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Jet Reconstruction Internal Documentation ​

Documentation for JetReconstruction.jl's internal methods and types.

N.B. no guarantee is made of stability of these interfaces or types.

Index ​

Public Interface ​

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julia
+(j1::PseudoJet, j2::PseudoJet)

Addition operator for PseudoJet objects.

Arguments

  • j1::PseudoJet: The first PseudoJet object.

  • j2::PseudoJet: The second PseudoJet object.

Returns

A new PseudoJet object with the sum of the momenta and energy of j1 and j2.

source

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julia
copy(j::TiledJet)

Create a copy of a TiledJet object.

Arguments

  • j::TiledJet: The TiledJet object to be copied.

Returns

A new TiledJet object with the same attributes as the input object.

source

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julia
Base.iterate(t::rightmost_tiles, state=1)

Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.

source

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julia
Base.iterate(t::neighbour_tiles, state=1)

Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.

source

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julia
Base.iterate(tj::TiledJet)

Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).

Arguments

  • tj::TiledJet: The TiledJet object to start to iterate over.

source

',5))]),t("details",b,[t("summary",null,[e[15]||(e[15]=t("a",{id:"Base.show-Tuple{IO, PseudoJet}",href:"#Base.show-Tuple{IO, PseudoJet}"},[t("span",{class:"jlbinding"},"Base.show")],-1)),e[16]||(e[16]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=s('
julia
show(io::IO, jet::PseudoJet)

Print a PseudoJet object to the specified IO stream.

Arguments

  • io::IO: The IO stream to which the information will be printed.

  • jet::PseudoJet: The PseudoJet object whose information will be printed.

source

',5))]),t("details",m,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Base.tryparse-Tuple{Type{<:Enum}, String}",href:"#Base.tryparse-Tuple{Type{<:Enum}, String}"},[t("span",{class:"jlbinding"},"Base.tryparse")],-1)),e[19]||(e[19]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=s('
julia
Base.tryparse(E::Type{<:Enum}, str::String)

Parser that converts a string to an enum value if it exists, otherwise returns nothing.

source

',3))]),t("details",y,[t("summary",null,[e[21]||(e[21]=t("a",{id:"JetReconstruction.CosTheta-Tuple{PseudoJet}",href:"#JetReconstruction.CosTheta-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.CosTheta")],-1)),e[22]||(e[22]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=s('
julia
CosTheta(p::PseudoJet)

Compute the cosine of the angle between the momentum vector p and the z-axis.

Arguments

  • p::PseudoJet: The PseudoJet object representing the momentum vector.

Returns

  • The cosine of the angle between p and the z-axis.

source

',7))]),t("details",j,[t("summary",null,[e[24]||(e[24]=t("a",{id:"JetReconstruction._ee_genkt_algorithm-Tuple{}",href:"#JetReconstruction._ee_genkt_algorithm-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction._ee_genkt_algorithm")],-1)),e[25]||(e[25]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=s(`
julia
_ee_genkt_algorithm(; particles::Vector{EEjet}, p = 1, R = 4.0,
+import{_ as d,c as l,a5 as s,j as t,a as i,G as o,B as r,o as a}from"./chunks/framework.BtBDBbTG.js";const $e=JSON.parse('{"title":"Jet Reconstruction Internal Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/internal.md","filePath":"lib/internal.md","lastUpdated":null}'),p={name:"lib/internal.md"},c={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},x={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},_={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.666ex"},xmlns:"http://www.w3.org/2000/svg",width:"11.255ex",height:"2.363ex",role:"img",focusable:"false",viewBox:"0 -750 4974.7 1044.2","aria-hidden":"true"},Q={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},P={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.186ex"},xmlns:"http://www.w3.org/2000/svg",width:"11.406ex",height:"1.756ex",role:"img",focusable:"false",viewBox:"0 -694 5041.4 776","aria-hidden":"true"},w={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},H={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},I={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"4.973ex",height:"1.779ex",role:"img",focusable:"false",viewBox:"0 -775.2 2198.3 786.2","aria-hidden":"true"},q={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"},te={class:"jldocstring custom-block"},ie={class:"jldocstring custom-block"},se={class:"jldocstring custom-block"},ne={class:"jldocstring custom-block"},oe={class:"jldocstring custom-block"},le={class:"jldocstring custom-block"},ae={class:"jldocstring custom-block"},de={class:"jldocstring custom-block"},re={class:"jldocstring custom-block"},pe={class:"jldocstring custom-block"},ce={class:"jldocstring custom-block"},he={class:"jldocstring custom-block"},ue={class:"jldocstring custom-block"},ge={class:"jldocstring custom-block"},ke={class:"jldocstring custom-block"},be={class:"jldocstring custom-block"},me={class:"jldocstring custom-block"},ye={class:"jldocstring custom-block"},je={class:"jldocstring custom-block"},fe={class:"jldocstring custom-block"},Te={class:"jldocstring custom-block"},Je={class:"jldocstring custom-block"},Ee={class:"jldocstring custom-block"},ve={class:"jldocstring custom-block"},Re={class:"jldocstring custom-block"},Ce={class:"jldocstring custom-block"},Fe={class:"jldocstring custom-block"},Ae={class:"jldocstring custom-block"},xe={class:"jldocstring custom-block"},_e={class:"jldocstring custom-block"},Qe={class:"jldocstring custom-block"},Pe={class:"jldocstring custom-block"},we={class:"jldocstring custom-block"},Le={class:"jldocstring custom-block"},Be={class:"jldocstring custom-block"},Me={class:"jldocstring custom-block"},De={class:"jldocstring custom-block"},He={class:"jldocstring custom-block"},Ie={class:"jldocstring custom-block"},qe={class:"jldocstring custom-block"},Se={class:"jldocstring custom-block"},Oe={class:"jldocstring custom-block"},Ne={class:"jldocstring custom-block"};function Ve(ze,e,Ze,Ue,Xe,Ge){const n=r("Badge");return a(),l("div",null,[e[270]||(e[270]=s('
Jet Reconstruction Internal Documentation ​
Documentation for JetReconstruction.jl's internal methods and types.
N.B. no guarantee is made of stability of these interfaces or types.
Index ​
Public Interface ​
',6)),t("details",c,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Base.:+-Tuple{PseudoJet, PseudoJet}",href:"#Base.:+-Tuple{PseudoJet, PseudoJet}"},[t("span",{class:"jlbinding"},"Base.:+")],-1)),e[1]||(e[1]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=s('
julia
+(j1::PseudoJet, j2::PseudoJet)
Addition operator for PseudoJet objects.
Arguments
  • j1::PseudoJet: The first PseudoJet object.
  • j2::PseudoJet: The second PseudoJet object.
Returns
A new PseudoJet object with the sum of the momenta and energy of j1 and j2.
source
',7))]),t("details",h,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Base.copy-Tuple{JetReconstruction.TiledJet}",href:"#Base.copy-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"Base.copy")],-1)),e[4]||(e[4]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=s('
julia
copy(j::TiledJet)
Create a copy of a TiledJet object.
Arguments
  • j::TiledJet: The TiledJet object to be copied.
Returns
A new TiledJet object with the same attributes as the input object.
source
',7))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Base.iterate",href:"#Base.iterate"},[t("span",{class:"jlbinding"},"Base.iterate")],-1)),e[7]||(e[7]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[8]||(e[8]=s('
julia
Base.iterate(t::neighbour_tiles, state=1)
Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.
source
',3))]),t("details",g,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Base.iterate-2",href:"#Base.iterate-2"},[t("span",{class:"jlbinding"},"Base.iterate")],-1)),e[10]||(e[10]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[11]||(e[11]=s('
julia
Base.iterate(t::rightmost_tiles, state=1)
Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.
source
',3))]),t("details",k,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Base.iterate-Tuple{JetReconstruction.TiledJet}",href:"#Base.iterate-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"Base.iterate")],-1)),e[13]||(e[13]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=s('
julia
Base.iterate(tj::TiledJet)
Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).
Arguments
  • tj::TiledJet: The TiledJet object to start to iterate over.
source
',5))]),t("details",b,[t("summary",null,[e[15]||(e[15]=t("a",{id:"Base.show-Tuple{IO, PseudoJet}",href:"#Base.show-Tuple{IO, PseudoJet}"},[t("span",{class:"jlbinding"},"Base.show")],-1)),e[16]||(e[16]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=s('
julia
show(io::IO, jet::PseudoJet)
Print a PseudoJet object to the specified IO stream.
Arguments
  • io::IO: The IO stream to which the information will be printed.
  • jet::PseudoJet: The PseudoJet object whose information will be printed.
source
',5))]),t("details",m,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Base.tryparse-Tuple{Type{<:Enum}, String}",href:"#Base.tryparse-Tuple{Type{<:Enum}, String}"},[t("span",{class:"jlbinding"},"Base.tryparse")],-1)),e[19]||(e[19]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=s('
julia
Base.tryparse(E::Type{<:Enum}, str::String)
Parser that converts a string to an enum value if it exists, otherwise returns nothing.
source
',3))]),t("details",y,[t("summary",null,[e[21]||(e[21]=t("a",{id:"JetReconstruction.CosTheta-Tuple{PseudoJet}",href:"#JetReconstruction.CosTheta-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.CosTheta")],-1)),e[22]||(e[22]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=s('
julia
CosTheta(p::PseudoJet)
Compute the cosine of the angle between the momentum vector p and the z-axis.
Arguments
  • p::PseudoJet: The PseudoJet object representing the momentum vector.
Returns
  • The cosine of the angle between p and the z-axis.
source
',7))]),t("details",j,[t("summary",null,[e[24]||(e[24]=t("a",{id:"JetReconstruction._ee_genkt_algorithm-Tuple{}",href:"#JetReconstruction._ee_genkt_algorithm-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction._ee_genkt_algorithm")],-1)),e[25]||(e[25]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=s(`
julia
_ee_genkt_algorithm(; particles::Vector{EEjet}, p = 1, R = 4.0,
                    algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham,
-                   recombine = +)
This function is the actual implementation of the e+e- jet clustering algorithm.
source
`,3))]),t("details",T,[t("summary",null,[e[27]||(e[27]=t("a",{id:"JetReconstruction._ensure_valid_rap_phi-Tuple{PseudoJet}",href:"#JetReconstruction._ensure_valid_rap_phi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction._ensure_valid_rap_phi")],-1)),e[28]||(e[28]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=s('
julia
_ensure_valid_rap_phi(p::PseudoJet)
Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.
Arguments
  • p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.
source
',5))]),t("details",f,[t("summary",null,[e[30]||(e[30]=t("a",{id:"JetReconstruction._plain_jet_reconstruct-Tuple{}",href:"#JetReconstruction._plain_jet_reconstruct-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction._plain_jet_reconstruct")],-1)),e[31]||(e[31]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=s('
julia
_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)
This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.
Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.
The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt. Floating point values are allowed for generalised k_t algorithm.
Arguments
  • particles: A vector of PseudoJet objects representing the input particles.
  • p=-1: The power to which the transverse momentum (pt) of each particle is raised.
  • R=1.0: The jet radius parameter.
  • recombine: The recombination function used to merge two jets. Default is + (additive recombination).
Returns
  • clusterseq: The resulting ClusterSequence object representing the reconstructed jets.
source
',9))]),t("details",J,[t("summary",null,[e[33]||(e[33]=t("a",{id:"JetReconstruction._set_rap_phi!-Tuple{PseudoJet}",href:"#JetReconstruction._set_rap_phi!-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction._set_rap_phi!")],-1)),e[34]||(e[34]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=s('
_set_rap_phi!(p::PseudoJet)
Set the rapidity and azimuthal angle of the PseudoJet p.
Arguments
  • p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.
Description
This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.
Note - the ϕ angle is calculated in the range [0, 2π).
source
',8))]),t("details",E,[t("summary",null,[e[36]||(e[36]=t("a",{id:"JetReconstruction._tiled_jet_reconstruct-Tuple{Vector{PseudoJet}}",href:"#JetReconstruction._tiled_jet_reconstruct-Tuple{Vector{PseudoJet}}"},[t("span",{class:"jlbinding"},"JetReconstruction._tiled_jet_reconstruct")],-1)),e[37]||(e[37]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=s('
julia
_tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets once preprocessing of data types are done.
Arguments
  • particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.
  • p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)
source
',9))]),t("details",v,[t("summary",null,[e[39]||(e[39]=t("a",{id:"JetReconstruction._tj_diJ-Tuple{Any}",href:"#JetReconstruction._tj_diJ-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction._tj_diJ")],-1)),e[40]||(e[40]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=s('
julia
_tj_diJ(jet)
Compute the dij metric value for a given jet.
Arguments
  • jet: The input jet.
Returns
  • The dij value for the jet.
Example
source
',8))]),t("details",R,[t("summary",null,[e[42]||(e[42]=t("a",{id:"JetReconstruction._tj_dist-Tuple{Any, Any}",href:"#JetReconstruction._tj_dist-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction._tj_dist")],-1)),e[43]||(e[43]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[44]||(e[44]=s('
julia
_tj_dist(jetA, jetB)
Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.
Arguments
  • jetA: The first jet.
  • jetB: The second jet.
Returns
The squared distance between jetA and jetB.
Examples
source
',8))]),t("details",C,[t("summary",null,[e[45]||(e[45]=t("a",{id:"JetReconstruction.add_step_to_history!-Tuple{ClusterSequence, Vararg{Any, 4}}",href:"#JetReconstruction.add_step_to_history!-Tuple{ClusterSequence, Vararg{Any, 4}}"},[t("span",{class:"jlbinding"},"JetReconstruction.add_step_to_history!")],-1)),e[46]||(e[46]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[47]||(e[47]=s('
julia
add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)
Add a new jet's history into the recombination sequence.
Arguments:
  • clusterseq::ClusterSequence: The cluster sequence object.
  • parent1: The index of the first parent.
  • parent2: The index of the second parent.
  • jetp_index: The index of the jet.
  • dij: The dij value.
This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.
If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.
source
',7))]),t("details",F,[t("summary",null,[e[48]||(e[48]=t("a",{id:"JetReconstruction.add_untagged_neighbours_to_tile_union-NTuple{4, Any}",href:"#JetReconstruction.add_untagged_neighbours_to_tile_union-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.add_untagged_neighbours_to_tile_union")],-1)),e[49]||(e[49]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[50]||(e[50]=s('
julia
add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)
Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.
Arguments
  • center_index: The index of the center tile.
  • tile_union: An array to store the indices of neighbouring tiles.
  • n_near_tiles: The number of neighbouring tiles.
  • tiling: The tiling object containing the tile tags.
Returns
The updated number of near tiles.
source
',7))]),t("details",A,[t("summary",null,[e[51]||(e[51]=t("a",{id:"JetReconstruction.angular_distance-Tuple{Any, Any, Any}",href:"#JetReconstruction.angular_distance-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.angular_distance")],-1)),e[52]||(e[52]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=s('
julia
angular_distance(eereco, i, j) -> Float64
',1)),t("p",null,[e[55]||(e[55]=i("Calculate the angular distance between two jets ")),e[56]||(e[56]=t("code",null,"i",-1)),e[57]||(e[57]=i(" and ")),e[58]||(e[58]=t("code",null,"j",-1)),e[59]||(e[59]=i(" using the formula ")),t("mjx-container",x,[(a(),l("svg",_,e[53]||(e[53]=[s('',1)]))),e[54]||(e[54]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mn",null,"1"),t("mo",null,"−"),t("mi",null,"c"),t("mi",null,"o"),t("mi",null,"s"),t("mo",{stretchy:"false"},"("),t("msub",null,[t("mi",null,"θ"),t("mrow",{"data-mjx-texclass":"ORD"},[t("mi",null,"i"),t("mi",null,"j")])]),t("mo",{stretchy:"false"},")")])],-1))]),e[60]||(e[60]=i("."))]),e[71]||(e[71]=s("
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
Returns
",3)),t("ul",null,[t("li",null,[e[63]||(e[63]=t("code",null,"Float64",-1)),e[64]||(e[64]=i(": The angular distance between ")),e[65]||(e[65]=t("code",null,"i",-1)),e[66]||(e[66]=i(" and ")),e[67]||(e[67]=t("code",null,"j",-1)),e[68]||(e[68]=i(", which is ")),t("mjx-container",Q,[(a(),l("svg",P,e[61]||(e[61]=[s('',1)]))),e[62]||(e[62]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mn",null,"1"),t("mo",null,"−"),t("mi",null,"c"),t("mi",null,"o"),t("mi",null,"s"),t("mi",null,"h"),t("mi",null,"e"),t("mi",null,"t"),t("mi",null,"a")])],-1))]),e[69]||(e[69]=i("."))])]),e[72]||(e[72]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/EEAlgorithm.jl#L6-L20",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",w,[t("summary",null,[e[73]||(e[73]=t("a",{id:"JetReconstruction.check_algorithm_power_consistency-Tuple{}",href:"#JetReconstruction.check_algorithm_power_consistency-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction.check_algorithm_power_consistency")],-1)),e[74]||(e[74]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[75]||(e[75]=t("p",null,"Allow a check for algorithm and power consistency",-1)),e[76]||(e[76]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/AlgorithmStrategyEnums.jl#L129",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",L,[t("summary",null,[e[77]||(e[77]=t("a",{id:"JetReconstruction.detach!-Tuple{JetReconstruction.TiledJet}",href:"#JetReconstruction.detach!-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.detach!")],-1)),e[78]||(e[78]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[79]||(e[79]=s('
julia
detach!(jet::TiledJet)
Detach a TiledJet from its linked list by updating the previous and next pointers.
Arguments
  • jet::TiledJet: The TiledJet object to detach.
source
',5))]),t("details",B,[t("summary",null,[e[80]||(e[80]=t("a",{id:"JetReconstruction.determine_rapidity_extent-Union{Tuple{Vector{T}}, Tuple{T}} where T<:AbstractFloat",href:"#JetReconstruction.determine_rapidity_extent-Union{Tuple{Vector{T}}, Tuple{T}} where T<:AbstractFloat"},[t("span",{class:"jlbinding"},"JetReconstruction.determine_rapidity_extent")],-1)),e[81]||(e[81]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[82]||(e[82]=s('
julia
determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat
Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.
This is the heuristic which is used by FastJet (inline comments are from FastJet).
Arguments
  • eta::Vector{T}: A vector of rapidity values.
Returns
  • minrap::T: The minimum rapidity value.
  • maxrap::T: The maximum rapidity value.
source
',8))]),t("details",M,[t("summary",null,[e[83]||(e[83]=t("a",{id:"JetReconstruction.dij-NTuple{4, Any}",href:"#JetReconstruction.dij-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dij")],-1)),e[84]||(e[84]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[85]||(e[85]=s('
julia
dij(i, kt2_array, nn, nndist)
Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß
Arguments
  • i: The index of the element.
  • kt2_array: An array of kt2 values.
  • nn: An array of nearest neighbors.
  • nndist: An array of nearest neighbor distances.
Returns
  • The computed dij value.
source
',7))]),t("details",D,[t("summary",null,[e[86]||(e[86]=t("a",{id:"JetReconstruction.dij_dist-NTuple{4, Any}",href:"#JetReconstruction.dij_dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dij_dist")],-1)),e[87]||(e[87]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[92]||(e[92]=s('
julia
dij_dist(eereco, i, j, dij_factor)
',1)),t("p",null,[e[90]||(e[90]=i("Calculate the dij distance between two ")),t("mjx-container",H,[(a(),l("svg",I,e[88]||(e[88]=[s('',1)]))),e[89]||(e[89]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("msup",null,[t("mi",null,"e"),t("mo",null,"+")]),t("msup",null,[t("mi",null,"e"),t("mo",null,"−")])])],-1))]),e[91]||(e[91]=i("jets."))]),e[93]||(e[93]=s('
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
  • dij_factor: The scaling factor to multiply the dij distance by.
Returns
  • The dij distance between i and j.
source
',5))]),t("details",q,[t("summary",null,[e[94]||(e[94]=t("a",{id:"JetReconstruction.dist-NTuple{4, Any}",href:"#JetReconstruction.dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dist")],-1)),e[95]||(e[95]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[96]||(e[96]=s('
julia
dist(i, j, rapidity_array, phi_array)
Compute the distance between points in a 2D space defined by rapidity and phi coordinates.
Arguments
  • i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).
  • j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).
  • rapidity_array::Vector{Float64}: Array of rapidity coordinates.
  • phi_array::Vector{Float64}: Array of phi coordinates.
Returns
  • distance::Float64: The distance between the two points.
source
',7))]),t("details",S,[t("summary",null,[e[97]||(e[97]=t("a",{id:"JetReconstruction.do_iB_recombination_step!-Tuple{ClusterSequence, Any, Any}",href:"#JetReconstruction.do_iB_recombination_step!-Tuple{ClusterSequence, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.do_iB_recombination_step!")],-1)),e[98]||(e[98]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[99]||(e[99]=s('
julia
do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)
Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the jet.
  • diB: The diB value.
source
',5))]),t("details",O,[t("summary",null,[e[100]||(e[100]=t("a",{id:"JetReconstruction.do_ij_recombination_step!",href:"#JetReconstruction.do_ij_recombination_step!"},[t("span",{class:"jlbinding"},"JetReconstruction.do_ij_recombination_step!")],-1)),e[101]||(e[101]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[102]||(e[102]=s('
julia
do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)
Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the first jet to be recombined.
  • jet_j: The index of the second jet to be recombined.
  • dij: The distance between the two jets.
  • recombine=+: The recombination function to be used. Default is addition.
Returns
  • newjet_k: The index of the newly created jet.
Description
This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.
source
',9))]),t("details",N,[t("summary",null,[e[103]||(e[103]=t("a",{id:"JetReconstruction.energy-Tuple{PseudoJet}",href:"#JetReconstruction.energy-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.energy")],-1)),e[104]||(e[104]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[105]||(e[105]=s('
julia
energy(p::PseudoJet)
Return the energy of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The energy of the PseudoJet.
source
',7))]),t("details",V,[t("summary",null,[e[106]||(e[106]=t("a",{id:"JetReconstruction.eta-Tuple{PseudoJet}",href:"#JetReconstruction.eta-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.eta")],-1)),e[107]||(e[107]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[108]||(e[108]=s('
julia
eta(p::PseudoJet)
Compute the pseudorapidity (η) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.
Returns
  • The pseudorapidity (η) of the PseudoJet.
source
',7))]),t("details",z,[t("summary",null,[e[109]||(e[109]=t("a",{id:"JetReconstruction.fast_findmin-Tuple{Any, Any}",href:"#JetReconstruction.fast_findmin-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.fast_findmin")],-1)),e[110]||(e[110]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[111]||(e[111]=s('
julia
fast_findmin(dij, n)
Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.
Arguments
  • dij: An array of values.
  • n: The number of elements to consider in the dij array.
Returns
  • dij_min: The minimum value in the first n elements of the dij array.
  • best: The index of the minimum value in the dij array.
source
',7))]),t("details",Z,[t("summary",null,[e[112]||(e[112]=t("a",{id:"JetReconstruction.find_tile_neighbours!-NTuple{5, Any}",href:"#JetReconstruction.find_tile_neighbours!-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.find_tile_neighbours!")],-1)),e[113]||(e[113]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[114]||(e[114]=s('
julia
find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)
Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run
Arguments
  • tile_union: The tile union to which the neighbouring tiles will be added.
  • jetA: The first jet.
  • jetB: The second jet.
  • oldB: The old second jet.
  • tiling: The tiling information.
Returns
The number of neighbouring tiles added to the tile_union.
source
',7))]),t("details",U,[t("summary",null,[e[115]||(e[115]=t("a",{id:"JetReconstruction.geometric_distance-NTuple{4, AbstractFloat}",href:"#JetReconstruction.geometric_distance-NTuple{4, AbstractFloat}"},[t("span",{class:"jlbinding"},"JetReconstruction.geometric_distance")],-1)),e[116]||(e[116]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[117]||(e[117]=s('
julia
geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)
Compute the geometric distance between two points in the rap-phi plane.
Arguments
  • eta1::AbstractFloat: The eta coordinate of the first point.
  • phi1::AbstractFloat: The phi coordinate of the first point.
  • eta2::AbstractFloat: The eta coordinate of the second point.
  • phi2::AbstractFloat: The phi coordinate of the second point.
Returns
  • distance::Float64: The geometric distance between the two points.
source
',7))]),t("details",X,[t("summary",null,[e[118]||(e[118]=t("a",{id:"JetReconstruction.get_algorithm_power_consistency-Tuple{}",href:"#JetReconstruction.get_algorithm_power_consistency-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_algorithm_power_consistency")],-1)),e[119]||(e[119]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[120]||(e[120]=s('
julia
get_algorithm_power_consistency(; p::Union{Real, Nothing}, algorithm::Union{JetAlgorithm, Nothing})
Get the algorithm and power consistency correct
This function checks the consistency between the algorithm and power parameters. If the algorithm is specified, it checks if the power parameter is consistent with the algorithm's known power. If the power parameter is not specified, it sets the power parameter based on the algorithm. If neither the algorithm nor the power parameter is specified, it throws an ArgumentError.
Arguments
  • p::Union{Real, Nothing}: The power value.
  • algorithm::Union{JetAlgorithm, Nothing}: The algorithm.
Returns
A named tuple of the consistent power and algorithm values.
Throws
  • ArgumentError: If the algorithm and power are inconsistent or if neither the algorithm nor the power is specified.
source
',10))]),t("details",G,[t("summary",null,[e[121]||(e[121]=t("a",{id:"JetReconstruction.get_all_ancestors-Tuple{Any, ClusterSequence}",href:"#JetReconstruction.get_all_ancestors-Tuple{Any, ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_all_ancestors")],-1)),e[122]||(e[122]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[123]||(e[123]=s('
julia
get_all_ancestors(idx, cs::ClusterSequence)
Recursively finds all ancestors of a given index in a ClusterSequence object.
Arguments
  • idx: The index of the jet for which to find ancestors.
  • cs: The ClusterSequence object containing the jet history.
Returns
An array of indices representing the ancestors of the given jet.
source
',7))]),t("details",W,[t("summary",null,[e[124]||(e[124]=t("a",{id:"JetReconstruction.get_dij_dist-NTuple{4, Any}",href:"#JetReconstruction.get_dij_dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_dij_dist")],-1)),e[125]||(e[125]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[126]||(e[126]=s('
julia
get_dij_dist(nn_dist, kt2_1, kt2_2, R2)
Compute the dij metric distance between two jets.
Arguments
  • nn_dist: The nearest-neighbor distance between two jets.
  • kt2_1: The squared momentum metric value of the first jet.
  • kt2_2: The squared momentum metric value of the second jet.
  • R2: The jet radius parameter squared.
Returns
The distance between the two jets.
If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.
source
',8))]),t("details",$,[t("summary",null,[e[127]||(e[127]=t("a",{id:"JetReconstruction.get_tile-Tuple{JetReconstruction.TilingDef, AbstractFloat, AbstractFloat}",href:"#JetReconstruction.get_tile-Tuple{JetReconstruction.TilingDef, AbstractFloat, AbstractFloat}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_tile")],-1)),e[128]||(e[128]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[129]||(e[129]=s('
julia
get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)
Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.
Arguments
  • tiling_setup: A TilingDef object that contains the tiling setup parameters.
  • eta: The eta value for which to calculate the tile index.
  • phi: The phi value for which to calculate the tile index.
Returns
  • ieta: The tile index along the eta direction.
  • iphi: The tile index along the phi direction.
source
',7))]),t("details",K,[t("summary",null,[e[130]||(e[130]=t("a",{id:"JetReconstruction.get_tile_cartesian_indices-Tuple{JetReconstruction.TilingDef, Int64}",href:"#JetReconstruction.get_tile_cartesian_indices-Tuple{JetReconstruction.TilingDef, Int64}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_tile_cartesian_indices")],-1)),e[131]||(e[131]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[132]||(e[132]=s('
julia
get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)
Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)
Arguments
  • tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.
  • i_η::Int: The index of the tile in the η dimension.
  • i_ϕ::Int: The index of the tile in the ϕ dimension.
Returns
  • The linear index of the tile.
source
',7))]),t("details",Y,[t("summary",null,[e[133]||(e[133]=t("a",{id:"JetReconstruction.initial_history-Tuple{Any}",href:"#JetReconstruction.initial_history-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.initial_history")],-1)),e[134]||(e[134]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[135]||(e[135]=s('
julia
initial_history(particles)
Create an initial history for the given particles.
Arguments
  • particles: The initial vector of stable particles.
Returns
  • history: An array of HistoryElement objects.
  • Qtot: The total energy in the event.
source
',7))]),t("details",ee,[t("summary",null,[e[136]||(e[136]=t("a",{id:"JetReconstruction.insert!-Tuple{JetReconstruction.TiledJet, JetReconstruction.TiledJet}",href:"#JetReconstruction.insert!-Tuple{JetReconstruction.TiledJet, JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.insert!")],-1)),e[137]||(e[137]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[138]||(e[138]=s('
julia
insert!(nextjet::TiledJet, jettomove::TiledJet)
Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list
Arguments
  • nextjet::TiledJet: The TiledJet object after which jettomove should be inserted.
  • jettomove::TiledJet: The TiledJet object to be inserted.
Example
source
',6))]),t("details",te,[t("summary",null,[e[139]||(e[139]=t("a",{id:"JetReconstruction.is_ee-Tuple{JetReconstruction.JetAlgorithm.Algorithm}",href:"#JetReconstruction.is_ee-Tuple{JetReconstruction.JetAlgorithm.Algorithm}"},[t("span",{class:"jlbinding"},"JetReconstruction.is_ee")],-1)),e[140]||(e[140]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[141]||(e[141]=s('
julia
is_ee(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a e+e- reconstruction algorithm.
Returns
true if the algorithm is a e+e- reconstruction algorithm, false otherwise.
source
',5))]),t("details",ie,[t("summary",null,[e[142]||(e[142]=t("a",{id:"JetReconstruction.is_pp-Tuple{JetReconstruction.JetAlgorithm.Algorithm}",href:"#JetReconstruction.is_pp-Tuple{JetReconstruction.JetAlgorithm.Algorithm}"},[t("span",{class:"jlbinding"},"JetReconstruction.is_pp")],-1)),e[143]||(e[143]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[144]||(e[144]=s('
julia
is_pp(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a pp reconstruction algorithm.
Returns
true if the algorithm is a pp reconstruction algorithm, false otherwise.
source
',5))]),t("details",se,[t("summary",null,[e[145]||(e[145]=t("a",{id:"JetReconstruction.isvalid-Tuple{JetReconstruction.TiledJet}",href:"#JetReconstruction.isvalid-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.isvalid")],-1)),e[146]||(e[146]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[147]||(e[147]=s('
julia
isvalid(t::TiledJet)
Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.
Arguments
  • t::TiledJet: The TiledJet object to check.
Returns
  • Bool: true if the TiledJet object is valid, false otherwise.
source
',7))]),t("details",ne,[t("summary",null,[e[148]||(e[148]=t("a",{id:"JetReconstruction.jet_ranks-Tuple{ClusterSequence}",href:"#JetReconstruction.jet_ranks-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.jet_ranks")],-1)),e[149]||(e[149]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[150]||(e[150]=s('
julia
jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)
Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.
  • compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.
Returns
A dictionary mapping each jet index to its rank.
Note
This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.
source
',9))]),t("details",oe,[t("summary",null,[e[151]||(e[151]=t("a",{id:"JetReconstruction.m-Tuple{PseudoJet}",href:"#JetReconstruction.m-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.m")],-1)),e[152]||(e[152]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[153]||(e[153]=s('
julia
m(p::PseudoJet)
Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the invariant mass.
Returns
The invariant mass of the PseudoJet object.
source
',7))]),t("details",le,[t("summary",null,[e[154]||(e[154]=t("a",{id:"JetReconstruction.m2-Tuple{PseudoJet}",href:"#JetReconstruction.m2-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.m2")],-1)),e[155]||(e[155]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[156]||(e[156]=s('
julia
m2(p::PseudoJet)
Calculate the invariant mass squared (m^2) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.
Returns
  • The invariant mass squared (m^2) of the PseudoJet.
source
',7))]),t("details",ae,[t("summary",null,[e[157]||(e[157]=t("a",{id:"JetReconstruction.mag-Tuple{PseudoJet}",href:"#JetReconstruction.mag-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.mag")],-1)),e[158]||(e[158]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[159]||(e[159]=s('
julia
mag(p::PseudoJet)
Return the magnitude of the momentum of a PseudoJet, |p|.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the magnitude.
Returns
The magnitude of the PseudoJet object.
source
',7))]),t("details",re,[t("summary",null,[e[160]||(e[160]=t("a",{id:"JetReconstruction.mass-Tuple{PseudoJet}",href:"#JetReconstruction.mass-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.mass")],-1)),e[161]||(e[161]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[162]||(e[162]=s('
julia
mass(p::PseudoJet)
Compute the invariant mass (alias for m(p)).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the mass.
Returns
  • The mass of the PseudoJet.
source
',7))]),t("details",de,[t("summary",null,[e[163]||(e[163]=t("a",{id:"JetReconstruction.mass2",href:"#JetReconstruction.mass2"},[t("span",{class:"jlbinding"},"JetReconstruction.mass2")],-1)),e[164]||(e[164]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[165]||(e[165]=t("p",null,[i("Alias for "),t("code",null,"m2"),i(" function")],-1)),e[166]||(e[166]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/Pseudojet.jl#L370",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",pe,[t("summary",null,[e[167]||(e[167]=t("a",{id:"JetReconstruction.merge_steps-Tuple{ClusterSequence}",href:"#JetReconstruction.merge_steps-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.merge_steps")],-1)),e[168]||(e[168]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[169]||(e[169]=s('
julia
merge_steps(clusterseq::ClusterSequence)
Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
Returns
  • merge_steps::Int: The number of merge steps.
source
',7))]),t("details",ce,[t("summary",null,[e[170]||(e[170]=t("a",{id:"JetReconstruction.phi-Tuple{PseudoJet}",href:"#JetReconstruction.phi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.phi")],-1)),e[171]||(e[171]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[172]||(e[172]=s('
julia
phi(p::PseudoJet)
Compute the ϕ angle of a PseudoJet object p.
Note this function is a wrapper for phi_02pi(p).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
',8))]),t("details",he,[t("summary",null,[e[173]||(e[173]=t("a",{id:"JetReconstruction.phi_02pi-Tuple{PseudoJet}",href:"#JetReconstruction.phi_02pi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.phi_02pi")],-1)),e[174]||(e[174]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[175]||(e[175]=s('
julia
phi_02pi(p::PseudoJet)
Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
',7))]),t("details",ue,[t("summary",null,[e[176]||(e[176]=t("a",{id:"JetReconstruction.pt-Tuple{PseudoJet}",href:"#JetReconstruction.pt-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pt")],-1)),e[177]||(e[177]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[178]||(e[178]=s('
julia
pt(p::PseudoJet)
Compute the scalar transverse momentum (pt) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.
Returns
  • The transverse momentum (pt) of the PseudoJet.
source
',7))]),t("details",ge,[t("summary",null,[e[179]||(e[179]=t("a",{id:"JetReconstruction.pt2-Tuple{PseudoJet}",href:"#JetReconstruction.pt2-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pt2")],-1)),e[180]||(e[180]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[181]||(e[181]=s('
julia
pt2(p::PseudoJet)
Get the squared transverse momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The squared transverse momentum of the PseudoJet.
source
',7))]),t("details",ke,[t("summary",null,[e[182]||(e[182]=t("a",{id:"JetReconstruction.px-Tuple{PseudoJet}",href:"#JetReconstruction.px-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.px")],-1)),e[183]||(e[183]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[184]||(e[184]=s('
julia
px(p::PseudoJet)
Return the x-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The x-component of the momentum of the PseudoJet.
source
',7))]),t("details",be,[t("summary",null,[e[185]||(e[185]=t("a",{id:"JetReconstruction.py-Tuple{PseudoJet}",href:"#JetReconstruction.py-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.py")],-1)),e[186]||(e[186]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[187]||(e[187]=s('
julia
py(p::PseudoJet)
Return the y-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The y-component of the momentum of the PseudoJet.
source
',7))]),t("details",me,[t("summary",null,[e[188]||(e[188]=t("a",{id:"JetReconstruction.pz-Tuple{PseudoJet}",href:"#JetReconstruction.pz-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pz")],-1)),e[189]||(e[189]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[190]||(e[190]=s('
julia
pz(p::PseudoJet)
Return the z-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The z-component of the momentum of the PseudoJet.
source
',7))]),t("details",ye,[t("summary",null,[e[191]||(e[191]=t("a",{id:"JetReconstruction.rapidity-Tuple{PseudoJet}",href:"#JetReconstruction.rapidity-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.rapidity")],-1)),e[192]||(e[192]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[193]||(e[193]=s('
julia
rapidity(p::PseudoJet)
Compute the rapidity of a PseudoJet object.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the rapidity.
Returns
The rapidity of the PseudoJet object.
source
',7))]),t("details",je,[t("summary",null,[e[194]||(e[194]=t("a",{id:"JetReconstruction.reco_state-Tuple{ClusterSequence, Any}",href:"#JetReconstruction.reco_state-Tuple{ClusterSequence, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.reco_state")],-1)),e[195]||(e[195]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[196]||(e[196]=s('
julia
reco_state(cs::ClusterSequence, pt_ranks; iteration=0)
This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.
Arguments
  • cs::ClusterSequence: The ClusterSequence object to update.
  • ranks: The ranks of the original clusters, that are inherited by peudojets
during the reconstruction process.
  • iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).
  • ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).
Returns
A dictionary representing a snapshot of the reconstruction state.
Details
The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.
source
',11))]),t("details",Te,[t("summary",null,[e[197]||(e[197]=t("a",{id:"JetReconstruction.rightneighbours-Tuple{Int64, JetReconstruction.Tiling}",href:"#JetReconstruction.rightneighbours-Tuple{Int64, JetReconstruction.Tiling}"},[t("span",{class:"jlbinding"},"JetReconstruction.rightneighbours")],-1)),e[198]||(e[198]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[199]||(e[199]=s('
julia
rightneighbours(center::Int, tiling::Tiling)
Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the indices of the right neighbors.
source
',7))]),t("details",fe,[t("summary",null,[e[200]||(e[200]=t("a",{id:"JetReconstruction.set_momentum!-Tuple{PseudoJet, Vararg{Any, 4}}",href:"#JetReconstruction.set_momentum!-Tuple{PseudoJet, Vararg{Any, 4}}"},[t("span",{class:"jlbinding"},"JetReconstruction.set_momentum!")],-1)),e[201]||(e[201]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[202]||(e[202]=s('
julia
set_momentum!(j::PseudoJet, px, py, pz, E)
Set the momentum components and energy of a PseudoJet object.
Arguments
  • j::PseudoJet: The PseudoJet object to set the momentum for.
  • px: The x-component of the momentum.
  • py: The y-component of the momentum.
  • pz: The z-component of the momentum.
  • E: The energy of the particle.
source
',5))]),t("details",Je,[t("summary",null,[e[203]||(e[203]=t("a",{id:"JetReconstruction.set_nearest_neighbours!-Tuple{ClusterSequence, JetReconstruction.Tiling, Vector{JetReconstruction.TiledJet}}",href:"#JetReconstruction.set_nearest_neighbours!-Tuple{ClusterSequence, JetReconstruction.Tiling, Vector{JetReconstruction.TiledJet}}"},[t("span",{class:"jlbinding"},"JetReconstruction.set_nearest_neighbours!")],-1)),e[204]||(e[204]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[205]||(e[205]=s('
julia
set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})
This function sets the nearest neighbor information for all jets in the tiledjets vector.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • tiling::Tiling: The tiling object.
  • tiledjets::Vector{TiledJet}: The vector of tiled jets.
Returns
  • NNs::Vector{TiledJet}: The vector of nearest neighbor jets.
  • diJ::Vector{Float64}: The vector of diJ values.
The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.
Note: The diJ values are calculated as the kt distance multiplied by R^2.
source
',9))]),t("details",Ee,[t("summary",null,[e[206]||(e[206]=t("a",{id:"JetReconstruction.setup_tiling-Union{Tuple{T}, Tuple{Vector{T}, AbstractFloat}} where T<:AbstractFloat",href:"#JetReconstruction.setup_tiling-Union{Tuple{T}, Tuple{Vector{T}, AbstractFloat}} where T<:AbstractFloat"},[t("span",{class:"jlbinding"},"JetReconstruction.setup_tiling")],-1)),e[207]||(e[207]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[208]||(e[208]=s('
julia
setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat
This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.
Arguments
  • eta::Vector{T}: A vector of rapidities.
  • Rparam::AbstractFloat: The jet radius parameter.
Returns
  • tiling_setup: A TilingDef object containing the tiling setup parameters.
Description
The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.
source
',9))]),t("details",ve,[t("summary",null,[e[209]||(e[209]=t("a",{id:"JetReconstruction.surrounding-Tuple{Int64, JetReconstruction.Tiling}",href:"#JetReconstruction.surrounding-Tuple{Int64, JetReconstruction.Tiling}"},[t("span",{class:"jlbinding"},"JetReconstruction.surrounding")],-1)),e[210]||(e[210]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[211]||(e[211]=s('
julia
surrounding(center::Int, tiling::Tiling)
Compute the surrounding indices of a given center index in a tiling.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the surrounding indices.
source
',7))]),t("details",Re,[t("summary",null,[e[212]||(e[212]=t("a",{id:"JetReconstruction.tile_index-Tuple{Any, Float64, Float64}",href:"#JetReconstruction.tile_index-Tuple{Any, Float64, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.tile_index")],-1)),e[213]||(e[213]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[214]||(e[214]=s('
julia
tile_index(tiling_setup, eta::Float64, phi::Float64)
Compute the tile index for a given (eta, phi) coordinate.
Arguments
  • tiling_setup: The tiling setup object containing the tile size and number of tiles.
  • eta::Float64: The eta coordinate.
  • phi::Float64: The phi coordinate.
Returns
The tile index corresponding to the (eta, phi) coordinate.
source
',7))]),t("details",Ce,[t("summary",null,[e[215]||(e[215]=t("a",{id:"JetReconstruction.tiledjet_remove_from_tiles!-Tuple{Any, Any}",href:"#JetReconstruction.tiledjet_remove_from_tiles!-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.tiledjet_remove_from_tiles!")],-1)),e[216]||(e[216]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[217]||(e[217]=s('
julia
tiledjet_remove_from_tiles!(tiling, jet)
Remove a jet from the given tiling structure.
Arguments
  • tiling: The tiling structure from which the jet will be removed.
  • jet: The jet to be removed from the tiling structure.
Description
This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.
source
',7))]),t("details",Fe,[t("summary",null,[e[218]||(e[218]=t("a",{id:"JetReconstruction.tiledjet_set_jetinfo!-Tuple{JetReconstruction.TiledJet, ClusterSequence, JetReconstruction.Tiling, Any, Any, Any}",href:"#JetReconstruction.tiledjet_set_jetinfo!-Tuple{JetReconstruction.TiledJet, ClusterSequence, JetReconstruction.Tiling, Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.tiledjet_set_jetinfo!")],-1)),e[219]||(e[219]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[220]||(e[220]=s('
julia
tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)
Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)
Arguments:
  • jet::TiledJet: The TiledJet object to set the information for.
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets.
  • tiling::Tiling: The Tiling object containing the tile information.
  • jets_index: The index of the jet in the ClusterSequence.
  • R2: The jet radius parameter squared.
  • p: The power to raise the pt2 value to.
This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.
Returns:
  • nothing
source
',8))]),t("details",Ae,[t("summary",null,[e[221]||(e[221]=t("a",{id:"JetReconstruction.upd_nn_crosscheck!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}",href:"#JetReconstruction.upd_nn_crosscheck!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_crosscheck!")],-1)),e[222]||(e[222]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[223]||(e[223]=s('
julia
upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
',5))]),t("details",xe,[t("summary",null,[e[224]||(e[224]=t("a",{id:"JetReconstruction.upd_nn_nocross!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}",href:"#JetReconstruction.upd_nn_nocross!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_nocross!")],-1)),e[225]||(e[225]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[226]||(e[226]=s('
julia
upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
',5))]),t("details",_e,[t("summary",null,[e[227]||(e[227]=t("a",{id:"JetReconstruction.upd_nn_step!-NTuple{12, Any}",href:"#JetReconstruction.upd_nn_step!-NTuple{12, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_step!")],-1)),e[228]||(e[228]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[229]||(e[229]=s('
julia
upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)
Update the nearest neighbor information after a jet merge step.
Arguments:
  • i: Index of the first particle in the last merge step.
  • j: Index of the second particle in the last merge step.
  • k: Index of the current particle for which the nearest neighbour will be updated.
  • N: Total number of particles (currently vaild array indexes are [1:N]).
  • Nn: Number of nearest neighbors to consider.
  • kt2_array: Array of transverse momentum squared values.
  • rapidity_array: Array of rapidity values.
  • phi_array: Array of azimuthal angle values.
  • R2: Distance threshold squared for nearest neighbors.
  • nndist: Array of nearest neighbor geometric distances.
  • nn: Array of nearest neighbor indices.
  • nndij: Array of metric distances between particles.
This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.
source
',6))]),t("details",Qe,[t("summary",null,[e[230]||(e[230]=t("a",{id:"JetReconstruction.η",href:"#JetReconstruction.η"},[t("span",{class:"jlbinding"},"JetReconstruction.η")],-1)),e[231]||(e[231]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[232]||(e[232]=s('
julia
const η = eta
Alias for the pseudorapidity function, eta.
source
',3))]),t("details",Pe,[t("summary",null,[e[233]||(e[233]=t("a",{id:"JetReconstruction.FourMomentum",href:"#JetReconstruction.FourMomentum"},[t("span",{class:"jlbinding"},"JetReconstruction.FourMomentum")],-1)),e[234]||(e[234]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[235]||(e[235]=t("p",null,"Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.",-1)),e[236]||(e[236]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/Pseudojet.jl#L6-L8",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",we,[t("summary",null,[e[237]||(e[237]=t("a",{id:"JetReconstruction.HistoryElement",href:"#JetReconstruction.HistoryElement"},[t("span",{class:"jlbinding"},"JetReconstruction.HistoryElement")],-1)),e[238]||(e[238]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[239]||(e[239]=s('
julia
struct HistoryElement
A struct holding a record of jet mergers and finalisations
Fields:
  • parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)
  • parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)
  • child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.
  • jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.
  • dij: The distance corresponding to the recombination at this stage of the clustering.
  • max_dij_so_far: The largest recombination distance seen so far in the clustering history.
source
',5))]),t("details",Le,[t("summary",null,[e[240]||(e[240]=t("a",{id:"JetReconstruction.HistoryElement-Tuple{Any}",href:"#JetReconstruction.HistoryElement-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.HistoryElement")],-1)),e[241]||(e[241]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[242]||(e[242]=s('
julia
HistoryElement(jetp_index)
Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.
Arguments
  • jetp_index: The index of the jetp.
Returns
A HistoryElement object.
source
',7))]),t("details",Be,[t("summary",null,[e[243]||(e[243]=t("a",{id:"JetReconstruction.JetWithAncestors",href:"#JetReconstruction.JetWithAncestors"},[t("span",{class:"jlbinding"},"JetReconstruction.JetWithAncestors")],-1)),e[244]||(e[244]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[245]||(e[245]=s('
julia
struct JetWithAncestors
A struct representing a jet with its origin ancestors.
Fields
  • self::PseudoJet: The PseudoJet object for this jet.
  • jetp_index::Int: The index of the jet in the corresponding cluster sequence.
  • ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).
  • jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors
Note
This structure needs its associated cluster sequence origin to be useful.
source
',7))]),t("details",Me,[t("summary",null,[e[246]||(e[246]=t("a",{id:"JetReconstruction.Surrounding",href:"#JetReconstruction.Surrounding"},[t("span",{class:"jlbinding"},"JetReconstruction.Surrounding")],-1)),e[247]||(e[247]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[248]||(e[248]=s('
julia
struct Surrounding{N}
Structure used for iterating over neighbour tiles.
Fields
  • indices::NTuple{N, Int}: A tuple of N integers representing the indices.
source
',5))]),t("details",De,[t("summary",null,[e[249]||(e[249]=t("a",{id:"JetReconstruction.TiledJet",href:"#JetReconstruction.TiledJet"},[t("span",{class:"jlbinding"},"JetReconstruction.TiledJet")],-1)),e[250]||(e[250]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[251]||(e[251]=s('
julia
struct TiledJet
TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.
Fields
  • id::Int: The ID of the jet.
  • eta::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • kt2::Float64: The transverse momentum squared of the jet.
  • NN_dist::Float64: The distance to the nearest neighbor.
  • jets_index::Int: The index of the jet in the jet array.
  • tile_index::Int: The index of the tile in the tile array.
  • dij_posn::Int: The position of this jet in the dij compact array.
  • NN::TiledJet: The nearest neighbor.
  • previous::TiledJet: The previous jet.
  • next::TiledJet: The next jet.
source
',5))]),t("details",He,[t("summary",null,[e[252]||(e[252]=t("a",{id:"JetReconstruction.TiledJet-Tuple{Any}",href:"#JetReconstruction.TiledJet-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.TiledJet")],-1)),e[253]||(e[253]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[254]||(e[254]=s('
julia
TiledJet(id)
Constructs a TiledJet object with the given id and initializes its properties to zero.
Arguments
  • id: The ID of the TiledJet object.
Returns
A TiledJet object with the specified id and values set to zero or noTiledJet.
source
',7))]),t("details",Ie,[t("summary",null,[e[255]||(e[255]=t("a",{id:"JetReconstruction.Tiling",href:"#JetReconstruction.Tiling"},[t("span",{class:"jlbinding"},"JetReconstruction.Tiling")],-1)),e[256]||(e[256]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[257]||(e[257]=s('
julia
struct Tiling
The Tiling struct represents a tiling configuration for jet reconstruction.
Fields
  • setup::TilingDef: The tiling definition used for the configuration.
  • tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).
  • positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.
  • tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).
source
',5))]),t("details",qe,[t("summary",null,[e[258]||(e[258]=t("a",{id:"JetReconstruction.Tiling-Tuple{JetReconstruction.TilingDef}",href:"#JetReconstruction.Tiling-Tuple{JetReconstruction.TilingDef}"},[t("span",{class:"jlbinding"},"JetReconstruction.Tiling")],-1)),e[259]||(e[259]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[260]||(e[260]=s('
julia
Tiling(setup::TilingDef)
Constructs a intial Tiling object based on the provided setup parameters.
Arguments
  • setup::TilingDef: The setup parameters for the tiling.
Returns
A Tiling object.
source
',7))]),t("details",Se,[t("summary",null,[e[261]||(e[261]=t("a",{id:"JetReconstruction.TilingDef",href:"#JetReconstruction.TilingDef"},[t("span",{class:"jlbinding"},"JetReconstruction.TilingDef")],-1)),e[262]||(e[262]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[263]||(e[263]=s(`
julia
struct TilingDef
A struct representing the definition of a spcific tiling scheme.
Fields
  • _tiles_eta_min::Float64: The minimum rapidity of the tiles.
  • _tiles_eta_max::Float64: The maximum rapidity of the tiles.
  • _tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).
  • _tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).
  • _n_tiles_eta::Int: The number of tiles across rapidity.
  • _n_tiles_phi::Int: The number of tiles across phi.
  • _n_tiles::Int: The total number of tiles.
  • _tiles_ieta_min::Int: The minimum rapidity tile index.
  • _tiles_ieta_max::Int: The maximum rapidity tile index.
Constructor
TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
-	_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)
Constructs a TilingDef object with the given parameters.
source
`,8))]),t("details",Oe,[t("summary",null,[e[264]||(e[264]=t("a",{id:"JetReconstruction.neighbour_tiles",href:"#JetReconstruction.neighbour_tiles"},[t("span",{class:"jlbinding"},"JetReconstruction.neighbour_tiles")],-1)),e[265]||(e[265]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[266]||(e[266]=s(`
julia
struct neighbour_tiles
A struct representing the neighbouring tiles.
A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
+                   recombine = +)
This function is the actual implementation of the e+e- jet clustering algorithm.
source
`,3))]),t("details",f,[t("summary",null,[e[27]||(e[27]=t("a",{id:"JetReconstruction._ensure_valid_rap_phi-Tuple{PseudoJet}",href:"#JetReconstruction._ensure_valid_rap_phi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction._ensure_valid_rap_phi")],-1)),e[28]||(e[28]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=s('
julia
_ensure_valid_rap_phi(p::PseudoJet)
Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.
Arguments
  • p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.
source
',5))]),t("details",T,[t("summary",null,[e[30]||(e[30]=t("a",{id:"JetReconstruction._plain_jet_reconstruct-Tuple{}",href:"#JetReconstruction._plain_jet_reconstruct-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction._plain_jet_reconstruct")],-1)),e[31]||(e[31]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=s('
julia
_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)
This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.
Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.
The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt. Floating point values are allowed for generalised k_t algorithm.
Arguments
  • particles: A vector of PseudoJet objects representing the input particles.
  • p=-1: The power to which the transverse momentum (pt) of each particle is raised.
  • R=1.0: The jet radius parameter.
  • recombine: The recombination function used to merge two jets. Default is + (additive recombination).
Returns
  • clusterseq: The resulting ClusterSequence object representing the reconstructed jets.
source
',9))]),t("details",J,[t("summary",null,[e[33]||(e[33]=t("a",{id:"JetReconstruction._set_rap_phi!-Tuple{PseudoJet}",href:"#JetReconstruction._set_rap_phi!-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction._set_rap_phi!")],-1)),e[34]||(e[34]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=s('
_set_rap_phi!(p::PseudoJet)
Set the rapidity and azimuthal angle of the PseudoJet p.
Arguments
  • p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.
Description
This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.
Note - the ϕ angle is calculated in the range [0, 2π).
source
',8))]),t("details",E,[t("summary",null,[e[36]||(e[36]=t("a",{id:"JetReconstruction._tiled_jet_reconstruct-Tuple{Vector{PseudoJet}}",href:"#JetReconstruction._tiled_jet_reconstruct-Tuple{Vector{PseudoJet}}"},[t("span",{class:"jlbinding"},"JetReconstruction._tiled_jet_reconstruct")],-1)),e[37]||(e[37]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=s('
julia
_tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets once preprocessing of data types are done.
Arguments
  • particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.
  • p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)
source
',9))]),t("details",v,[t("summary",null,[e[39]||(e[39]=t("a",{id:"JetReconstruction._tj_diJ-Tuple{Any}",href:"#JetReconstruction._tj_diJ-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction._tj_diJ")],-1)),e[40]||(e[40]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=s('
julia
_tj_diJ(jet)
Compute the dij metric value for a given jet.
Arguments
  • jet: The input jet.
Returns
  • The dij value for the jet.
Example
source
',8))]),t("details",R,[t("summary",null,[e[42]||(e[42]=t("a",{id:"JetReconstruction._tj_dist-Tuple{Any, Any}",href:"#JetReconstruction._tj_dist-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction._tj_dist")],-1)),e[43]||(e[43]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[44]||(e[44]=s('
julia
_tj_dist(jetA, jetB)
Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.
Arguments
  • jetA: The first jet.
  • jetB: The second jet.
Returns
The squared distance between jetA and jetB.
Examples
source
',8))]),t("details",C,[t("summary",null,[e[45]||(e[45]=t("a",{id:"JetReconstruction.add_step_to_history!-Tuple{ClusterSequence, Vararg{Any, 4}}",href:"#JetReconstruction.add_step_to_history!-Tuple{ClusterSequence, Vararg{Any, 4}}"},[t("span",{class:"jlbinding"},"JetReconstruction.add_step_to_history!")],-1)),e[46]||(e[46]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[47]||(e[47]=s('
julia
add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)
Add a new jet's history into the recombination sequence.
Arguments:
  • clusterseq::ClusterSequence: The cluster sequence object.
  • parent1: The index of the first parent.
  • parent2: The index of the second parent.
  • jetp_index: The index of the jet.
  • dij: The dij value.
This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.
If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.
source
',7))]),t("details",F,[t("summary",null,[e[48]||(e[48]=t("a",{id:"JetReconstruction.add_untagged_neighbours_to_tile_union-NTuple{4, Any}",href:"#JetReconstruction.add_untagged_neighbours_to_tile_union-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.add_untagged_neighbours_to_tile_union")],-1)),e[49]||(e[49]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[50]||(e[50]=s('
julia
add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)
Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.
Arguments
  • center_index: The index of the center tile.
  • tile_union: An array to store the indices of neighbouring tiles.
  • n_near_tiles: The number of neighbouring tiles.
  • tiling: The tiling object containing the tile tags.
Returns
The updated number of near tiles.
source
',7))]),t("details",A,[t("summary",null,[e[51]||(e[51]=t("a",{id:"JetReconstruction.angular_distance-Tuple{Any, Any, Any}",href:"#JetReconstruction.angular_distance-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.angular_distance")],-1)),e[52]||(e[52]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=s('
julia
angular_distance(eereco, i, j) -> Float64
',1)),t("p",null,[e[55]||(e[55]=i("Calculate the angular distance between two jets ")),e[56]||(e[56]=t("code",null,"i",-1)),e[57]||(e[57]=i(" and ")),e[58]||(e[58]=t("code",null,"j",-1)),e[59]||(e[59]=i(" using the formula ")),t("mjx-container",x,[(a(),l("svg",_,e[53]||(e[53]=[s('',1)]))),e[54]||(e[54]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mn",null,"1"),t("mo",null,"−"),t("mi",null,"c"),t("mi",null,"o"),t("mi",null,"s"),t("mo",{stretchy:"false"},"("),t("msub",null,[t("mi",null,"θ"),t("mrow",{"data-mjx-texclass":"ORD"},[t("mi",null,"i"),t("mi",null,"j")])]),t("mo",{stretchy:"false"},")")])],-1))]),e[60]||(e[60]=i("."))]),e[71]||(e[71]=s("
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
Returns
",3)),t("ul",null,[t("li",null,[e[63]||(e[63]=t("code",null,"Float64",-1)),e[64]||(e[64]=i(": The angular distance between ")),e[65]||(e[65]=t("code",null,"i",-1)),e[66]||(e[66]=i(" and ")),e[67]||(e[67]=t("code",null,"j",-1)),e[68]||(e[68]=i(", which is ")),t("mjx-container",Q,[(a(),l("svg",P,e[61]||(e[61]=[s('',1)]))),e[62]||(e[62]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mn",null,"1"),t("mo",null,"−"),t("mi",null,"c"),t("mi",null,"o"),t("mi",null,"s"),t("mi",null,"h"),t("mi",null,"e"),t("mi",null,"t"),t("mi",null,"a")])],-1))]),e[69]||(e[69]=i("."))])]),e[72]||(e[72]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/EEAlgorithm.jl#L6-L20",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",w,[t("summary",null,[e[73]||(e[73]=t("a",{id:"JetReconstruction.check_algorithm_power_consistency-Tuple{}",href:"#JetReconstruction.check_algorithm_power_consistency-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction.check_algorithm_power_consistency")],-1)),e[74]||(e[74]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[75]||(e[75]=t("p",null,"Allow a check for algorithm and power consistency",-1)),e[76]||(e[76]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/AlgorithmStrategyEnums.jl#L129",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",L,[t("summary",null,[e[77]||(e[77]=t("a",{id:"JetReconstruction.detach!-Tuple{JetReconstruction.TiledJet}",href:"#JetReconstruction.detach!-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.detach!")],-1)),e[78]||(e[78]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[79]||(e[79]=s('
julia
detach!(jet::TiledJet)
Detach a TiledJet from its linked list by updating the previous and next pointers.
Arguments
  • jet::TiledJet: The TiledJet object to detach.
source
',5))]),t("details",B,[t("summary",null,[e[80]||(e[80]=t("a",{id:"JetReconstruction.determine_rapidity_extent-Union{Tuple{Vector{T}}, Tuple{T}} where T<:AbstractFloat",href:"#JetReconstruction.determine_rapidity_extent-Union{Tuple{Vector{T}}, Tuple{T}} where T<:AbstractFloat"},[t("span",{class:"jlbinding"},"JetReconstruction.determine_rapidity_extent")],-1)),e[81]||(e[81]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[82]||(e[82]=s('
julia
determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat
Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.
This is the heuristic which is used by FastJet (inline comments are from FastJet).
Arguments
  • eta::Vector{T}: A vector of rapidity values.
Returns
  • minrap::T: The minimum rapidity value.
  • maxrap::T: The maximum rapidity value.
source
',8))]),t("details",M,[t("summary",null,[e[83]||(e[83]=t("a",{id:"JetReconstruction.dij-NTuple{4, Any}",href:"#JetReconstruction.dij-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dij")],-1)),e[84]||(e[84]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[85]||(e[85]=s('
julia
dij(i, kt2_array, nn, nndist)
Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß
Arguments
  • i: The index of the element.
  • kt2_array: An array of kt2 values.
  • nn: An array of nearest neighbors.
  • nndist: An array of nearest neighbor distances.
Returns
  • The computed dij value.
source
',7))]),t("details",D,[t("summary",null,[e[86]||(e[86]=t("a",{id:"JetReconstruction.dij_dist-NTuple{4, Any}",href:"#JetReconstruction.dij_dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dij_dist")],-1)),e[87]||(e[87]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[92]||(e[92]=s('
julia
dij_dist(eereco, i, j, dij_factor)
',1)),t("p",null,[e[90]||(e[90]=i("Calculate the dij distance between two ")),t("mjx-container",H,[(a(),l("svg",I,e[88]||(e[88]=[s('',1)]))),e[89]||(e[89]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("msup",null,[t("mi",null,"e"),t("mo",null,"+")]),t("msup",null,[t("mi",null,"e"),t("mo",null,"−")])])],-1))]),e[91]||(e[91]=i("jets."))]),e[93]||(e[93]=s('
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
  • dij_factor: The scaling factor to multiply the dij distance by.
Returns
  • The dij distance between i and j.
source
',5))]),t("details",q,[t("summary",null,[e[94]||(e[94]=t("a",{id:"JetReconstruction.dist-NTuple{4, Any}",href:"#JetReconstruction.dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dist")],-1)),e[95]||(e[95]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[96]||(e[96]=s('
julia
dist(i, j, rapidity_array, phi_array)
Compute the distance between points in a 2D space defined by rapidity and phi coordinates.
Arguments
  • i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).
  • j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).
  • rapidity_array::Vector{Float64}: Array of rapidity coordinates.
  • phi_array::Vector{Float64}: Array of phi coordinates.
Returns
  • distance::Float64: The distance between the two points.
source
',7))]),t("details",S,[t("summary",null,[e[97]||(e[97]=t("a",{id:"JetReconstruction.do_iB_recombination_step!-Tuple{ClusterSequence, Any, Any}",href:"#JetReconstruction.do_iB_recombination_step!-Tuple{ClusterSequence, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.do_iB_recombination_step!")],-1)),e[98]||(e[98]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[99]||(e[99]=s('
julia
do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)
Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the jet.
  • diB: The diB value.
source
',5))]),t("details",O,[t("summary",null,[e[100]||(e[100]=t("a",{id:"JetReconstruction.do_ij_recombination_step!",href:"#JetReconstruction.do_ij_recombination_step!"},[t("span",{class:"jlbinding"},"JetReconstruction.do_ij_recombination_step!")],-1)),e[101]||(e[101]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[102]||(e[102]=s('
julia
do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)
Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the first jet to be recombined.
  • jet_j: The index of the second jet to be recombined.
  • dij: The distance between the two jets.
  • recombine=+: The recombination function to be used. Default is addition.
Returns
  • newjet_k: The index of the newly created jet.
Description
This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.
source
',9))]),t("details",N,[t("summary",null,[e[103]||(e[103]=t("a",{id:"JetReconstruction.energy-Tuple{PseudoJet}",href:"#JetReconstruction.energy-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.energy")],-1)),e[104]||(e[104]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[105]||(e[105]=s('
julia
energy(p::PseudoJet)
Return the energy of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The energy of the PseudoJet.
source
',7))]),t("details",V,[t("summary",null,[e[106]||(e[106]=t("a",{id:"JetReconstruction.eta-Tuple{PseudoJet}",href:"#JetReconstruction.eta-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.eta")],-1)),e[107]||(e[107]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[108]||(e[108]=s('
julia
eta(p::PseudoJet)
Compute the pseudorapidity (η) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.
Returns
  • The pseudorapidity (η) of the PseudoJet.
source
',7))]),t("details",z,[t("summary",null,[e[109]||(e[109]=t("a",{id:"JetReconstruction.fast_findmin-Tuple{Any, Any}",href:"#JetReconstruction.fast_findmin-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.fast_findmin")],-1)),e[110]||(e[110]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[111]||(e[111]=s('
julia
fast_findmin(dij, n)
Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.
Arguments
  • dij: An array of values.
  • n: The number of elements to consider in the dij array.
Returns
  • dij_min: The minimum value in the first n elements of the dij array.
  • best: The index of the minimum value in the dij array.
source
',7))]),t("details",Z,[t("summary",null,[e[112]||(e[112]=t("a",{id:"JetReconstruction.find_tile_neighbours!-NTuple{5, Any}",href:"#JetReconstruction.find_tile_neighbours!-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.find_tile_neighbours!")],-1)),e[113]||(e[113]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[114]||(e[114]=s('
julia
find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)
Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run
Arguments
  • tile_union: The tile union to which the neighbouring tiles will be added.
  • jetA: The first jet.
  • jetB: The second jet.
  • oldB: The old second jet.
  • tiling: The tiling information.
Returns
The number of neighbouring tiles added to the tile_union.
source
',7))]),t("details",U,[t("summary",null,[e[115]||(e[115]=t("a",{id:"JetReconstruction.geometric_distance-NTuple{4, AbstractFloat}",href:"#JetReconstruction.geometric_distance-NTuple{4, AbstractFloat}"},[t("span",{class:"jlbinding"},"JetReconstruction.geometric_distance")],-1)),e[116]||(e[116]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[117]||(e[117]=s('
julia
geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)
Compute the geometric distance between two points in the rap-phi plane.
Arguments
  • eta1::AbstractFloat: The eta coordinate of the first point.
  • phi1::AbstractFloat: The phi coordinate of the first point.
  • eta2::AbstractFloat: The eta coordinate of the second point.
  • phi2::AbstractFloat: The phi coordinate of the second point.
Returns
  • distance::Float64: The geometric distance between the two points.
source
',7))]),t("details",X,[t("summary",null,[e[118]||(e[118]=t("a",{id:"JetReconstruction.get_algorithm_power_consistency-Tuple{}",href:"#JetReconstruction.get_algorithm_power_consistency-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_algorithm_power_consistency")],-1)),e[119]||(e[119]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[120]||(e[120]=s('
julia
get_algorithm_power_consistency(; p::Union{Real, Nothing}, algorithm::Union{JetAlgorithm, Nothing})
Get the algorithm and power consistency correct
This function checks the consistency between the algorithm and power parameters. If the algorithm is specified, it checks if the power parameter is consistent with the algorithm's known power. If the power parameter is not specified, it sets the power parameter based on the algorithm. If neither the algorithm nor the power parameter is specified, it throws an ArgumentError.
Arguments
  • p::Union{Real, Nothing}: The power value.
  • algorithm::Union{JetAlgorithm, Nothing}: The algorithm.
Returns
A named tuple of the consistent power and algorithm values.
Throws
  • ArgumentError: If the algorithm and power are inconsistent or if neither the algorithm nor the power is specified.
source
',10))]),t("details",G,[t("summary",null,[e[121]||(e[121]=t("a",{id:"JetReconstruction.get_all_ancestors-Tuple{Any, ClusterSequence}",href:"#JetReconstruction.get_all_ancestors-Tuple{Any, ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_all_ancestors")],-1)),e[122]||(e[122]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[123]||(e[123]=s('
julia
get_all_ancestors(idx, cs::ClusterSequence)
Recursively finds all ancestors of a given index in a ClusterSequence object.
Arguments
  • idx: The index of the jet for which to find ancestors.
  • cs: The ClusterSequence object containing the jet history.
Returns
An array of indices representing the ancestors of the given jet.
source
',7))]),t("details",W,[t("summary",null,[e[124]||(e[124]=t("a",{id:"JetReconstruction.get_dij_dist-NTuple{4, Any}",href:"#JetReconstruction.get_dij_dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_dij_dist")],-1)),e[125]||(e[125]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[126]||(e[126]=s('
julia
get_dij_dist(nn_dist, kt2_1, kt2_2, R2)
Compute the dij metric distance between two jets.
Arguments
  • nn_dist: The nearest-neighbor distance between two jets.
  • kt2_1: The squared momentum metric value of the first jet.
  • kt2_2: The squared momentum metric value of the second jet.
  • R2: The jet radius parameter squared.
Returns
The distance between the two jets.
If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.
source
',8))]),t("details",$,[t("summary",null,[e[127]||(e[127]=t("a",{id:"JetReconstruction.get_tile-Tuple{JetReconstruction.TilingDef, AbstractFloat, AbstractFloat}",href:"#JetReconstruction.get_tile-Tuple{JetReconstruction.TilingDef, AbstractFloat, AbstractFloat}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_tile")],-1)),e[128]||(e[128]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[129]||(e[129]=s('
julia
get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)
Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.
Arguments
  • tiling_setup: A TilingDef object that contains the tiling setup parameters.
  • eta: The eta value for which to calculate the tile index.
  • phi: The phi value for which to calculate the tile index.
Returns
  • ieta: The tile index along the eta direction.
  • iphi: The tile index along the phi direction.
source
',7))]),t("details",K,[t("summary",null,[e[130]||(e[130]=t("a",{id:"JetReconstruction.get_tile_cartesian_indices-Tuple{JetReconstruction.TilingDef, Int64}",href:"#JetReconstruction.get_tile_cartesian_indices-Tuple{JetReconstruction.TilingDef, Int64}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_tile_cartesian_indices")],-1)),e[131]||(e[131]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[132]||(e[132]=s('
julia
get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)
Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)
Arguments
  • tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.
  • i_η::Int: The index of the tile in the η dimension.
  • i_ϕ::Int: The index of the tile in the ϕ dimension.
Returns
  • The linear index of the tile.
source
',7))]),t("details",Y,[t("summary",null,[e[133]||(e[133]=t("a",{id:"JetReconstruction.initial_history-Tuple{Any}",href:"#JetReconstruction.initial_history-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.initial_history")],-1)),e[134]||(e[134]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[135]||(e[135]=s('
julia
initial_history(particles)
Create an initial history for the given particles.
Arguments
  • particles: The initial vector of stable particles.
Returns
  • history: An array of HistoryElement objects.
  • Qtot: The total energy in the event.
source
',7))]),t("details",ee,[t("summary",null,[e[136]||(e[136]=t("a",{id:"JetReconstruction.insert!-Tuple{JetReconstruction.TiledJet, JetReconstruction.TiledJet}",href:"#JetReconstruction.insert!-Tuple{JetReconstruction.TiledJet, JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.insert!")],-1)),e[137]||(e[137]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[138]||(e[138]=s('
julia
insert!(nextjet::TiledJet, jettomove::TiledJet)
Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list
Arguments
  • nextjet::TiledJet: The TiledJet object after which jettomove should be inserted.
  • jettomove::TiledJet: The TiledJet object to be inserted.
Example
source
',6))]),t("details",te,[t("summary",null,[e[139]||(e[139]=t("a",{id:"JetReconstruction.is_ee-Tuple{JetReconstruction.JetAlgorithm.Algorithm}",href:"#JetReconstruction.is_ee-Tuple{JetReconstruction.JetAlgorithm.Algorithm}"},[t("span",{class:"jlbinding"},"JetReconstruction.is_ee")],-1)),e[140]||(e[140]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[141]||(e[141]=s('
julia
is_ee(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a e+e- reconstruction algorithm.
Returns
true if the algorithm is a e+e- reconstruction algorithm, false otherwise.
source
',5))]),t("details",ie,[t("summary",null,[e[142]||(e[142]=t("a",{id:"JetReconstruction.is_pp-Tuple{JetReconstruction.JetAlgorithm.Algorithm}",href:"#JetReconstruction.is_pp-Tuple{JetReconstruction.JetAlgorithm.Algorithm}"},[t("span",{class:"jlbinding"},"JetReconstruction.is_pp")],-1)),e[143]||(e[143]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[144]||(e[144]=s('
julia
is_pp(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a pp reconstruction algorithm.
Returns
true if the algorithm is a pp reconstruction algorithm, false otherwise.
source
',5))]),t("details",se,[t("summary",null,[e[145]||(e[145]=t("a",{id:"JetReconstruction.isvalid-Tuple{JetReconstruction.TiledJet}",href:"#JetReconstruction.isvalid-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.isvalid")],-1)),e[146]||(e[146]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[147]||(e[147]=s('
julia
isvalid(t::TiledJet)
Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.
Arguments
  • t::TiledJet: The TiledJet object to check.
Returns
  • Bool: true if the TiledJet object is valid, false otherwise.
source
',7))]),t("details",ne,[t("summary",null,[e[148]||(e[148]=t("a",{id:"JetReconstruction.jet_ranks-Tuple{ClusterSequence}",href:"#JetReconstruction.jet_ranks-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.jet_ranks")],-1)),e[149]||(e[149]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[150]||(e[150]=s('
julia
jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)
Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.
  • compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.
Returns
A dictionary mapping each jet index to its rank.
Note
This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.
source
',9))]),t("details",oe,[t("summary",null,[e[151]||(e[151]=t("a",{id:"JetReconstruction.m-Tuple{PseudoJet}",href:"#JetReconstruction.m-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.m")],-1)),e[152]||(e[152]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[153]||(e[153]=s('
julia
m(p::PseudoJet)
Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the invariant mass.
Returns
The invariant mass of the PseudoJet object.
source
',7))]),t("details",le,[t("summary",null,[e[154]||(e[154]=t("a",{id:"JetReconstruction.m2-Tuple{PseudoJet}",href:"#JetReconstruction.m2-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.m2")],-1)),e[155]||(e[155]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[156]||(e[156]=s('
julia
m2(p::PseudoJet)
Calculate the invariant mass squared (m^2) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.
Returns
  • The invariant mass squared (m^2) of the PseudoJet.
source
',7))]),t("details",ae,[t("summary",null,[e[157]||(e[157]=t("a",{id:"JetReconstruction.mag-Tuple{PseudoJet}",href:"#JetReconstruction.mag-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.mag")],-1)),e[158]||(e[158]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[159]||(e[159]=s('
julia
mag(p::PseudoJet)
Return the magnitude of the momentum of a PseudoJet, |p|.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the magnitude.
Returns
The magnitude of the PseudoJet object.
source
',7))]),t("details",de,[t("summary",null,[e[160]||(e[160]=t("a",{id:"JetReconstruction.mass-Tuple{PseudoJet}",href:"#JetReconstruction.mass-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.mass")],-1)),e[161]||(e[161]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[162]||(e[162]=s('
julia
mass(p::PseudoJet)
Compute the invariant mass (alias for m(p)).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the mass.
Returns
  • The mass of the PseudoJet.
source
',7))]),t("details",re,[t("summary",null,[e[163]||(e[163]=t("a",{id:"JetReconstruction.mass2",href:"#JetReconstruction.mass2"},[t("span",{class:"jlbinding"},"JetReconstruction.mass2")],-1)),e[164]||(e[164]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[165]||(e[165]=t("p",null,[i("Alias for "),t("code",null,"m2"),i(" function")],-1)),e[166]||(e[166]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/Pseudojet.jl#L370",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",pe,[t("summary",null,[e[167]||(e[167]=t("a",{id:"JetReconstruction.merge_steps-Tuple{ClusterSequence}",href:"#JetReconstruction.merge_steps-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.merge_steps")],-1)),e[168]||(e[168]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[169]||(e[169]=s('
julia
merge_steps(clusterseq::ClusterSequence)
Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
Returns
  • merge_steps::Int: The number of merge steps.
source
',7))]),t("details",ce,[t("summary",null,[e[170]||(e[170]=t("a",{id:"JetReconstruction.phi-Tuple{PseudoJet}",href:"#JetReconstruction.phi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.phi")],-1)),e[171]||(e[171]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[172]||(e[172]=s('
julia
phi(p::PseudoJet)
Compute the ϕ angle of a PseudoJet object p.
Note this function is a wrapper for phi_02pi(p).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
',8))]),t("details",he,[t("summary",null,[e[173]||(e[173]=t("a",{id:"JetReconstruction.phi_02pi-Tuple{PseudoJet}",href:"#JetReconstruction.phi_02pi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.phi_02pi")],-1)),e[174]||(e[174]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[175]||(e[175]=s('
julia
phi_02pi(p::PseudoJet)
Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
',7))]),t("details",ue,[t("summary",null,[e[176]||(e[176]=t("a",{id:"JetReconstruction.pt-Tuple{PseudoJet}",href:"#JetReconstruction.pt-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pt")],-1)),e[177]||(e[177]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[178]||(e[178]=s('
julia
pt(p::PseudoJet)
Compute the scalar transverse momentum (pt) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.
Returns
  • The transverse momentum (pt) of the PseudoJet.
source
',7))]),t("details",ge,[t("summary",null,[e[179]||(e[179]=t("a",{id:"JetReconstruction.pt2-Tuple{PseudoJet}",href:"#JetReconstruction.pt2-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pt2")],-1)),e[180]||(e[180]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[181]||(e[181]=s('
julia
pt2(p::PseudoJet)
Get the squared transverse momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The squared transverse momentum of the PseudoJet.
source
',7))]),t("details",ke,[t("summary",null,[e[182]||(e[182]=t("a",{id:"JetReconstruction.px-Tuple{PseudoJet}",href:"#JetReconstruction.px-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.px")],-1)),e[183]||(e[183]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[184]||(e[184]=s('
julia
px(p::PseudoJet)
Return the x-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The x-component of the momentum of the PseudoJet.
source
',7))]),t("details",be,[t("summary",null,[e[185]||(e[185]=t("a",{id:"JetReconstruction.py-Tuple{PseudoJet}",href:"#JetReconstruction.py-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.py")],-1)),e[186]||(e[186]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[187]||(e[187]=s('
julia
py(p::PseudoJet)
Return the y-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The y-component of the momentum of the PseudoJet.
source
',7))]),t("details",me,[t("summary",null,[e[188]||(e[188]=t("a",{id:"JetReconstruction.pz-Tuple{PseudoJet}",href:"#JetReconstruction.pz-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pz")],-1)),e[189]||(e[189]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[190]||(e[190]=s('
julia
pz(p::PseudoJet)
Return the z-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The z-component of the momentum of the PseudoJet.
source
',7))]),t("details",ye,[t("summary",null,[e[191]||(e[191]=t("a",{id:"JetReconstruction.rapidity-Tuple{PseudoJet}",href:"#JetReconstruction.rapidity-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.rapidity")],-1)),e[192]||(e[192]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[193]||(e[193]=s('
julia
rapidity(p::PseudoJet)
Compute the rapidity of a PseudoJet object.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the rapidity.
Returns
The rapidity of the PseudoJet object.
source
',7))]),t("details",je,[t("summary",null,[e[194]||(e[194]=t("a",{id:"JetReconstruction.reco_state-Tuple{ClusterSequence, Any}",href:"#JetReconstruction.reco_state-Tuple{ClusterSequence, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.reco_state")],-1)),e[195]||(e[195]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[196]||(e[196]=s('
julia
reco_state(cs::ClusterSequence, pt_ranks; iteration=0)
This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.
Arguments
  • cs::ClusterSequence: The ClusterSequence object to update.
  • ranks: The ranks of the original clusters, that are inherited by peudojets
during the reconstruction process.
  • iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).
  • ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).
Returns
A dictionary representing a snapshot of the reconstruction state.
Details
The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.
source
',11))]),t("details",fe,[t("summary",null,[e[197]||(e[197]=t("a",{id:"JetReconstruction.rightneighbours-Tuple{Int64, JetReconstruction.Tiling}",href:"#JetReconstruction.rightneighbours-Tuple{Int64, JetReconstruction.Tiling}"},[t("span",{class:"jlbinding"},"JetReconstruction.rightneighbours")],-1)),e[198]||(e[198]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[199]||(e[199]=s('
julia
rightneighbours(center::Int, tiling::Tiling)
Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the indices of the right neighbors.
source
',7))]),t("details",Te,[t("summary",null,[e[200]||(e[200]=t("a",{id:"JetReconstruction.set_momentum!-Tuple{PseudoJet, Vararg{Any, 4}}",href:"#JetReconstruction.set_momentum!-Tuple{PseudoJet, Vararg{Any, 4}}"},[t("span",{class:"jlbinding"},"JetReconstruction.set_momentum!")],-1)),e[201]||(e[201]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[202]||(e[202]=s('
julia
set_momentum!(j::PseudoJet, px, py, pz, E)
Set the momentum components and energy of a PseudoJet object.
Arguments
  • j::PseudoJet: The PseudoJet object to set the momentum for.
  • px: The x-component of the momentum.
  • py: The y-component of the momentum.
  • pz: The z-component of the momentum.
  • E: The energy of the particle.
source
',5))]),t("details",Je,[t("summary",null,[e[203]||(e[203]=t("a",{id:"JetReconstruction.set_nearest_neighbours!-Tuple{ClusterSequence, JetReconstruction.Tiling, Vector{JetReconstruction.TiledJet}}",href:"#JetReconstruction.set_nearest_neighbours!-Tuple{ClusterSequence, JetReconstruction.Tiling, Vector{JetReconstruction.TiledJet}}"},[t("span",{class:"jlbinding"},"JetReconstruction.set_nearest_neighbours!")],-1)),e[204]||(e[204]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[205]||(e[205]=s('
julia
set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})
This function sets the nearest neighbor information for all jets in the tiledjets vector.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • tiling::Tiling: The tiling object.
  • tiledjets::Vector{TiledJet}: The vector of tiled jets.
Returns
  • NNs::Vector{TiledJet}: The vector of nearest neighbor jets.
  • diJ::Vector{Float64}: The vector of diJ values.
The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.
Note: The diJ values are calculated as the kt distance multiplied by R^2.
source
',9))]),t("details",Ee,[t("summary",null,[e[206]||(e[206]=t("a",{id:"JetReconstruction.setup_tiling-Union{Tuple{T}, Tuple{Vector{T}, AbstractFloat}} where T<:AbstractFloat",href:"#JetReconstruction.setup_tiling-Union{Tuple{T}, Tuple{Vector{T}, AbstractFloat}} where T<:AbstractFloat"},[t("span",{class:"jlbinding"},"JetReconstruction.setup_tiling")],-1)),e[207]||(e[207]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[208]||(e[208]=s('
julia
setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat
This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.
Arguments
  • eta::Vector{T}: A vector of rapidities.
  • Rparam::AbstractFloat: The jet radius parameter.
Returns
  • tiling_setup: A TilingDef object containing the tiling setup parameters.
Description
The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.
source
',9))]),t("details",ve,[t("summary",null,[e[209]||(e[209]=t("a",{id:"JetReconstruction.surrounding-Tuple{Int64, JetReconstruction.Tiling}",href:"#JetReconstruction.surrounding-Tuple{Int64, JetReconstruction.Tiling}"},[t("span",{class:"jlbinding"},"JetReconstruction.surrounding")],-1)),e[210]||(e[210]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[211]||(e[211]=s('
julia
surrounding(center::Int, tiling::Tiling)
Compute the surrounding indices of a given center index in a tiling.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the surrounding indices.
source
',7))]),t("details",Re,[t("summary",null,[e[212]||(e[212]=t("a",{id:"JetReconstruction.tile_index-Tuple{Any, Float64, Float64}",href:"#JetReconstruction.tile_index-Tuple{Any, Float64, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.tile_index")],-1)),e[213]||(e[213]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[214]||(e[214]=s('
julia
tile_index(tiling_setup, eta::Float64, phi::Float64)
Compute the tile index for a given (eta, phi) coordinate.
Arguments
  • tiling_setup: The tiling setup object containing the tile size and number of tiles.
  • eta::Float64: The eta coordinate.
  • phi::Float64: The phi coordinate.
Returns
The tile index corresponding to the (eta, phi) coordinate.
source
',7))]),t("details",Ce,[t("summary",null,[e[215]||(e[215]=t("a",{id:"JetReconstruction.tiledjet_remove_from_tiles!-Tuple{Any, Any}",href:"#JetReconstruction.tiledjet_remove_from_tiles!-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.tiledjet_remove_from_tiles!")],-1)),e[216]||(e[216]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[217]||(e[217]=s('
julia
tiledjet_remove_from_tiles!(tiling, jet)
Remove a jet from the given tiling structure.
Arguments
  • tiling: The tiling structure from which the jet will be removed.
  • jet: The jet to be removed from the tiling structure.
Description
This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.
source
',7))]),t("details",Fe,[t("summary",null,[e[218]||(e[218]=t("a",{id:"JetReconstruction.tiledjet_set_jetinfo!-Tuple{JetReconstruction.TiledJet, ClusterSequence, JetReconstruction.Tiling, Any, Any, Any}",href:"#JetReconstruction.tiledjet_set_jetinfo!-Tuple{JetReconstruction.TiledJet, ClusterSequence, JetReconstruction.Tiling, Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.tiledjet_set_jetinfo!")],-1)),e[219]||(e[219]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[220]||(e[220]=s('
julia
tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)
Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)
Arguments:
  • jet::TiledJet: The TiledJet object to set the information for.
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets.
  • tiling::Tiling: The Tiling object containing the tile information.
  • jets_index: The index of the jet in the ClusterSequence.
  • R2: The jet radius parameter squared.
  • p: The power to raise the pt2 value to.
This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.
Returns:
  • nothing
source
',8))]),t("details",Ae,[t("summary",null,[e[221]||(e[221]=t("a",{id:"JetReconstruction.upd_nn_crosscheck!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}",href:"#JetReconstruction.upd_nn_crosscheck!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_crosscheck!")],-1)),e[222]||(e[222]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[223]||(e[223]=s('
julia
upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
',5))]),t("details",xe,[t("summary",null,[e[224]||(e[224]=t("a",{id:"JetReconstruction.upd_nn_nocross!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}",href:"#JetReconstruction.upd_nn_nocross!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_nocross!")],-1)),e[225]||(e[225]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[226]||(e[226]=s('
julia
upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
',5))]),t("details",_e,[t("summary",null,[e[227]||(e[227]=t("a",{id:"JetReconstruction.upd_nn_step!-NTuple{12, Any}",href:"#JetReconstruction.upd_nn_step!-NTuple{12, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_step!")],-1)),e[228]||(e[228]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[229]||(e[229]=s('
julia
upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)
Update the nearest neighbor information after a jet merge step.
Arguments:
  • i: Index of the first particle in the last merge step.
  • j: Index of the second particle in the last merge step.
  • k: Index of the current particle for which the nearest neighbour will be updated.
  • N: Total number of particles (currently vaild array indexes are [1:N]).
  • Nn: Number of nearest neighbors to consider.
  • kt2_array: Array of transverse momentum squared values.
  • rapidity_array: Array of rapidity values.
  • phi_array: Array of azimuthal angle values.
  • R2: Distance threshold squared for nearest neighbors.
  • nndist: Array of nearest neighbor geometric distances.
  • nn: Array of nearest neighbor indices.
  • nndij: Array of metric distances between particles.
This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.
source
',6))]),t("details",Qe,[t("summary",null,[e[230]||(e[230]=t("a",{id:"JetReconstruction.η",href:"#JetReconstruction.η"},[t("span",{class:"jlbinding"},"JetReconstruction.η")],-1)),e[231]||(e[231]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[232]||(e[232]=s('
julia
const η = eta
Alias for the pseudorapidity function, eta.
source
',3))]),t("details",Pe,[t("summary",null,[e[233]||(e[233]=t("a",{id:"JetReconstruction.FourMomentum",href:"#JetReconstruction.FourMomentum"},[t("span",{class:"jlbinding"},"JetReconstruction.FourMomentum")],-1)),e[234]||(e[234]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[235]||(e[235]=t("p",null,"Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.",-1)),e[236]||(e[236]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/Pseudojet.jl#L6-L8",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",we,[t("summary",null,[e[237]||(e[237]=t("a",{id:"JetReconstruction.HistoryElement",href:"#JetReconstruction.HistoryElement"},[t("span",{class:"jlbinding"},"JetReconstruction.HistoryElement")],-1)),e[238]||(e[238]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[239]||(e[239]=s('
julia
struct HistoryElement
A struct holding a record of jet mergers and finalisations
Fields:
  • parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)
  • parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)
  • child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.
  • jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.
  • dij: The distance corresponding to the recombination at this stage of the clustering.
  • max_dij_so_far: The largest recombination distance seen so far in the clustering history.
source
',5))]),t("details",Le,[t("summary",null,[e[240]||(e[240]=t("a",{id:"JetReconstruction.HistoryElement-Tuple{Any}",href:"#JetReconstruction.HistoryElement-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.HistoryElement")],-1)),e[241]||(e[241]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[242]||(e[242]=s('
julia
HistoryElement(jetp_index)
Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.
Arguments
  • jetp_index: The index of the jetp.
Returns
A HistoryElement object.
source
',7))]),t("details",Be,[t("summary",null,[e[243]||(e[243]=t("a",{id:"JetReconstruction.JetWithAncestors",href:"#JetReconstruction.JetWithAncestors"},[t("span",{class:"jlbinding"},"JetReconstruction.JetWithAncestors")],-1)),e[244]||(e[244]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[245]||(e[245]=s('
julia
struct JetWithAncestors
A struct representing a jet with its origin ancestors.
Fields
  • self::PseudoJet: The PseudoJet object for this jet.
  • jetp_index::Int: The index of the jet in the corresponding cluster sequence.
  • ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).
  • jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors
Note
This structure needs its associated cluster sequence origin to be useful.
source
',7))]),t("details",Me,[t("summary",null,[e[246]||(e[246]=t("a",{id:"JetReconstruction.Surrounding",href:"#JetReconstruction.Surrounding"},[t("span",{class:"jlbinding"},"JetReconstruction.Surrounding")],-1)),e[247]||(e[247]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[248]||(e[248]=s('
julia
struct Surrounding{N}
Structure used for iterating over neighbour tiles.
Fields
  • indices::NTuple{N, Int}: A tuple of N integers representing the indices.
source
',5))]),t("details",De,[t("summary",null,[e[249]||(e[249]=t("a",{id:"JetReconstruction.TiledJet",href:"#JetReconstruction.TiledJet"},[t("span",{class:"jlbinding"},"JetReconstruction.TiledJet")],-1)),e[250]||(e[250]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[251]||(e[251]=s('
julia
struct TiledJet
TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.
Fields
  • id::Int: The ID of the jet.
  • eta::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • kt2::Float64: The transverse momentum squared of the jet.
  • NN_dist::Float64: The distance to the nearest neighbor.
  • jets_index::Int: The index of the jet in the jet array.
  • tile_index::Int: The index of the tile in the tile array.
  • dij_posn::Int: The position of this jet in the dij compact array.
  • NN::TiledJet: The nearest neighbor.
  • previous::TiledJet: The previous jet.
  • next::TiledJet: The next jet.
source
',5))]),t("details",He,[t("summary",null,[e[252]||(e[252]=t("a",{id:"JetReconstruction.TiledJet-Tuple{Any}",href:"#JetReconstruction.TiledJet-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.TiledJet")],-1)),e[253]||(e[253]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[254]||(e[254]=s('
julia
TiledJet(id)
Constructs a TiledJet object with the given id and initializes its properties to zero.
Arguments
  • id: The ID of the TiledJet object.
Returns
A TiledJet object with the specified id and values set to zero or noTiledJet.
source
',7))]),t("details",Ie,[t("summary",null,[e[255]||(e[255]=t("a",{id:"JetReconstruction.Tiling",href:"#JetReconstruction.Tiling"},[t("span",{class:"jlbinding"},"JetReconstruction.Tiling")],-1)),e[256]||(e[256]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[257]||(e[257]=s('
julia
struct Tiling
The Tiling struct represents a tiling configuration for jet reconstruction.
Fields
  • setup::TilingDef: The tiling definition used for the configuration.
  • tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).
  • positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.
  • tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).
source
',5))]),t("details",qe,[t("summary",null,[e[258]||(e[258]=t("a",{id:"JetReconstruction.Tiling-Tuple{JetReconstruction.TilingDef}",href:"#JetReconstruction.Tiling-Tuple{JetReconstruction.TilingDef}"},[t("span",{class:"jlbinding"},"JetReconstruction.Tiling")],-1)),e[259]||(e[259]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[260]||(e[260]=s('
julia
Tiling(setup::TilingDef)
Constructs a intial Tiling object based on the provided setup parameters.
Arguments
  • setup::TilingDef: The setup parameters for the tiling.
Returns
A Tiling object.
source
',7))]),t("details",Se,[t("summary",null,[e[261]||(e[261]=t("a",{id:"JetReconstruction.TilingDef",href:"#JetReconstruction.TilingDef"},[t("span",{class:"jlbinding"},"JetReconstruction.TilingDef")],-1)),e[262]||(e[262]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[263]||(e[263]=s(`
julia
struct TilingDef
A struct representing the definition of a spcific tiling scheme.
Fields
  • _tiles_eta_min::Float64: The minimum rapidity of the tiles.
  • _tiles_eta_max::Float64: The maximum rapidity of the tiles.
  • _tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).
  • _tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).
  • _n_tiles_eta::Int: The number of tiles across rapidity.
  • _n_tiles_phi::Int: The number of tiles across phi.
  • _n_tiles::Int: The total number of tiles.
  • _tiles_ieta_min::Int: The minimum rapidity tile index.
  • _tiles_ieta_max::Int: The maximum rapidity tile index.
Constructor
TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
+	_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)
Constructs a TilingDef object with the given parameters.
source
`,8))]),t("details",Oe,[t("summary",null,[e[264]||(e[264]=t("a",{id:"JetReconstruction.neighbour_tiles",href:"#JetReconstruction.neighbour_tiles"},[t("span",{class:"jlbinding"},"JetReconstruction.neighbour_tiles")],-1)),e[265]||(e[265]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[266]||(e[266]=s(`
julia
struct neighbour_tiles
A struct representing the neighbouring tiles.
A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
 X.X
-XXX
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source
`,8))]),t("details",Ne,[t("summary",null,[e[267]||(e[267]=t("a",{id:"JetReconstruction.rightmost_tiles",href:"#JetReconstruction.rightmost_tiles"},[t("span",{class:"jlbinding"},"JetReconstruction.rightmost_tiles")],-1)),e[268]||(e[268]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[269]||(e[269]=s(`
julia
struct rightmost_tiles
A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
+XXX
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source
`,8))]),t("details",Ne,[t("summary",null,[e[267]||(e[267]=t("a",{id:"JetReconstruction.rightmost_tiles",href:"#JetReconstruction.rightmost_tiles"},[t("span",{class:"jlbinding"},"JetReconstruction.rightmost_tiles")],-1)),e[268]||(e[268]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[269]||(e[269]=s(`
julia
struct rightmost_tiles
A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
 O.X
-OOO
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source
`,7))])])}const Ke=r(p,[["render",Ve]]);export{$e as __pageData,Ke as default};
+OOO

Note, rapidity coordinate must be in range, ϕ coordinate wraps

Fields

  • n_η::Int: Number of η tiles

  • n_ϕ::Int: Number of ϕ tiles

  • start_η::Int: Centre η tile coordinate

  • start_ϕ::Int: Centre ϕ tile coordinate

source

`,7))])])}const Ke=d(p,[["render",Ve]]);export{$e as __pageData,Ke as default}; diff --git a/dev/assets/lib_internal.md.oZWVn3YV.lean.js b/dev/assets/lib_internal.md.CnFs4lT2.lean.js similarity index 94% rename from dev/assets/lib_internal.md.oZWVn3YV.lean.js rename to dev/assets/lib_internal.md.CnFs4lT2.lean.js index 7209f23..251d132 100644 --- a/dev/assets/lib_internal.md.oZWVn3YV.lean.js +++ b/dev/assets/lib_internal.md.CnFs4lT2.lean.js @@ -1,8 +1,8 @@ -import{_ as r,c as l,a5 as s,j as t,a as i,G as o,B as d,o as a}from"./chunks/framework.BtBDBbTG.js";const $e=JSON.parse('{"title":"Jet Reconstruction Internal Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/internal.md","filePath":"lib/internal.md","lastUpdated":null}'),p={name:"lib/internal.md"},c={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},x={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},_={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.666ex"},xmlns:"http://www.w3.org/2000/svg",width:"11.255ex",height:"2.363ex",role:"img",focusable:"false",viewBox:"0 -750 4974.7 1044.2","aria-hidden":"true"},Q={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},P={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.186ex"},xmlns:"http://www.w3.org/2000/svg",width:"11.406ex",height:"1.756ex",role:"img",focusable:"false",viewBox:"0 -694 5041.4 776","aria-hidden":"true"},w={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},H={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},I={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"4.973ex",height:"1.779ex",role:"img",focusable:"false",viewBox:"0 -775.2 2198.3 786.2","aria-hidden":"true"},q={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"},te={class:"jldocstring custom-block"},ie={class:"jldocstring custom-block"},se={class:"jldocstring custom-block"},ne={class:"jldocstring custom-block"},oe={class:"jldocstring custom-block"},le={class:"jldocstring custom-block"},ae={class:"jldocstring custom-block"},re={class:"jldocstring custom-block"},de={class:"jldocstring custom-block"},pe={class:"jldocstring custom-block"},ce={class:"jldocstring custom-block"},he={class:"jldocstring custom-block"},ue={class:"jldocstring custom-block"},ge={class:"jldocstring custom-block"},ke={class:"jldocstring custom-block"},be={class:"jldocstring custom-block"},me={class:"jldocstring custom-block"},ye={class:"jldocstring custom-block"},je={class:"jldocstring custom-block"},Te={class:"jldocstring custom-block"},fe={class:"jldocstring custom-block"},Je={class:"jldocstring custom-block"},Ee={class:"jldocstring custom-block"},ve={class:"jldocstring custom-block"},Re={class:"jldocstring custom-block"},Ce={class:"jldocstring custom-block"},Fe={class:"jldocstring custom-block"},Ae={class:"jldocstring custom-block"},xe={class:"jldocstring custom-block"},_e={class:"jldocstring custom-block"},Qe={class:"jldocstring custom-block"},Pe={class:"jldocstring custom-block"},we={class:"jldocstring custom-block"},Le={class:"jldocstring custom-block"},Be={class:"jldocstring custom-block"},Me={class:"jldocstring custom-block"},De={class:"jldocstring custom-block"},He={class:"jldocstring custom-block"},Ie={class:"jldocstring custom-block"},qe={class:"jldocstring custom-block"},Se={class:"jldocstring custom-block"},Oe={class:"jldocstring custom-block"},Ne={class:"jldocstring custom-block"};function Ve(ze,e,Ze,Ue,Xe,Ge){const n=d("Badge");return a(),l("div",null,[e[270]||(e[270]=s('

Jet Reconstruction Internal Documentation ​

Documentation for JetReconstruction.jl's internal methods and types.

N.B. no guarantee is made of stability of these interfaces or types.

Index ​

Public Interface ​

',6)),t("details",c,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Base.:+-Tuple{PseudoJet, PseudoJet}",href:"#Base.:+-Tuple{PseudoJet, PseudoJet}"},[t("span",{class:"jlbinding"},"Base.:+")],-1)),e[1]||(e[1]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=s('
julia
+(j1::PseudoJet, j2::PseudoJet)

Addition operator for PseudoJet objects.

Arguments

  • j1::PseudoJet: The first PseudoJet object.

  • j2::PseudoJet: The second PseudoJet object.

Returns

A new PseudoJet object with the sum of the momenta and energy of j1 and j2.

source

',7))]),t("details",h,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Base.copy-Tuple{JetReconstruction.TiledJet}",href:"#Base.copy-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"Base.copy")],-1)),e[4]||(e[4]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=s('
julia
copy(j::TiledJet)

Create a copy of a TiledJet object.

Arguments

  • j::TiledJet: The TiledJet object to be copied.

Returns

A new TiledJet object with the same attributes as the input object.

source

',7))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Base.iterate",href:"#Base.iterate"},[t("span",{class:"jlbinding"},"Base.iterate")],-1)),e[7]||(e[7]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[8]||(e[8]=s('
julia
Base.iterate(t::rightmost_tiles, state=1)

Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.

source

',3))]),t("details",g,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Base.iterate-2",href:"#Base.iterate-2"},[t("span",{class:"jlbinding"},"Base.iterate")],-1)),e[10]||(e[10]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[11]||(e[11]=s('
julia
Base.iterate(t::neighbour_tiles, state=1)

Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.

source

',3))]),t("details",k,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Base.iterate-Tuple{JetReconstruction.TiledJet}",href:"#Base.iterate-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"Base.iterate")],-1)),e[13]||(e[13]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=s('
julia
Base.iterate(tj::TiledJet)

Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).

Arguments

  • tj::TiledJet: The TiledJet object to start to iterate over.

source

',5))]),t("details",b,[t("summary",null,[e[15]||(e[15]=t("a",{id:"Base.show-Tuple{IO, PseudoJet}",href:"#Base.show-Tuple{IO, PseudoJet}"},[t("span",{class:"jlbinding"},"Base.show")],-1)),e[16]||(e[16]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=s('
julia
show(io::IO, jet::PseudoJet)

Print a PseudoJet object to the specified IO stream.

Arguments

  • io::IO: The IO stream to which the information will be printed.

  • jet::PseudoJet: The PseudoJet object whose information will be printed.

source

',5))]),t("details",m,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Base.tryparse-Tuple{Type{<:Enum}, String}",href:"#Base.tryparse-Tuple{Type{<:Enum}, String}"},[t("span",{class:"jlbinding"},"Base.tryparse")],-1)),e[19]||(e[19]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=s('
julia
Base.tryparse(E::Type{<:Enum}, str::String)

Parser that converts a string to an enum value if it exists, otherwise returns nothing.

source

',3))]),t("details",y,[t("summary",null,[e[21]||(e[21]=t("a",{id:"JetReconstruction.CosTheta-Tuple{PseudoJet}",href:"#JetReconstruction.CosTheta-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.CosTheta")],-1)),e[22]||(e[22]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=s('
julia
CosTheta(p::PseudoJet)

Compute the cosine of the angle between the momentum vector p and the z-axis.

Arguments

  • p::PseudoJet: The PseudoJet object representing the momentum vector.

Returns

  • The cosine of the angle between p and the z-axis.

source

',7))]),t("details",j,[t("summary",null,[e[24]||(e[24]=t("a",{id:"JetReconstruction._ee_genkt_algorithm-Tuple{}",href:"#JetReconstruction._ee_genkt_algorithm-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction._ee_genkt_algorithm")],-1)),e[25]||(e[25]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=s(`
julia
_ee_genkt_algorithm(; particles::Vector{EEjet}, p = 1, R = 4.0,
+import{_ as d,c as l,a5 as s,j as t,a as i,G as o,B as r,o as a}from"./chunks/framework.BtBDBbTG.js";const $e=JSON.parse('{"title":"Jet Reconstruction Internal Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/internal.md","filePath":"lib/internal.md","lastUpdated":null}'),p={name:"lib/internal.md"},c={class:"jldocstring custom-block"},h={class:"jldocstring custom-block"},u={class:"jldocstring custom-block"},g={class:"jldocstring custom-block"},k={class:"jldocstring custom-block"},b={class:"jldocstring custom-block"},m={class:"jldocstring custom-block"},y={class:"jldocstring custom-block"},j={class:"jldocstring custom-block"},f={class:"jldocstring custom-block"},T={class:"jldocstring custom-block"},J={class:"jldocstring custom-block"},E={class:"jldocstring custom-block"},v={class:"jldocstring custom-block"},R={class:"jldocstring custom-block"},C={class:"jldocstring custom-block"},F={class:"jldocstring custom-block"},A={class:"jldocstring custom-block"},x={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},_={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.666ex"},xmlns:"http://www.w3.org/2000/svg",width:"11.255ex",height:"2.363ex",role:"img",focusable:"false",viewBox:"0 -750 4974.7 1044.2","aria-hidden":"true"},Q={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},P={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.186ex"},xmlns:"http://www.w3.org/2000/svg",width:"11.406ex",height:"1.756ex",role:"img",focusable:"false",viewBox:"0 -694 5041.4 776","aria-hidden":"true"},w={class:"jldocstring custom-block"},L={class:"jldocstring custom-block"},B={class:"jldocstring custom-block"},M={class:"jldocstring custom-block"},D={class:"jldocstring custom-block"},H={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},I={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"4.973ex",height:"1.779ex",role:"img",focusable:"false",viewBox:"0 -775.2 2198.3 786.2","aria-hidden":"true"},q={class:"jldocstring custom-block"},S={class:"jldocstring custom-block"},O={class:"jldocstring custom-block"},N={class:"jldocstring custom-block"},V={class:"jldocstring custom-block"},z={class:"jldocstring custom-block"},Z={class:"jldocstring custom-block"},U={class:"jldocstring custom-block"},X={class:"jldocstring custom-block"},G={class:"jldocstring custom-block"},W={class:"jldocstring custom-block"},$={class:"jldocstring custom-block"},K={class:"jldocstring custom-block"},Y={class:"jldocstring custom-block"},ee={class:"jldocstring custom-block"},te={class:"jldocstring custom-block"},ie={class:"jldocstring custom-block"},se={class:"jldocstring custom-block"},ne={class:"jldocstring custom-block"},oe={class:"jldocstring custom-block"},le={class:"jldocstring custom-block"},ae={class:"jldocstring custom-block"},de={class:"jldocstring custom-block"},re={class:"jldocstring custom-block"},pe={class:"jldocstring custom-block"},ce={class:"jldocstring custom-block"},he={class:"jldocstring custom-block"},ue={class:"jldocstring custom-block"},ge={class:"jldocstring custom-block"},ke={class:"jldocstring custom-block"},be={class:"jldocstring custom-block"},me={class:"jldocstring custom-block"},ye={class:"jldocstring custom-block"},je={class:"jldocstring custom-block"},fe={class:"jldocstring custom-block"},Te={class:"jldocstring custom-block"},Je={class:"jldocstring custom-block"},Ee={class:"jldocstring custom-block"},ve={class:"jldocstring custom-block"},Re={class:"jldocstring custom-block"},Ce={class:"jldocstring custom-block"},Fe={class:"jldocstring custom-block"},Ae={class:"jldocstring custom-block"},xe={class:"jldocstring custom-block"},_e={class:"jldocstring custom-block"},Qe={class:"jldocstring custom-block"},Pe={class:"jldocstring custom-block"},we={class:"jldocstring custom-block"},Le={class:"jldocstring custom-block"},Be={class:"jldocstring custom-block"},Me={class:"jldocstring custom-block"},De={class:"jldocstring custom-block"},He={class:"jldocstring custom-block"},Ie={class:"jldocstring custom-block"},qe={class:"jldocstring custom-block"},Se={class:"jldocstring custom-block"},Oe={class:"jldocstring custom-block"},Ne={class:"jldocstring custom-block"};function Ve(ze,e,Ze,Ue,Xe,Ge){const n=r("Badge");return a(),l("div",null,[e[270]||(e[270]=s('
Jet Reconstruction Internal Documentation ​
Documentation for JetReconstruction.jl's internal methods and types.
N.B. no guarantee is made of stability of these interfaces or types.
Index ​
Public Interface ​
',6)),t("details",c,[t("summary",null,[e[0]||(e[0]=t("a",{id:"Base.:+-Tuple{PseudoJet, PseudoJet}",href:"#Base.:+-Tuple{PseudoJet, PseudoJet}"},[t("span",{class:"jlbinding"},"Base.:+")],-1)),e[1]||(e[1]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=s('
julia
+(j1::PseudoJet, j2::PseudoJet)
Addition operator for PseudoJet objects.
Arguments
  • j1::PseudoJet: The first PseudoJet object.
  • j2::PseudoJet: The second PseudoJet object.
Returns
A new PseudoJet object with the sum of the momenta and energy of j1 and j2.
source
',7))]),t("details",h,[t("summary",null,[e[3]||(e[3]=t("a",{id:"Base.copy-Tuple{JetReconstruction.TiledJet}",href:"#Base.copy-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"Base.copy")],-1)),e[4]||(e[4]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=s('
julia
copy(j::TiledJet)
Create a copy of a TiledJet object.
Arguments
  • j::TiledJet: The TiledJet object to be copied.
Returns
A new TiledJet object with the same attributes as the input object.
source
',7))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"Base.iterate",href:"#Base.iterate"},[t("span",{class:"jlbinding"},"Base.iterate")],-1)),e[7]||(e[7]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[8]||(e[8]=s('
julia
Base.iterate(t::neighbour_tiles, state=1)
Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.
source
',3))]),t("details",g,[t("summary",null,[e[9]||(e[9]=t("a",{id:"Base.iterate-2",href:"#Base.iterate-2"},[t("span",{class:"jlbinding"},"Base.iterate")],-1)),e[10]||(e[10]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[11]||(e[11]=s('
julia
Base.iterate(t::rightmost_tiles, state=1)
Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.
source
',3))]),t("details",k,[t("summary",null,[e[12]||(e[12]=t("a",{id:"Base.iterate-Tuple{JetReconstruction.TiledJet}",href:"#Base.iterate-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"Base.iterate")],-1)),e[13]||(e[13]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[14]||(e[14]=s('
julia
Base.iterate(tj::TiledJet)
Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).
Arguments
  • tj::TiledJet: The TiledJet object to start to iterate over.
source
',5))]),t("details",b,[t("summary",null,[e[15]||(e[15]=t("a",{id:"Base.show-Tuple{IO, PseudoJet}",href:"#Base.show-Tuple{IO, PseudoJet}"},[t("span",{class:"jlbinding"},"Base.show")],-1)),e[16]||(e[16]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[17]||(e[17]=s('
julia
show(io::IO, jet::PseudoJet)
Print a PseudoJet object to the specified IO stream.
Arguments
  • io::IO: The IO stream to which the information will be printed.
  • jet::PseudoJet: The PseudoJet object whose information will be printed.
source
',5))]),t("details",m,[t("summary",null,[e[18]||(e[18]=t("a",{id:"Base.tryparse-Tuple{Type{<:Enum}, String}",href:"#Base.tryparse-Tuple{Type{<:Enum}, String}"},[t("span",{class:"jlbinding"},"Base.tryparse")],-1)),e[19]||(e[19]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[20]||(e[20]=s('
julia
Base.tryparse(E::Type{<:Enum}, str::String)
Parser that converts a string to an enum value if it exists, otherwise returns nothing.
source
',3))]),t("details",y,[t("summary",null,[e[21]||(e[21]=t("a",{id:"JetReconstruction.CosTheta-Tuple{PseudoJet}",href:"#JetReconstruction.CosTheta-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.CosTheta")],-1)),e[22]||(e[22]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[23]||(e[23]=s('
julia
CosTheta(p::PseudoJet)
Compute the cosine of the angle between the momentum vector p and the z-axis.
Arguments
  • p::PseudoJet: The PseudoJet object representing the momentum vector.
Returns
  • The cosine of the angle between p and the z-axis.
source
',7))]),t("details",j,[t("summary",null,[e[24]||(e[24]=t("a",{id:"JetReconstruction._ee_genkt_algorithm-Tuple{}",href:"#JetReconstruction._ee_genkt_algorithm-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction._ee_genkt_algorithm")],-1)),e[25]||(e[25]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[26]||(e[26]=s(`
julia
_ee_genkt_algorithm(; particles::Vector{EEjet}, p = 1, R = 4.0,
                    algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham,
-                   recombine = +)
This function is the actual implementation of the e+e- jet clustering algorithm.
source
`,3))]),t("details",T,[t("summary",null,[e[27]||(e[27]=t("a",{id:"JetReconstruction._ensure_valid_rap_phi-Tuple{PseudoJet}",href:"#JetReconstruction._ensure_valid_rap_phi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction._ensure_valid_rap_phi")],-1)),e[28]||(e[28]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=s('
julia
_ensure_valid_rap_phi(p::PseudoJet)
Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.
Arguments
  • p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.
source
',5))]),t("details",f,[t("summary",null,[e[30]||(e[30]=t("a",{id:"JetReconstruction._plain_jet_reconstruct-Tuple{}",href:"#JetReconstruction._plain_jet_reconstruct-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction._plain_jet_reconstruct")],-1)),e[31]||(e[31]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=s('
julia
_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)
This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.
Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.
The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt. Floating point values are allowed for generalised k_t algorithm.
Arguments
  • particles: A vector of PseudoJet objects representing the input particles.
  • p=-1: The power to which the transverse momentum (pt) of each particle is raised.
  • R=1.0: The jet radius parameter.
  • recombine: The recombination function used to merge two jets. Default is + (additive recombination).
Returns
  • clusterseq: The resulting ClusterSequence object representing the reconstructed jets.
source
',9))]),t("details",J,[t("summary",null,[e[33]||(e[33]=t("a",{id:"JetReconstruction._set_rap_phi!-Tuple{PseudoJet}",href:"#JetReconstruction._set_rap_phi!-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction._set_rap_phi!")],-1)),e[34]||(e[34]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=s('
_set_rap_phi!(p::PseudoJet)
Set the rapidity and azimuthal angle of the PseudoJet p.
Arguments
  • p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.
Description
This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.
Note - the ϕ angle is calculated in the range [0, 2π).
source
',8))]),t("details",E,[t("summary",null,[e[36]||(e[36]=t("a",{id:"JetReconstruction._tiled_jet_reconstruct-Tuple{Vector{PseudoJet}}",href:"#JetReconstruction._tiled_jet_reconstruct-Tuple{Vector{PseudoJet}}"},[t("span",{class:"jlbinding"},"JetReconstruction._tiled_jet_reconstruct")],-1)),e[37]||(e[37]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=s('
julia
_tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets once preprocessing of data types are done.
Arguments
  • particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.
  • p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)
source
',9))]),t("details",v,[t("summary",null,[e[39]||(e[39]=t("a",{id:"JetReconstruction._tj_diJ-Tuple{Any}",href:"#JetReconstruction._tj_diJ-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction._tj_diJ")],-1)),e[40]||(e[40]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=s('
julia
_tj_diJ(jet)
Compute the dij metric value for a given jet.
Arguments
  • jet: The input jet.
Returns
  • The dij value for the jet.
Example
source
',8))]),t("details",R,[t("summary",null,[e[42]||(e[42]=t("a",{id:"JetReconstruction._tj_dist-Tuple{Any, Any}",href:"#JetReconstruction._tj_dist-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction._tj_dist")],-1)),e[43]||(e[43]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[44]||(e[44]=s('
julia
_tj_dist(jetA, jetB)
Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.
Arguments
  • jetA: The first jet.
  • jetB: The second jet.
Returns
The squared distance between jetA and jetB.
Examples
source
',8))]),t("details",C,[t("summary",null,[e[45]||(e[45]=t("a",{id:"JetReconstruction.add_step_to_history!-Tuple{ClusterSequence, Vararg{Any, 4}}",href:"#JetReconstruction.add_step_to_history!-Tuple{ClusterSequence, Vararg{Any, 4}}"},[t("span",{class:"jlbinding"},"JetReconstruction.add_step_to_history!")],-1)),e[46]||(e[46]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[47]||(e[47]=s('
julia
add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)
Add a new jet's history into the recombination sequence.
Arguments:
  • clusterseq::ClusterSequence: The cluster sequence object.
  • parent1: The index of the first parent.
  • parent2: The index of the second parent.
  • jetp_index: The index of the jet.
  • dij: The dij value.
This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.
If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.
source
',7))]),t("details",F,[t("summary",null,[e[48]||(e[48]=t("a",{id:"JetReconstruction.add_untagged_neighbours_to_tile_union-NTuple{4, Any}",href:"#JetReconstruction.add_untagged_neighbours_to_tile_union-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.add_untagged_neighbours_to_tile_union")],-1)),e[49]||(e[49]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[50]||(e[50]=s('
julia
add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)
Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.
Arguments
  • center_index: The index of the center tile.
  • tile_union: An array to store the indices of neighbouring tiles.
  • n_near_tiles: The number of neighbouring tiles.
  • tiling: The tiling object containing the tile tags.
Returns
The updated number of near tiles.
source
',7))]),t("details",A,[t("summary",null,[e[51]||(e[51]=t("a",{id:"JetReconstruction.angular_distance-Tuple{Any, Any, Any}",href:"#JetReconstruction.angular_distance-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.angular_distance")],-1)),e[52]||(e[52]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=s('
julia
angular_distance(eereco, i, j) -> Float64
',1)),t("p",null,[e[55]||(e[55]=i("Calculate the angular distance between two jets ")),e[56]||(e[56]=t("code",null,"i",-1)),e[57]||(e[57]=i(" and ")),e[58]||(e[58]=t("code",null,"j",-1)),e[59]||(e[59]=i(" using the formula ")),t("mjx-container",x,[(a(),l("svg",_,e[53]||(e[53]=[s('',1)]))),e[54]||(e[54]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mn",null,"1"),t("mo",null,"−"),t("mi",null,"c"),t("mi",null,"o"),t("mi",null,"s"),t("mo",{stretchy:"false"},"("),t("msub",null,[t("mi",null,"θ"),t("mrow",{"data-mjx-texclass":"ORD"},[t("mi",null,"i"),t("mi",null,"j")])]),t("mo",{stretchy:"false"},")")])],-1))]),e[60]||(e[60]=i("."))]),e[71]||(e[71]=s("
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
Returns
",3)),t("ul",null,[t("li",null,[e[63]||(e[63]=t("code",null,"Float64",-1)),e[64]||(e[64]=i(": The angular distance between ")),e[65]||(e[65]=t("code",null,"i",-1)),e[66]||(e[66]=i(" and ")),e[67]||(e[67]=t("code",null,"j",-1)),e[68]||(e[68]=i(", which is ")),t("mjx-container",Q,[(a(),l("svg",P,e[61]||(e[61]=[s('',1)]))),e[62]||(e[62]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mn",null,"1"),t("mo",null,"−"),t("mi",null,"c"),t("mi",null,"o"),t("mi",null,"s"),t("mi",null,"h"),t("mi",null,"e"),t("mi",null,"t"),t("mi",null,"a")])],-1))]),e[69]||(e[69]=i("."))])]),e[72]||(e[72]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/EEAlgorithm.jl#L6-L20",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",w,[t("summary",null,[e[73]||(e[73]=t("a",{id:"JetReconstruction.check_algorithm_power_consistency-Tuple{}",href:"#JetReconstruction.check_algorithm_power_consistency-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction.check_algorithm_power_consistency")],-1)),e[74]||(e[74]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[75]||(e[75]=t("p",null,"Allow a check for algorithm and power consistency",-1)),e[76]||(e[76]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/AlgorithmStrategyEnums.jl#L129",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",L,[t("summary",null,[e[77]||(e[77]=t("a",{id:"JetReconstruction.detach!-Tuple{JetReconstruction.TiledJet}",href:"#JetReconstruction.detach!-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.detach!")],-1)),e[78]||(e[78]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[79]||(e[79]=s('
julia
detach!(jet::TiledJet)
Detach a TiledJet from its linked list by updating the previous and next pointers.
Arguments
  • jet::TiledJet: The TiledJet object to detach.
source
',5))]),t("details",B,[t("summary",null,[e[80]||(e[80]=t("a",{id:"JetReconstruction.determine_rapidity_extent-Union{Tuple{Vector{T}}, Tuple{T}} where T<:AbstractFloat",href:"#JetReconstruction.determine_rapidity_extent-Union{Tuple{Vector{T}}, Tuple{T}} where T<:AbstractFloat"},[t("span",{class:"jlbinding"},"JetReconstruction.determine_rapidity_extent")],-1)),e[81]||(e[81]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[82]||(e[82]=s('
julia
determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat
Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.
This is the heuristic which is used by FastJet (inline comments are from FastJet).
Arguments
  • eta::Vector{T}: A vector of rapidity values.
Returns
  • minrap::T: The minimum rapidity value.
  • maxrap::T: The maximum rapidity value.
source
',8))]),t("details",M,[t("summary",null,[e[83]||(e[83]=t("a",{id:"JetReconstruction.dij-NTuple{4, Any}",href:"#JetReconstruction.dij-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dij")],-1)),e[84]||(e[84]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[85]||(e[85]=s('
julia
dij(i, kt2_array, nn, nndist)
Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß
Arguments
  • i: The index of the element.
  • kt2_array: An array of kt2 values.
  • nn: An array of nearest neighbors.
  • nndist: An array of nearest neighbor distances.
Returns
  • The computed dij value.
source
',7))]),t("details",D,[t("summary",null,[e[86]||(e[86]=t("a",{id:"JetReconstruction.dij_dist-NTuple{4, Any}",href:"#JetReconstruction.dij_dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dij_dist")],-1)),e[87]||(e[87]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[92]||(e[92]=s('
julia
dij_dist(eereco, i, j, dij_factor)
',1)),t("p",null,[e[90]||(e[90]=i("Calculate the dij distance between two ")),t("mjx-container",H,[(a(),l("svg",I,e[88]||(e[88]=[s('',1)]))),e[89]||(e[89]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("msup",null,[t("mi",null,"e"),t("mo",null,"+")]),t("msup",null,[t("mi",null,"e"),t("mo",null,"−")])])],-1))]),e[91]||(e[91]=i("jets."))]),e[93]||(e[93]=s('
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
  • dij_factor: The scaling factor to multiply the dij distance by.
Returns
  • The dij distance between i and j.
source
',5))]),t("details",q,[t("summary",null,[e[94]||(e[94]=t("a",{id:"JetReconstruction.dist-NTuple{4, Any}",href:"#JetReconstruction.dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dist")],-1)),e[95]||(e[95]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[96]||(e[96]=s('
julia
dist(i, j, rapidity_array, phi_array)
Compute the distance between points in a 2D space defined by rapidity and phi coordinates.
Arguments
  • i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).
  • j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).
  • rapidity_array::Vector{Float64}: Array of rapidity coordinates.
  • phi_array::Vector{Float64}: Array of phi coordinates.
Returns
  • distance::Float64: The distance between the two points.
source
',7))]),t("details",S,[t("summary",null,[e[97]||(e[97]=t("a",{id:"JetReconstruction.do_iB_recombination_step!-Tuple{ClusterSequence, Any, Any}",href:"#JetReconstruction.do_iB_recombination_step!-Tuple{ClusterSequence, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.do_iB_recombination_step!")],-1)),e[98]||(e[98]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[99]||(e[99]=s('
julia
do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)
Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the jet.
  • diB: The diB value.
source
',5))]),t("details",O,[t("summary",null,[e[100]||(e[100]=t("a",{id:"JetReconstruction.do_ij_recombination_step!",href:"#JetReconstruction.do_ij_recombination_step!"},[t("span",{class:"jlbinding"},"JetReconstruction.do_ij_recombination_step!")],-1)),e[101]||(e[101]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[102]||(e[102]=s('
julia
do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)
Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the first jet to be recombined.
  • jet_j: The index of the second jet to be recombined.
  • dij: The distance between the two jets.
  • recombine=+: The recombination function to be used. Default is addition.
Returns
  • newjet_k: The index of the newly created jet.
Description
This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.
source
',9))]),t("details",N,[t("summary",null,[e[103]||(e[103]=t("a",{id:"JetReconstruction.energy-Tuple{PseudoJet}",href:"#JetReconstruction.energy-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.energy")],-1)),e[104]||(e[104]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[105]||(e[105]=s('
julia
energy(p::PseudoJet)
Return the energy of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The energy of the PseudoJet.
source
',7))]),t("details",V,[t("summary",null,[e[106]||(e[106]=t("a",{id:"JetReconstruction.eta-Tuple{PseudoJet}",href:"#JetReconstruction.eta-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.eta")],-1)),e[107]||(e[107]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[108]||(e[108]=s('
julia
eta(p::PseudoJet)
Compute the pseudorapidity (η) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.
Returns
  • The pseudorapidity (η) of the PseudoJet.
source
',7))]),t("details",z,[t("summary",null,[e[109]||(e[109]=t("a",{id:"JetReconstruction.fast_findmin-Tuple{Any, Any}",href:"#JetReconstruction.fast_findmin-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.fast_findmin")],-1)),e[110]||(e[110]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[111]||(e[111]=s('
julia
fast_findmin(dij, n)
Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.
Arguments
  • dij: An array of values.
  • n: The number of elements to consider in the dij array.
Returns
  • dij_min: The minimum value in the first n elements of the dij array.
  • best: The index of the minimum value in the dij array.
source
',7))]),t("details",Z,[t("summary",null,[e[112]||(e[112]=t("a",{id:"JetReconstruction.find_tile_neighbours!-NTuple{5, Any}",href:"#JetReconstruction.find_tile_neighbours!-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.find_tile_neighbours!")],-1)),e[113]||(e[113]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[114]||(e[114]=s('
julia
find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)
Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run
Arguments
  • tile_union: The tile union to which the neighbouring tiles will be added.
  • jetA: The first jet.
  • jetB: The second jet.
  • oldB: The old second jet.
  • tiling: The tiling information.
Returns
The number of neighbouring tiles added to the tile_union.
source
',7))]),t("details",U,[t("summary",null,[e[115]||(e[115]=t("a",{id:"JetReconstruction.geometric_distance-NTuple{4, AbstractFloat}",href:"#JetReconstruction.geometric_distance-NTuple{4, AbstractFloat}"},[t("span",{class:"jlbinding"},"JetReconstruction.geometric_distance")],-1)),e[116]||(e[116]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[117]||(e[117]=s('
julia
geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)
Compute the geometric distance between two points in the rap-phi plane.
Arguments
  • eta1::AbstractFloat: The eta coordinate of the first point.
  • phi1::AbstractFloat: The phi coordinate of the first point.
  • eta2::AbstractFloat: The eta coordinate of the second point.
  • phi2::AbstractFloat: The phi coordinate of the second point.
Returns
  • distance::Float64: The geometric distance between the two points.
source
',7))]),t("details",X,[t("summary",null,[e[118]||(e[118]=t("a",{id:"JetReconstruction.get_algorithm_power_consistency-Tuple{}",href:"#JetReconstruction.get_algorithm_power_consistency-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_algorithm_power_consistency")],-1)),e[119]||(e[119]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[120]||(e[120]=s('
julia
get_algorithm_power_consistency(; p::Union{Real, Nothing}, algorithm::Union{JetAlgorithm, Nothing})
Get the algorithm and power consistency correct
This function checks the consistency between the algorithm and power parameters. If the algorithm is specified, it checks if the power parameter is consistent with the algorithm's known power. If the power parameter is not specified, it sets the power parameter based on the algorithm. If neither the algorithm nor the power parameter is specified, it throws an ArgumentError.
Arguments
  • p::Union{Real, Nothing}: The power value.
  • algorithm::Union{JetAlgorithm, Nothing}: The algorithm.
Returns
A named tuple of the consistent power and algorithm values.
Throws
  • ArgumentError: If the algorithm and power are inconsistent or if neither the algorithm nor the power is specified.
source
',10))]),t("details",G,[t("summary",null,[e[121]||(e[121]=t("a",{id:"JetReconstruction.get_all_ancestors-Tuple{Any, ClusterSequence}",href:"#JetReconstruction.get_all_ancestors-Tuple{Any, ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_all_ancestors")],-1)),e[122]||(e[122]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[123]||(e[123]=s('
julia
get_all_ancestors(idx, cs::ClusterSequence)
Recursively finds all ancestors of a given index in a ClusterSequence object.
Arguments
  • idx: The index of the jet for which to find ancestors.
  • cs: The ClusterSequence object containing the jet history.
Returns
An array of indices representing the ancestors of the given jet.
source
',7))]),t("details",W,[t("summary",null,[e[124]||(e[124]=t("a",{id:"JetReconstruction.get_dij_dist-NTuple{4, Any}",href:"#JetReconstruction.get_dij_dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_dij_dist")],-1)),e[125]||(e[125]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[126]||(e[126]=s('
julia
get_dij_dist(nn_dist, kt2_1, kt2_2, R2)
Compute the dij metric distance between two jets.
Arguments
  • nn_dist: The nearest-neighbor distance between two jets.
  • kt2_1: The squared momentum metric value of the first jet.
  • kt2_2: The squared momentum metric value of the second jet.
  • R2: The jet radius parameter squared.
Returns
The distance between the two jets.
If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.
source
',8))]),t("details",$,[t("summary",null,[e[127]||(e[127]=t("a",{id:"JetReconstruction.get_tile-Tuple{JetReconstruction.TilingDef, AbstractFloat, AbstractFloat}",href:"#JetReconstruction.get_tile-Tuple{JetReconstruction.TilingDef, AbstractFloat, AbstractFloat}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_tile")],-1)),e[128]||(e[128]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[129]||(e[129]=s('
julia
get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)
Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.
Arguments
  • tiling_setup: A TilingDef object that contains the tiling setup parameters.
  • eta: The eta value for which to calculate the tile index.
  • phi: The phi value for which to calculate the tile index.
Returns
  • ieta: The tile index along the eta direction.
  • iphi: The tile index along the phi direction.
source
',7))]),t("details",K,[t("summary",null,[e[130]||(e[130]=t("a",{id:"JetReconstruction.get_tile_cartesian_indices-Tuple{JetReconstruction.TilingDef, Int64}",href:"#JetReconstruction.get_tile_cartesian_indices-Tuple{JetReconstruction.TilingDef, Int64}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_tile_cartesian_indices")],-1)),e[131]||(e[131]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[132]||(e[132]=s('
julia
get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)
Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)
Arguments
  • tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.
  • i_η::Int: The index of the tile in the η dimension.
  • i_ϕ::Int: The index of the tile in the ϕ dimension.
Returns
  • The linear index of the tile.
source
',7))]),t("details",Y,[t("summary",null,[e[133]||(e[133]=t("a",{id:"JetReconstruction.initial_history-Tuple{Any}",href:"#JetReconstruction.initial_history-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.initial_history")],-1)),e[134]||(e[134]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[135]||(e[135]=s('
julia
initial_history(particles)
Create an initial history for the given particles.
Arguments
  • particles: The initial vector of stable particles.
Returns
  • history: An array of HistoryElement objects.
  • Qtot: The total energy in the event.
source
',7))]),t("details",ee,[t("summary",null,[e[136]||(e[136]=t("a",{id:"JetReconstruction.insert!-Tuple{JetReconstruction.TiledJet, JetReconstruction.TiledJet}",href:"#JetReconstruction.insert!-Tuple{JetReconstruction.TiledJet, JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.insert!")],-1)),e[137]||(e[137]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[138]||(e[138]=s('
julia
insert!(nextjet::TiledJet, jettomove::TiledJet)
Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list
Arguments
  • nextjet::TiledJet: The TiledJet object after which jettomove should be inserted.
  • jettomove::TiledJet: The TiledJet object to be inserted.
Example
source
',6))]),t("details",te,[t("summary",null,[e[139]||(e[139]=t("a",{id:"JetReconstruction.is_ee-Tuple{JetReconstruction.JetAlgorithm.Algorithm}",href:"#JetReconstruction.is_ee-Tuple{JetReconstruction.JetAlgorithm.Algorithm}"},[t("span",{class:"jlbinding"},"JetReconstruction.is_ee")],-1)),e[140]||(e[140]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[141]||(e[141]=s('
julia
is_ee(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a e+e- reconstruction algorithm.
Returns
true if the algorithm is a e+e- reconstruction algorithm, false otherwise.
source
',5))]),t("details",ie,[t("summary",null,[e[142]||(e[142]=t("a",{id:"JetReconstruction.is_pp-Tuple{JetReconstruction.JetAlgorithm.Algorithm}",href:"#JetReconstruction.is_pp-Tuple{JetReconstruction.JetAlgorithm.Algorithm}"},[t("span",{class:"jlbinding"},"JetReconstruction.is_pp")],-1)),e[143]||(e[143]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[144]||(e[144]=s('
julia
is_pp(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a pp reconstruction algorithm.
Returns
true if the algorithm is a pp reconstruction algorithm, false otherwise.
source
',5))]),t("details",se,[t("summary",null,[e[145]||(e[145]=t("a",{id:"JetReconstruction.isvalid-Tuple{JetReconstruction.TiledJet}",href:"#JetReconstruction.isvalid-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.isvalid")],-1)),e[146]||(e[146]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[147]||(e[147]=s('
julia
isvalid(t::TiledJet)
Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.
Arguments
  • t::TiledJet: The TiledJet object to check.
Returns
  • Bool: true if the TiledJet object is valid, false otherwise.
source
',7))]),t("details",ne,[t("summary",null,[e[148]||(e[148]=t("a",{id:"JetReconstruction.jet_ranks-Tuple{ClusterSequence}",href:"#JetReconstruction.jet_ranks-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.jet_ranks")],-1)),e[149]||(e[149]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[150]||(e[150]=s('
julia
jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)
Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.
  • compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.
Returns
A dictionary mapping each jet index to its rank.
Note
This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.
source
',9))]),t("details",oe,[t("summary",null,[e[151]||(e[151]=t("a",{id:"JetReconstruction.m-Tuple{PseudoJet}",href:"#JetReconstruction.m-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.m")],-1)),e[152]||(e[152]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[153]||(e[153]=s('
julia
m(p::PseudoJet)
Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the invariant mass.
Returns
The invariant mass of the PseudoJet object.
source
',7))]),t("details",le,[t("summary",null,[e[154]||(e[154]=t("a",{id:"JetReconstruction.m2-Tuple{PseudoJet}",href:"#JetReconstruction.m2-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.m2")],-1)),e[155]||(e[155]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[156]||(e[156]=s('
julia
m2(p::PseudoJet)
Calculate the invariant mass squared (m^2) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.
Returns
  • The invariant mass squared (m^2) of the PseudoJet.
source
',7))]),t("details",ae,[t("summary",null,[e[157]||(e[157]=t("a",{id:"JetReconstruction.mag-Tuple{PseudoJet}",href:"#JetReconstruction.mag-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.mag")],-1)),e[158]||(e[158]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[159]||(e[159]=s('
julia
mag(p::PseudoJet)
Return the magnitude of the momentum of a PseudoJet, |p|.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the magnitude.
Returns
The magnitude of the PseudoJet object.
source
',7))]),t("details",re,[t("summary",null,[e[160]||(e[160]=t("a",{id:"JetReconstruction.mass-Tuple{PseudoJet}",href:"#JetReconstruction.mass-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.mass")],-1)),e[161]||(e[161]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[162]||(e[162]=s('
julia
mass(p::PseudoJet)
Compute the invariant mass (alias for m(p)).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the mass.
Returns
  • The mass of the PseudoJet.
source
',7))]),t("details",de,[t("summary",null,[e[163]||(e[163]=t("a",{id:"JetReconstruction.mass2",href:"#JetReconstruction.mass2"},[t("span",{class:"jlbinding"},"JetReconstruction.mass2")],-1)),e[164]||(e[164]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[165]||(e[165]=t("p",null,[i("Alias for "),t("code",null,"m2"),i(" function")],-1)),e[166]||(e[166]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/Pseudojet.jl#L370",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",pe,[t("summary",null,[e[167]||(e[167]=t("a",{id:"JetReconstruction.merge_steps-Tuple{ClusterSequence}",href:"#JetReconstruction.merge_steps-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.merge_steps")],-1)),e[168]||(e[168]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[169]||(e[169]=s('
julia
merge_steps(clusterseq::ClusterSequence)
Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
Returns
  • merge_steps::Int: The number of merge steps.
source
',7))]),t("details",ce,[t("summary",null,[e[170]||(e[170]=t("a",{id:"JetReconstruction.phi-Tuple{PseudoJet}",href:"#JetReconstruction.phi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.phi")],-1)),e[171]||(e[171]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[172]||(e[172]=s('
julia
phi(p::PseudoJet)
Compute the ϕ angle of a PseudoJet object p.
Note this function is a wrapper for phi_02pi(p).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
',8))]),t("details",he,[t("summary",null,[e[173]||(e[173]=t("a",{id:"JetReconstruction.phi_02pi-Tuple{PseudoJet}",href:"#JetReconstruction.phi_02pi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.phi_02pi")],-1)),e[174]||(e[174]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[175]||(e[175]=s('
julia
phi_02pi(p::PseudoJet)
Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
',7))]),t("details",ue,[t("summary",null,[e[176]||(e[176]=t("a",{id:"JetReconstruction.pt-Tuple{PseudoJet}",href:"#JetReconstruction.pt-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pt")],-1)),e[177]||(e[177]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[178]||(e[178]=s('
julia
pt(p::PseudoJet)
Compute the scalar transverse momentum (pt) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.
Returns
  • The transverse momentum (pt) of the PseudoJet.
source
',7))]),t("details",ge,[t("summary",null,[e[179]||(e[179]=t("a",{id:"JetReconstruction.pt2-Tuple{PseudoJet}",href:"#JetReconstruction.pt2-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pt2")],-1)),e[180]||(e[180]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[181]||(e[181]=s('
julia
pt2(p::PseudoJet)
Get the squared transverse momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The squared transverse momentum of the PseudoJet.
source
',7))]),t("details",ke,[t("summary",null,[e[182]||(e[182]=t("a",{id:"JetReconstruction.px-Tuple{PseudoJet}",href:"#JetReconstruction.px-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.px")],-1)),e[183]||(e[183]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[184]||(e[184]=s('
julia
px(p::PseudoJet)
Return the x-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The x-component of the momentum of the PseudoJet.
source
',7))]),t("details",be,[t("summary",null,[e[185]||(e[185]=t("a",{id:"JetReconstruction.py-Tuple{PseudoJet}",href:"#JetReconstruction.py-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.py")],-1)),e[186]||(e[186]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[187]||(e[187]=s('
julia
py(p::PseudoJet)
Return the y-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The y-component of the momentum of the PseudoJet.
source
',7))]),t("details",me,[t("summary",null,[e[188]||(e[188]=t("a",{id:"JetReconstruction.pz-Tuple{PseudoJet}",href:"#JetReconstruction.pz-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pz")],-1)),e[189]||(e[189]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[190]||(e[190]=s('
julia
pz(p::PseudoJet)
Return the z-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The z-component of the momentum of the PseudoJet.
source
',7))]),t("details",ye,[t("summary",null,[e[191]||(e[191]=t("a",{id:"JetReconstruction.rapidity-Tuple{PseudoJet}",href:"#JetReconstruction.rapidity-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.rapidity")],-1)),e[192]||(e[192]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[193]||(e[193]=s('
julia
rapidity(p::PseudoJet)
Compute the rapidity of a PseudoJet object.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the rapidity.
Returns
The rapidity of the PseudoJet object.
source
',7))]),t("details",je,[t("summary",null,[e[194]||(e[194]=t("a",{id:"JetReconstruction.reco_state-Tuple{ClusterSequence, Any}",href:"#JetReconstruction.reco_state-Tuple{ClusterSequence, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.reco_state")],-1)),e[195]||(e[195]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[196]||(e[196]=s('
julia
reco_state(cs::ClusterSequence, pt_ranks; iteration=0)
This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.
Arguments
  • cs::ClusterSequence: The ClusterSequence object to update.
  • ranks: The ranks of the original clusters, that are inherited by peudojets
during the reconstruction process.
  • iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).
  • ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).
Returns
A dictionary representing a snapshot of the reconstruction state.
Details
The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.
source
',11))]),t("details",Te,[t("summary",null,[e[197]||(e[197]=t("a",{id:"JetReconstruction.rightneighbours-Tuple{Int64, JetReconstruction.Tiling}",href:"#JetReconstruction.rightneighbours-Tuple{Int64, JetReconstruction.Tiling}"},[t("span",{class:"jlbinding"},"JetReconstruction.rightneighbours")],-1)),e[198]||(e[198]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[199]||(e[199]=s('
julia
rightneighbours(center::Int, tiling::Tiling)
Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the indices of the right neighbors.
source
',7))]),t("details",fe,[t("summary",null,[e[200]||(e[200]=t("a",{id:"JetReconstruction.set_momentum!-Tuple{PseudoJet, Vararg{Any, 4}}",href:"#JetReconstruction.set_momentum!-Tuple{PseudoJet, Vararg{Any, 4}}"},[t("span",{class:"jlbinding"},"JetReconstruction.set_momentum!")],-1)),e[201]||(e[201]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[202]||(e[202]=s('
julia
set_momentum!(j::PseudoJet, px, py, pz, E)
Set the momentum components and energy of a PseudoJet object.
Arguments
  • j::PseudoJet: The PseudoJet object to set the momentum for.
  • px: The x-component of the momentum.
  • py: The y-component of the momentum.
  • pz: The z-component of the momentum.
  • E: The energy of the particle.
source
',5))]),t("details",Je,[t("summary",null,[e[203]||(e[203]=t("a",{id:"JetReconstruction.set_nearest_neighbours!-Tuple{ClusterSequence, JetReconstruction.Tiling, Vector{JetReconstruction.TiledJet}}",href:"#JetReconstruction.set_nearest_neighbours!-Tuple{ClusterSequence, JetReconstruction.Tiling, Vector{JetReconstruction.TiledJet}}"},[t("span",{class:"jlbinding"},"JetReconstruction.set_nearest_neighbours!")],-1)),e[204]||(e[204]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[205]||(e[205]=s('
julia
set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})
This function sets the nearest neighbor information for all jets in the tiledjets vector.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • tiling::Tiling: The tiling object.
  • tiledjets::Vector{TiledJet}: The vector of tiled jets.
Returns
  • NNs::Vector{TiledJet}: The vector of nearest neighbor jets.
  • diJ::Vector{Float64}: The vector of diJ values.
The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.
Note: The diJ values are calculated as the kt distance multiplied by R^2.
source
',9))]),t("details",Ee,[t("summary",null,[e[206]||(e[206]=t("a",{id:"JetReconstruction.setup_tiling-Union{Tuple{T}, Tuple{Vector{T}, AbstractFloat}} where T<:AbstractFloat",href:"#JetReconstruction.setup_tiling-Union{Tuple{T}, Tuple{Vector{T}, AbstractFloat}} where T<:AbstractFloat"},[t("span",{class:"jlbinding"},"JetReconstruction.setup_tiling")],-1)),e[207]||(e[207]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[208]||(e[208]=s('
julia
setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat
This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.
Arguments
  • eta::Vector{T}: A vector of rapidities.
  • Rparam::AbstractFloat: The jet radius parameter.
Returns
  • tiling_setup: A TilingDef object containing the tiling setup parameters.
Description
The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.
source
',9))]),t("details",ve,[t("summary",null,[e[209]||(e[209]=t("a",{id:"JetReconstruction.surrounding-Tuple{Int64, JetReconstruction.Tiling}",href:"#JetReconstruction.surrounding-Tuple{Int64, JetReconstruction.Tiling}"},[t("span",{class:"jlbinding"},"JetReconstruction.surrounding")],-1)),e[210]||(e[210]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[211]||(e[211]=s('
julia
surrounding(center::Int, tiling::Tiling)
Compute the surrounding indices of a given center index in a tiling.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the surrounding indices.
source
',7))]),t("details",Re,[t("summary",null,[e[212]||(e[212]=t("a",{id:"JetReconstruction.tile_index-Tuple{Any, Float64, Float64}",href:"#JetReconstruction.tile_index-Tuple{Any, Float64, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.tile_index")],-1)),e[213]||(e[213]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[214]||(e[214]=s('
julia
tile_index(tiling_setup, eta::Float64, phi::Float64)
Compute the tile index for a given (eta, phi) coordinate.
Arguments
  • tiling_setup: The tiling setup object containing the tile size and number of tiles.
  • eta::Float64: The eta coordinate.
  • phi::Float64: The phi coordinate.
Returns
The tile index corresponding to the (eta, phi) coordinate.
source
',7))]),t("details",Ce,[t("summary",null,[e[215]||(e[215]=t("a",{id:"JetReconstruction.tiledjet_remove_from_tiles!-Tuple{Any, Any}",href:"#JetReconstruction.tiledjet_remove_from_tiles!-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.tiledjet_remove_from_tiles!")],-1)),e[216]||(e[216]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[217]||(e[217]=s('
julia
tiledjet_remove_from_tiles!(tiling, jet)
Remove a jet from the given tiling structure.
Arguments
  • tiling: The tiling structure from which the jet will be removed.
  • jet: The jet to be removed from the tiling structure.
Description
This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.
source
',7))]),t("details",Fe,[t("summary",null,[e[218]||(e[218]=t("a",{id:"JetReconstruction.tiledjet_set_jetinfo!-Tuple{JetReconstruction.TiledJet, ClusterSequence, JetReconstruction.Tiling, Any, Any, Any}",href:"#JetReconstruction.tiledjet_set_jetinfo!-Tuple{JetReconstruction.TiledJet, ClusterSequence, JetReconstruction.Tiling, Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.tiledjet_set_jetinfo!")],-1)),e[219]||(e[219]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[220]||(e[220]=s('
julia
tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)
Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)
Arguments:
  • jet::TiledJet: The TiledJet object to set the information for.
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets.
  • tiling::Tiling: The Tiling object containing the tile information.
  • jets_index: The index of the jet in the ClusterSequence.
  • R2: The jet radius parameter squared.
  • p: The power to raise the pt2 value to.
This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.
Returns:
  • nothing
source
',8))]),t("details",Ae,[t("summary",null,[e[221]||(e[221]=t("a",{id:"JetReconstruction.upd_nn_crosscheck!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}",href:"#JetReconstruction.upd_nn_crosscheck!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_crosscheck!")],-1)),e[222]||(e[222]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[223]||(e[223]=s('
julia
upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
',5))]),t("details",xe,[t("summary",null,[e[224]||(e[224]=t("a",{id:"JetReconstruction.upd_nn_nocross!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}",href:"#JetReconstruction.upd_nn_nocross!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_nocross!")],-1)),e[225]||(e[225]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[226]||(e[226]=s('
julia
upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
',5))]),t("details",_e,[t("summary",null,[e[227]||(e[227]=t("a",{id:"JetReconstruction.upd_nn_step!-NTuple{12, Any}",href:"#JetReconstruction.upd_nn_step!-NTuple{12, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_step!")],-1)),e[228]||(e[228]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[229]||(e[229]=s('
julia
upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)
Update the nearest neighbor information after a jet merge step.
Arguments:
  • i: Index of the first particle in the last merge step.
  • j: Index of the second particle in the last merge step.
  • k: Index of the current particle for which the nearest neighbour will be updated.
  • N: Total number of particles (currently vaild array indexes are [1:N]).
  • Nn: Number of nearest neighbors to consider.
  • kt2_array: Array of transverse momentum squared values.
  • rapidity_array: Array of rapidity values.
  • phi_array: Array of azimuthal angle values.
  • R2: Distance threshold squared for nearest neighbors.
  • nndist: Array of nearest neighbor geometric distances.
  • nn: Array of nearest neighbor indices.
  • nndij: Array of metric distances between particles.
This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.
source
',6))]),t("details",Qe,[t("summary",null,[e[230]||(e[230]=t("a",{id:"JetReconstruction.η",href:"#JetReconstruction.η"},[t("span",{class:"jlbinding"},"JetReconstruction.η")],-1)),e[231]||(e[231]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[232]||(e[232]=s('
julia
const η = eta
Alias for the pseudorapidity function, eta.
source
',3))]),t("details",Pe,[t("summary",null,[e[233]||(e[233]=t("a",{id:"JetReconstruction.FourMomentum",href:"#JetReconstruction.FourMomentum"},[t("span",{class:"jlbinding"},"JetReconstruction.FourMomentum")],-1)),e[234]||(e[234]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[235]||(e[235]=t("p",null,"Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.",-1)),e[236]||(e[236]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/Pseudojet.jl#L6-L8",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",we,[t("summary",null,[e[237]||(e[237]=t("a",{id:"JetReconstruction.HistoryElement",href:"#JetReconstruction.HistoryElement"},[t("span",{class:"jlbinding"},"JetReconstruction.HistoryElement")],-1)),e[238]||(e[238]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[239]||(e[239]=s('
julia
struct HistoryElement
A struct holding a record of jet mergers and finalisations
Fields:
  • parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)
  • parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)
  • child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.
  • jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.
  • dij: The distance corresponding to the recombination at this stage of the clustering.
  • max_dij_so_far: The largest recombination distance seen so far in the clustering history.
source
',5))]),t("details",Le,[t("summary",null,[e[240]||(e[240]=t("a",{id:"JetReconstruction.HistoryElement-Tuple{Any}",href:"#JetReconstruction.HistoryElement-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.HistoryElement")],-1)),e[241]||(e[241]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[242]||(e[242]=s('
julia
HistoryElement(jetp_index)
Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.
Arguments
  • jetp_index: The index of the jetp.
Returns
A HistoryElement object.
source
',7))]),t("details",Be,[t("summary",null,[e[243]||(e[243]=t("a",{id:"JetReconstruction.JetWithAncestors",href:"#JetReconstruction.JetWithAncestors"},[t("span",{class:"jlbinding"},"JetReconstruction.JetWithAncestors")],-1)),e[244]||(e[244]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[245]||(e[245]=s('
julia
struct JetWithAncestors
A struct representing a jet with its origin ancestors.
Fields
  • self::PseudoJet: The PseudoJet object for this jet.
  • jetp_index::Int: The index of the jet in the corresponding cluster sequence.
  • ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).
  • jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors
Note
This structure needs its associated cluster sequence origin to be useful.
source
',7))]),t("details",Me,[t("summary",null,[e[246]||(e[246]=t("a",{id:"JetReconstruction.Surrounding",href:"#JetReconstruction.Surrounding"},[t("span",{class:"jlbinding"},"JetReconstruction.Surrounding")],-1)),e[247]||(e[247]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[248]||(e[248]=s('
julia
struct Surrounding{N}
Structure used for iterating over neighbour tiles.
Fields
  • indices::NTuple{N, Int}: A tuple of N integers representing the indices.
source
',5))]),t("details",De,[t("summary",null,[e[249]||(e[249]=t("a",{id:"JetReconstruction.TiledJet",href:"#JetReconstruction.TiledJet"},[t("span",{class:"jlbinding"},"JetReconstruction.TiledJet")],-1)),e[250]||(e[250]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[251]||(e[251]=s('
julia
struct TiledJet
TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.
Fields
  • id::Int: The ID of the jet.
  • eta::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • kt2::Float64: The transverse momentum squared of the jet.
  • NN_dist::Float64: The distance to the nearest neighbor.
  • jets_index::Int: The index of the jet in the jet array.
  • tile_index::Int: The index of the tile in the tile array.
  • dij_posn::Int: The position of this jet in the dij compact array.
  • NN::TiledJet: The nearest neighbor.
  • previous::TiledJet: The previous jet.
  • next::TiledJet: The next jet.
source
',5))]),t("details",He,[t("summary",null,[e[252]||(e[252]=t("a",{id:"JetReconstruction.TiledJet-Tuple{Any}",href:"#JetReconstruction.TiledJet-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.TiledJet")],-1)),e[253]||(e[253]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[254]||(e[254]=s('
julia
TiledJet(id)
Constructs a TiledJet object with the given id and initializes its properties to zero.
Arguments
  • id: The ID of the TiledJet object.
Returns
A TiledJet object with the specified id and values set to zero or noTiledJet.
source
',7))]),t("details",Ie,[t("summary",null,[e[255]||(e[255]=t("a",{id:"JetReconstruction.Tiling",href:"#JetReconstruction.Tiling"},[t("span",{class:"jlbinding"},"JetReconstruction.Tiling")],-1)),e[256]||(e[256]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[257]||(e[257]=s('
julia
struct Tiling
The Tiling struct represents a tiling configuration for jet reconstruction.
Fields
  • setup::TilingDef: The tiling definition used for the configuration.
  • tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).
  • positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.
  • tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).
source
',5))]),t("details",qe,[t("summary",null,[e[258]||(e[258]=t("a",{id:"JetReconstruction.Tiling-Tuple{JetReconstruction.TilingDef}",href:"#JetReconstruction.Tiling-Tuple{JetReconstruction.TilingDef}"},[t("span",{class:"jlbinding"},"JetReconstruction.Tiling")],-1)),e[259]||(e[259]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[260]||(e[260]=s('
julia
Tiling(setup::TilingDef)
Constructs a intial Tiling object based on the provided setup parameters.
Arguments
  • setup::TilingDef: The setup parameters for the tiling.
Returns
A Tiling object.
source
',7))]),t("details",Se,[t("summary",null,[e[261]||(e[261]=t("a",{id:"JetReconstruction.TilingDef",href:"#JetReconstruction.TilingDef"},[t("span",{class:"jlbinding"},"JetReconstruction.TilingDef")],-1)),e[262]||(e[262]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[263]||(e[263]=s(`
julia
struct TilingDef
A struct representing the definition of a spcific tiling scheme.
Fields
  • _tiles_eta_min::Float64: The minimum rapidity of the tiles.
  • _tiles_eta_max::Float64: The maximum rapidity of the tiles.
  • _tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).
  • _tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).
  • _n_tiles_eta::Int: The number of tiles across rapidity.
  • _n_tiles_phi::Int: The number of tiles across phi.
  • _n_tiles::Int: The total number of tiles.
  • _tiles_ieta_min::Int: The minimum rapidity tile index.
  • _tiles_ieta_max::Int: The maximum rapidity tile index.
Constructor
TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
-	_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)
Constructs a TilingDef object with the given parameters.
source
`,8))]),t("details",Oe,[t("summary",null,[e[264]||(e[264]=t("a",{id:"JetReconstruction.neighbour_tiles",href:"#JetReconstruction.neighbour_tiles"},[t("span",{class:"jlbinding"},"JetReconstruction.neighbour_tiles")],-1)),e[265]||(e[265]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[266]||(e[266]=s(`
julia
struct neighbour_tiles
A struct representing the neighbouring tiles.
A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
+                   recombine = +)
This function is the actual implementation of the e+e- jet clustering algorithm.
source
`,3))]),t("details",f,[t("summary",null,[e[27]||(e[27]=t("a",{id:"JetReconstruction._ensure_valid_rap_phi-Tuple{PseudoJet}",href:"#JetReconstruction._ensure_valid_rap_phi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction._ensure_valid_rap_phi")],-1)),e[28]||(e[28]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[29]||(e[29]=s('
julia
_ensure_valid_rap_phi(p::PseudoJet)
Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.
Arguments
  • p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.
source
',5))]),t("details",T,[t("summary",null,[e[30]||(e[30]=t("a",{id:"JetReconstruction._plain_jet_reconstruct-Tuple{}",href:"#JetReconstruction._plain_jet_reconstruct-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction._plain_jet_reconstruct")],-1)),e[31]||(e[31]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[32]||(e[32]=s('
julia
_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)
This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.
Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.
The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt. Floating point values are allowed for generalised k_t algorithm.
Arguments
  • particles: A vector of PseudoJet objects representing the input particles.
  • p=-1: The power to which the transverse momentum (pt) of each particle is raised.
  • R=1.0: The jet radius parameter.
  • recombine: The recombination function used to merge two jets. Default is + (additive recombination).
Returns
  • clusterseq: The resulting ClusterSequence object representing the reconstructed jets.
source
',9))]),t("details",J,[t("summary",null,[e[33]||(e[33]=t("a",{id:"JetReconstruction._set_rap_phi!-Tuple{PseudoJet}",href:"#JetReconstruction._set_rap_phi!-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction._set_rap_phi!")],-1)),e[34]||(e[34]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[35]||(e[35]=s('
_set_rap_phi!(p::PseudoJet)
Set the rapidity and azimuthal angle of the PseudoJet p.
Arguments
  • p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.
Description
This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.
Note - the ϕ angle is calculated in the range [0, 2π).
source
',8))]),t("details",E,[t("summary",null,[e[36]||(e[36]=t("a",{id:"JetReconstruction._tiled_jet_reconstruct-Tuple{Vector{PseudoJet}}",href:"#JetReconstruction._tiled_jet_reconstruct-Tuple{Vector{PseudoJet}}"},[t("span",{class:"jlbinding"},"JetReconstruction._tiled_jet_reconstruct")],-1)),e[37]||(e[37]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[38]||(e[38]=s('
julia
_tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets once preprocessing of data types are done.
Arguments
  • particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.
  • p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)
source
',9))]),t("details",v,[t("summary",null,[e[39]||(e[39]=t("a",{id:"JetReconstruction._tj_diJ-Tuple{Any}",href:"#JetReconstruction._tj_diJ-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction._tj_diJ")],-1)),e[40]||(e[40]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[41]||(e[41]=s('
julia
_tj_diJ(jet)
Compute the dij metric value for a given jet.
Arguments
  • jet: The input jet.
Returns
  • The dij value for the jet.
Example
source
',8))]),t("details",R,[t("summary",null,[e[42]||(e[42]=t("a",{id:"JetReconstruction._tj_dist-Tuple{Any, Any}",href:"#JetReconstruction._tj_dist-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction._tj_dist")],-1)),e[43]||(e[43]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[44]||(e[44]=s('
julia
_tj_dist(jetA, jetB)
Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.
Arguments
  • jetA: The first jet.
  • jetB: The second jet.
Returns
The squared distance between jetA and jetB.
Examples
source
',8))]),t("details",C,[t("summary",null,[e[45]||(e[45]=t("a",{id:"JetReconstruction.add_step_to_history!-Tuple{ClusterSequence, Vararg{Any, 4}}",href:"#JetReconstruction.add_step_to_history!-Tuple{ClusterSequence, Vararg{Any, 4}}"},[t("span",{class:"jlbinding"},"JetReconstruction.add_step_to_history!")],-1)),e[46]||(e[46]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[47]||(e[47]=s('
julia
add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)
Add a new jet's history into the recombination sequence.
Arguments:
  • clusterseq::ClusterSequence: The cluster sequence object.
  • parent1: The index of the first parent.
  • parent2: The index of the second parent.
  • jetp_index: The index of the jet.
  • dij: The dij value.
This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.
If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.
source
',7))]),t("details",F,[t("summary",null,[e[48]||(e[48]=t("a",{id:"JetReconstruction.add_untagged_neighbours_to_tile_union-NTuple{4, Any}",href:"#JetReconstruction.add_untagged_neighbours_to_tile_union-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.add_untagged_neighbours_to_tile_union")],-1)),e[49]||(e[49]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[50]||(e[50]=s('
julia
add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)
Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.
Arguments
  • center_index: The index of the center tile.
  • tile_union: An array to store the indices of neighbouring tiles.
  • n_near_tiles: The number of neighbouring tiles.
  • tiling: The tiling object containing the tile tags.
Returns
The updated number of near tiles.
source
',7))]),t("details",A,[t("summary",null,[e[51]||(e[51]=t("a",{id:"JetReconstruction.angular_distance-Tuple{Any, Any, Any}",href:"#JetReconstruction.angular_distance-Tuple{Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.angular_distance")],-1)),e[52]||(e[52]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=s('
julia
angular_distance(eereco, i, j) -> Float64
',1)),t("p",null,[e[55]||(e[55]=i("Calculate the angular distance between two jets ")),e[56]||(e[56]=t("code",null,"i",-1)),e[57]||(e[57]=i(" and ")),e[58]||(e[58]=t("code",null,"j",-1)),e[59]||(e[59]=i(" using the formula ")),t("mjx-container",x,[(a(),l("svg",_,e[53]||(e[53]=[s('',1)]))),e[54]||(e[54]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mn",null,"1"),t("mo",null,"−"),t("mi",null,"c"),t("mi",null,"o"),t("mi",null,"s"),t("mo",{stretchy:"false"},"("),t("msub",null,[t("mi",null,"θ"),t("mrow",{"data-mjx-texclass":"ORD"},[t("mi",null,"i"),t("mi",null,"j")])]),t("mo",{stretchy:"false"},")")])],-1))]),e[60]||(e[60]=i("."))]),e[71]||(e[71]=s("
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
Returns
",3)),t("ul",null,[t("li",null,[e[63]||(e[63]=t("code",null,"Float64",-1)),e[64]||(e[64]=i(": The angular distance between ")),e[65]||(e[65]=t("code",null,"i",-1)),e[66]||(e[66]=i(" and ")),e[67]||(e[67]=t("code",null,"j",-1)),e[68]||(e[68]=i(", which is ")),t("mjx-container",Q,[(a(),l("svg",P,e[61]||(e[61]=[s('',1)]))),e[62]||(e[62]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mn",null,"1"),t("mo",null,"−"),t("mi",null,"c"),t("mi",null,"o"),t("mi",null,"s"),t("mi",null,"h"),t("mi",null,"e"),t("mi",null,"t"),t("mi",null,"a")])],-1))]),e[69]||(e[69]=i("."))])]),e[72]||(e[72]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/EEAlgorithm.jl#L6-L20",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",w,[t("summary",null,[e[73]||(e[73]=t("a",{id:"JetReconstruction.check_algorithm_power_consistency-Tuple{}",href:"#JetReconstruction.check_algorithm_power_consistency-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction.check_algorithm_power_consistency")],-1)),e[74]||(e[74]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[75]||(e[75]=t("p",null,"Allow a check for algorithm and power consistency",-1)),e[76]||(e[76]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/AlgorithmStrategyEnums.jl#L129",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",L,[t("summary",null,[e[77]||(e[77]=t("a",{id:"JetReconstruction.detach!-Tuple{JetReconstruction.TiledJet}",href:"#JetReconstruction.detach!-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.detach!")],-1)),e[78]||(e[78]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[79]||(e[79]=s('
julia
detach!(jet::TiledJet)
Detach a TiledJet from its linked list by updating the previous and next pointers.
Arguments
  • jet::TiledJet: The TiledJet object to detach.
source
',5))]),t("details",B,[t("summary",null,[e[80]||(e[80]=t("a",{id:"JetReconstruction.determine_rapidity_extent-Union{Tuple{Vector{T}}, Tuple{T}} where T<:AbstractFloat",href:"#JetReconstruction.determine_rapidity_extent-Union{Tuple{Vector{T}}, Tuple{T}} where T<:AbstractFloat"},[t("span",{class:"jlbinding"},"JetReconstruction.determine_rapidity_extent")],-1)),e[81]||(e[81]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[82]||(e[82]=s('
julia
determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat
Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.
This is the heuristic which is used by FastJet (inline comments are from FastJet).
Arguments
  • eta::Vector{T}: A vector of rapidity values.
Returns
  • minrap::T: The minimum rapidity value.
  • maxrap::T: The maximum rapidity value.
source
',8))]),t("details",M,[t("summary",null,[e[83]||(e[83]=t("a",{id:"JetReconstruction.dij-NTuple{4, Any}",href:"#JetReconstruction.dij-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dij")],-1)),e[84]||(e[84]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[85]||(e[85]=s('
julia
dij(i, kt2_array, nn, nndist)
Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß
Arguments
  • i: The index of the element.
  • kt2_array: An array of kt2 values.
  • nn: An array of nearest neighbors.
  • nndist: An array of nearest neighbor distances.
Returns
  • The computed dij value.
source
',7))]),t("details",D,[t("summary",null,[e[86]||(e[86]=t("a",{id:"JetReconstruction.dij_dist-NTuple{4, Any}",href:"#JetReconstruction.dij_dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dij_dist")],-1)),e[87]||(e[87]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[92]||(e[92]=s('
julia
dij_dist(eereco, i, j, dij_factor)
',1)),t("p",null,[e[90]||(e[90]=i("Calculate the dij distance between two ")),t("mjx-container",H,[(a(),l("svg",I,e[88]||(e[88]=[s('',1)]))),e[89]||(e[89]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("msup",null,[t("mi",null,"e"),t("mo",null,"+")]),t("msup",null,[t("mi",null,"e"),t("mo",null,"−")])])],-1))]),e[91]||(e[91]=i("jets."))]),e[93]||(e[93]=s('
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
  • dij_factor: The scaling factor to multiply the dij distance by.
Returns
  • The dij distance between i and j.
source
',5))]),t("details",q,[t("summary",null,[e[94]||(e[94]=t("a",{id:"JetReconstruction.dist-NTuple{4, Any}",href:"#JetReconstruction.dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.dist")],-1)),e[95]||(e[95]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[96]||(e[96]=s('
julia
dist(i, j, rapidity_array, phi_array)
Compute the distance between points in a 2D space defined by rapidity and phi coordinates.
Arguments
  • i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).
  • j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).
  • rapidity_array::Vector{Float64}: Array of rapidity coordinates.
  • phi_array::Vector{Float64}: Array of phi coordinates.
Returns
  • distance::Float64: The distance between the two points.
source
',7))]),t("details",S,[t("summary",null,[e[97]||(e[97]=t("a",{id:"JetReconstruction.do_iB_recombination_step!-Tuple{ClusterSequence, Any, Any}",href:"#JetReconstruction.do_iB_recombination_step!-Tuple{ClusterSequence, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.do_iB_recombination_step!")],-1)),e[98]||(e[98]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[99]||(e[99]=s('
julia
do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)
Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the jet.
  • diB: The diB value.
source
',5))]),t("details",O,[t("summary",null,[e[100]||(e[100]=t("a",{id:"JetReconstruction.do_ij_recombination_step!",href:"#JetReconstruction.do_ij_recombination_step!"},[t("span",{class:"jlbinding"},"JetReconstruction.do_ij_recombination_step!")],-1)),e[101]||(e[101]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[102]||(e[102]=s('
julia
do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)
Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the first jet to be recombined.
  • jet_j: The index of the second jet to be recombined.
  • dij: The distance between the two jets.
  • recombine=+: The recombination function to be used. Default is addition.
Returns
  • newjet_k: The index of the newly created jet.
Description
This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.
source
',9))]),t("details",N,[t("summary",null,[e[103]||(e[103]=t("a",{id:"JetReconstruction.energy-Tuple{PseudoJet}",href:"#JetReconstruction.energy-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.energy")],-1)),e[104]||(e[104]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[105]||(e[105]=s('
julia
energy(p::PseudoJet)
Return the energy of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The energy of the PseudoJet.
source
',7))]),t("details",V,[t("summary",null,[e[106]||(e[106]=t("a",{id:"JetReconstruction.eta-Tuple{PseudoJet}",href:"#JetReconstruction.eta-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.eta")],-1)),e[107]||(e[107]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[108]||(e[108]=s('
julia
eta(p::PseudoJet)
Compute the pseudorapidity (η) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.
Returns
  • The pseudorapidity (η) of the PseudoJet.
source
',7))]),t("details",z,[t("summary",null,[e[109]||(e[109]=t("a",{id:"JetReconstruction.fast_findmin-Tuple{Any, Any}",href:"#JetReconstruction.fast_findmin-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.fast_findmin")],-1)),e[110]||(e[110]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[111]||(e[111]=s('
julia
fast_findmin(dij, n)
Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.
Arguments
  • dij: An array of values.
  • n: The number of elements to consider in the dij array.
Returns
  • dij_min: The minimum value in the first n elements of the dij array.
  • best: The index of the minimum value in the dij array.
source
',7))]),t("details",Z,[t("summary",null,[e[112]||(e[112]=t("a",{id:"JetReconstruction.find_tile_neighbours!-NTuple{5, Any}",href:"#JetReconstruction.find_tile_neighbours!-NTuple{5, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.find_tile_neighbours!")],-1)),e[113]||(e[113]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[114]||(e[114]=s('
julia
find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)
Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run
Arguments
  • tile_union: The tile union to which the neighbouring tiles will be added.
  • jetA: The first jet.
  • jetB: The second jet.
  • oldB: The old second jet.
  • tiling: The tiling information.
Returns
The number of neighbouring tiles added to the tile_union.
source
',7))]),t("details",U,[t("summary",null,[e[115]||(e[115]=t("a",{id:"JetReconstruction.geometric_distance-NTuple{4, AbstractFloat}",href:"#JetReconstruction.geometric_distance-NTuple{4, AbstractFloat}"},[t("span",{class:"jlbinding"},"JetReconstruction.geometric_distance")],-1)),e[116]||(e[116]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[117]||(e[117]=s('
julia
geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)
Compute the geometric distance between two points in the rap-phi plane.
Arguments
  • eta1::AbstractFloat: The eta coordinate of the first point.
  • phi1::AbstractFloat: The phi coordinate of the first point.
  • eta2::AbstractFloat: The eta coordinate of the second point.
  • phi2::AbstractFloat: The phi coordinate of the second point.
Returns
  • distance::Float64: The geometric distance between the two points.
source
',7))]),t("details",X,[t("summary",null,[e[118]||(e[118]=t("a",{id:"JetReconstruction.get_algorithm_power_consistency-Tuple{}",href:"#JetReconstruction.get_algorithm_power_consistency-Tuple{}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_algorithm_power_consistency")],-1)),e[119]||(e[119]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[120]||(e[120]=s('
julia
get_algorithm_power_consistency(; p::Union{Real, Nothing}, algorithm::Union{JetAlgorithm, Nothing})
Get the algorithm and power consistency correct
This function checks the consistency between the algorithm and power parameters. If the algorithm is specified, it checks if the power parameter is consistent with the algorithm's known power. If the power parameter is not specified, it sets the power parameter based on the algorithm. If neither the algorithm nor the power parameter is specified, it throws an ArgumentError.
Arguments
  • p::Union{Real, Nothing}: The power value.
  • algorithm::Union{JetAlgorithm, Nothing}: The algorithm.
Returns
A named tuple of the consistent power and algorithm values.
Throws
  • ArgumentError: If the algorithm and power are inconsistent or if neither the algorithm nor the power is specified.
source
',10))]),t("details",G,[t("summary",null,[e[121]||(e[121]=t("a",{id:"JetReconstruction.get_all_ancestors-Tuple{Any, ClusterSequence}",href:"#JetReconstruction.get_all_ancestors-Tuple{Any, ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_all_ancestors")],-1)),e[122]||(e[122]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[123]||(e[123]=s('
julia
get_all_ancestors(idx, cs::ClusterSequence)
Recursively finds all ancestors of a given index in a ClusterSequence object.
Arguments
  • idx: The index of the jet for which to find ancestors.
  • cs: The ClusterSequence object containing the jet history.
Returns
An array of indices representing the ancestors of the given jet.
source
',7))]),t("details",W,[t("summary",null,[e[124]||(e[124]=t("a",{id:"JetReconstruction.get_dij_dist-NTuple{4, Any}",href:"#JetReconstruction.get_dij_dist-NTuple{4, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_dij_dist")],-1)),e[125]||(e[125]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[126]||(e[126]=s('
julia
get_dij_dist(nn_dist, kt2_1, kt2_2, R2)
Compute the dij metric distance between two jets.
Arguments
  • nn_dist: The nearest-neighbor distance between two jets.
  • kt2_1: The squared momentum metric value of the first jet.
  • kt2_2: The squared momentum metric value of the second jet.
  • R2: The jet radius parameter squared.
Returns
The distance between the two jets.
If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.
source
',8))]),t("details",$,[t("summary",null,[e[127]||(e[127]=t("a",{id:"JetReconstruction.get_tile-Tuple{JetReconstruction.TilingDef, AbstractFloat, AbstractFloat}",href:"#JetReconstruction.get_tile-Tuple{JetReconstruction.TilingDef, AbstractFloat, AbstractFloat}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_tile")],-1)),e[128]||(e[128]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[129]||(e[129]=s('
julia
get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)
Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.
Arguments
  • tiling_setup: A TilingDef object that contains the tiling setup parameters.
  • eta: The eta value for which to calculate the tile index.
  • phi: The phi value for which to calculate the tile index.
Returns
  • ieta: The tile index along the eta direction.
  • iphi: The tile index along the phi direction.
source
',7))]),t("details",K,[t("summary",null,[e[130]||(e[130]=t("a",{id:"JetReconstruction.get_tile_cartesian_indices-Tuple{JetReconstruction.TilingDef, Int64}",href:"#JetReconstruction.get_tile_cartesian_indices-Tuple{JetReconstruction.TilingDef, Int64}"},[t("span",{class:"jlbinding"},"JetReconstruction.get_tile_cartesian_indices")],-1)),e[131]||(e[131]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[132]||(e[132]=s('
julia
get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)
Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)
Arguments
  • tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.
  • i_η::Int: The index of the tile in the η dimension.
  • i_ϕ::Int: The index of the tile in the ϕ dimension.
Returns
  • The linear index of the tile.
source
',7))]),t("details",Y,[t("summary",null,[e[133]||(e[133]=t("a",{id:"JetReconstruction.initial_history-Tuple{Any}",href:"#JetReconstruction.initial_history-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.initial_history")],-1)),e[134]||(e[134]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[135]||(e[135]=s('
julia
initial_history(particles)
Create an initial history for the given particles.
Arguments
  • particles: The initial vector of stable particles.
Returns
  • history: An array of HistoryElement objects.
  • Qtot: The total energy in the event.
source
',7))]),t("details",ee,[t("summary",null,[e[136]||(e[136]=t("a",{id:"JetReconstruction.insert!-Tuple{JetReconstruction.TiledJet, JetReconstruction.TiledJet}",href:"#JetReconstruction.insert!-Tuple{JetReconstruction.TiledJet, JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.insert!")],-1)),e[137]||(e[137]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[138]||(e[138]=s('
julia
insert!(nextjet::TiledJet, jettomove::TiledJet)
Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list
Arguments
  • nextjet::TiledJet: The TiledJet object after which jettomove should be inserted.
  • jettomove::TiledJet: The TiledJet object to be inserted.
Example
source
',6))]),t("details",te,[t("summary",null,[e[139]||(e[139]=t("a",{id:"JetReconstruction.is_ee-Tuple{JetReconstruction.JetAlgorithm.Algorithm}",href:"#JetReconstruction.is_ee-Tuple{JetReconstruction.JetAlgorithm.Algorithm}"},[t("span",{class:"jlbinding"},"JetReconstruction.is_ee")],-1)),e[140]||(e[140]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[141]||(e[141]=s('
julia
is_ee(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a e+e- reconstruction algorithm.
Returns
true if the algorithm is a e+e- reconstruction algorithm, false otherwise.
source
',5))]),t("details",ie,[t("summary",null,[e[142]||(e[142]=t("a",{id:"JetReconstruction.is_pp-Tuple{JetReconstruction.JetAlgorithm.Algorithm}",href:"#JetReconstruction.is_pp-Tuple{JetReconstruction.JetAlgorithm.Algorithm}"},[t("span",{class:"jlbinding"},"JetReconstruction.is_pp")],-1)),e[143]||(e[143]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[144]||(e[144]=s('
julia
is_pp(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a pp reconstruction algorithm.
Returns
true if the algorithm is a pp reconstruction algorithm, false otherwise.
source
',5))]),t("details",se,[t("summary",null,[e[145]||(e[145]=t("a",{id:"JetReconstruction.isvalid-Tuple{JetReconstruction.TiledJet}",href:"#JetReconstruction.isvalid-Tuple{JetReconstruction.TiledJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.isvalid")],-1)),e[146]||(e[146]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[147]||(e[147]=s('
julia
isvalid(t::TiledJet)
Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.
Arguments
  • t::TiledJet: The TiledJet object to check.
Returns
  • Bool: true if the TiledJet object is valid, false otherwise.
source
',7))]),t("details",ne,[t("summary",null,[e[148]||(e[148]=t("a",{id:"JetReconstruction.jet_ranks-Tuple{ClusterSequence}",href:"#JetReconstruction.jet_ranks-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.jet_ranks")],-1)),e[149]||(e[149]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[150]||(e[150]=s('
julia
jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)
Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.
  • compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.
Returns
A dictionary mapping each jet index to its rank.
Note
This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.
source
',9))]),t("details",oe,[t("summary",null,[e[151]||(e[151]=t("a",{id:"JetReconstruction.m-Tuple{PseudoJet}",href:"#JetReconstruction.m-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.m")],-1)),e[152]||(e[152]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[153]||(e[153]=s('
julia
m(p::PseudoJet)
Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the invariant mass.
Returns
The invariant mass of the PseudoJet object.
source
',7))]),t("details",le,[t("summary",null,[e[154]||(e[154]=t("a",{id:"JetReconstruction.m2-Tuple{PseudoJet}",href:"#JetReconstruction.m2-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.m2")],-1)),e[155]||(e[155]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[156]||(e[156]=s('
julia
m2(p::PseudoJet)
Calculate the invariant mass squared (m^2) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.
Returns
  • The invariant mass squared (m^2) of the PseudoJet.
source
',7))]),t("details",ae,[t("summary",null,[e[157]||(e[157]=t("a",{id:"JetReconstruction.mag-Tuple{PseudoJet}",href:"#JetReconstruction.mag-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.mag")],-1)),e[158]||(e[158]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[159]||(e[159]=s('
julia
mag(p::PseudoJet)
Return the magnitude of the momentum of a PseudoJet, |p|.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the magnitude.
Returns
The magnitude of the PseudoJet object.
source
',7))]),t("details",de,[t("summary",null,[e[160]||(e[160]=t("a",{id:"JetReconstruction.mass-Tuple{PseudoJet}",href:"#JetReconstruction.mass-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.mass")],-1)),e[161]||(e[161]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[162]||(e[162]=s('
julia
mass(p::PseudoJet)
Compute the invariant mass (alias for m(p)).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the mass.
Returns
  • The mass of the PseudoJet.
source
',7))]),t("details",re,[t("summary",null,[e[163]||(e[163]=t("a",{id:"JetReconstruction.mass2",href:"#JetReconstruction.mass2"},[t("span",{class:"jlbinding"},"JetReconstruction.mass2")],-1)),e[164]||(e[164]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[165]||(e[165]=t("p",null,[i("Alias for "),t("code",null,"m2"),i(" function")],-1)),e[166]||(e[166]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/Pseudojet.jl#L370",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",pe,[t("summary",null,[e[167]||(e[167]=t("a",{id:"JetReconstruction.merge_steps-Tuple{ClusterSequence}",href:"#JetReconstruction.merge_steps-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.merge_steps")],-1)),e[168]||(e[168]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[169]||(e[169]=s('
julia
merge_steps(clusterseq::ClusterSequence)
Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
Returns
  • merge_steps::Int: The number of merge steps.
source
',7))]),t("details",ce,[t("summary",null,[e[170]||(e[170]=t("a",{id:"JetReconstruction.phi-Tuple{PseudoJet}",href:"#JetReconstruction.phi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.phi")],-1)),e[171]||(e[171]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[172]||(e[172]=s('
julia
phi(p::PseudoJet)
Compute the ϕ angle of a PseudoJet object p.
Note this function is a wrapper for phi_02pi(p).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
',8))]),t("details",he,[t("summary",null,[e[173]||(e[173]=t("a",{id:"JetReconstruction.phi_02pi-Tuple{PseudoJet}",href:"#JetReconstruction.phi_02pi-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.phi_02pi")],-1)),e[174]||(e[174]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[175]||(e[175]=s('
julia
phi_02pi(p::PseudoJet)
Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
',7))]),t("details",ue,[t("summary",null,[e[176]||(e[176]=t("a",{id:"JetReconstruction.pt-Tuple{PseudoJet}",href:"#JetReconstruction.pt-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pt")],-1)),e[177]||(e[177]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[178]||(e[178]=s('
julia
pt(p::PseudoJet)
Compute the scalar transverse momentum (pt) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.
Returns
  • The transverse momentum (pt) of the PseudoJet.
source
',7))]),t("details",ge,[t("summary",null,[e[179]||(e[179]=t("a",{id:"JetReconstruction.pt2-Tuple{PseudoJet}",href:"#JetReconstruction.pt2-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pt2")],-1)),e[180]||(e[180]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[181]||(e[181]=s('
julia
pt2(p::PseudoJet)
Get the squared transverse momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The squared transverse momentum of the PseudoJet.
source
',7))]),t("details",ke,[t("summary",null,[e[182]||(e[182]=t("a",{id:"JetReconstruction.px-Tuple{PseudoJet}",href:"#JetReconstruction.px-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.px")],-1)),e[183]||(e[183]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[184]||(e[184]=s('
julia
px(p::PseudoJet)
Return the x-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The x-component of the momentum of the PseudoJet.
source
',7))]),t("details",be,[t("summary",null,[e[185]||(e[185]=t("a",{id:"JetReconstruction.py-Tuple{PseudoJet}",href:"#JetReconstruction.py-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.py")],-1)),e[186]||(e[186]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[187]||(e[187]=s('
julia
py(p::PseudoJet)
Return the y-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The y-component of the momentum of the PseudoJet.
source
',7))]),t("details",me,[t("summary",null,[e[188]||(e[188]=t("a",{id:"JetReconstruction.pz-Tuple{PseudoJet}",href:"#JetReconstruction.pz-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.pz")],-1)),e[189]||(e[189]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[190]||(e[190]=s('
julia
pz(p::PseudoJet)
Return the z-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The z-component of the momentum of the PseudoJet.
source
',7))]),t("details",ye,[t("summary",null,[e[191]||(e[191]=t("a",{id:"JetReconstruction.rapidity-Tuple{PseudoJet}",href:"#JetReconstruction.rapidity-Tuple{PseudoJet}"},[t("span",{class:"jlbinding"},"JetReconstruction.rapidity")],-1)),e[192]||(e[192]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[193]||(e[193]=s('
julia
rapidity(p::PseudoJet)
Compute the rapidity of a PseudoJet object.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the rapidity.
Returns
The rapidity of the PseudoJet object.
source
',7))]),t("details",je,[t("summary",null,[e[194]||(e[194]=t("a",{id:"JetReconstruction.reco_state-Tuple{ClusterSequence, Any}",href:"#JetReconstruction.reco_state-Tuple{ClusterSequence, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.reco_state")],-1)),e[195]||(e[195]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[196]||(e[196]=s('
julia
reco_state(cs::ClusterSequence, pt_ranks; iteration=0)
This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.
Arguments
  • cs::ClusterSequence: The ClusterSequence object to update.
  • ranks: The ranks of the original clusters, that are inherited by peudojets
during the reconstruction process.
  • iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).
  • ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).
Returns
A dictionary representing a snapshot of the reconstruction state.
Details
The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.
source
',11))]),t("details",fe,[t("summary",null,[e[197]||(e[197]=t("a",{id:"JetReconstruction.rightneighbours-Tuple{Int64, JetReconstruction.Tiling}",href:"#JetReconstruction.rightneighbours-Tuple{Int64, JetReconstruction.Tiling}"},[t("span",{class:"jlbinding"},"JetReconstruction.rightneighbours")],-1)),e[198]||(e[198]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[199]||(e[199]=s('
julia
rightneighbours(center::Int, tiling::Tiling)
Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the indices of the right neighbors.
source
',7))]),t("details",Te,[t("summary",null,[e[200]||(e[200]=t("a",{id:"JetReconstruction.set_momentum!-Tuple{PseudoJet, Vararg{Any, 4}}",href:"#JetReconstruction.set_momentum!-Tuple{PseudoJet, Vararg{Any, 4}}"},[t("span",{class:"jlbinding"},"JetReconstruction.set_momentum!")],-1)),e[201]||(e[201]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[202]||(e[202]=s('
julia
set_momentum!(j::PseudoJet, px, py, pz, E)
Set the momentum components and energy of a PseudoJet object.
Arguments
  • j::PseudoJet: The PseudoJet object to set the momentum for.
  • px: The x-component of the momentum.
  • py: The y-component of the momentum.
  • pz: The z-component of the momentum.
  • E: The energy of the particle.
source
',5))]),t("details",Je,[t("summary",null,[e[203]||(e[203]=t("a",{id:"JetReconstruction.set_nearest_neighbours!-Tuple{ClusterSequence, JetReconstruction.Tiling, Vector{JetReconstruction.TiledJet}}",href:"#JetReconstruction.set_nearest_neighbours!-Tuple{ClusterSequence, JetReconstruction.Tiling, Vector{JetReconstruction.TiledJet}}"},[t("span",{class:"jlbinding"},"JetReconstruction.set_nearest_neighbours!")],-1)),e[204]||(e[204]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[205]||(e[205]=s('
julia
set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})
This function sets the nearest neighbor information for all jets in the tiledjets vector.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • tiling::Tiling: The tiling object.
  • tiledjets::Vector{TiledJet}: The vector of tiled jets.
Returns
  • NNs::Vector{TiledJet}: The vector of nearest neighbor jets.
  • diJ::Vector{Float64}: The vector of diJ values.
The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.
Note: The diJ values are calculated as the kt distance multiplied by R^2.
source
',9))]),t("details",Ee,[t("summary",null,[e[206]||(e[206]=t("a",{id:"JetReconstruction.setup_tiling-Union{Tuple{T}, Tuple{Vector{T}, AbstractFloat}} where T<:AbstractFloat",href:"#JetReconstruction.setup_tiling-Union{Tuple{T}, Tuple{Vector{T}, AbstractFloat}} where T<:AbstractFloat"},[t("span",{class:"jlbinding"},"JetReconstruction.setup_tiling")],-1)),e[207]||(e[207]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[208]||(e[208]=s('
julia
setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat
This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.
Arguments
  • eta::Vector{T}: A vector of rapidities.
  • Rparam::AbstractFloat: The jet radius parameter.
Returns
  • tiling_setup: A TilingDef object containing the tiling setup parameters.
Description
The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.
source
',9))]),t("details",ve,[t("summary",null,[e[209]||(e[209]=t("a",{id:"JetReconstruction.surrounding-Tuple{Int64, JetReconstruction.Tiling}",href:"#JetReconstruction.surrounding-Tuple{Int64, JetReconstruction.Tiling}"},[t("span",{class:"jlbinding"},"JetReconstruction.surrounding")],-1)),e[210]||(e[210]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[211]||(e[211]=s('
julia
surrounding(center::Int, tiling::Tiling)
Compute the surrounding indices of a given center index in a tiling.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the surrounding indices.
source
',7))]),t("details",Re,[t("summary",null,[e[212]||(e[212]=t("a",{id:"JetReconstruction.tile_index-Tuple{Any, Float64, Float64}",href:"#JetReconstruction.tile_index-Tuple{Any, Float64, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.tile_index")],-1)),e[213]||(e[213]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[214]||(e[214]=s('
julia
tile_index(tiling_setup, eta::Float64, phi::Float64)
Compute the tile index for a given (eta, phi) coordinate.
Arguments
  • tiling_setup: The tiling setup object containing the tile size and number of tiles.
  • eta::Float64: The eta coordinate.
  • phi::Float64: The phi coordinate.
Returns
The tile index corresponding to the (eta, phi) coordinate.
source
',7))]),t("details",Ce,[t("summary",null,[e[215]||(e[215]=t("a",{id:"JetReconstruction.tiledjet_remove_from_tiles!-Tuple{Any, Any}",href:"#JetReconstruction.tiledjet_remove_from_tiles!-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.tiledjet_remove_from_tiles!")],-1)),e[216]||(e[216]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[217]||(e[217]=s('
julia
tiledjet_remove_from_tiles!(tiling, jet)
Remove a jet from the given tiling structure.
Arguments
  • tiling: The tiling structure from which the jet will be removed.
  • jet: The jet to be removed from the tiling structure.
Description
This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.
source
',7))]),t("details",Fe,[t("summary",null,[e[218]||(e[218]=t("a",{id:"JetReconstruction.tiledjet_set_jetinfo!-Tuple{JetReconstruction.TiledJet, ClusterSequence, JetReconstruction.Tiling, Any, Any, Any}",href:"#JetReconstruction.tiledjet_set_jetinfo!-Tuple{JetReconstruction.TiledJet, ClusterSequence, JetReconstruction.Tiling, Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.tiledjet_set_jetinfo!")],-1)),e[219]||(e[219]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[220]||(e[220]=s('
julia
tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)
Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)
Arguments:
  • jet::TiledJet: The TiledJet object to set the information for.
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets.
  • tiling::Tiling: The Tiling object containing the tile information.
  • jets_index: The index of the jet in the ClusterSequence.
  • R2: The jet radius parameter squared.
  • p: The power to raise the pt2 value to.
This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.
Returns:
  • nothing
source
',8))]),t("details",Ae,[t("summary",null,[e[221]||(e[221]=t("a",{id:"JetReconstruction.upd_nn_crosscheck!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}",href:"#JetReconstruction.upd_nn_crosscheck!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_crosscheck!")],-1)),e[222]||(e[222]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[223]||(e[223]=s('
julia
upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
',5))]),t("details",xe,[t("summary",null,[e[224]||(e[224]=t("a",{id:"JetReconstruction.upd_nn_nocross!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}",href:"#JetReconstruction.upd_nn_nocross!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_nocross!")],-1)),e[225]||(e[225]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[226]||(e[226]=s('
julia
upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
',5))]),t("details",_e,[t("summary",null,[e[227]||(e[227]=t("a",{id:"JetReconstruction.upd_nn_step!-NTuple{12, Any}",href:"#JetReconstruction.upd_nn_step!-NTuple{12, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.upd_nn_step!")],-1)),e[228]||(e[228]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[229]||(e[229]=s('
julia
upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)
Update the nearest neighbor information after a jet merge step.
Arguments:
  • i: Index of the first particle in the last merge step.
  • j: Index of the second particle in the last merge step.
  • k: Index of the current particle for which the nearest neighbour will be updated.
  • N: Total number of particles (currently vaild array indexes are [1:N]).
  • Nn: Number of nearest neighbors to consider.
  • kt2_array: Array of transverse momentum squared values.
  • rapidity_array: Array of rapidity values.
  • phi_array: Array of azimuthal angle values.
  • R2: Distance threshold squared for nearest neighbors.
  • nndist: Array of nearest neighbor geometric distances.
  • nn: Array of nearest neighbor indices.
  • nndij: Array of metric distances between particles.
This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.
source
',6))]),t("details",Qe,[t("summary",null,[e[230]||(e[230]=t("a",{id:"JetReconstruction.η",href:"#JetReconstruction.η"},[t("span",{class:"jlbinding"},"JetReconstruction.η")],-1)),e[231]||(e[231]=i()),o(n,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[232]||(e[232]=s('
julia
const η = eta
Alias for the pseudorapidity function, eta.
source
',3))]),t("details",Pe,[t("summary",null,[e[233]||(e[233]=t("a",{id:"JetReconstruction.FourMomentum",href:"#JetReconstruction.FourMomentum"},[t("span",{class:"jlbinding"},"JetReconstruction.FourMomentum")],-1)),e[234]||(e[234]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[235]||(e[235]=t("p",null,"Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.",-1)),e[236]||(e[236]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/Pseudojet.jl#L6-L8",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",we,[t("summary",null,[e[237]||(e[237]=t("a",{id:"JetReconstruction.HistoryElement",href:"#JetReconstruction.HistoryElement"},[t("span",{class:"jlbinding"},"JetReconstruction.HistoryElement")],-1)),e[238]||(e[238]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[239]||(e[239]=s('
julia
struct HistoryElement
A struct holding a record of jet mergers and finalisations
Fields:
  • parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)
  • parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)
  • child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.
  • jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.
  • dij: The distance corresponding to the recombination at this stage of the clustering.
  • max_dij_so_far: The largest recombination distance seen so far in the clustering history.
source
',5))]),t("details",Le,[t("summary",null,[e[240]||(e[240]=t("a",{id:"JetReconstruction.HistoryElement-Tuple{Any}",href:"#JetReconstruction.HistoryElement-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.HistoryElement")],-1)),e[241]||(e[241]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[242]||(e[242]=s('
julia
HistoryElement(jetp_index)
Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.
Arguments
  • jetp_index: The index of the jetp.
Returns
A HistoryElement object.
source
',7))]),t("details",Be,[t("summary",null,[e[243]||(e[243]=t("a",{id:"JetReconstruction.JetWithAncestors",href:"#JetReconstruction.JetWithAncestors"},[t("span",{class:"jlbinding"},"JetReconstruction.JetWithAncestors")],-1)),e[244]||(e[244]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[245]||(e[245]=s('
julia
struct JetWithAncestors
A struct representing a jet with its origin ancestors.
Fields
  • self::PseudoJet: The PseudoJet object for this jet.
  • jetp_index::Int: The index of the jet in the corresponding cluster sequence.
  • ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).
  • jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors
Note
This structure needs its associated cluster sequence origin to be useful.
source
',7))]),t("details",Me,[t("summary",null,[e[246]||(e[246]=t("a",{id:"JetReconstruction.Surrounding",href:"#JetReconstruction.Surrounding"},[t("span",{class:"jlbinding"},"JetReconstruction.Surrounding")],-1)),e[247]||(e[247]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[248]||(e[248]=s('
julia
struct Surrounding{N}
Structure used for iterating over neighbour tiles.
Fields
  • indices::NTuple{N, Int}: A tuple of N integers representing the indices.
source
',5))]),t("details",De,[t("summary",null,[e[249]||(e[249]=t("a",{id:"JetReconstruction.TiledJet",href:"#JetReconstruction.TiledJet"},[t("span",{class:"jlbinding"},"JetReconstruction.TiledJet")],-1)),e[250]||(e[250]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[251]||(e[251]=s('
julia
struct TiledJet
TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.
Fields
  • id::Int: The ID of the jet.
  • eta::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • kt2::Float64: The transverse momentum squared of the jet.
  • NN_dist::Float64: The distance to the nearest neighbor.
  • jets_index::Int: The index of the jet in the jet array.
  • tile_index::Int: The index of the tile in the tile array.
  • dij_posn::Int: The position of this jet in the dij compact array.
  • NN::TiledJet: The nearest neighbor.
  • previous::TiledJet: The previous jet.
  • next::TiledJet: The next jet.
source
',5))]),t("details",He,[t("summary",null,[e[252]||(e[252]=t("a",{id:"JetReconstruction.TiledJet-Tuple{Any}",href:"#JetReconstruction.TiledJet-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.TiledJet")],-1)),e[253]||(e[253]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[254]||(e[254]=s('
julia
TiledJet(id)
Constructs a TiledJet object with the given id and initializes its properties to zero.
Arguments
  • id: The ID of the TiledJet object.
Returns
A TiledJet object with the specified id and values set to zero or noTiledJet.
source
',7))]),t("details",Ie,[t("summary",null,[e[255]||(e[255]=t("a",{id:"JetReconstruction.Tiling",href:"#JetReconstruction.Tiling"},[t("span",{class:"jlbinding"},"JetReconstruction.Tiling")],-1)),e[256]||(e[256]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[257]||(e[257]=s('
julia
struct Tiling
The Tiling struct represents a tiling configuration for jet reconstruction.
Fields
  • setup::TilingDef: The tiling definition used for the configuration.
  • tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).
  • positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.
  • tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).
source
',5))]),t("details",qe,[t("summary",null,[e[258]||(e[258]=t("a",{id:"JetReconstruction.Tiling-Tuple{JetReconstruction.TilingDef}",href:"#JetReconstruction.Tiling-Tuple{JetReconstruction.TilingDef}"},[t("span",{class:"jlbinding"},"JetReconstruction.Tiling")],-1)),e[259]||(e[259]=i()),o(n,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[260]||(e[260]=s('
julia
Tiling(setup::TilingDef)
Constructs a intial Tiling object based on the provided setup parameters.
Arguments
  • setup::TilingDef: The setup parameters for the tiling.
Returns
A Tiling object.
source
',7))]),t("details",Se,[t("summary",null,[e[261]||(e[261]=t("a",{id:"JetReconstruction.TilingDef",href:"#JetReconstruction.TilingDef"},[t("span",{class:"jlbinding"},"JetReconstruction.TilingDef")],-1)),e[262]||(e[262]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[263]||(e[263]=s(`
julia
struct TilingDef
A struct representing the definition of a spcific tiling scheme.
Fields
  • _tiles_eta_min::Float64: The minimum rapidity of the tiles.
  • _tiles_eta_max::Float64: The maximum rapidity of the tiles.
  • _tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).
  • _tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).
  • _n_tiles_eta::Int: The number of tiles across rapidity.
  • _n_tiles_phi::Int: The number of tiles across phi.
  • _n_tiles::Int: The total number of tiles.
  • _tiles_ieta_min::Int: The minimum rapidity tile index.
  • _tiles_ieta_max::Int: The maximum rapidity tile index.
Constructor
TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
+	_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)
Constructs a TilingDef object with the given parameters.
source
`,8))]),t("details",Oe,[t("summary",null,[e[264]||(e[264]=t("a",{id:"JetReconstruction.neighbour_tiles",href:"#JetReconstruction.neighbour_tiles"},[t("span",{class:"jlbinding"},"JetReconstruction.neighbour_tiles")],-1)),e[265]||(e[265]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[266]||(e[266]=s(`
julia
struct neighbour_tiles
A struct representing the neighbouring tiles.
A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
 X.X
-XXX
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source
`,8))]),t("details",Ne,[t("summary",null,[e[267]||(e[267]=t("a",{id:"JetReconstruction.rightmost_tiles",href:"#JetReconstruction.rightmost_tiles"},[t("span",{class:"jlbinding"},"JetReconstruction.rightmost_tiles")],-1)),e[268]||(e[268]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[269]||(e[269]=s(`
julia
struct rightmost_tiles
A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
+XXX
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source
`,8))]),t("details",Ne,[t("summary",null,[e[267]||(e[267]=t("a",{id:"JetReconstruction.rightmost_tiles",href:"#JetReconstruction.rightmost_tiles"},[t("span",{class:"jlbinding"},"JetReconstruction.rightmost_tiles")],-1)),e[268]||(e[268]=i()),o(n,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[269]||(e[269]=s(`
julia
struct rightmost_tiles
A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
 O.X
-OOO
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source
`,7))])])}const Ke=r(p,[["render",Ve]]);export{$e as __pageData,Ke as default};
+OOO

Note, rapidity coordinate must be in range, ϕ coordinate wraps

Fields

  • n_η::Int: Number of η tiles

  • n_ϕ::Int: Number of ϕ tiles

  • start_η::Int: Centre η tile coordinate

  • start_ϕ::Int: Centre ϕ tile coordinate

source

`,7))])])}const Ke=d(p,[["render",Ve]]);export{$e as __pageData,Ke as default}; diff --git a/dev/assets/lib_public.md.ClckhHDw.js b/dev/assets/lib_public.md.DSnuIYVw.js similarity index 92% rename from dev/assets/lib_public.md.ClckhHDw.js rename to dev/assets/lib_public.md.DSnuIYVw.js index 89a6ebe..72a33b0 100644 --- a/dev/assets/lib_public.md.ClckhHDw.js +++ b/dev/assets/lib_public.md.DSnuIYVw.js @@ -1,13 +1,13 @@ -import{_ as l,c as o,a5 as a,j as t,a as i,G as n,B as p,o as r}from"./chunks/framework.BtBDBbTG.js";const I=JSON.parse('{"title":"Jet Reconstruction Public Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/public.md","filePath":"lib/public.md","lastUpdated":null}'),h={name:"lib/public.md"},c={class:"jldocstring custom-block",open:""},d={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""};function _(S,e,L,w,z,V){const s=p("Badge");return r(),o("div",null,[e[71]||(e[71]=a('

Jet Reconstruction Public Documentation ​

Documentation for JetReconstruction.jl's public interfaces.

Index ​

Public Methods and Types ​

',5)),t("details",c,[t("summary",null,[e[0]||(e[0]=t("a",{id:"JetReconstruction.constituents-Tuple{PseudoJet, ClusterSequence}",href:"#JetReconstruction.constituents-Tuple{PseudoJet, ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.constituents")],-1)),e[1]||(e[1]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=a('
julia
constituents(j::PseudoJet, cs::ClusterSequence)

Get the constituents of a given jet in a cluster sequence.

Arguments

  • cs::ClusterSequence: The cluster sequence object.

  • j::PseudoJet: The jet for which to retrieve the constituents.

Returns

An array of PseudoJet objects representing the constituents of the given jet. (That is, the original clusters that were recombined to form this jet.)

source

',7))]),t("details",d,[t("summary",null,[e[3]||(e[3]=t("a",{id:"JetReconstruction.ee_genkt_algorithm-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.ee_genkt_algorithm-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.ee_genkt_algorithm")],-1)),e[4]||(e[4]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=a(`
julia
ee_genkt_algorithm(particles::Vector{T}; p = -1, R = 4.0,
+import{_ as l,c as o,a5 as a,j as t,a as i,G as n,B as p,o as r}from"./chunks/framework.BtBDBbTG.js";const I=JSON.parse('{"title":"Jet Reconstruction Public Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/public.md","filePath":"lib/public.md","lastUpdated":null}'),h={name:"lib/public.md"},d={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""};function _(S,e,L,w,z,V){const s=p("Badge");return r(),o("div",null,[e[71]||(e[71]=a('
Jet Reconstruction Public Documentation ​
Documentation for JetReconstruction.jl's public interfaces.
Index ​
Public Methods and Types ​
',5)),t("details",d,[t("summary",null,[e[0]||(e[0]=t("a",{id:"JetReconstruction.constituents-Tuple{PseudoJet, ClusterSequence}",href:"#JetReconstruction.constituents-Tuple{PseudoJet, ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.constituents")],-1)),e[1]||(e[1]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=a('
julia
constituents(j::PseudoJet, cs::ClusterSequence)
Get the constituents of a given jet in a cluster sequence.
Arguments
  • cs::ClusterSequence: The cluster sequence object.
  • j::PseudoJet: The jet for which to retrieve the constituents.
Returns
An array of PseudoJet objects representing the constituents of the given jet. (That is, the original clusters that were recombined to form this jet.)
source
',7))]),t("details",c,[t("summary",null,[e[3]||(e[3]=t("a",{id:"JetReconstruction.ee_genkt_algorithm-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.ee_genkt_algorithm-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.ee_genkt_algorithm")],-1)),e[4]||(e[4]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=a(`
julia
ee_genkt_algorithm(particles::Vector{T}; p = -1, R = 4.0,
                    algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham,
-                   recombine = +) where {T}
Run an e+e- reconstruction algorithm on a set of initial particles.
Arguments
  • particles::Vector{T}: A vector of particles to be clustered.
  • p = 1: The power parameter for the algorithm. Not required / ignored for the Durham algorithm when it is set to 1.
  • R = 4.0: The jet radius parameter. Not required / ignored for the Durham algorithm.
  • algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham: The specific jet algorithm to use.
  • recombine: The recombination scheme to use. Defaults to +.
Returns
  • The result of the jet clustering as a ClusterSequence object.
Notes
This is the public interface to the e+e- jet clustering algorithm. The function will check for consistency between the algorithm and the power parameter as needed. It will then prepare the internal EDM particles for the clustering itself, and call the actual reconstruction method _ee_genkt_algorithm.
If the algorithm is Durham, p is set to 1 and R is nominally set to 4.
Note that unlike pp reconstruction the algorithm has to be specified explicitly.
source
`,11))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"JetReconstruction.exclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.exclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.exclusive_jets")],-1)),e[7]||(e[7]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=a(`
julia
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing, T = LorentzVectorCyl)
Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.
  • njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Note: Either dcut or njets must be provided (but not both).
Returns
  • An array of T objects representing the exclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl)
Exceptions
  • ArgumentError: If neither dcut nor njets is provided.
  • ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.
  • ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.
Examples
julia
exclusive_jets(clusterseq, dcut = 20.0)
-exclusive_jets(clusterseq, njets = 3, T = PseudoJet)
source
`,13))]),t("details",k,[t("summary",null,[e[9]||(e[9]=t("a",{id:"JetReconstruction.final_jets",href:"#JetReconstruction.final_jets"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[10]||(e[10]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[11]||(e[11]=t("p",null,"Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)",-1)),e[12]||(e[12]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/Utils.jl#L105",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",g,[t("summary",null,[e[13]||(e[13]=t("a",{id:"JetReconstruction.final_jets-2",href:"#JetReconstruction.final_jets-2"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[14]||(e[14]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[15]||(e[15]=t("p",null,"Specialisation for final jets from LorentzVectors (TODO: merge into more general function)",-1)),e[16]||(e[16]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/Utils.jl#L88",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",y,[t("summary",null,[e[17]||(e[17]=t("a",{id:"JetReconstruction.final_jets-3",href:"#JetReconstruction.final_jets-3"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[18]||(e[18]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[19]||(e[19]=a('
julia
final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)
This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.
Arguments
  • jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.
  • ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.
Returns
A vector of FinalJet objects that satisfy the transverse momentum condition.
source
',7))]),t("details",E,[t("summary",null,[e[20]||(e[20]=t("a",{id:"JetReconstruction.inclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.inclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.inclusive_jets")],-1)),e[21]||(e[21]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[22]||(e[22]=a('
julia
inclusive_jets(clusterseq::ClusterSequence; ptmin = 0.0, T = LorentzVectorCyl)
Return all inclusive jets of a ClusterSequence with pt > ptmin.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • ptmin::Float64 = 0.0: The minimum transverse momentum (pt) threshold for the inclusive jets.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Returns
An array of T objects representing the inclusive jets.
Description
This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl).
Example
julia
inclusive_jets(clusterseq; ptmin = 10.0)
source
',12))]),t("details",m,[t("summary",null,[e[23]||(e[23]=t("a",{id:"JetReconstruction.jet_reconstruct-Tuple{Any}",href:"#JetReconstruction.jet_reconstruct-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.jet_reconstruct")],-1)),e[24]||(e[24]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[25]||(e[25]=a(`
julia
jet_reconstruct(particles; p = -1, algorithm = nothing, R = 1.0, recombine = +, strategy = RecoStrategy.Best)
Reconstructs jets from a collection of particles using a specified algorithm and strategy
Arguments
  • particles: A collection of particles used for jet reconstruction.
  • p::Union{Real, Nothing} = -1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).
  • algorithm::Union{JetAlgorithm.Algorithm, Nothing} = nothing: The algorithm to use for jet reconstruction.
  • R=1.0: The jet radius parameter.
  • recombine=+: The recombination scheme used for combining particles.
  • strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.
Returns
A cluster sequence object containing the reconstructed jets and the merging history.
Details
particles argument
Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.
recombine argument
The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E-scheme.
Consitency of p, algorithm and R arguments
If an algorithm is explicitly specified the p value should be consistent with it or nothing. If the algorithm is one where p can vary, then it has to be given, along with the algorithm.\`\`
If the p parameter is passed and algorithm=nothing, then pp-type reconstruction is implied (i.e., AntiKt, CA, Kt or GenKt will be used, depending on the value of p).
When an algorithm has no R dependence the R parameter is ignored.
Example
julia
jet_reconstruct(particles; p = -1, R = 0.4)
+                   recombine = +) where {T}
Run an e+e- reconstruction algorithm on a set of initial particles.
Arguments
  • particles::Vector{T}: A vector of particles to be clustered.
  • p = 1: The power parameter for the algorithm. Not required / ignored for the Durham algorithm when it is set to 1.
  • R = 4.0: The jet radius parameter. Not required / ignored for the Durham algorithm.
  • algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham: The specific jet algorithm to use.
  • recombine: The recombination scheme to use. Defaults to +.
Returns
  • The result of the jet clustering as a ClusterSequence object.
Notes
This is the public interface to the e+e- jet clustering algorithm. The function will check for consistency between the algorithm and the power parameter as needed. It will then prepare the internal EDM particles for the clustering itself, and call the actual reconstruction method _ee_genkt_algorithm.
If the algorithm is Durham, p is set to 1 and R is nominally set to 4.
Note that unlike pp reconstruction the algorithm has to be specified explicitly.
source
`,11))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"JetReconstruction.exclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.exclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.exclusive_jets")],-1)),e[7]||(e[7]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=a(`
julia
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing, T = LorentzVectorCyl)
Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.
  • njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Note: Either dcut or njets must be provided (but not both).
Returns
  • An array of T objects representing the exclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl)
Exceptions
  • ArgumentError: If neither dcut nor njets is provided.
  • ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.
  • ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.
Examples
julia
exclusive_jets(clusterseq, dcut = 20.0)
+exclusive_jets(clusterseq, njets = 3, T = PseudoJet)
source
`,13))]),t("details",k,[t("summary",null,[e[9]||(e[9]=t("a",{id:"JetReconstruction.final_jets",href:"#JetReconstruction.final_jets"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[10]||(e[10]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[11]||(e[11]=t("p",null,"Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)",-1)),e[12]||(e[12]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/Utils.jl#L105",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",g,[t("summary",null,[e[13]||(e[13]=t("a",{id:"JetReconstruction.final_jets-2",href:"#JetReconstruction.final_jets-2"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[14]||(e[14]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[15]||(e[15]=a('
julia
final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)
This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.
Arguments
  • jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.
  • ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.
Returns
A vector of FinalJet objects that satisfy the transverse momentum condition.
source
',7))]),t("details",y,[t("summary",null,[e[16]||(e[16]=t("a",{id:"JetReconstruction.final_jets-3",href:"#JetReconstruction.final_jets-3"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[17]||(e[17]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[18]||(e[18]=t("p",null,"Specialisation for final jets from LorentzVectors (TODO: merge into more general function)",-1)),e[19]||(e[19]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/Utils.jl#L88",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",E,[t("summary",null,[e[20]||(e[20]=t("a",{id:"JetReconstruction.inclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.inclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.inclusive_jets")],-1)),e[21]||(e[21]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[22]||(e[22]=a('
julia
inclusive_jets(clusterseq::ClusterSequence; ptmin = 0.0, T = LorentzVectorCyl)
Return all inclusive jets of a ClusterSequence with pt > ptmin.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • ptmin::Float64 = 0.0: The minimum transverse momentum (pt) threshold for the inclusive jets.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Returns
An array of T objects representing the inclusive jets.
Description
This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl).
Example
julia
inclusive_jets(clusterseq; ptmin = 10.0)
source
',12))]),t("details",m,[t("summary",null,[e[23]||(e[23]=t("a",{id:"JetReconstruction.jet_reconstruct-Tuple{Any}",href:"#JetReconstruction.jet_reconstruct-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.jet_reconstruct")],-1)),e[24]||(e[24]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[25]||(e[25]=a(`
julia
jet_reconstruct(particles; p = -1, algorithm = nothing, R = 1.0, recombine = +, strategy = RecoStrategy.Best)
Reconstructs jets from a collection of particles using a specified algorithm and strategy
Arguments
  • particles: A collection of particles used for jet reconstruction.
  • p::Union{Real, Nothing} = -1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).
  • algorithm::Union{JetAlgorithm.Algorithm, Nothing} = nothing: The algorithm to use for jet reconstruction.
  • R=1.0: The jet radius parameter.
  • recombine=+: The recombination scheme used for combining particles.
  • strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.
Returns
A cluster sequence object containing the reconstructed jets and the merging history.
Details
particles argument
Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.
recombine argument
The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E-scheme.
Consitency of p, algorithm and R arguments
If an algorithm is explicitly specified the p value should be consistent with it or nothing. If the algorithm is one where p can vary, then it has to be given, along with the algorithm.\`\`
If the p parameter is passed and algorithm=nothing, then pp-type reconstruction is implied (i.e., AntiKt, CA, Kt or GenKt will be used, depending on the value of p).
When an algorithm has no R dependence the R parameter is ignored.
Example
julia
jet_reconstruct(particles; p = -1, R = 0.4)
 jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
 jet_reconstruct(particles; algorithm = JetAlgorithm.Durham)
-jet_reconstruct(particles; algorithm = JetAlgorithm.GenKt, p = 0.5, R = 1.0)
source
`,18))]),t("details",b,[t("summary",null,[e[26]||(e[26]=t("a",{id:"JetReconstruction.loadjets!-Tuple{Any, Any}",href:"#JetReconstruction.loadjets!-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.loadjets!")],-1)),e[27]||(e[27]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[28]||(e[28]=a(`
julia
loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)
Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.
Everything that was already in jets is not affected as we only use push! on it.
Example
julia
# Load jets from two files into one array
+jet_reconstruct(particles; algorithm = JetAlgorithm.GenKt, p = 0.5, R = 1.0)
source
`,18))]),t("details",b,[t("summary",null,[e[26]||(e[26]=t("a",{id:"JetReconstruction.loadjets!-Tuple{Any, Any}",href:"#JetReconstruction.loadjets!-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.loadjets!")],-1)),e[27]||(e[27]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[28]||(e[28]=a(`
julia
loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)
Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.
Everything that was already in jets is not affected as we only use push! on it.
Example
julia
# Load jets from two files into one array
 jets = []
 loadjets!("myjets1.dat", jets)
-loadjets!("myjets2.dat", jets)
source
`,6))]),t("details",j,[t("summary",null,[e[29]||(e[29]=t("a",{id:"JetReconstruction.loadjets-Tuple{Any}",href:"#JetReconstruction.loadjets-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.loadjets")],-1)),e[30]||(e[30]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[31]||(e[31]=a('
julia
loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)
Load jets from a file.
Arguments
  • filename: The name of the file to load jets from.
  • splitby: The delimiter used to split the data in the file. Default is isspace.
  • constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
  • VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
  • A vector of VT objects representing the loaded jets.
source
',7))]),t("details",f,[t("summary",null,[e[32]||(e[32]=t("a",{id:"JetReconstruction.n_exclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.n_exclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.n_exclusive_jets")],-1)),e[33]||(e[33]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[34]||(e[34]=a('
julia
n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)
Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.
  • dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia
n_exclusive_jets(clusterseq, dcut = 20.0)
source
',9))]),t("details",F,[t("summary",null,[e[35]||(e[35]=t("a",{id:"JetReconstruction.plain_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.plain_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.plain_jet_reconstruct")],-1)),e[36]||(e[36]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[37]||(e[37]=a(`
julia
plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where T
Perform pp jet reconstruction using the plain algorithm.
Arguments
  • particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • p::Int=-1: The integer value used for jet reconstruction.
  • R::Float64=1.0: The radius parameter used for jet reconstruction.
  • recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
jets = plain_jet_reconstruct(particles; p = -1, R = 0.4)
-jets = plain_jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
source
`,12))]),t("details",C,[t("summary",null,[e[38]||(e[38]=t("a",{id:"JetReconstruction.read_final_state_particles-Tuple{Any}",href:"#JetReconstruction.read_final_state_particles-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.read_final_state_particles")],-1)),e[39]||(e[39]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[40]||(e[40]=a('
julia
read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)
Reads final state particles from a file and returns them as a vector of type T.
Arguments
  • fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
  • maxevents=-1: The maximum number of events to read. -1 means all events will be read.
  • skipevents=0: The number of events to skip before an event is included.
  • T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source
',7))]),t("details",v,[t("summary",null,[e[41]||(e[41]=t("a",{id:"JetReconstruction.savejets-Tuple{Any, Any}",href:"#JetReconstruction.savejets-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.savejets")],-1)),e[42]||(e[42]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[43]||(e[43]=a('
julia
savejets(filename, jets; format="px py pz E")
Save jet data to a file.
Arguments
  • filename: The name of the file to save the jet data to.
  • jets: An array of jet objects to save.
  • format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
  • "E" or "energy": Jet energy
  • "px": Momentum along the x-axis
  • "py": Momentum along the y-axis
  • "pz": Momentum along the z-axis
  • "pt2": Square of the transverse momentum
  • "phi": Azimuth angle
  • "rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source
',10))]),t("details",J,[t("summary",null,[e[44]||(e[44]=t("a",{id:"JetReconstruction.tiled_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.tiled_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.tiled_jet_reconstruct")],-1)),e[45]||(e[45]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[46]||(e[46]=a('
julia
tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Arguments
  • particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
  • p::Union{Real, Nothing} = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)
source
',12))]),t("details",T,[t("summary",null,[e[47]||(e[47]=t("a",{id:"JetReconstruction.ClusterSequence",href:"#JetReconstruction.ClusterSequence"},[t("span",{class:"jlbinding"},"JetReconstruction.ClusterSequence")],-1)),e[48]||(e[48]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[49]||(e[49]=a('
julia
struct ClusterSequence
A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • power::Float64: The power value used for the clustering algorithm (not that this value is always stored as a Float64 to be type stable)
  • R::Float64: The R parameter used for the clustering algorithm.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
  • n_initial_jets::Int: The initial number of particles used for exclusive jets.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
  • Qtot::Any: The total energy of the event.
source
',5))]),t("details",A,[t("summary",null,[e[50]||(e[50]=t("a",{id:"JetReconstruction.ClusterSequence-Tuple{JetReconstruction.JetAlgorithm.Algorithm, Real, Float64, JetReconstruction.RecoStrategy.Strategy, Any, Any, Any}",href:"#JetReconstruction.ClusterSequence-Tuple{JetReconstruction.JetAlgorithm.Algorithm, Real, Float64, JetReconstruction.RecoStrategy.Strategy, Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.ClusterSequence")],-1)),e[51]||(e[51]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[52]||(e[52]=a('
julia
ClusterSequence(algorithm::JetAlgorithm.Algorithm, p::Real, R::Float64, strategy::RecoStrategy.Strategy, jets, history, Qtot)
Construct a ClusterSequence object.
Arguments
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • p::Real: The power value used for the clustering algorithm.
  • R::Float64: The R parameter used for the clustering algorithm.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence.
  • Qtot::Any: The total energy of the event.
source
',5))]),t("details",R,[t("summary",null,[e[53]||(e[53]=t("a",{id:"JetReconstruction.EEjet",href:"#JetReconstruction.EEjet"},[t("span",{class:"jlbinding"},"JetReconstruction.EEjet")],-1)),e[54]||(e[54]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[55]||(e[55]=a('
julia
struct EEjet
The EEjet struct is a 4-momentum object used for the e+e jet reconstruction routines.
Fields
  • px::Float64: The x-component of the jet momentum.
  • py::Float64: The y-component of the jet momentum.
  • pz::Float64: The z-component of the jet momentum.
  • E::Float64: The energy of the jet.
  • _cluster_hist_index::Int: The index of the cluster histogram.
  • _p2::Float64: The squared momentum of the jet.
  • _inv_p::Float64: The inverse momentum of the jet.
source
',5))]),t("details",x,[t("summary",null,[e[56]||(e[56]=t("a",{id:"JetReconstruction.FinalJet",href:"#JetReconstruction.FinalJet"},[t("span",{class:"jlbinding"},"JetReconstruction.FinalJet")],-1)),e[57]||(e[57]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[58]||(e[58]=a('
julia
struct FinalJet
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
  • rap::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • pt::Float64: The transverse momentum of the jet.
source
',5))]),t("details",D,[t("summary",null,[e[59]||(e[59]=t("a",{id:"JetReconstruction.FinalJets",href:"#JetReconstruction.FinalJets"},[t("span",{class:"jlbinding"},"JetReconstruction.FinalJets")],-1)),e[60]||(e[60]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[61]||(e[61]=a('
julia
struct FinalJets
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
  • jetid::Int64: The ID of the jet.
  • jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.
source
',5))]),t("details",q,[t("summary",null,[e[62]||(e[62]=t("a",{id:"JetReconstruction.PseudoJet",href:"#JetReconstruction.PseudoJet"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[63]||(e[63]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[64]||(e[64]=a('
julia
mutable struct PseudoJet <: FourMomentum
The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy component of the momentum.
  • _cluster_hist_index::Int: The index of the cluster history.
  • _pt2::Float64: The squared transverse momentum.
  • _inv_pt2::Float64: The inverse squared transverse momentum.
  • _rap::Float64: The rapidity.
  • _phi::Float64: The azimuthal angle.
source
',5))]),t("details",B,[t("summary",null,[e[65]||(e[65]=t("a",{id:"JetReconstruction.PseudoJet-NTuple{4, Float64}",href:"#JetReconstruction.PseudoJet-NTuple{4, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[66]||(e[66]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[67]||(e[67]=a('
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)
Constructs a PseudoJet object with the given momentum components and energy.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
Returns
A PseudoJet object.
source
',7))]),t("details",P,[t("summary",null,[e[68]||(e[68]=t("a",{id:"JetReconstruction.PseudoJet-Tuple{Float64, Float64, Float64, Float64, Int64, Float64}",href:"#JetReconstruction.PseudoJet-Tuple{Float64, Float64, Float64, Float64, Int64, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[69]||(e[69]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=a(`
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
+loadjets!("myjets2.dat", jets)
source
`,6))]),t("details",j,[t("summary",null,[e[29]||(e[29]=t("a",{id:"JetReconstruction.loadjets-Tuple{Any}",href:"#JetReconstruction.loadjets-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.loadjets")],-1)),e[30]||(e[30]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[31]||(e[31]=a('
julia
loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)
Load jets from a file.
Arguments
  • filename: The name of the file to load jets from.
  • splitby: The delimiter used to split the data in the file. Default is isspace.
  • constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
  • VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
  • A vector of VT objects representing the loaded jets.
source
',7))]),t("details",f,[t("summary",null,[e[32]||(e[32]=t("a",{id:"JetReconstruction.n_exclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.n_exclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.n_exclusive_jets")],-1)),e[33]||(e[33]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[34]||(e[34]=a('
julia
n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)
Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.
  • dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia
n_exclusive_jets(clusterseq, dcut = 20.0)
source
',9))]),t("details",F,[t("summary",null,[e[35]||(e[35]=t("a",{id:"JetReconstruction.plain_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.plain_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.plain_jet_reconstruct")],-1)),e[36]||(e[36]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[37]||(e[37]=a(`
julia
plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where T
Perform pp jet reconstruction using the plain algorithm.
Arguments
  • particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • p::Int=-1: The integer value used for jet reconstruction.
  • R::Float64=1.0: The radius parameter used for jet reconstruction.
  • recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
jets = plain_jet_reconstruct(particles; p = -1, R = 0.4)
+jets = plain_jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
source
`,12))]),t("details",C,[t("summary",null,[e[38]||(e[38]=t("a",{id:"JetReconstruction.read_final_state_particles-Tuple{Any}",href:"#JetReconstruction.read_final_state_particles-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.read_final_state_particles")],-1)),e[39]||(e[39]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[40]||(e[40]=a('
julia
read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)
Reads final state particles from a file and returns them as a vector of type T.
Arguments
  • fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
  • maxevents=-1: The maximum number of events to read. -1 means all events will be read.
  • skipevents=0: The number of events to skip before an event is included.
  • T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source
',7))]),t("details",v,[t("summary",null,[e[41]||(e[41]=t("a",{id:"JetReconstruction.savejets-Tuple{Any, Any}",href:"#JetReconstruction.savejets-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.savejets")],-1)),e[42]||(e[42]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[43]||(e[43]=a('
julia
savejets(filename, jets; format="px py pz E")
Save jet data to a file.
Arguments
  • filename: The name of the file to save the jet data to.
  • jets: An array of jet objects to save.
  • format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
  • "E" or "energy": Jet energy
  • "px": Momentum along the x-axis
  • "py": Momentum along the y-axis
  • "pz": Momentum along the z-axis
  • "pt2": Square of the transverse momentum
  • "phi": Azimuth angle
  • "rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source
',10))]),t("details",J,[t("summary",null,[e[44]||(e[44]=t("a",{id:"JetReconstruction.tiled_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.tiled_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.tiled_jet_reconstruct")],-1)),e[45]||(e[45]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[46]||(e[46]=a('
julia
tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Arguments
  • particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
  • p::Union{Real, Nothing} = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)
source
',12))]),t("details",T,[t("summary",null,[e[47]||(e[47]=t("a",{id:"JetReconstruction.ClusterSequence",href:"#JetReconstruction.ClusterSequence"},[t("span",{class:"jlbinding"},"JetReconstruction.ClusterSequence")],-1)),e[48]||(e[48]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[49]||(e[49]=a('
julia
struct ClusterSequence
A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • power::Float64: The power value used for the clustering algorithm (not that this value is always stored as a Float64 to be type stable)
  • R::Float64: The R parameter used for the clustering algorithm.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
  • n_initial_jets::Int: The initial number of particles used for exclusive jets.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
  • Qtot::Any: The total energy of the event.
source
',5))]),t("details",A,[t("summary",null,[e[50]||(e[50]=t("a",{id:"JetReconstruction.ClusterSequence-Tuple{JetReconstruction.JetAlgorithm.Algorithm, Real, Float64, JetReconstruction.RecoStrategy.Strategy, Any, Any, Any}",href:"#JetReconstruction.ClusterSequence-Tuple{JetReconstruction.JetAlgorithm.Algorithm, Real, Float64, JetReconstruction.RecoStrategy.Strategy, Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.ClusterSequence")],-1)),e[51]||(e[51]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[52]||(e[52]=a('
julia
ClusterSequence(algorithm::JetAlgorithm.Algorithm, p::Real, R::Float64, strategy::RecoStrategy.Strategy, jets, history, Qtot)
Construct a ClusterSequence object.
Arguments
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • p::Real: The power value used for the clustering algorithm.
  • R::Float64: The R parameter used for the clustering algorithm.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence.
  • Qtot::Any: The total energy of the event.
source
',5))]),t("details",R,[t("summary",null,[e[53]||(e[53]=t("a",{id:"JetReconstruction.EEjet",href:"#JetReconstruction.EEjet"},[t("span",{class:"jlbinding"},"JetReconstruction.EEjet")],-1)),e[54]||(e[54]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[55]||(e[55]=a('
julia
struct EEjet
The EEjet struct is a 4-momentum object used for the e+e jet reconstruction routines.
Fields
  • px::Float64: The x-component of the jet momentum.
  • py::Float64: The y-component of the jet momentum.
  • pz::Float64: The z-component of the jet momentum.
  • E::Float64: The energy of the jet.
  • _cluster_hist_index::Int: The index of the cluster histogram.
  • _p2::Float64: The squared momentum of the jet.
  • _inv_p::Float64: The inverse momentum of the jet.
source
',5))]),t("details",x,[t("summary",null,[e[56]||(e[56]=t("a",{id:"JetReconstruction.FinalJet",href:"#JetReconstruction.FinalJet"},[t("span",{class:"jlbinding"},"JetReconstruction.FinalJet")],-1)),e[57]||(e[57]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[58]||(e[58]=a('
julia
struct FinalJet
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
  • rap::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • pt::Float64: The transverse momentum of the jet.
source
',5))]),t("details",D,[t("summary",null,[e[59]||(e[59]=t("a",{id:"JetReconstruction.FinalJets",href:"#JetReconstruction.FinalJets"},[t("span",{class:"jlbinding"},"JetReconstruction.FinalJets")],-1)),e[60]||(e[60]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[61]||(e[61]=a('
julia
struct FinalJets
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
  • jetid::Int64: The ID of the jet.
  • jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.
source
',5))]),t("details",q,[t("summary",null,[e[62]||(e[62]=t("a",{id:"JetReconstruction.PseudoJet",href:"#JetReconstruction.PseudoJet"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[63]||(e[63]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[64]||(e[64]=a('
julia
mutable struct PseudoJet <: FourMomentum
The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy component of the momentum.
  • _cluster_hist_index::Int: The index of the cluster history.
  • _pt2::Float64: The squared transverse momentum.
  • _inv_pt2::Float64: The inverse squared transverse momentum.
  • _rap::Float64: The rapidity.
  • _phi::Float64: The azimuthal angle.
source
',5))]),t("details",B,[t("summary",null,[e[65]||(e[65]=t("a",{id:"JetReconstruction.PseudoJet-NTuple{4, Float64}",href:"#JetReconstruction.PseudoJet-NTuple{4, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[66]||(e[66]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[67]||(e[67]=a('
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)
Constructs a PseudoJet object with the given momentum components and energy.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
Returns
A PseudoJet object.
source
',7))]),t("details",P,[t("summary",null,[e[68]||(e[68]=t("a",{id:"JetReconstruction.PseudoJet-Tuple{Float64, Float64, Float64, Float64, Int64, Float64}",href:"#JetReconstruction.PseudoJet-Tuple{Float64, Float64, Float64, Float64, Int64, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[69]||(e[69]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=a(`
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
     _cluster_hist_index::Int,
-    pt2::Float64)
Constructs a PseudoJet object with the given momentum components and energy and history index.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
  • _cluster_hist_index::Int: The cluster history index.
  • pt2::Float64: The transverse momentum squared.
Returns
A PseudoJet object.
source
`,7))])])}const O=l(h,[["render",_]]);export{I as __pageData,O as default};
+    pt2::Float64)

Constructs a PseudoJet object with the given momentum components and energy and history index.

Arguments

  • px::Float64: The x-component of the momentum.

  • py::Float64: The y-component of the momentum.

  • pz::Float64: The z-component of the momentum.

  • E::Float64: The energy.

  • _cluster_hist_index::Int: The cluster history index.

  • pt2::Float64: The transverse momentum squared.

Returns

A PseudoJet object.

source

`,7))])])}const O=l(h,[["render",_]]);export{I as __pageData,O as default}; diff --git a/dev/assets/lib_public.md.ClckhHDw.lean.js b/dev/assets/lib_public.md.DSnuIYVw.lean.js similarity index 92% rename from dev/assets/lib_public.md.ClckhHDw.lean.js rename to dev/assets/lib_public.md.DSnuIYVw.lean.js index 89a6ebe..72a33b0 100644 --- a/dev/assets/lib_public.md.ClckhHDw.lean.js +++ b/dev/assets/lib_public.md.DSnuIYVw.lean.js @@ -1,13 +1,13 @@ -import{_ as l,c as o,a5 as a,j as t,a as i,G as n,B as p,o as r}from"./chunks/framework.BtBDBbTG.js";const I=JSON.parse('{"title":"Jet Reconstruction Public Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/public.md","filePath":"lib/public.md","lastUpdated":null}'),h={name:"lib/public.md"},c={class:"jldocstring custom-block",open:""},d={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""};function _(S,e,L,w,z,V){const s=p("Badge");return r(),o("div",null,[e[71]||(e[71]=a('

Jet Reconstruction Public Documentation ​

Documentation for JetReconstruction.jl's public interfaces.

Index ​

Public Methods and Types ​

',5)),t("details",c,[t("summary",null,[e[0]||(e[0]=t("a",{id:"JetReconstruction.constituents-Tuple{PseudoJet, ClusterSequence}",href:"#JetReconstruction.constituents-Tuple{PseudoJet, ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.constituents")],-1)),e[1]||(e[1]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=a('
julia
constituents(j::PseudoJet, cs::ClusterSequence)

Get the constituents of a given jet in a cluster sequence.

Arguments

  • cs::ClusterSequence: The cluster sequence object.

  • j::PseudoJet: The jet for which to retrieve the constituents.

Returns

An array of PseudoJet objects representing the constituents of the given jet. (That is, the original clusters that were recombined to form this jet.)

source

',7))]),t("details",d,[t("summary",null,[e[3]||(e[3]=t("a",{id:"JetReconstruction.ee_genkt_algorithm-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.ee_genkt_algorithm-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.ee_genkt_algorithm")],-1)),e[4]||(e[4]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=a(`
julia
ee_genkt_algorithm(particles::Vector{T}; p = -1, R = 4.0,
+import{_ as l,c as o,a5 as a,j as t,a as i,G as n,B as p,o as r}from"./chunks/framework.BtBDBbTG.js";const I=JSON.parse('{"title":"Jet Reconstruction Public Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/public.md","filePath":"lib/public.md","lastUpdated":null}'),h={name:"lib/public.md"},d={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},j={class:"jldocstring custom-block",open:""},f={class:"jldocstring custom-block",open:""},F={class:"jldocstring custom-block",open:""},C={class:"jldocstring custom-block",open:""},v={class:"jldocstring custom-block",open:""},J={class:"jldocstring custom-block",open:""},T={class:"jldocstring custom-block",open:""},A={class:"jldocstring custom-block",open:""},R={class:"jldocstring custom-block",open:""},x={class:"jldocstring custom-block",open:""},D={class:"jldocstring custom-block",open:""},q={class:"jldocstring custom-block",open:""},B={class:"jldocstring custom-block",open:""},P={class:"jldocstring custom-block",open:""};function _(S,e,L,w,z,V){const s=p("Badge");return r(),o("div",null,[e[71]||(e[71]=a('
Jet Reconstruction Public Documentation ​
Documentation for JetReconstruction.jl's public interfaces.
Index ​
Public Methods and Types ​
',5)),t("details",d,[t("summary",null,[e[0]||(e[0]=t("a",{id:"JetReconstruction.constituents-Tuple{PseudoJet, ClusterSequence}",href:"#JetReconstruction.constituents-Tuple{PseudoJet, ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.constituents")],-1)),e[1]||(e[1]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=a('
julia
constituents(j::PseudoJet, cs::ClusterSequence)
Get the constituents of a given jet in a cluster sequence.
Arguments
  • cs::ClusterSequence: The cluster sequence object.
  • j::PseudoJet: The jet for which to retrieve the constituents.
Returns
An array of PseudoJet objects representing the constituents of the given jet. (That is, the original clusters that were recombined to form this jet.)
source
',7))]),t("details",c,[t("summary",null,[e[3]||(e[3]=t("a",{id:"JetReconstruction.ee_genkt_algorithm-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.ee_genkt_algorithm-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.ee_genkt_algorithm")],-1)),e[4]||(e[4]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=a(`
julia
ee_genkt_algorithm(particles::Vector{T}; p = -1, R = 4.0,
                    algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham,
-                   recombine = +) where {T}
Run an e+e- reconstruction algorithm on a set of initial particles.
Arguments
  • particles::Vector{T}: A vector of particles to be clustered.
  • p = 1: The power parameter for the algorithm. Not required / ignored for the Durham algorithm when it is set to 1.
  • R = 4.0: The jet radius parameter. Not required / ignored for the Durham algorithm.
  • algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham: The specific jet algorithm to use.
  • recombine: The recombination scheme to use. Defaults to +.
Returns
  • The result of the jet clustering as a ClusterSequence object.
Notes
This is the public interface to the e+e- jet clustering algorithm. The function will check for consistency between the algorithm and the power parameter as needed. It will then prepare the internal EDM particles for the clustering itself, and call the actual reconstruction method _ee_genkt_algorithm.
If the algorithm is Durham, p is set to 1 and R is nominally set to 4.
Note that unlike pp reconstruction the algorithm has to be specified explicitly.
source
`,11))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"JetReconstruction.exclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.exclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.exclusive_jets")],-1)),e[7]||(e[7]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=a(`
julia
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing, T = LorentzVectorCyl)
Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.
  • njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Note: Either dcut or njets must be provided (but not both).
Returns
  • An array of T objects representing the exclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl)
Exceptions
  • ArgumentError: If neither dcut nor njets is provided.
  • ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.
  • ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.
Examples
julia
exclusive_jets(clusterseq, dcut = 20.0)
-exclusive_jets(clusterseq, njets = 3, T = PseudoJet)
source
`,13))]),t("details",k,[t("summary",null,[e[9]||(e[9]=t("a",{id:"JetReconstruction.final_jets",href:"#JetReconstruction.final_jets"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[10]||(e[10]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[11]||(e[11]=t("p",null,"Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)",-1)),e[12]||(e[12]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/Utils.jl#L105",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",g,[t("summary",null,[e[13]||(e[13]=t("a",{id:"JetReconstruction.final_jets-2",href:"#JetReconstruction.final_jets-2"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[14]||(e[14]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[15]||(e[15]=t("p",null,"Specialisation for final jets from LorentzVectors (TODO: merge into more general function)",-1)),e[16]||(e[16]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/70c372b379cb343a4cade52fc0231b7a7721dd16/src/Utils.jl#L88",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",y,[t("summary",null,[e[17]||(e[17]=t("a",{id:"JetReconstruction.final_jets-3",href:"#JetReconstruction.final_jets-3"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[18]||(e[18]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[19]||(e[19]=a('
julia
final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)
This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.
Arguments
  • jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.
  • ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.
Returns
A vector of FinalJet objects that satisfy the transverse momentum condition.
source
',7))]),t("details",E,[t("summary",null,[e[20]||(e[20]=t("a",{id:"JetReconstruction.inclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.inclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.inclusive_jets")],-1)),e[21]||(e[21]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[22]||(e[22]=a('
julia
inclusive_jets(clusterseq::ClusterSequence; ptmin = 0.0, T = LorentzVectorCyl)
Return all inclusive jets of a ClusterSequence with pt > ptmin.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • ptmin::Float64 = 0.0: The minimum transverse momentum (pt) threshold for the inclusive jets.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Returns
An array of T objects representing the inclusive jets.
Description
This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl).
Example
julia
inclusive_jets(clusterseq; ptmin = 10.0)
source
',12))]),t("details",m,[t("summary",null,[e[23]||(e[23]=t("a",{id:"JetReconstruction.jet_reconstruct-Tuple{Any}",href:"#JetReconstruction.jet_reconstruct-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.jet_reconstruct")],-1)),e[24]||(e[24]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[25]||(e[25]=a(`
julia
jet_reconstruct(particles; p = -1, algorithm = nothing, R = 1.0, recombine = +, strategy = RecoStrategy.Best)
Reconstructs jets from a collection of particles using a specified algorithm and strategy
Arguments
  • particles: A collection of particles used for jet reconstruction.
  • p::Union{Real, Nothing} = -1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).
  • algorithm::Union{JetAlgorithm.Algorithm, Nothing} = nothing: The algorithm to use for jet reconstruction.
  • R=1.0: The jet radius parameter.
  • recombine=+: The recombination scheme used for combining particles.
  • strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.
Returns
A cluster sequence object containing the reconstructed jets and the merging history.
Details
particles argument
Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.
recombine argument
The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E-scheme.
Consitency of p, algorithm and R arguments
If an algorithm is explicitly specified the p value should be consistent with it or nothing. If the algorithm is one where p can vary, then it has to be given, along with the algorithm.\`\`
If the p parameter is passed and algorithm=nothing, then pp-type reconstruction is implied (i.e., AntiKt, CA, Kt or GenKt will be used, depending on the value of p).
When an algorithm has no R dependence the R parameter is ignored.
Example
julia
jet_reconstruct(particles; p = -1, R = 0.4)
+                   recombine = +) where {T}
Run an e+e- reconstruction algorithm on a set of initial particles.
Arguments
  • particles::Vector{T}: A vector of particles to be clustered.
  • p = 1: The power parameter for the algorithm. Not required / ignored for the Durham algorithm when it is set to 1.
  • R = 4.0: The jet radius parameter. Not required / ignored for the Durham algorithm.
  • algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham: The specific jet algorithm to use.
  • recombine: The recombination scheme to use. Defaults to +.
Returns
  • The result of the jet clustering as a ClusterSequence object.
Notes
This is the public interface to the e+e- jet clustering algorithm. The function will check for consistency between the algorithm and the power parameter as needed. It will then prepare the internal EDM particles for the clustering itself, and call the actual reconstruction method _ee_genkt_algorithm.
If the algorithm is Durham, p is set to 1 and R is nominally set to 4.
Note that unlike pp reconstruction the algorithm has to be specified explicitly.
source
`,11))]),t("details",u,[t("summary",null,[e[6]||(e[6]=t("a",{id:"JetReconstruction.exclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.exclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.exclusive_jets")],-1)),e[7]||(e[7]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=a(`
julia
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing, T = LorentzVectorCyl)
Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.
  • njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Note: Either dcut or njets must be provided (but not both).
Returns
  • An array of T objects representing the exclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl)
Exceptions
  • ArgumentError: If neither dcut nor njets is provided.
  • ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.
  • ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.
Examples
julia
exclusive_jets(clusterseq, dcut = 20.0)
+exclusive_jets(clusterseq, njets = 3, T = PseudoJet)
source
`,13))]),t("details",k,[t("summary",null,[e[9]||(e[9]=t("a",{id:"JetReconstruction.final_jets",href:"#JetReconstruction.final_jets"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[10]||(e[10]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[11]||(e[11]=t("p",null,"Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)",-1)),e[12]||(e[12]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/Utils.jl#L105",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",g,[t("summary",null,[e[13]||(e[13]=t("a",{id:"JetReconstruction.final_jets-2",href:"#JetReconstruction.final_jets-2"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[14]||(e[14]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[15]||(e[15]=a('
julia
final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)
This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.
Arguments
  • jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.
  • ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.
Returns
A vector of FinalJet objects that satisfy the transverse momentum condition.
source
',7))]),t("details",y,[t("summary",null,[e[16]||(e[16]=t("a",{id:"JetReconstruction.final_jets-3",href:"#JetReconstruction.final_jets-3"},[t("span",{class:"jlbinding"},"JetReconstruction.final_jets")],-1)),e[17]||(e[17]=i()),n(s,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),e[18]||(e[18]=t("p",null,"Specialisation for final jets from LorentzVectors (TODO: merge into more general function)",-1)),e[19]||(e[19]=t("p",null,[t("a",{href:"https://github.com/JuliaHEP/JetReconstruction.jl/blob/63d59d3d6213deb76e3d3358857ad7dfcbbbdf2a/src/Utils.jl#L88",target:"_blank",rel:"noreferrer"},"source")],-1))]),t("details",E,[t("summary",null,[e[20]||(e[20]=t("a",{id:"JetReconstruction.inclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.inclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.inclusive_jets")],-1)),e[21]||(e[21]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[22]||(e[22]=a('
julia
inclusive_jets(clusterseq::ClusterSequence; ptmin = 0.0, T = LorentzVectorCyl)
Return all inclusive jets of a ClusterSequence with pt > ptmin.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • ptmin::Float64 = 0.0: The minimum transverse momentum (pt) threshold for the inclusive jets.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Returns
An array of T objects representing the inclusive jets.
Description
This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl).
Example
julia
inclusive_jets(clusterseq; ptmin = 10.0)
source
',12))]),t("details",m,[t("summary",null,[e[23]||(e[23]=t("a",{id:"JetReconstruction.jet_reconstruct-Tuple{Any}",href:"#JetReconstruction.jet_reconstruct-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.jet_reconstruct")],-1)),e[24]||(e[24]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[25]||(e[25]=a(`
julia
jet_reconstruct(particles; p = -1, algorithm = nothing, R = 1.0, recombine = +, strategy = RecoStrategy.Best)
Reconstructs jets from a collection of particles using a specified algorithm and strategy
Arguments
  • particles: A collection of particles used for jet reconstruction.
  • p::Union{Real, Nothing} = -1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).
  • algorithm::Union{JetAlgorithm.Algorithm, Nothing} = nothing: The algorithm to use for jet reconstruction.
  • R=1.0: The jet radius parameter.
  • recombine=+: The recombination scheme used for combining particles.
  • strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.
Returns
A cluster sequence object containing the reconstructed jets and the merging history.
Details
particles argument
Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.
recombine argument
The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E-scheme.
Consitency of p, algorithm and R arguments
If an algorithm is explicitly specified the p value should be consistent with it or nothing. If the algorithm is one where p can vary, then it has to be given, along with the algorithm.\`\`
If the p parameter is passed and algorithm=nothing, then pp-type reconstruction is implied (i.e., AntiKt, CA, Kt or GenKt will be used, depending on the value of p).
When an algorithm has no R dependence the R parameter is ignored.
Example
julia
jet_reconstruct(particles; p = -1, R = 0.4)
 jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
 jet_reconstruct(particles; algorithm = JetAlgorithm.Durham)
-jet_reconstruct(particles; algorithm = JetAlgorithm.GenKt, p = 0.5, R = 1.0)
source
`,18))]),t("details",b,[t("summary",null,[e[26]||(e[26]=t("a",{id:"JetReconstruction.loadjets!-Tuple{Any, Any}",href:"#JetReconstruction.loadjets!-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.loadjets!")],-1)),e[27]||(e[27]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[28]||(e[28]=a(`
julia
loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)
Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.
Everything that was already in jets is not affected as we only use push! on it.
Example
julia
# Load jets from two files into one array
+jet_reconstruct(particles; algorithm = JetAlgorithm.GenKt, p = 0.5, R = 1.0)
source
`,18))]),t("details",b,[t("summary",null,[e[26]||(e[26]=t("a",{id:"JetReconstruction.loadjets!-Tuple{Any, Any}",href:"#JetReconstruction.loadjets!-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.loadjets!")],-1)),e[27]||(e[27]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[28]||(e[28]=a(`
julia
loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)
Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.
Everything that was already in jets is not affected as we only use push! on it.
Example
julia
# Load jets from two files into one array
 jets = []
 loadjets!("myjets1.dat", jets)
-loadjets!("myjets2.dat", jets)
source
`,6))]),t("details",j,[t("summary",null,[e[29]||(e[29]=t("a",{id:"JetReconstruction.loadjets-Tuple{Any}",href:"#JetReconstruction.loadjets-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.loadjets")],-1)),e[30]||(e[30]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[31]||(e[31]=a('
julia
loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)
Load jets from a file.
Arguments
  • filename: The name of the file to load jets from.
  • splitby: The delimiter used to split the data in the file. Default is isspace.
  • constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
  • VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
  • A vector of VT objects representing the loaded jets.
source
',7))]),t("details",f,[t("summary",null,[e[32]||(e[32]=t("a",{id:"JetReconstruction.n_exclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.n_exclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.n_exclusive_jets")],-1)),e[33]||(e[33]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[34]||(e[34]=a('
julia
n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)
Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.
  • dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia
n_exclusive_jets(clusterseq, dcut = 20.0)
source
',9))]),t("details",F,[t("summary",null,[e[35]||(e[35]=t("a",{id:"JetReconstruction.plain_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.plain_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.plain_jet_reconstruct")],-1)),e[36]||(e[36]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[37]||(e[37]=a(`
julia
plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where T
Perform pp jet reconstruction using the plain algorithm.
Arguments
  • particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • p::Int=-1: The integer value used for jet reconstruction.
  • R::Float64=1.0: The radius parameter used for jet reconstruction.
  • recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
jets = plain_jet_reconstruct(particles; p = -1, R = 0.4)
-jets = plain_jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
source
`,12))]),t("details",C,[t("summary",null,[e[38]||(e[38]=t("a",{id:"JetReconstruction.read_final_state_particles-Tuple{Any}",href:"#JetReconstruction.read_final_state_particles-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.read_final_state_particles")],-1)),e[39]||(e[39]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[40]||(e[40]=a('
julia
read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)
Reads final state particles from a file and returns them as a vector of type T.
Arguments
  • fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
  • maxevents=-1: The maximum number of events to read. -1 means all events will be read.
  • skipevents=0: The number of events to skip before an event is included.
  • T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source
',7))]),t("details",v,[t("summary",null,[e[41]||(e[41]=t("a",{id:"JetReconstruction.savejets-Tuple{Any, Any}",href:"#JetReconstruction.savejets-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.savejets")],-1)),e[42]||(e[42]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[43]||(e[43]=a('
julia
savejets(filename, jets; format="px py pz E")
Save jet data to a file.
Arguments
  • filename: The name of the file to save the jet data to.
  • jets: An array of jet objects to save.
  • format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
  • "E" or "energy": Jet energy
  • "px": Momentum along the x-axis
  • "py": Momentum along the y-axis
  • "pz": Momentum along the z-axis
  • "pt2": Square of the transverse momentum
  • "phi": Azimuth angle
  • "rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source
',10))]),t("details",J,[t("summary",null,[e[44]||(e[44]=t("a",{id:"JetReconstruction.tiled_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.tiled_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.tiled_jet_reconstruct")],-1)),e[45]||(e[45]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[46]||(e[46]=a('
julia
tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Arguments
  • particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
  • p::Union{Real, Nothing} = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)
source
',12))]),t("details",T,[t("summary",null,[e[47]||(e[47]=t("a",{id:"JetReconstruction.ClusterSequence",href:"#JetReconstruction.ClusterSequence"},[t("span",{class:"jlbinding"},"JetReconstruction.ClusterSequence")],-1)),e[48]||(e[48]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[49]||(e[49]=a('
julia
struct ClusterSequence
A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • power::Float64: The power value used for the clustering algorithm (not that this value is always stored as a Float64 to be type stable)
  • R::Float64: The R parameter used for the clustering algorithm.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
  • n_initial_jets::Int: The initial number of particles used for exclusive jets.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
  • Qtot::Any: The total energy of the event.
source
',5))]),t("details",A,[t("summary",null,[e[50]||(e[50]=t("a",{id:"JetReconstruction.ClusterSequence-Tuple{JetReconstruction.JetAlgorithm.Algorithm, Real, Float64, JetReconstruction.RecoStrategy.Strategy, Any, Any, Any}",href:"#JetReconstruction.ClusterSequence-Tuple{JetReconstruction.JetAlgorithm.Algorithm, Real, Float64, JetReconstruction.RecoStrategy.Strategy, Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.ClusterSequence")],-1)),e[51]||(e[51]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[52]||(e[52]=a('
julia
ClusterSequence(algorithm::JetAlgorithm.Algorithm, p::Real, R::Float64, strategy::RecoStrategy.Strategy, jets, history, Qtot)
Construct a ClusterSequence object.
Arguments
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • p::Real: The power value used for the clustering algorithm.
  • R::Float64: The R parameter used for the clustering algorithm.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence.
  • Qtot::Any: The total energy of the event.
source
',5))]),t("details",R,[t("summary",null,[e[53]||(e[53]=t("a",{id:"JetReconstruction.EEjet",href:"#JetReconstruction.EEjet"},[t("span",{class:"jlbinding"},"JetReconstruction.EEjet")],-1)),e[54]||(e[54]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[55]||(e[55]=a('
julia
struct EEjet
The EEjet struct is a 4-momentum object used for the e+e jet reconstruction routines.
Fields
  • px::Float64: The x-component of the jet momentum.
  • py::Float64: The y-component of the jet momentum.
  • pz::Float64: The z-component of the jet momentum.
  • E::Float64: The energy of the jet.
  • _cluster_hist_index::Int: The index of the cluster histogram.
  • _p2::Float64: The squared momentum of the jet.
  • _inv_p::Float64: The inverse momentum of the jet.
source
',5))]),t("details",x,[t("summary",null,[e[56]||(e[56]=t("a",{id:"JetReconstruction.FinalJet",href:"#JetReconstruction.FinalJet"},[t("span",{class:"jlbinding"},"JetReconstruction.FinalJet")],-1)),e[57]||(e[57]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[58]||(e[58]=a('
julia
struct FinalJet
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
  • rap::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • pt::Float64: The transverse momentum of the jet.
source
',5))]),t("details",D,[t("summary",null,[e[59]||(e[59]=t("a",{id:"JetReconstruction.FinalJets",href:"#JetReconstruction.FinalJets"},[t("span",{class:"jlbinding"},"JetReconstruction.FinalJets")],-1)),e[60]||(e[60]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[61]||(e[61]=a('
julia
struct FinalJets
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
  • jetid::Int64: The ID of the jet.
  • jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.
source
',5))]),t("details",q,[t("summary",null,[e[62]||(e[62]=t("a",{id:"JetReconstruction.PseudoJet",href:"#JetReconstruction.PseudoJet"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[63]||(e[63]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[64]||(e[64]=a('
julia
mutable struct PseudoJet <: FourMomentum
The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy component of the momentum.
  • _cluster_hist_index::Int: The index of the cluster history.
  • _pt2::Float64: The squared transverse momentum.
  • _inv_pt2::Float64: The inverse squared transverse momentum.
  • _rap::Float64: The rapidity.
  • _phi::Float64: The azimuthal angle.
source
',5))]),t("details",B,[t("summary",null,[e[65]||(e[65]=t("a",{id:"JetReconstruction.PseudoJet-NTuple{4, Float64}",href:"#JetReconstruction.PseudoJet-NTuple{4, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[66]||(e[66]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[67]||(e[67]=a('
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)
Constructs a PseudoJet object with the given momentum components and energy.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
Returns
A PseudoJet object.
source
',7))]),t("details",P,[t("summary",null,[e[68]||(e[68]=t("a",{id:"JetReconstruction.PseudoJet-Tuple{Float64, Float64, Float64, Float64, Int64, Float64}",href:"#JetReconstruction.PseudoJet-Tuple{Float64, Float64, Float64, Float64, Int64, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[69]||(e[69]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=a(`
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
+loadjets!("myjets2.dat", jets)
source
`,6))]),t("details",j,[t("summary",null,[e[29]||(e[29]=t("a",{id:"JetReconstruction.loadjets-Tuple{Any}",href:"#JetReconstruction.loadjets-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.loadjets")],-1)),e[30]||(e[30]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[31]||(e[31]=a('
julia
loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)
Load jets from a file.
Arguments
  • filename: The name of the file to load jets from.
  • splitby: The delimiter used to split the data in the file. Default is isspace.
  • constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
  • VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
  • A vector of VT objects representing the loaded jets.
source
',7))]),t("details",f,[t("summary",null,[e[32]||(e[32]=t("a",{id:"JetReconstruction.n_exclusive_jets-Tuple{ClusterSequence}",href:"#JetReconstruction.n_exclusive_jets-Tuple{ClusterSequence}"},[t("span",{class:"jlbinding"},"JetReconstruction.n_exclusive_jets")],-1)),e[33]||(e[33]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[34]||(e[34]=a('
julia
n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)
Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.
  • dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia
n_exclusive_jets(clusterseq, dcut = 20.0)
source
',9))]),t("details",F,[t("summary",null,[e[35]||(e[35]=t("a",{id:"JetReconstruction.plain_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.plain_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.plain_jet_reconstruct")],-1)),e[36]||(e[36]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[37]||(e[37]=a(`
julia
plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where T
Perform pp jet reconstruction using the plain algorithm.
Arguments
  • particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • p::Int=-1: The integer value used for jet reconstruction.
  • R::Float64=1.0: The radius parameter used for jet reconstruction.
  • recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
jets = plain_jet_reconstruct(particles; p = -1, R = 0.4)
+jets = plain_jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
source
`,12))]),t("details",C,[t("summary",null,[e[38]||(e[38]=t("a",{id:"JetReconstruction.read_final_state_particles-Tuple{Any}",href:"#JetReconstruction.read_final_state_particles-Tuple{Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.read_final_state_particles")],-1)),e[39]||(e[39]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[40]||(e[40]=a('
julia
read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)
Reads final state particles from a file and returns them as a vector of type T.
Arguments
  • fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
  • maxevents=-1: The maximum number of events to read. -1 means all events will be read.
  • skipevents=0: The number of events to skip before an event is included.
  • T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source
',7))]),t("details",v,[t("summary",null,[e[41]||(e[41]=t("a",{id:"JetReconstruction.savejets-Tuple{Any, Any}",href:"#JetReconstruction.savejets-Tuple{Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.savejets")],-1)),e[42]||(e[42]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[43]||(e[43]=a('
julia
savejets(filename, jets; format="px py pz E")
Save jet data to a file.
Arguments
  • filename: The name of the file to save the jet data to.
  • jets: An array of jet objects to save.
  • format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
  • "E" or "energy": Jet energy
  • "px": Momentum along the x-axis
  • "py": Momentum along the y-axis
  • "pz": Momentum along the z-axis
  • "pt2": Square of the transverse momentum
  • "phi": Azimuth angle
  • "rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source
',10))]),t("details",J,[t("summary",null,[e[44]||(e[44]=t("a",{id:"JetReconstruction.tiled_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T",href:"#JetReconstruction.tiled_jet_reconstruct-Union{Tuple{Vector{T}}, Tuple{T}} where T"},[t("span",{class:"jlbinding"},"JetReconstruction.tiled_jet_reconstruct")],-1)),e[45]||(e[45]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[46]||(e[46]=a('
julia
tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Arguments
  • particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
  • p::Union{Real, Nothing} = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)
source
',12))]),t("details",T,[t("summary",null,[e[47]||(e[47]=t("a",{id:"JetReconstruction.ClusterSequence",href:"#JetReconstruction.ClusterSequence"},[t("span",{class:"jlbinding"},"JetReconstruction.ClusterSequence")],-1)),e[48]||(e[48]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[49]||(e[49]=a('
julia
struct ClusterSequence
A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • power::Float64: The power value used for the clustering algorithm (not that this value is always stored as a Float64 to be type stable)
  • R::Float64: The R parameter used for the clustering algorithm.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
  • n_initial_jets::Int: The initial number of particles used for exclusive jets.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
  • Qtot::Any: The total energy of the event.
source
',5))]),t("details",A,[t("summary",null,[e[50]||(e[50]=t("a",{id:"JetReconstruction.ClusterSequence-Tuple{JetReconstruction.JetAlgorithm.Algorithm, Real, Float64, JetReconstruction.RecoStrategy.Strategy, Any, Any, Any}",href:"#JetReconstruction.ClusterSequence-Tuple{JetReconstruction.JetAlgorithm.Algorithm, Real, Float64, JetReconstruction.RecoStrategy.Strategy, Any, Any, Any}"},[t("span",{class:"jlbinding"},"JetReconstruction.ClusterSequence")],-1)),e[51]||(e[51]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[52]||(e[52]=a('
julia
ClusterSequence(algorithm::JetAlgorithm.Algorithm, p::Real, R::Float64, strategy::RecoStrategy.Strategy, jets, history, Qtot)
Construct a ClusterSequence object.
Arguments
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • p::Real: The power value used for the clustering algorithm.
  • R::Float64: The R parameter used for the clustering algorithm.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence.
  • Qtot::Any: The total energy of the event.
source
',5))]),t("details",R,[t("summary",null,[e[53]||(e[53]=t("a",{id:"JetReconstruction.EEjet",href:"#JetReconstruction.EEjet"},[t("span",{class:"jlbinding"},"JetReconstruction.EEjet")],-1)),e[54]||(e[54]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[55]||(e[55]=a('
julia
struct EEjet
The EEjet struct is a 4-momentum object used for the e+e jet reconstruction routines.
Fields
  • px::Float64: The x-component of the jet momentum.
  • py::Float64: The y-component of the jet momentum.
  • pz::Float64: The z-component of the jet momentum.
  • E::Float64: The energy of the jet.
  • _cluster_hist_index::Int: The index of the cluster histogram.
  • _p2::Float64: The squared momentum of the jet.
  • _inv_p::Float64: The inverse momentum of the jet.
source
',5))]),t("details",x,[t("summary",null,[e[56]||(e[56]=t("a",{id:"JetReconstruction.FinalJet",href:"#JetReconstruction.FinalJet"},[t("span",{class:"jlbinding"},"JetReconstruction.FinalJet")],-1)),e[57]||(e[57]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[58]||(e[58]=a('
julia
struct FinalJet
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
  • rap::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • pt::Float64: The transverse momentum of the jet.
source
',5))]),t("details",D,[t("summary",null,[e[59]||(e[59]=t("a",{id:"JetReconstruction.FinalJets",href:"#JetReconstruction.FinalJets"},[t("span",{class:"jlbinding"},"JetReconstruction.FinalJets")],-1)),e[60]||(e[60]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[61]||(e[61]=a('
julia
struct FinalJets
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
  • jetid::Int64: The ID of the jet.
  • jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.
source
',5))]),t("details",q,[t("summary",null,[e[62]||(e[62]=t("a",{id:"JetReconstruction.PseudoJet",href:"#JetReconstruction.PseudoJet"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[63]||(e[63]=i()),n(s,{type:"info",class:"jlObjectType jlType",text:"Type"})]),e[64]||(e[64]=a('
julia
mutable struct PseudoJet <: FourMomentum
The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy component of the momentum.
  • _cluster_hist_index::Int: The index of the cluster history.
  • _pt2::Float64: The squared transverse momentum.
  • _inv_pt2::Float64: The inverse squared transverse momentum.
  • _rap::Float64: The rapidity.
  • _phi::Float64: The azimuthal angle.
source
',5))]),t("details",B,[t("summary",null,[e[65]||(e[65]=t("a",{id:"JetReconstruction.PseudoJet-NTuple{4, Float64}",href:"#JetReconstruction.PseudoJet-NTuple{4, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[66]||(e[66]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[67]||(e[67]=a('
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)
Constructs a PseudoJet object with the given momentum components and energy.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
Returns
A PseudoJet object.
source
',7))]),t("details",P,[t("summary",null,[e[68]||(e[68]=t("a",{id:"JetReconstruction.PseudoJet-Tuple{Float64, Float64, Float64, Float64, Int64, Float64}",href:"#JetReconstruction.PseudoJet-Tuple{Float64, Float64, Float64, Float64, Int64, Float64}"},[t("span",{class:"jlbinding"},"JetReconstruction.PseudoJet")],-1)),e[69]||(e[69]=i()),n(s,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[70]||(e[70]=a(`
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
     _cluster_hist_index::Int,
-    pt2::Float64)
Constructs a PseudoJet object with the given momentum components and energy and history index.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
  • _cluster_hist_index::Int: The cluster history index.
  • pt2::Float64: The transverse momentum squared.
Returns
A PseudoJet object.
source
`,7))])])}const O=l(h,[["render",_]]);export{I as __pageData,O as default};
+    pt2::Float64)

Constructs a PseudoJet object with the given momentum components and energy and history index.

Arguments

  • px::Float64: The x-component of the momentum.

  • py::Float64: The y-component of the momentum.

  • pz::Float64: The z-component of the momentum.

  • E::Float64: The energy.

  • _cluster_hist_index::Int: The cluster history index.

  • pt2::Float64: The transverse momentum squared.

Returns

A PseudoJet object.

source

`,7))])])}const O=l(h,[["render",_]]);export{I as __pageData,O as default}; diff --git a/dev/assets/lib_visualisation.md.Bjkk6jY3.js b/dev/assets/lib_visualisation.md.BGOOfkLR.js similarity index 96% rename from dev/assets/lib_visualisation.md.Bjkk6jY3.js rename to dev/assets/lib_visualisation.md.BGOOfkLR.js index d423ddb..afc8143 100644 --- a/dev/assets/lib_visualisation.md.Bjkk6jY3.js +++ b/dev/assets/lib_visualisation.md.BGOOfkLR.js @@ -1,4 +1,4 @@ -import{_ as o,c as l,a5 as a,j as s,a as t,G as n,B as p,o as c}from"./chunks/framework.BtBDBbTG.js";const j=JSON.parse('{"title":"Jet Visualisation Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/visualisation.md","filePath":"lib/visualisation.md","lastUpdated":null}'),r={name:"lib/visualisation.md"},h={class:"jldocstring custom-block",open:""},d={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""};function k(g,e,y,b,m,E){const i=p("Badge");return c(),l("div",null,[e[9]||(e[9]=a('

Jet Visualisation Documentation ​

Documentation for visualisation interfaces extension module.

Index ​

Jet Visualisation Public Interfaces ​

',5)),s("details",h,[s("summary",null,[e[0]||(e[0]=s("a",{id:"JetReconstruction.animatereco-Tuple{ClusterSequence, Any}",href:"#JetReconstruction.animatereco-Tuple{ClusterSequence, Any}"},[s("span",{class:"jlbinding"},"JetReconstruction.animatereco")],-1)),e[1]||(e[1]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=a(`
julia
animatereco(cs::ClusterSequence, filename;
+import{_ as o,c as l,a5 as a,j as s,a as t,G as n,B as p,o as c}from"./chunks/framework.BtBDBbTG.js";const j=JSON.parse('{"title":"Jet Visualisation Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/visualisation.md","filePath":"lib/visualisation.md","lastUpdated":null}'),r={name:"lib/visualisation.md"},d={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""};function k(g,e,y,b,m,E){const i=p("Badge");return c(),l("div",null,[e[9]||(e[9]=a('
Jet Visualisation Documentation ​
Documentation for visualisation interfaces extension module.
Index ​
Jet Visualisation Public Interfaces ​
',5)),s("details",d,[s("summary",null,[e[0]||(e[0]=s("a",{id:"JetReconstruction.animatereco-Tuple{ClusterSequence, Any}",href:"#JetReconstruction.animatereco-Tuple{ClusterSequence, Any}"},[s("span",{class:"jlbinding"},"JetReconstruction.animatereco")],-1)),e[1]||(e[1]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=a(`
julia
animatereco(cs::ClusterSequence, filename;
             barsize_phi = 0.1,
             barsize_y = 0.1,
             colormap = :glasbey_category10_n256,
@@ -7,7 +7,7 @@ import{_ as o,c as l,a5 as a,j as s,a as t,G as n,B as p,o as c}from"./chunks/fr
             elevation = 0.5,
             framerate = 5,
             ancestors = false,
-            Module = Makie)
Animate the jet reconstruction process and save it as a video file.
Arguments
  • cs::ClusterSequence: The cluster sequence object containing the jets.
  • filename: The name of the output video file.
Optional Arguments
  • barsize_phi=0.1: The size of the bars in the phi direction.
  • barsize_y=0.1: The size of the bars in the y direction.
  • colormap=:glasbey_category10_n256: The colormap to use for coloring the jets.
  • perspective=0.5: The perspective of the plot.
  • azimuth=2.7: The azimuth angle of the plot.
  • elevation=0.5: The elevation angle of the plot.
  • framerate=5: The framerate of the output video.
  • end_frames=0: The number of static frames to show at the end of the animation. This can be useful to show the final state of the jets for a longer time.
  • title=nothing: The title to add to the plot.
  • ancestors=false: Whether to include ancestors of the jets in the animation. When true the ancestors of the jets will be plotted as well, as height zero bars, with the same colour as the jet they are ancestors of.
  • Module: The plotting module to use. Default is Makie.
For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.
Returns
  • fig: The figure object representing the final frame.
source
`,10))]),s("details",d,[s("summary",null,[e[3]||(e[3]=s("a",{id:"JetReconstruction.jetsplot-Tuple{Any, Any}",href:"#JetReconstruction.jetsplot-Tuple{Any, Any}"},[s("span",{class:"jlbinding"},"JetReconstruction.jetsplot")],-1)),e[4]||(e[4]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=a(`
jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)
Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example
+            Module = Makie)
Animate the jet reconstruction process and save it as a video file.
Arguments
  • cs::ClusterSequence: The cluster sequence object containing the jets.
  • filename: The name of the output video file.
Optional Arguments
  • barsize_phi=0.1: The size of the bars in the phi direction.
  • barsize_y=0.1: The size of the bars in the y direction.
  • colormap=:glasbey_category10_n256: The colormap to use for coloring the jets.
  • perspective=0.5: The perspective of the plot.
  • azimuth=2.7: The azimuth angle of the plot.
  • elevation=0.5: The elevation angle of the plot.
  • framerate=5: The framerate of the output video.
  • end_frames=0: The number of static frames to show at the end of the animation. This can be useful to show the final state of the jets for a longer time.
  • title=nothing: The title to add to the plot.
  • ancestors=false: Whether to include ancestors of the jets in the animation. When true the ancestors of the jets will be plotted as well, as height zero bars, with the same colour as the jet they are ancestors of.
  • Module: The plotting module to use. Default is Makie.
For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.
Returns
  • fig: The figure object representing the final frame.
source
`,10))]),s("details",h,[s("summary",null,[e[3]||(e[3]=s("a",{id:"JetReconstruction.jetsplot-Tuple{Any, Any}",href:"#JetReconstruction.jetsplot-Tuple{Any, Any}"},[s("span",{class:"jlbinding"},"JetReconstruction.jetsplot")],-1)),e[4]||(e[4]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=a(`
jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)
Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example
 using CairoMakie # use any other Makie that you have here
 
 jetsplot([object1, object2, object3], [[1], [2, 3]])
The example above plots object1 as a separate jet in one colour and object2 and object3 together in another colour.
This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:
import CairoMakie
@@ -17,7 +17,7 @@ import{_ as o,c as l,a5 as a,j as s,a as t,G as n,B as p,o as c}from"./chunks/fr
 jetsplot(my_objects, my_colour_arrays, Module=GLMakie)
 
 using WGLMakie
-jetsplot(my_objects, my_colour_arrays, Module=Main) #default
source
`,8))]),s("details",u,[s("summary",null,[e[6]||(e[6]=s("a",{id:"JetReconstruction.jetsplot-Tuple{Any, ClusterSequence}",href:"#JetReconstruction.jetsplot-Tuple{Any, ClusterSequence}"},[s("span",{class:"jlbinding"},"JetReconstruction.jetsplot")],-1)),e[7]||(e[7]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=a(`
julia
jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)
Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example
+jetsplot(my_objects, my_colour_arrays, Module=Main) #default
source
`,8))]),s("details",u,[s("summary",null,[e[6]||(e[6]=s("a",{id:"JetReconstruction.jetsplot-Tuple{Any, ClusterSequence}",href:"#JetReconstruction.jetsplot-Tuple{Any, ClusterSequence}"},[s("span",{class:"jlbinding"},"JetReconstruction.jetsplot")],-1)),e[7]||(e[7]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=a(`
julia
jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)
Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example
 using CairoMakie # use any other Makie that you have here
 jetsplot([object1, object2, object3], cluster_sequence_I_got_from_jet_reconstruct; Module=CairoMakie)
This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:
import CairoMakie
 jetsplot(my_objects, cs, Module=CairoMakie)
@@ -26,4 +26,4 @@ import{_ as o,c as l,a5 as a,j as s,a as t,G as n,B as p,o as c}from"./chunks/fr
 jetsplot(my_objects, cs, Module=GLMakie)
 
 using WGLMakie
-jetsplot(my_objects, cs, Module=Main) #default
source
`,7))])])}const v=o(r,[["render",k]]);export{j as __pageData,v as default};
+jetsplot(my_objects, cs, Module=Main) #default

source

`,7))])])}const v=o(r,[["render",k]]);export{j as __pageData,v as default}; diff --git a/dev/assets/lib_visualisation.md.Bjkk6jY3.lean.js b/dev/assets/lib_visualisation.md.BGOOfkLR.lean.js similarity index 96% rename from dev/assets/lib_visualisation.md.Bjkk6jY3.lean.js rename to dev/assets/lib_visualisation.md.BGOOfkLR.lean.js index d423ddb..afc8143 100644 --- a/dev/assets/lib_visualisation.md.Bjkk6jY3.lean.js +++ b/dev/assets/lib_visualisation.md.BGOOfkLR.lean.js @@ -1,4 +1,4 @@ -import{_ as o,c as l,a5 as a,j as s,a as t,G as n,B as p,o as c}from"./chunks/framework.BtBDBbTG.js";const j=JSON.parse('{"title":"Jet Visualisation Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/visualisation.md","filePath":"lib/visualisation.md","lastUpdated":null}'),r={name:"lib/visualisation.md"},h={class:"jldocstring custom-block",open:""},d={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""};function k(g,e,y,b,m,E){const i=p("Badge");return c(),l("div",null,[e[9]||(e[9]=a('

Jet Visualisation Documentation ​

Documentation for visualisation interfaces extension module.

Index ​

Jet Visualisation Public Interfaces ​

',5)),s("details",h,[s("summary",null,[e[0]||(e[0]=s("a",{id:"JetReconstruction.animatereco-Tuple{ClusterSequence, Any}",href:"#JetReconstruction.animatereco-Tuple{ClusterSequence, Any}"},[s("span",{class:"jlbinding"},"JetReconstruction.animatereco")],-1)),e[1]||(e[1]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=a(`
julia
animatereco(cs::ClusterSequence, filename;
+import{_ as o,c as l,a5 as a,j as s,a as t,G as n,B as p,o as c}from"./chunks/framework.BtBDBbTG.js";const j=JSON.parse('{"title":"Jet Visualisation Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/visualisation.md","filePath":"lib/visualisation.md","lastUpdated":null}'),r={name:"lib/visualisation.md"},d={class:"jldocstring custom-block",open:""},h={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""};function k(g,e,y,b,m,E){const i=p("Badge");return c(),l("div",null,[e[9]||(e[9]=a('
Jet Visualisation Documentation ​
Documentation for visualisation interfaces extension module.
Index ​
Jet Visualisation Public Interfaces ​
',5)),s("details",d,[s("summary",null,[e[0]||(e[0]=s("a",{id:"JetReconstruction.animatereco-Tuple{ClusterSequence, Any}",href:"#JetReconstruction.animatereco-Tuple{ClusterSequence, Any}"},[s("span",{class:"jlbinding"},"JetReconstruction.animatereco")],-1)),e[1]||(e[1]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[2]||(e[2]=a(`
julia
animatereco(cs::ClusterSequence, filename;
             barsize_phi = 0.1,
             barsize_y = 0.1,
             colormap = :glasbey_category10_n256,
@@ -7,7 +7,7 @@ import{_ as o,c as l,a5 as a,j as s,a as t,G as n,B as p,o as c}from"./chunks/fr
             elevation = 0.5,
             framerate = 5,
             ancestors = false,
-            Module = Makie)
Animate the jet reconstruction process and save it as a video file.
Arguments
  • cs::ClusterSequence: The cluster sequence object containing the jets.
  • filename: The name of the output video file.
Optional Arguments
  • barsize_phi=0.1: The size of the bars in the phi direction.
  • barsize_y=0.1: The size of the bars in the y direction.
  • colormap=:glasbey_category10_n256: The colormap to use for coloring the jets.
  • perspective=0.5: The perspective of the plot.
  • azimuth=2.7: The azimuth angle of the plot.
  • elevation=0.5: The elevation angle of the plot.
  • framerate=5: The framerate of the output video.
  • end_frames=0: The number of static frames to show at the end of the animation. This can be useful to show the final state of the jets for a longer time.
  • title=nothing: The title to add to the plot.
  • ancestors=false: Whether to include ancestors of the jets in the animation. When true the ancestors of the jets will be plotted as well, as height zero bars, with the same colour as the jet they are ancestors of.
  • Module: The plotting module to use. Default is Makie.
For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.
Returns
  • fig: The figure object representing the final frame.
source
`,10))]),s("details",d,[s("summary",null,[e[3]||(e[3]=s("a",{id:"JetReconstruction.jetsplot-Tuple{Any, Any}",href:"#JetReconstruction.jetsplot-Tuple{Any, Any}"},[s("span",{class:"jlbinding"},"JetReconstruction.jetsplot")],-1)),e[4]||(e[4]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=a(`
jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)
Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example
+            Module = Makie)
Animate the jet reconstruction process and save it as a video file.
Arguments
  • cs::ClusterSequence: The cluster sequence object containing the jets.
  • filename: The name of the output video file.
Optional Arguments
  • barsize_phi=0.1: The size of the bars in the phi direction.
  • barsize_y=0.1: The size of the bars in the y direction.
  • colormap=:glasbey_category10_n256: The colormap to use for coloring the jets.
  • perspective=0.5: The perspective of the plot.
  • azimuth=2.7: The azimuth angle of the plot.
  • elevation=0.5: The elevation angle of the plot.
  • framerate=5: The framerate of the output video.
  • end_frames=0: The number of static frames to show at the end of the animation. This can be useful to show the final state of the jets for a longer time.
  • title=nothing: The title to add to the plot.
  • ancestors=false: Whether to include ancestors of the jets in the animation. When true the ancestors of the jets will be plotted as well, as height zero bars, with the same colour as the jet they are ancestors of.
  • Module: The plotting module to use. Default is Makie.
For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.
Returns
  • fig: The figure object representing the final frame.
source
`,10))]),s("details",h,[s("summary",null,[e[3]||(e[3]=s("a",{id:"JetReconstruction.jetsplot-Tuple{Any, Any}",href:"#JetReconstruction.jetsplot-Tuple{Any, Any}"},[s("span",{class:"jlbinding"},"JetReconstruction.jetsplot")],-1)),e[4]||(e[4]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[5]||(e[5]=a(`
jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)
Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example
 using CairoMakie # use any other Makie that you have here
 
 jetsplot([object1, object2, object3], [[1], [2, 3]])
The example above plots object1 as a separate jet in one colour and object2 and object3 together in another colour.
This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:
import CairoMakie
@@ -17,7 +17,7 @@ import{_ as o,c as l,a5 as a,j as s,a as t,G as n,B as p,o as c}from"./chunks/fr
 jetsplot(my_objects, my_colour_arrays, Module=GLMakie)
 
 using WGLMakie
-jetsplot(my_objects, my_colour_arrays, Module=Main) #default
source
`,8))]),s("details",u,[s("summary",null,[e[6]||(e[6]=s("a",{id:"JetReconstruction.jetsplot-Tuple{Any, ClusterSequence}",href:"#JetReconstruction.jetsplot-Tuple{Any, ClusterSequence}"},[s("span",{class:"jlbinding"},"JetReconstruction.jetsplot")],-1)),e[7]||(e[7]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=a(`
julia
jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)
Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example
+jetsplot(my_objects, my_colour_arrays, Module=Main) #default
source
`,8))]),s("details",u,[s("summary",null,[e[6]||(e[6]=s("a",{id:"JetReconstruction.jetsplot-Tuple{Any, ClusterSequence}",href:"#JetReconstruction.jetsplot-Tuple{Any, ClusterSequence}"},[s("span",{class:"jlbinding"},"JetReconstruction.jetsplot")],-1)),e[7]||(e[7]=t()),n(i,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),e[8]||(e[8]=a(`
julia
jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)
Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example
 using CairoMakie # use any other Makie that you have here
 jetsplot([object1, object2, object3], cluster_sequence_I_got_from_jet_reconstruct; Module=CairoMakie)
This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:
import CairoMakie
 jetsplot(my_objects, cs, Module=CairoMakie)
@@ -26,4 +26,4 @@ import{_ as o,c as l,a5 as a,j as s,a as t,G as n,B as p,o as c}from"./chunks/fr
 jetsplot(my_objects, cs, Module=GLMakie)
 
 using WGLMakie
-jetsplot(my_objects, cs, Module=Main) #default
source
`,7))])])}const v=o(r,[["render",k]]);export{j as __pageData,v as default};
+jetsplot(my_objects, cs, Module=Main) #default

source

`,7))])])}const v=o(r,[["render",k]]);export{j as __pageData,v as default}; diff --git a/dev/examples.html b/dev/examples.html index 8fa598f..2eedebc 100644 --- a/dev/examples.html +++ b/dev/examples.html @@ -8,9 +8,9 @@ - + - + @@ -23,7 +23,7 @@ ... julia --project=examples examples/jetreco.jl --maxevents=10 --strategy=N2Plain --algorithm=Kt --exclusive-njets=3 test/data/events.pp13TeV.hepmc3.gz ...

There are options to explicitly set the algorithm (use --help to see these).

instrumented-jetreco.jl ​

This is a more sophisticated example that allows performance measurements to be made of the reconstruction, as well as profiling (flamegraphs and memory profiling). Use the --help option to see usage. e.g., to extract timing performance for the AntiKt algorithm using the tiled strategy:

sh
julia --project instrumented-jetreco.jl -S N2Tiled -A AntiKt --nsamples 100 ../test/data/events.hepmc3

visualise-jets.jl ​

This script will produce a PNG/PDF showing the results of a jet reconstruction. This is a 3D plot where all the initial energy deposits are visualised, with colours that indicate in which final cluster the deposit ended up in.

visualise-jets.ipynb ​

Similar to visualise-jets.jl this notebook will produce a visualisation of jet reconstruction in the browser. This is a 3D plot where all the initial energy deposits are visualised, with colours that indicate in which final cluster the deposit ended up in.

animate-reconstruction.jl ​

Performs jet reconstruction and then produces and animation of the process, showing how the jets merge from their different constituents.

- + \ No newline at end of file diff --git a/dev/hashmap.json b/dev/hashmap.json index e2b1dd2..a429c3b 100644 --- a/dev/hashmap.json +++ b/dev/hashmap.json @@ -1 +1 @@ -{"examples.md":"0mYAaYCb","index.md":"CDGdS60h","lib_internal.md":"oZWVn3YV","lib_public.md":"ClckhHDw","lib_visualisation.md":"Bjkk6jY3"} +{"examples.md":"0mYAaYCb","index.md":"CDGdS60h","lib_internal.md":"CnFs4lT2","lib_public.md":"DSnuIYVw","lib_visualisation.md":"BGOOfkLR"} diff --git a/dev/index.html b/dev/index.html index 6d106d4..ed98164 100644 --- a/dev/index.html +++ b/dev/index.html @@ -8,9 +8,9 @@ - + - + @@ -39,7 +39,7 @@ archivePrefix={arXiv}, primaryClass={hep-ex} }

Code in this package is authored by:

  • Atell Krasnopolski <delta_atell@protonmail.com>

  • Graeme A Stewart <graeme.andrew.stewart@cern.ch>

  • Philippe Gras <philippe.gras@cern.ch>

and is Copyright 2022-2024 The Authors, CERN.

The code is under the MIT License.

- + \ No newline at end of file diff --git a/dev/lib/internal.html b/dev/lib/internal.html index 09e0ccd..8800881 100644 --- a/dev/lib/internal.html +++ b/dev/lib/internal.html @@ -8,24 +8,24 @@ - + - + - + -
JetReconstruction.jl

Appearance

Jet Reconstruction Internal Documentation ​

Documentation for JetReconstruction.jl's internal methods and types.

N.B. no guarantee is made of stability of these interfaces or types.

Index ​

Public Interface ​

Base.:+ Method
julia
+(j1::PseudoJet, j2::PseudoJet)

Addition operator for PseudoJet objects.

Arguments

  • j1::PseudoJet: The first PseudoJet object.

  • j2::PseudoJet: The second PseudoJet object.

Returns

A new PseudoJet object with the sum of the momenta and energy of j1 and j2.

source

Base.copy Method
julia
copy(j::TiledJet)

Create a copy of a TiledJet object.

Arguments

  • j::TiledJet: The TiledJet object to be copied.

Returns

A new TiledJet object with the same attributes as the input object.

source

Base.iterate Function
julia
Base.iterate(t::rightmost_tiles, state=1)

Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.

source

Base.iterate Function
julia
Base.iterate(t::neighbour_tiles, state=1)

Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.

source

Base.iterate Method
julia
Base.iterate(tj::TiledJet)

Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).

Arguments

  • tj::TiledJet: The TiledJet object to start to iterate over.

source

Base.show Method
julia
show(io::IO, jet::PseudoJet)

Print a PseudoJet object to the specified IO stream.

Arguments

  • io::IO: The IO stream to which the information will be printed.

  • jet::PseudoJet: The PseudoJet object whose information will be printed.

source

Base.tryparse Method
julia
Base.tryparse(E::Type{<:Enum}, str::String)

Parser that converts a string to an enum value if it exists, otherwise returns nothing.

source

JetReconstruction.CosTheta Method
julia
CosTheta(p::PseudoJet)

Compute the cosine of the angle between the momentum vector p and the z-axis.

Arguments

  • p::PseudoJet: The PseudoJet object representing the momentum vector.

Returns

  • The cosine of the angle between p and the z-axis.

source

JetReconstruction._ee_genkt_algorithm Method
julia
_ee_genkt_algorithm(; particles::Vector{EEjet}, p = 1, R = 4.0,
+    
JetReconstruction.jl
Appearance
Jet Reconstruction Internal Documentation ​
Documentation for JetReconstruction.jl's internal methods and types.
N.B. no guarantee is made of stability of these interfaces or types.
Index ​
Public Interface ​
Base.:+ Method
julia
+(j1::PseudoJet, j2::PseudoJet)
Addition operator for PseudoJet objects.
Arguments
  • j1::PseudoJet: The first PseudoJet object.
  • j2::PseudoJet: The second PseudoJet object.
Returns
A new PseudoJet object with the sum of the momenta and energy of j1 and j2.
source
Base.copy Method
julia
copy(j::TiledJet)
Create a copy of a TiledJet object.
Arguments
  • j::TiledJet: The TiledJet object to be copied.
Returns
A new TiledJet object with the same attributes as the input object.
source
Base.iterate Function
julia
Base.iterate(t::neighbour_tiles, state=1)
Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.
source
Base.iterate Function
julia
Base.iterate(t::rightmost_tiles, state=1)
Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.
source
Base.iterate Method
julia
Base.iterate(tj::TiledJet)
Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).
Arguments
  • tj::TiledJet: The TiledJet object to start to iterate over.
source
Base.show Method
julia
show(io::IO, jet::PseudoJet)
Print a PseudoJet object to the specified IO stream.
Arguments
  • io::IO: The IO stream to which the information will be printed.
  • jet::PseudoJet: The PseudoJet object whose information will be printed.
source
Base.tryparse Method
julia
Base.tryparse(E::Type{<:Enum}, str::String)
Parser that converts a string to an enum value if it exists, otherwise returns nothing.
source
JetReconstruction.CosTheta Method
julia
CosTheta(p::PseudoJet)
Compute the cosine of the angle between the momentum vector p and the z-axis.
Arguments
  • p::PseudoJet: The PseudoJet object representing the momentum vector.
Returns
  • The cosine of the angle between p and the z-axis.
source
JetReconstruction._ee_genkt_algorithm Method
julia
_ee_genkt_algorithm(; particles::Vector{EEjet}, p = 1, R = 4.0,
                    algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham,
-                   recombine = +)
This function is the actual implementation of the e+e- jet clustering algorithm.
source
JetReconstruction._ensure_valid_rap_phi Method
julia
_ensure_valid_rap_phi(p::PseudoJet)
Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.
Arguments
  • p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.
source
JetReconstruction._plain_jet_reconstruct Method
julia
_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)
This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.
Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.
The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt. Floating point values are allowed for generalised k_t algorithm.
Arguments
  • particles: A vector of PseudoJet objects representing the input particles.
  • p=-1: The power to which the transverse momentum (pt) of each particle is raised.
  • R=1.0: The jet radius parameter.
  • recombine: The recombination function used to merge two jets. Default is + (additive recombination).
Returns
  • clusterseq: The resulting ClusterSequence object representing the reconstructed jets.
source
JetReconstruction._set_rap_phi! Method
_set_rap_phi!(p::PseudoJet)
Set the rapidity and azimuthal angle of the PseudoJet p.
Arguments
  • p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.
Description
This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.
Note - the ϕ angle is calculated in the range [0, 2π).
source
JetReconstruction._tiled_jet_reconstruct Method
julia
_tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets once preprocessing of data types are done.
Arguments
  • particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.
  • p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)
source
JetReconstruction._tj_diJ Method
julia
_tj_diJ(jet)
Compute the dij metric value for a given jet.
Arguments
  • jet: The input jet.
Returns
  • The dij value for the jet.
Example
source
JetReconstruction._tj_dist Method
julia
_tj_dist(jetA, jetB)
Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.
Arguments
  • jetA: The first jet.
  • jetB: The second jet.
Returns
The squared distance between jetA and jetB.
Examples
source
JetReconstruction.add_step_to_history! Method
julia
add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)
Add a new jet's history into the recombination sequence.
Arguments:
  • clusterseq::ClusterSequence: The cluster sequence object.
  • parent1: The index of the first parent.
  • parent2: The index of the second parent.
  • jetp_index: The index of the jet.
  • dij: The dij value.
This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.
If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.
source
JetReconstruction.add_untagged_neighbours_to_tile_union Method
julia
add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)
Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.
Arguments
  • center_index: The index of the center tile.
  • tile_union: An array to store the indices of neighbouring tiles.
  • n_near_tiles: The number of neighbouring tiles.
  • tiling: The tiling object containing the tile tags.
Returns
The updated number of near tiles.
source
JetReconstruction.angular_distance Method
julia
angular_distance(eereco, i, j) -> Float64
Calculate the angular distance between two jets i and j using the formula 1−cos(θij).
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
Returns
  • Float64: The angular distance between i and j, which is 1−cosheta.
source
JetReconstruction.check_algorithm_power_consistency Method
Allow a check for algorithm and power consistency
source
JetReconstruction.detach! Method
julia
detach!(jet::TiledJet)
Detach a TiledJet from its linked list by updating the previous and next pointers.
Arguments
  • jet::TiledJet: The TiledJet object to detach.
source
JetReconstruction.determine_rapidity_extent Method
julia
determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat
Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.
This is the heuristic which is used by FastJet (inline comments are from FastJet).
Arguments
  • eta::Vector{T}: A vector of rapidity values.
Returns
  • minrap::T: The minimum rapidity value.
  • maxrap::T: The maximum rapidity value.
source
JetReconstruction.dij Method
julia
dij(i, kt2_array, nn, nndist)
Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß
Arguments
  • i: The index of the element.
  • kt2_array: An array of kt2 values.
  • nn: An array of nearest neighbors.
  • nndist: An array of nearest neighbor distances.
Returns
  • The computed dij value.
source
JetReconstruction.dij_dist Method
julia
dij_dist(eereco, i, j, dij_factor)
Calculate the dij distance between two e+e−jets.
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
  • dij_factor: The scaling factor to multiply the dij distance by.
Returns
  • The dij distance between i and j.
source
JetReconstruction.dist Method
julia
dist(i, j, rapidity_array, phi_array)
Compute the distance between points in a 2D space defined by rapidity and phi coordinates.
Arguments
  • i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).
  • j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).
  • rapidity_array::Vector{Float64}: Array of rapidity coordinates.
  • phi_array::Vector{Float64}: Array of phi coordinates.
Returns
  • distance::Float64: The distance between the two points.
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JetReconstruction.do_iB_recombination_step! Method
julia
do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)
Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the jet.
  • diB: The diB value.
source
JetReconstruction.do_ij_recombination_step! Function
julia
do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)
Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the first jet to be recombined.
  • jet_j: The index of the second jet to be recombined.
  • dij: The distance between the two jets.
  • recombine=+: The recombination function to be used. Default is addition.
Returns
  • newjet_k: The index of the newly created jet.
Description
This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.
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JetReconstruction.energy Method
julia
energy(p::PseudoJet)
Return the energy of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The energy of the PseudoJet.
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JetReconstruction.eta Method
julia
eta(p::PseudoJet)
Compute the pseudorapidity (η) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.
Returns
  • The pseudorapidity (η) of the PseudoJet.
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JetReconstruction.fast_findmin Method
julia
fast_findmin(dij, n)
Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.
Arguments
  • dij: An array of values.
  • n: The number of elements to consider in the dij array.
Returns
  • dij_min: The minimum value in the first n elements of the dij array.
  • best: The index of the minimum value in the dij array.
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JetReconstruction.find_tile_neighbours! Method
julia
find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)
Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run
Arguments
  • tile_union: The tile union to which the neighbouring tiles will be added.
  • jetA: The first jet.
  • jetB: The second jet.
  • oldB: The old second jet.
  • tiling: The tiling information.
Returns
The number of neighbouring tiles added to the tile_union.
source
JetReconstruction.geometric_distance Method
julia
geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)
Compute the geometric distance between two points in the rap-phi plane.
Arguments
  • eta1::AbstractFloat: The eta coordinate of the first point.
  • phi1::AbstractFloat: The phi coordinate of the first point.
  • eta2::AbstractFloat: The eta coordinate of the second point.
  • phi2::AbstractFloat: The phi coordinate of the second point.
Returns
  • distance::Float64: The geometric distance between the two points.
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JetReconstruction.get_algorithm_power_consistency Method
julia
get_algorithm_power_consistency(; p::Union{Real, Nothing}, algorithm::Union{JetAlgorithm, Nothing})
Get the algorithm and power consistency correct
This function checks the consistency between the algorithm and power parameters. If the algorithm is specified, it checks if the power parameter is consistent with the algorithm's known power. If the power parameter is not specified, it sets the power parameter based on the algorithm. If neither the algorithm nor the power parameter is specified, it throws an ArgumentError.
Arguments
  • p::Union{Real, Nothing}: The power value.
  • algorithm::Union{JetAlgorithm, Nothing}: The algorithm.
Returns
A named tuple of the consistent power and algorithm values.
Throws
  • ArgumentError: If the algorithm and power are inconsistent or if neither the algorithm nor the power is specified.
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JetReconstruction.get_all_ancestors Method
julia
get_all_ancestors(idx, cs::ClusterSequence)
Recursively finds all ancestors of a given index in a ClusterSequence object.
Arguments
  • idx: The index of the jet for which to find ancestors.
  • cs: The ClusterSequence object containing the jet history.
Returns
An array of indices representing the ancestors of the given jet.
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JetReconstruction.get_dij_dist Method
julia
get_dij_dist(nn_dist, kt2_1, kt2_2, R2)
Compute the dij metric distance between two jets.
Arguments
  • nn_dist: The nearest-neighbor distance between two jets.
  • kt2_1: The squared momentum metric value of the first jet.
  • kt2_2: The squared momentum metric value of the second jet.
  • R2: The jet radius parameter squared.
Returns
The distance between the two jets.
If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.
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JetReconstruction.get_tile Method
julia
get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)
Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.
Arguments
  • tiling_setup: A TilingDef object that contains the tiling setup parameters.
  • eta: The eta value for which to calculate the tile index.
  • phi: The phi value for which to calculate the tile index.
Returns
  • ieta: The tile index along the eta direction.
  • iphi: The tile index along the phi direction.
source
JetReconstruction.get_tile_cartesian_indices Method
julia
get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)
Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)
Arguments
  • tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.
  • i_η::Int: The index of the tile in the η dimension.
  • i_ϕ::Int: The index of the tile in the ϕ dimension.
Returns
  • The linear index of the tile.
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JetReconstruction.initial_history Method
julia
initial_history(particles)
Create an initial history for the given particles.
Arguments
  • particles: The initial vector of stable particles.
Returns
  • history: An array of HistoryElement objects.
  • Qtot: The total energy in the event.
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JetReconstruction.insert! Method
julia
insert!(nextjet::TiledJet, jettomove::TiledJet)
Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list
Arguments
  • nextjet::TiledJet: The TiledJet object after which jettomove should be inserted.
  • jettomove::TiledJet: The TiledJet object to be inserted.
Example
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JetReconstruction.is_ee Method
julia
is_ee(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a e+e- reconstruction algorithm.
Returns
true if the algorithm is a e+e- reconstruction algorithm, false otherwise.
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JetReconstruction.is_pp Method
julia
is_pp(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a pp reconstruction algorithm.
Returns
true if the algorithm is a pp reconstruction algorithm, false otherwise.
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JetReconstruction.isvalid Method
julia
isvalid(t::TiledJet)
Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.
Arguments
  • t::TiledJet: The TiledJet object to check.
Returns
  • Bool: true if the TiledJet object is valid, false otherwise.
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JetReconstruction.jet_ranks Method
julia
jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)
Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.
  • compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.
Returns
A dictionary mapping each jet index to its rank.
Note
This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.
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JetReconstruction.m Method
julia
m(p::PseudoJet)
Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the invariant mass.
Returns
The invariant mass of the PseudoJet object.
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JetReconstruction.m2 Method
julia
m2(p::PseudoJet)
Calculate the invariant mass squared (m^2) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.
Returns
  • The invariant mass squared (m^2) of the PseudoJet.
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JetReconstruction.mag Method
julia
mag(p::PseudoJet)
Return the magnitude of the momentum of a PseudoJet, |p|.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the magnitude.
Returns
The magnitude of the PseudoJet object.
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JetReconstruction.mass Method
julia
mass(p::PseudoJet)
Compute the invariant mass (alias for m(p)).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the mass.
Returns
  • The mass of the PseudoJet.
source
JetReconstruction.mass2 Function
Alias for m2 function
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JetReconstruction.merge_steps Method
julia
merge_steps(clusterseq::ClusterSequence)
Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
Returns
  • merge_steps::Int: The number of merge steps.
source
JetReconstruction.phi Method
julia
phi(p::PseudoJet)
Compute the ϕ angle of a PseudoJet object p.
Note this function is a wrapper for phi_02pi(p).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
JetReconstruction.phi_02pi Method
julia
phi_02pi(p::PseudoJet)
Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
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JetReconstruction.pt Method
julia
pt(p::PseudoJet)
Compute the scalar transverse momentum (pt) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.
Returns
  • The transverse momentum (pt) of the PseudoJet.
source
JetReconstruction.pt2 Method
julia
pt2(p::PseudoJet)
Get the squared transverse momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The squared transverse momentum of the PseudoJet.
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JetReconstruction.px Method
julia
px(p::PseudoJet)
Return the x-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The x-component of the momentum of the PseudoJet.
source
JetReconstruction.py Method
julia
py(p::PseudoJet)
Return the y-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The y-component of the momentum of the PseudoJet.
source
JetReconstruction.pz Method
julia
pz(p::PseudoJet)
Return the z-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The z-component of the momentum of the PseudoJet.
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JetReconstruction.rapidity Method
julia
rapidity(p::PseudoJet)
Compute the rapidity of a PseudoJet object.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the rapidity.
Returns
The rapidity of the PseudoJet object.
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JetReconstruction.reco_state Method
julia
reco_state(cs::ClusterSequence, pt_ranks; iteration=0)
This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.
Arguments
  • cs::ClusterSequence: The ClusterSequence object to update.
  • ranks: The ranks of the original clusters, that are inherited by peudojets
during the reconstruction process.
  • iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).
  • ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).
Returns
A dictionary representing a snapshot of the reconstruction state.
Details
The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.
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JetReconstruction.rightneighbours Method
julia
rightneighbours(center::Int, tiling::Tiling)
Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the indices of the right neighbors.
source
JetReconstruction.set_momentum! Method
julia
set_momentum!(j::PseudoJet, px, py, pz, E)
Set the momentum components and energy of a PseudoJet object.
Arguments
  • j::PseudoJet: The PseudoJet object to set the momentum for.
  • px: The x-component of the momentum.
  • py: The y-component of the momentum.
  • pz: The z-component of the momentum.
  • E: The energy of the particle.
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JetReconstruction.set_nearest_neighbours! Method
julia
set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})
This function sets the nearest neighbor information for all jets in the tiledjets vector.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • tiling::Tiling: The tiling object.
  • tiledjets::Vector{TiledJet}: The vector of tiled jets.
Returns
  • NNs::Vector{TiledJet}: The vector of nearest neighbor jets.
  • diJ::Vector{Float64}: The vector of diJ values.
The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.
Note: The diJ values are calculated as the kt distance multiplied by R^2.
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JetReconstruction.setup_tiling Method
julia
setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat
This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.
Arguments
  • eta::Vector{T}: A vector of rapidities.
  • Rparam::AbstractFloat: The jet radius parameter.
Returns
  • tiling_setup: A TilingDef object containing the tiling setup parameters.
Description
The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.
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JetReconstruction.surrounding Method
julia
surrounding(center::Int, tiling::Tiling)
Compute the surrounding indices of a given center index in a tiling.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the surrounding indices.
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JetReconstruction.tile_index Method
julia
tile_index(tiling_setup, eta::Float64, phi::Float64)
Compute the tile index for a given (eta, phi) coordinate.
Arguments
  • tiling_setup: The tiling setup object containing the tile size and number of tiles.
  • eta::Float64: The eta coordinate.
  • phi::Float64: The phi coordinate.
Returns
The tile index corresponding to the (eta, phi) coordinate.
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JetReconstruction.tiledjet_remove_from_tiles! Method
julia
tiledjet_remove_from_tiles!(tiling, jet)
Remove a jet from the given tiling structure.
Arguments
  • tiling: The tiling structure from which the jet will be removed.
  • jet: The jet to be removed from the tiling structure.
Description
This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.
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JetReconstruction.tiledjet_set_jetinfo! Method
julia
tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)
Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)
Arguments:
  • jet::TiledJet: The TiledJet object to set the information for.
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets.
  • tiling::Tiling: The Tiling object containing the tile information.
  • jets_index: The index of the jet in the ClusterSequence.
  • R2: The jet radius parameter squared.
  • p: The power to raise the pt2 value to.
This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.
Returns:
  • nothing
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JetReconstruction.upd_nn_crosscheck! Method
julia
upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
JetReconstruction.upd_nn_nocross! Method
julia
upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
JetReconstruction.upd_nn_step! Method
julia
upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)
Update the nearest neighbor information after a jet merge step.
Arguments:
  • i: Index of the first particle in the last merge step.
  • j: Index of the second particle in the last merge step.
  • k: Index of the current particle for which the nearest neighbour will be updated.
  • N: Total number of particles (currently vaild array indexes are [1:N]).
  • Nn: Number of nearest neighbors to consider.
  • kt2_array: Array of transverse momentum squared values.
  • rapidity_array: Array of rapidity values.
  • phi_array: Array of azimuthal angle values.
  • R2: Distance threshold squared for nearest neighbors.
  • nndist: Array of nearest neighbor geometric distances.
  • nn: Array of nearest neighbor indices.
  • nndij: Array of metric distances between particles.
This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.
source
JetReconstruction.η Function
julia
const η = eta
Alias for the pseudorapidity function, eta.
source
JetReconstruction.FourMomentum Type
Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.
source
JetReconstruction.HistoryElement Type
julia
struct HistoryElement
A struct holding a record of jet mergers and finalisations
Fields:
  • parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)
  • parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)
  • child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.
  • jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.
  • dij: The distance corresponding to the recombination at this stage of the clustering.
  • max_dij_so_far: The largest recombination distance seen so far in the clustering history.
source
JetReconstruction.HistoryElement Method
julia
HistoryElement(jetp_index)
Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.
Arguments
  • jetp_index: The index of the jetp.
Returns
A HistoryElement object.
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JetReconstruction.JetWithAncestors Type
julia
struct JetWithAncestors
A struct representing a jet with its origin ancestors.
Fields
  • self::PseudoJet: The PseudoJet object for this jet.
  • jetp_index::Int: The index of the jet in the corresponding cluster sequence.
  • ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).
  • jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors
Note
This structure needs its associated cluster sequence origin to be useful.
source
JetReconstruction.Surrounding Type
julia
struct Surrounding{N}
Structure used for iterating over neighbour tiles.
Fields
  • indices::NTuple{N, Int}: A tuple of N integers representing the indices.
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JetReconstruction.TiledJet Type
julia
struct TiledJet
TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.
Fields
  • id::Int: The ID of the jet.
  • eta::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • kt2::Float64: The transverse momentum squared of the jet.
  • NN_dist::Float64: The distance to the nearest neighbor.
  • jets_index::Int: The index of the jet in the jet array.
  • tile_index::Int: The index of the tile in the tile array.
  • dij_posn::Int: The position of this jet in the dij compact array.
  • NN::TiledJet: The nearest neighbor.
  • previous::TiledJet: The previous jet.
  • next::TiledJet: The next jet.
source
JetReconstruction.TiledJet Method
julia
TiledJet(id)
Constructs a TiledJet object with the given id and initializes its properties to zero.
Arguments
  • id: The ID of the TiledJet object.
Returns
A TiledJet object with the specified id and values set to zero or noTiledJet.
source
JetReconstruction.Tiling Type
julia
struct Tiling
The Tiling struct represents a tiling configuration for jet reconstruction.
Fields
  • setup::TilingDef: The tiling definition used for the configuration.
  • tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).
  • positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.
  • tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).
source
JetReconstruction.Tiling Method
julia
Tiling(setup::TilingDef)
Constructs a intial Tiling object based on the provided setup parameters.
Arguments
  • setup::TilingDef: The setup parameters for the tiling.
Returns
A Tiling object.
source
JetReconstruction.TilingDef Type
julia
struct TilingDef
A struct representing the definition of a spcific tiling scheme.
Fields
  • _tiles_eta_min::Float64: The minimum rapidity of the tiles.
  • _tiles_eta_max::Float64: The maximum rapidity of the tiles.
  • _tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).
  • _tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).
  • _n_tiles_eta::Int: The number of tiles across rapidity.
  • _n_tiles_phi::Int: The number of tiles across phi.
  • _n_tiles::Int: The total number of tiles.
  • _tiles_ieta_min::Int: The minimum rapidity tile index.
  • _tiles_ieta_max::Int: The maximum rapidity tile index.
Constructor
TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
-	_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)
Constructs a TilingDef object with the given parameters.
source
JetReconstruction.neighbour_tiles Type
julia
struct neighbour_tiles
A struct representing the neighbouring tiles.
A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
+                   recombine = +)
This function is the actual implementation of the e+e- jet clustering algorithm.
source
JetReconstruction._ensure_valid_rap_phi Method
julia
_ensure_valid_rap_phi(p::PseudoJet)
Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.
Arguments
  • p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.
source
JetReconstruction._plain_jet_reconstruct Method
julia
_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)
This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.
Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.
The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt. Floating point values are allowed for generalised k_t algorithm.
Arguments
  • particles: A vector of PseudoJet objects representing the input particles.
  • p=-1: The power to which the transverse momentum (pt) of each particle is raised.
  • R=1.0: The jet radius parameter.
  • recombine: The recombination function used to merge two jets. Default is + (additive recombination).
Returns
  • clusterseq: The resulting ClusterSequence object representing the reconstructed jets.
source
JetReconstruction._set_rap_phi! Method
_set_rap_phi!(p::PseudoJet)
Set the rapidity and azimuthal angle of the PseudoJet p.
Arguments
  • p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.
Description
This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.
Note - the ϕ angle is calculated in the range [0, 2π).
source
JetReconstruction._tiled_jet_reconstruct Method
julia
_tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets once preprocessing of data types are done.
Arguments
  • particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.
  • p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)
source
JetReconstruction._tj_diJ Method
julia
_tj_diJ(jet)
Compute the dij metric value for a given jet.
Arguments
  • jet: The input jet.
Returns
  • The dij value for the jet.
Example
source
JetReconstruction._tj_dist Method
julia
_tj_dist(jetA, jetB)
Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.
Arguments
  • jetA: The first jet.
  • jetB: The second jet.
Returns
The squared distance between jetA and jetB.
Examples
source
JetReconstruction.add_step_to_history! Method
julia
add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)
Add a new jet's history into the recombination sequence.
Arguments:
  • clusterseq::ClusterSequence: The cluster sequence object.
  • parent1: The index of the first parent.
  • parent2: The index of the second parent.
  • jetp_index: The index of the jet.
  • dij: The dij value.
This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.
If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.
source
JetReconstruction.add_untagged_neighbours_to_tile_union Method
julia
add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)
Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.
Arguments
  • center_index: The index of the center tile.
  • tile_union: An array to store the indices of neighbouring tiles.
  • n_near_tiles: The number of neighbouring tiles.
  • tiling: The tiling object containing the tile tags.
Returns
The updated number of near tiles.
source
JetReconstruction.angular_distance Method
julia
angular_distance(eereco, i, j) -> Float64
Calculate the angular distance between two jets i and j using the formula 1−cos(θij).
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
Returns
  • Float64: The angular distance between i and j, which is 1−cosheta.
source
JetReconstruction.check_algorithm_power_consistency Method
Allow a check for algorithm and power consistency
source
JetReconstruction.detach! Method
julia
detach!(jet::TiledJet)
Detach a TiledJet from its linked list by updating the previous and next pointers.
Arguments
  • jet::TiledJet: The TiledJet object to detach.
source
JetReconstruction.determine_rapidity_extent Method
julia
determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat
Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.
This is the heuristic which is used by FastJet (inline comments are from FastJet).
Arguments
  • eta::Vector{T}: A vector of rapidity values.
Returns
  • minrap::T: The minimum rapidity value.
  • maxrap::T: The maximum rapidity value.
source
JetReconstruction.dij Method
julia
dij(i, kt2_array, nn, nndist)
Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß
Arguments
  • i: The index of the element.
  • kt2_array: An array of kt2 values.
  • nn: An array of nearest neighbors.
  • nndist: An array of nearest neighbor distances.
Returns
  • The computed dij value.
source
JetReconstruction.dij_dist Method
julia
dij_dist(eereco, i, j, dij_factor)
Calculate the dij distance between two e+e−jets.
Arguments
  • eereco: The array of EERecoJet objects.
  • i: The first jet.
  • j: The second jet.
  • dij_factor: The scaling factor to multiply the dij distance by.
Returns
  • The dij distance between i and j.
source
JetReconstruction.dist Method
julia
dist(i, j, rapidity_array, phi_array)
Compute the distance between points in a 2D space defined by rapidity and phi coordinates.
Arguments
  • i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).
  • j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).
  • rapidity_array::Vector{Float64}: Array of rapidity coordinates.
  • phi_array::Vector{Float64}: Array of phi coordinates.
Returns
  • distance::Float64: The distance between the two points.
source
JetReconstruction.do_iB_recombination_step! Method
julia
do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)
Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the jet.
  • diB: The diB value.
source
JetReconstruction.do_ij_recombination_step! Function
julia
do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)
Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • jet_i: The index of the first jet to be recombined.
  • jet_j: The index of the second jet to be recombined.
  • dij: The distance between the two jets.
  • recombine=+: The recombination function to be used. Default is addition.
Returns
  • newjet_k: The index of the newly created jet.
Description
This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.
source
JetReconstruction.energy Method
julia
energy(p::PseudoJet)
Return the energy of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The energy of the PseudoJet.
source
JetReconstruction.eta Method
julia
eta(p::PseudoJet)
Compute the pseudorapidity (η) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.
Returns
  • The pseudorapidity (η) of the PseudoJet.
source
JetReconstruction.fast_findmin Method
julia
fast_findmin(dij, n)
Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.
Arguments
  • dij: An array of values.
  • n: The number of elements to consider in the dij array.
Returns
  • dij_min: The minimum value in the first n elements of the dij array.
  • best: The index of the minimum value in the dij array.
source
JetReconstruction.find_tile_neighbours! Method
julia
find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)
Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run
Arguments
  • tile_union: The tile union to which the neighbouring tiles will be added.
  • jetA: The first jet.
  • jetB: The second jet.
  • oldB: The old second jet.
  • tiling: The tiling information.
Returns
The number of neighbouring tiles added to the tile_union.
source
JetReconstruction.geometric_distance Method
julia
geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)
Compute the geometric distance between two points in the rap-phi plane.
Arguments
  • eta1::AbstractFloat: The eta coordinate of the first point.
  • phi1::AbstractFloat: The phi coordinate of the first point.
  • eta2::AbstractFloat: The eta coordinate of the second point.
  • phi2::AbstractFloat: The phi coordinate of the second point.
Returns
  • distance::Float64: The geometric distance between the two points.
source
JetReconstruction.get_algorithm_power_consistency Method
julia
get_algorithm_power_consistency(; p::Union{Real, Nothing}, algorithm::Union{JetAlgorithm, Nothing})
Get the algorithm and power consistency correct
This function checks the consistency between the algorithm and power parameters. If the algorithm is specified, it checks if the power parameter is consistent with the algorithm's known power. If the power parameter is not specified, it sets the power parameter based on the algorithm. If neither the algorithm nor the power parameter is specified, it throws an ArgumentError.
Arguments
  • p::Union{Real, Nothing}: The power value.
  • algorithm::Union{JetAlgorithm, Nothing}: The algorithm.
Returns
A named tuple of the consistent power and algorithm values.
Throws
  • ArgumentError: If the algorithm and power are inconsistent or if neither the algorithm nor the power is specified.
source
JetReconstruction.get_all_ancestors Method
julia
get_all_ancestors(idx, cs::ClusterSequence)
Recursively finds all ancestors of a given index in a ClusterSequence object.
Arguments
  • idx: The index of the jet for which to find ancestors.
  • cs: The ClusterSequence object containing the jet history.
Returns
An array of indices representing the ancestors of the given jet.
source
JetReconstruction.get_dij_dist Method
julia
get_dij_dist(nn_dist, kt2_1, kt2_2, R2)
Compute the dij metric distance between two jets.
Arguments
  • nn_dist: The nearest-neighbor distance between two jets.
  • kt2_1: The squared momentum metric value of the first jet.
  • kt2_2: The squared momentum metric value of the second jet.
  • R2: The jet radius parameter squared.
Returns
The distance between the two jets.
If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.
source
JetReconstruction.get_tile Method
julia
get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)
Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.
Arguments
  • tiling_setup: A TilingDef object that contains the tiling setup parameters.
  • eta: The eta value for which to calculate the tile index.
  • phi: The phi value for which to calculate the tile index.
Returns
  • ieta: The tile index along the eta direction.
  • iphi: The tile index along the phi direction.
source
JetReconstruction.get_tile_cartesian_indices Method
julia
get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)
Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)
Arguments
  • tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.
  • i_η::Int: The index of the tile in the η dimension.
  • i_ϕ::Int: The index of the tile in the ϕ dimension.
Returns
  • The linear index of the tile.
source
JetReconstruction.initial_history Method
julia
initial_history(particles)
Create an initial history for the given particles.
Arguments
  • particles: The initial vector of stable particles.
Returns
  • history: An array of HistoryElement objects.
  • Qtot: The total energy in the event.
source
JetReconstruction.insert! Method
julia
insert!(nextjet::TiledJet, jettomove::TiledJet)
Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list
Arguments
  • nextjet::TiledJet: The TiledJet object after which jettomove should be inserted.
  • jettomove::TiledJet: The TiledJet object to be inserted.
Example
source
JetReconstruction.is_ee Method
julia
is_ee(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a e+e- reconstruction algorithm.
Returns
true if the algorithm is a e+e- reconstruction algorithm, false otherwise.
source
JetReconstruction.is_pp Method
julia
is_pp(algorithm::JetAlgorithm.Algorithm)
Check if the algorithm is a pp reconstruction algorithm.
Returns
true if the algorithm is a pp reconstruction algorithm, false otherwise.
source
JetReconstruction.isvalid Method
julia
isvalid(t::TiledJet)
Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.
Arguments
  • t::TiledJet: The TiledJet object to check.
Returns
  • Bool: true if the TiledJet object is valid, false otherwise.
source
JetReconstruction.jet_ranks Method
julia
jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)
Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.
  • compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.
Returns
A dictionary mapping each jet index to its rank.
Note
This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.
source
JetReconstruction.m Method
julia
m(p::PseudoJet)
Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the invariant mass.
Returns
The invariant mass of the PseudoJet object.
source
JetReconstruction.m2 Method
julia
m2(p::PseudoJet)
Calculate the invariant mass squared (m^2) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.
Returns
  • The invariant mass squared (m^2) of the PseudoJet.
source
JetReconstruction.mag Method
julia
mag(p::PseudoJet)
Return the magnitude of the momentum of a PseudoJet, |p|.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the magnitude.
Returns
The magnitude of the PseudoJet object.
source
JetReconstruction.mass Method
julia
mass(p::PseudoJet)
Compute the invariant mass (alias for m(p)).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the mass.
Returns
  • The mass of the PseudoJet.
source
JetReconstruction.mass2 Function
Alias for m2 function
source
JetReconstruction.merge_steps Method
julia
merge_steps(clusterseq::ClusterSequence)
Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
Returns
  • merge_steps::Int: The number of merge steps.
source
JetReconstruction.phi Method
julia
phi(p::PseudoJet)
Compute the ϕ angle of a PseudoJet object p.
Note this function is a wrapper for phi_02pi(p).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
JetReconstruction.phi_02pi Method
julia
phi_02pi(p::PseudoJet)
Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.
Returns
  • The azimuthal angle of p in the range [0, 2π).
source
JetReconstruction.pt Method
julia
pt(p::PseudoJet)
Compute the scalar transverse momentum (pt) of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.
Returns
  • The transverse momentum (pt) of the PseudoJet.
source
JetReconstruction.pt2 Method
julia
pt2(p::PseudoJet)
Get the squared transverse momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The squared transverse momentum of the PseudoJet.
source
JetReconstruction.px Method
julia
px(p::PseudoJet)
Return the x-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The x-component of the momentum of the PseudoJet.
source
JetReconstruction.py Method
julia
py(p::PseudoJet)
Return the y-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The y-component of the momentum of the PseudoJet.
source
JetReconstruction.pz Method
julia
pz(p::PseudoJet)
Return the z-component of the momentum of a PseudoJet.
Arguments
  • p::PseudoJet: The PseudoJet object.
Returns
  • The z-component of the momentum of the PseudoJet.
source
JetReconstruction.rapidity Method
julia
rapidity(p::PseudoJet)
Compute the rapidity of a PseudoJet object.
Arguments
  • p::PseudoJet: The PseudoJet object for which to compute the rapidity.
Returns
The rapidity of the PseudoJet object.
source
JetReconstruction.reco_state Method
julia
reco_state(cs::ClusterSequence, pt_ranks; iteration=0)
This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.
Arguments
  • cs::ClusterSequence: The ClusterSequence object to update.
  • ranks: The ranks of the original clusters, that are inherited by peudojets
during the reconstruction process.
  • iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).
  • ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).
Returns
A dictionary representing a snapshot of the reconstruction state.
Details
The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.
source
JetReconstruction.rightneighbours Method
julia
rightneighbours(center::Int, tiling::Tiling)
Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the indices of the right neighbors.
source
JetReconstruction.set_momentum! Method
julia
set_momentum!(j::PseudoJet, px, py, pz, E)
Set the momentum components and energy of a PseudoJet object.
Arguments
  • j::PseudoJet: The PseudoJet object to set the momentum for.
  • px: The x-component of the momentum.
  • py: The y-component of the momentum.
  • pz: The z-component of the momentum.
  • E: The energy of the particle.
source
JetReconstruction.set_nearest_neighbours! Method
julia
set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})
This function sets the nearest neighbor information for all jets in the tiledjets vector.
Arguments
  • clusterseq::ClusterSequence: The cluster sequence object.
  • tiling::Tiling: The tiling object.
  • tiledjets::Vector{TiledJet}: The vector of tiled jets.
Returns
  • NNs::Vector{TiledJet}: The vector of nearest neighbor jets.
  • diJ::Vector{Float64}: The vector of diJ values.
The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.
Note: The diJ values are calculated as the kt distance multiplied by R^2.
source
JetReconstruction.setup_tiling Method
julia
setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat
This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.
Arguments
  • eta::Vector{T}: A vector of rapidities.
  • Rparam::AbstractFloat: The jet radius parameter.
Returns
  • tiling_setup: A TilingDef object containing the tiling setup parameters.
Description
The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.
source
JetReconstruction.surrounding Method
julia
surrounding(center::Int, tiling::Tiling)
Compute the surrounding indices of a given center index in a tiling.
Arguments
  • center::Int: The center index.
  • tiling::Tiling: The tiling object.
Returns
  • Surrounding: An object containing the surrounding indices.
source
JetReconstruction.tile_index Method
julia
tile_index(tiling_setup, eta::Float64, phi::Float64)
Compute the tile index for a given (eta, phi) coordinate.
Arguments
  • tiling_setup: The tiling setup object containing the tile size and number of tiles.
  • eta::Float64: The eta coordinate.
  • phi::Float64: The phi coordinate.
Returns
The tile index corresponding to the (eta, phi) coordinate.
source
JetReconstruction.tiledjet_remove_from_tiles! Method
julia
tiledjet_remove_from_tiles!(tiling, jet)
Remove a jet from the given tiling structure.
Arguments
  • tiling: The tiling structure from which the jet will be removed.
  • jet: The jet to be removed from the tiling structure.
Description
This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.
source
JetReconstruction.tiledjet_set_jetinfo! Method
julia
tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)
Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)
Arguments:
  • jet::TiledJet: The TiledJet object to set the information for.
  • clusterseq::ClusterSequence: The ClusterSequence object containing the jets.
  • tiling::Tiling: The Tiling object containing the tile information.
  • jets_index: The index of the jet in the ClusterSequence.
  • R2: The jet radius parameter squared.
  • p: The power to raise the pt2 value to.
This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.
Returns:
  • nothing
source
JetReconstruction.upd_nn_crosscheck! Method
julia
upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
JetReconstruction.upd_nn_nocross! Method
julia
upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)
Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).
Arguments
  • i::Int: The index of the particle to update and check against.
  • from::Int: The starting index of the range of particles to check against.
  • to::Int: The ending index of the range of particles to check against.
  • rapidity_array: An array containing the rapidity values of all particles.
  • phi_array: An array containing the phi values of the all particles.
  • R2: The squared jet distance threshold for considering a particle as a neighbour.
  • nndist: The array that stores the nearest neighbor distances.
  • nn: The array that stores the nearest neighbor indices.
source
JetReconstruction.upd_nn_step! Method
julia
upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)
Update the nearest neighbor information after a jet merge step.
Arguments:
  • i: Index of the first particle in the last merge step.
  • j: Index of the second particle in the last merge step.
  • k: Index of the current particle for which the nearest neighbour will be updated.
  • N: Total number of particles (currently vaild array indexes are [1:N]).
  • Nn: Number of nearest neighbors to consider.
  • kt2_array: Array of transverse momentum squared values.
  • rapidity_array: Array of rapidity values.
  • phi_array: Array of azimuthal angle values.
  • R2: Distance threshold squared for nearest neighbors.
  • nndist: Array of nearest neighbor geometric distances.
  • nn: Array of nearest neighbor indices.
  • nndij: Array of metric distances between particles.
This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.
source
JetReconstruction.η Function
julia
const η = eta
Alias for the pseudorapidity function, eta.
source
JetReconstruction.FourMomentum Type
Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.
source
JetReconstruction.HistoryElement Type
julia
struct HistoryElement
A struct holding a record of jet mergers and finalisations
Fields:
  • parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)
  • parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)
  • child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.
  • jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.
  • dij: The distance corresponding to the recombination at this stage of the clustering.
  • max_dij_so_far: The largest recombination distance seen so far in the clustering history.
source
JetReconstruction.HistoryElement Method
julia
HistoryElement(jetp_index)
Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.
Arguments
  • jetp_index: The index of the jetp.
Returns
A HistoryElement object.
source
JetReconstruction.JetWithAncestors Type
julia
struct JetWithAncestors
A struct representing a jet with its origin ancestors.
Fields
  • self::PseudoJet: The PseudoJet object for this jet.
  • jetp_index::Int: The index of the jet in the corresponding cluster sequence.
  • ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).
  • jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors
Note
This structure needs its associated cluster sequence origin to be useful.
source
JetReconstruction.Surrounding Type
julia
struct Surrounding{N}
Structure used for iterating over neighbour tiles.
Fields
  • indices::NTuple{N, Int}: A tuple of N integers representing the indices.
source
JetReconstruction.TiledJet Type
julia
struct TiledJet
TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.
Fields
  • id::Int: The ID of the jet.
  • eta::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • kt2::Float64: The transverse momentum squared of the jet.
  • NN_dist::Float64: The distance to the nearest neighbor.
  • jets_index::Int: The index of the jet in the jet array.
  • tile_index::Int: The index of the tile in the tile array.
  • dij_posn::Int: The position of this jet in the dij compact array.
  • NN::TiledJet: The nearest neighbor.
  • previous::TiledJet: The previous jet.
  • next::TiledJet: The next jet.
source
JetReconstruction.TiledJet Method
julia
TiledJet(id)
Constructs a TiledJet object with the given id and initializes its properties to zero.
Arguments
  • id: The ID of the TiledJet object.
Returns
A TiledJet object with the specified id and values set to zero or noTiledJet.
source
JetReconstruction.Tiling Type
julia
struct Tiling
The Tiling struct represents a tiling configuration for jet reconstruction.
Fields
  • setup::TilingDef: The tiling definition used for the configuration.
  • tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).
  • positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.
  • tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).
source
JetReconstruction.Tiling Method
julia
Tiling(setup::TilingDef)
Constructs a intial Tiling object based on the provided setup parameters.
Arguments
  • setup::TilingDef: The setup parameters for the tiling.
Returns
A Tiling object.
source
JetReconstruction.TilingDef Type
julia
struct TilingDef
A struct representing the definition of a spcific tiling scheme.
Fields
  • _tiles_eta_min::Float64: The minimum rapidity of the tiles.
  • _tiles_eta_max::Float64: The maximum rapidity of the tiles.
  • _tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).
  • _tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).
  • _n_tiles_eta::Int: The number of tiles across rapidity.
  • _n_tiles_phi::Int: The number of tiles across phi.
  • _n_tiles::Int: The total number of tiles.
  • _tiles_ieta_min::Int: The minimum rapidity tile index.
  • _tiles_ieta_max::Int: The maximum rapidity tile index.
Constructor
TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
+	_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)
Constructs a TilingDef object with the given parameters.
source
JetReconstruction.neighbour_tiles Type
julia
struct neighbour_tiles
A struct representing the neighbouring tiles.
A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
 X.X
-XXX
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source
JetReconstruction.rightmost_tiles Type
julia
struct rightmost_tiles
A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
+XXX
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source
JetReconstruction.rightmost_tiles Type
julia
struct rightmost_tiles
A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
 O.X
-OOO
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
  • n_η::Int: Number of η tiles
  • n_ϕ::Int: Number of ϕ tiles
  • start_η::Int: Centre η tile coordinate
  • start_ϕ::Int: Centre ϕ tile coordinate
source

-    
+OOO

Note, rapidity coordinate must be in range, ϕ coordinate wraps

Fields

  • n_η::Int: Number of η tiles

  • n_ϕ::Int: Number of ϕ tiles

  • start_η::Int: Centre η tile coordinate

  • start_ϕ::Int: Centre ϕ tile coordinate

source

+ \ No newline at end of file diff --git a/dev/lib/public.html b/dev/lib/public.html index 24b7317..fd7e4d4 100644 --- a/dev/lib/public.html +++ b/dev/lib/public.html @@ -8,29 +8,29 @@ - + - + - + -
JetReconstruction.jl

Appearance

Jet Reconstruction Public Documentation ​

Documentation for JetReconstruction.jl's public interfaces.

Index ​

Public Methods and Types ​

JetReconstruction.constituents Method
julia
constituents(j::PseudoJet, cs::ClusterSequence)

Get the constituents of a given jet in a cluster sequence.

Arguments

  • cs::ClusterSequence: The cluster sequence object.

  • j::PseudoJet: The jet for which to retrieve the constituents.

Returns

An array of PseudoJet objects representing the constituents of the given jet. (That is, the original clusters that were recombined to form this jet.)

source

JetReconstruction.ee_genkt_algorithm Method
julia
ee_genkt_algorithm(particles::Vector{T}; p = -1, R = 4.0,
+    
JetReconstruction.jl
Appearance
Jet Reconstruction Public Documentation ​
Documentation for JetReconstruction.jl's public interfaces.
Index ​
Public Methods and Types ​
JetReconstruction.constituents Method
julia
constituents(j::PseudoJet, cs::ClusterSequence)
Get the constituents of a given jet in a cluster sequence.
Arguments
  • cs::ClusterSequence: The cluster sequence object.
  • j::PseudoJet: The jet for which to retrieve the constituents.
Returns
An array of PseudoJet objects representing the constituents of the given jet. (That is, the original clusters that were recombined to form this jet.)
source
JetReconstruction.ee_genkt_algorithm Method
julia
ee_genkt_algorithm(particles::Vector{T}; p = -1, R = 4.0,
                    algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham,
-                   recombine = +) where {T}
Run an e+e- reconstruction algorithm on a set of initial particles.
Arguments
  • particles::Vector{T}: A vector of particles to be clustered.
  • p = 1: The power parameter for the algorithm. Not required / ignored for the Durham algorithm when it is set to 1.
  • R = 4.0: The jet radius parameter. Not required / ignored for the Durham algorithm.
  • algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham: The specific jet algorithm to use.
  • recombine: The recombination scheme to use. Defaults to +.
Returns
  • The result of the jet clustering as a ClusterSequence object.
Notes
This is the public interface to the e+e- jet clustering algorithm. The function will check for consistency between the algorithm and the power parameter as needed. It will then prepare the internal EDM particles for the clustering itself, and call the actual reconstruction method _ee_genkt_algorithm.
If the algorithm is Durham, p is set to 1 and R is nominally set to 4.
Note that unlike pp reconstruction the algorithm has to be specified explicitly.
source
JetReconstruction.exclusive_jets Method
julia
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing, T = LorentzVectorCyl)
Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.
  • njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Note: Either dcut or njets must be provided (but not both).
Returns
  • An array of T objects representing the exclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl)
Exceptions
  • ArgumentError: If neither dcut nor njets is provided.
  • ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.
  • ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.
Examples
julia
exclusive_jets(clusterseq, dcut = 20.0)
-exclusive_jets(clusterseq, njets = 3, T = PseudoJet)
source
JetReconstruction.final_jets Function
Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)
source
JetReconstruction.final_jets Function
Specialisation for final jets from LorentzVectors (TODO: merge into more general function)
source
JetReconstruction.final_jets Function
julia
final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)
This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.
Arguments
  • jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.
  • ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.
Returns
A vector of FinalJet objects that satisfy the transverse momentum condition.
source
JetReconstruction.inclusive_jets Method
julia
inclusive_jets(clusterseq::ClusterSequence; ptmin = 0.0, T = LorentzVectorCyl)
Return all inclusive jets of a ClusterSequence with pt > ptmin.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • ptmin::Float64 = 0.0: The minimum transverse momentum (pt) threshold for the inclusive jets.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Returns
An array of T objects representing the inclusive jets.
Description
This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl).
Example
julia
inclusive_jets(clusterseq; ptmin = 10.0)
source
JetReconstruction.jet_reconstruct Method
julia
jet_reconstruct(particles; p = -1, algorithm = nothing, R = 1.0, recombine = +, strategy = RecoStrategy.Best)
Reconstructs jets from a collection of particles using a specified algorithm and strategy
Arguments
  • particles: A collection of particles used for jet reconstruction.
  • p::Union{Real, Nothing} = -1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).
  • algorithm::Union{JetAlgorithm.Algorithm, Nothing} = nothing: The algorithm to use for jet reconstruction.
  • R=1.0: The jet radius parameter.
  • recombine=+: The recombination scheme used for combining particles.
  • strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.
Returns
A cluster sequence object containing the reconstructed jets and the merging history.
Details
particles argument
Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.
recombine argument
The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E-scheme.
Consitency of p, algorithm and R arguments
If an algorithm is explicitly specified the p value should be consistent with it or nothing. If the algorithm is one where p can vary, then it has to be given, along with the algorithm.``
If the p parameter is passed and algorithm=nothing, then pp-type reconstruction is implied (i.e., AntiKt, CA, Kt or GenKt will be used, depending on the value of p).
When an algorithm has no R dependence the R parameter is ignored.
Example
julia
jet_reconstruct(particles; p = -1, R = 0.4)
+                   recombine = +) where {T}
Run an e+e- reconstruction algorithm on a set of initial particles.
Arguments
  • particles::Vector{T}: A vector of particles to be clustered.
  • p = 1: The power parameter for the algorithm. Not required / ignored for the Durham algorithm when it is set to 1.
  • R = 4.0: The jet radius parameter. Not required / ignored for the Durham algorithm.
  • algorithm::JetAlgorithm.Algorithm = JetAlgorithm.Durham: The specific jet algorithm to use.
  • recombine: The recombination scheme to use. Defaults to +.
Returns
  • The result of the jet clustering as a ClusterSequence object.
Notes
This is the public interface to the e+e- jet clustering algorithm. The function will check for consistency between the algorithm and the power parameter as needed. It will then prepare the internal EDM particles for the clustering itself, and call the actual reconstruction method _ee_genkt_algorithm.
If the algorithm is Durham, p is set to 1 and R is nominally set to 4.
Note that unlike pp reconstruction the algorithm has to be specified explicitly.
source
JetReconstruction.exclusive_jets Method
julia
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing, T = LorentzVectorCyl)
Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.
  • njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Note: Either dcut or njets must be provided (but not both).
Returns
  • An array of T objects representing the exclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl)
Exceptions
  • ArgumentError: If neither dcut nor njets is provided.
  • ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.
  • ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.
Examples
julia
exclusive_jets(clusterseq, dcut = 20.0)
+exclusive_jets(clusterseq, njets = 3, T = PseudoJet)
source
JetReconstruction.final_jets Function
Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)
source
JetReconstruction.final_jets Function
julia
final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)
This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.
Arguments
  • jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.
  • ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.
Returns
A vector of FinalJet objects that satisfy the transverse momentum condition.
source
JetReconstruction.final_jets Function
Specialisation for final jets from LorentzVectors (TODO: merge into more general function)
source
JetReconstruction.inclusive_jets Method
julia
inclusive_jets(clusterseq::ClusterSequence; ptmin = 0.0, T = LorentzVectorCyl)
Return all inclusive jets of a ClusterSequence with pt > ptmin.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
  • ptmin::Float64 = 0.0: The minimum transverse momentum (pt) threshold for the inclusive jets.
  • T = LorentzVectorCyl: The return type used for the selected jets.
Returns
An array of T objects representing the inclusive jets.
Description
This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.
Valid return types are LorentzVectorCyl and PseudoJet (N.B. this will evolve in the future to be any subtype of FourMomemntumBase; currently unrecognised types will return LorentzVectorCyl).
Example
julia
inclusive_jets(clusterseq; ptmin = 10.0)
source
JetReconstruction.jet_reconstruct Method
julia
jet_reconstruct(particles; p = -1, algorithm = nothing, R = 1.0, recombine = +, strategy = RecoStrategy.Best)
Reconstructs jets from a collection of particles using a specified algorithm and strategy
Arguments
  • particles: A collection of particles used for jet reconstruction.
  • p::Union{Real, Nothing} = -1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).
  • algorithm::Union{JetAlgorithm.Algorithm, Nothing} = nothing: The algorithm to use for jet reconstruction.
  • R=1.0: The jet radius parameter.
  • recombine=+: The recombination scheme used for combining particles.
  • strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.
Returns
A cluster sequence object containing the reconstructed jets and the merging history.
Details
particles argument
Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.
recombine argument
The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E-scheme.
Consitency of p, algorithm and R arguments
If an algorithm is explicitly specified the p value should be consistent with it or nothing. If the algorithm is one where p can vary, then it has to be given, along with the algorithm.``
If the p parameter is passed and algorithm=nothing, then pp-type reconstruction is implied (i.e., AntiKt, CA, Kt or GenKt will be used, depending on the value of p).
When an algorithm has no R dependence the R parameter is ignored.
Example
julia
jet_reconstruct(particles; p = -1, R = 0.4)
 jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
 jet_reconstruct(particles; algorithm = JetAlgorithm.Durham)
-jet_reconstruct(particles; algorithm = JetAlgorithm.GenKt, p = 0.5, R = 1.0)
source
JetReconstruction.loadjets! Method
julia
loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)
Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.
Everything that was already in jets is not affected as we only use push! on it.
Example
julia
# Load jets from two files into one array
+jet_reconstruct(particles; algorithm = JetAlgorithm.GenKt, p = 0.5, R = 1.0)
source
JetReconstruction.loadjets! Method
julia
loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)
Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.
Everything that was already in jets is not affected as we only use push! on it.
Example
julia
# Load jets from two files into one array
 jets = []
 loadjets!("myjets1.dat", jets)
-loadjets!("myjets2.dat", jets)
source
JetReconstruction.loadjets Method
julia
loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)
Load jets from a file.
Arguments
  • filename: The name of the file to load jets from.
  • splitby: The delimiter used to split the data in the file. Default is isspace.
  • constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
  • VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
  • A vector of VT objects representing the loaded jets.
source
JetReconstruction.n_exclusive_jets Method
julia
n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)
Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.
  • dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia
n_exclusive_jets(clusterseq, dcut = 20.0)
source
JetReconstruction.plain_jet_reconstruct Method
julia
plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where T
Perform pp jet reconstruction using the plain algorithm.
Arguments
  • particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • p::Int=-1: The integer value used for jet reconstruction.
  • R::Float64=1.0: The radius parameter used for jet reconstruction.
  • recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
jets = plain_jet_reconstruct(particles; p = -1, R = 0.4)
-jets = plain_jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
source
JetReconstruction.read_final_state_particles Method
julia
read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)
Reads final state particles from a file and returns them as a vector of type T.
Arguments
  • fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
  • maxevents=-1: The maximum number of events to read. -1 means all events will be read.
  • skipevents=0: The number of events to skip before an event is included.
  • T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source
JetReconstruction.savejets Method
julia
savejets(filename, jets; format="px py pz E")
Save jet data to a file.
Arguments
  • filename: The name of the file to save the jet data to.
  • jets: An array of jet objects to save.
  • format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
  • "E" or "energy": Jet energy
  • "px": Momentum along the x-axis
  • "py": Momentum along the y-axis
  • "pz": Momentum along the z-axis
  • "pt2": Square of the transverse momentum
  • "phi": Azimuth angle
  • "rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source
JetReconstruction.tiled_jet_reconstruct Method
julia
tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Arguments
  • particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
  • p::Union{Real, Nothing} = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)
source
JetReconstruction.ClusterSequence Type
julia
struct ClusterSequence
A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • power::Float64: The power value used for the clustering algorithm (not that this value is always stored as a Float64 to be type stable)
  • R::Float64: The R parameter used for the clustering algorithm.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
  • n_initial_jets::Int: The initial number of particles used for exclusive jets.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
  • Qtot::Any: The total energy of the event.
source
JetReconstruction.ClusterSequence Method
julia
ClusterSequence(algorithm::JetAlgorithm.Algorithm, p::Real, R::Float64, strategy::RecoStrategy.Strategy, jets, history, Qtot)
Construct a ClusterSequence object.
Arguments
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • p::Real: The power value used for the clustering algorithm.
  • R::Float64: The R parameter used for the clustering algorithm.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence.
  • Qtot::Any: The total energy of the event.
source
JetReconstruction.EEjet Type
julia
struct EEjet
The EEjet struct is a 4-momentum object used for the e+e jet reconstruction routines.
Fields
  • px::Float64: The x-component of the jet momentum.
  • py::Float64: The y-component of the jet momentum.
  • pz::Float64: The z-component of the jet momentum.
  • E::Float64: The energy of the jet.
  • _cluster_hist_index::Int: The index of the cluster histogram.
  • _p2::Float64: The squared momentum of the jet.
  • _inv_p::Float64: The inverse momentum of the jet.
source
JetReconstruction.FinalJet Type
julia
struct FinalJet
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
  • rap::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • pt::Float64: The transverse momentum of the jet.
source
JetReconstruction.FinalJets Type
julia
struct FinalJets
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
  • jetid::Int64: The ID of the jet.
  • jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.
source
JetReconstruction.PseudoJet Type
julia
mutable struct PseudoJet <: FourMomentum
The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy component of the momentum.
  • _cluster_hist_index::Int: The index of the cluster history.
  • _pt2::Float64: The squared transverse momentum.
  • _inv_pt2::Float64: The inverse squared transverse momentum.
  • _rap::Float64: The rapidity.
  • _phi::Float64: The azimuthal angle.
source
JetReconstruction.PseudoJet Method
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)
Constructs a PseudoJet object with the given momentum components and energy.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
Returns
A PseudoJet object.
source
JetReconstruction.PseudoJet Method
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
+loadjets!("myjets2.dat", jets)
source
JetReconstruction.loadjets Method
julia
loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)
Load jets from a file.
Arguments
  • filename: The name of the file to load jets from.
  • splitby: The delimiter used to split the data in the file. Default is isspace.
  • constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
  • VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
  • A vector of VT objects representing the loaded jets.
source
JetReconstruction.n_exclusive_jets Method
julia
n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)
Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
  • clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.
  • dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia
n_exclusive_jets(clusterseq, dcut = 20.0)
source
JetReconstruction.plain_jet_reconstruct Method
julia
plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where T
Perform pp jet reconstruction using the plain algorithm.
Arguments
  • particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • p::Int=-1: The integer value used for jet reconstruction.
  • R::Float64=1.0: The radius parameter used for jet reconstruction.
  • recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
jets = plain_jet_reconstruct(particles; p = -1, R = 0.4)
+jets = plain_jet_reconstruct(particles; algorithm = JetAlgorithm.Kt, R = 1.0)
source
JetReconstruction.read_final_state_particles Method
julia
read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)
Reads final state particles from a file and returns them as a vector of type T.
Arguments
  • fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
  • maxevents=-1: The maximum number of events to read. -1 means all events will be read.
  • skipevents=0: The number of events to skip before an event is included.
  • T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source
JetReconstruction.savejets Method
julia
savejets(filename, jets; format="px py pz E")
Save jet data to a file.
Arguments
  • filename: The name of the file to save the jet data to.
  • jets: An array of jet objects to save.
  • format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
  • "E" or "energy": Jet energy
  • "px": Momentum along the x-axis
  • "py": Momentum along the y-axis
  • "pz": Momentum along the z-axis
  • "pt2": Square of the transverse momentum
  • "phi": Azimuth angle
  • "rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source
JetReconstruction.tiled_jet_reconstruct Method
julia
tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}
Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
This code will use the k_t algorithm types, operating in (rapidity, φ) space.
It is not necessary to specify both the algorithm and the p (power) value. If both are given they must be consistent or an exception is thrown.
Arguments
  • particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
  • p::Union{Real, Nothing} = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
  • algorithm::Union{JetAlgorithm, Nothing} = nothing: The explicit jet algorithm to use.
  • R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
  • recombine::Function = +: The recombination function used for combining pseudojets.
Returns
  • Vector{PseudoJet}: A vector of reconstructed jets.
Example
julia
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)
source
JetReconstruction.ClusterSequence Type
julia
struct ClusterSequence
A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • power::Float64: The power value used for the clustering algorithm (not that this value is always stored as a Float64 to be type stable)
  • R::Float64: The R parameter used for the clustering algorithm.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
  • n_initial_jets::Int: The initial number of particles used for exclusive jets.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
  • Qtot::Any: The total energy of the event.
source
JetReconstruction.ClusterSequence Method
julia
ClusterSequence(algorithm::JetAlgorithm.Algorithm, p::Real, R::Float64, strategy::RecoStrategy.Strategy, jets, history, Qtot)
Construct a ClusterSequence object.
Arguments
  • algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
  • p::Real: The power value used for the clustering algorithm.
  • R::Float64: The R parameter used for the clustering algorithm.
  • strategy::RecoStrategy.Strategy: The strategy used for clustering.
  • jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence.
  • history::Vector{HistoryElement}: The branching history of the cluster sequence.
  • Qtot::Any: The total energy of the event.
source
JetReconstruction.EEjet Type
julia
struct EEjet
The EEjet struct is a 4-momentum object used for the e+e jet reconstruction routines.
Fields
  • px::Float64: The x-component of the jet momentum.
  • py::Float64: The y-component of the jet momentum.
  • pz::Float64: The z-component of the jet momentum.
  • E::Float64: The energy of the jet.
  • _cluster_hist_index::Int: The index of the cluster histogram.
  • _p2::Float64: The squared momentum of the jet.
  • _inv_p::Float64: The inverse momentum of the jet.
source
JetReconstruction.FinalJet Type
julia
struct FinalJet
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
  • rap::Float64: The rapidity of the jet.
  • phi::Float64: The azimuthal angle of the jet.
  • pt::Float64: The transverse momentum of the jet.
source
JetReconstruction.FinalJets Type
julia
struct FinalJets
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
  • jetid::Int64: The ID of the jet.
  • jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.
source
JetReconstruction.PseudoJet Type
julia
mutable struct PseudoJet <: FourMomentum
The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy component of the momentum.
  • _cluster_hist_index::Int: The index of the cluster history.
  • _pt2::Float64: The squared transverse momentum.
  • _inv_pt2::Float64: The inverse squared transverse momentum.
  • _rap::Float64: The rapidity.
  • _phi::Float64: The azimuthal angle.
source
JetReconstruction.PseudoJet Method
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)
Constructs a PseudoJet object with the given momentum components and energy.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
Returns
A PseudoJet object.
source
JetReconstruction.PseudoJet Method
julia
PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
     _cluster_hist_index::Int,
-    pt2::Float64)
Constructs a PseudoJet object with the given momentum components and energy and history index.
Arguments
  • px::Float64: The x-component of the momentum.
  • py::Float64: The y-component of the momentum.
  • pz::Float64: The z-component of the momentum.
  • E::Float64: The energy.
  • _cluster_hist_index::Int: The cluster history index.
  • pt2::Float64: The transverse momentum squared.
Returns
A PseudoJet object.
source

-    
+    pt2::Float64)

Constructs a PseudoJet object with the given momentum components and energy and history index.

Arguments

  • px::Float64: The x-component of the momentum.

  • py::Float64: The y-component of the momentum.

  • pz::Float64: The z-component of the momentum.

  • E::Float64: The energy.

  • _cluster_hist_index::Int: The cluster history index.

  • pt2::Float64: The transverse momentum squared.

Returns

A PseudoJet object.

source

+ \ No newline at end of file diff --git a/dev/lib/visualisation.html b/dev/lib/visualisation.html index 7ba8761..f2aa990 100644 --- a/dev/lib/visualisation.html +++ b/dev/lib/visualisation.html @@ -8,11 +8,11 @@ - + - + - + @@ -26,7 +26,7 @@ elevation = 0.5, framerate = 5, ancestors = false, - Module = Makie)

Animate the jet reconstruction process and save it as a video file.

Arguments

  • cs::ClusterSequence: The cluster sequence object containing the jets.

  • filename: The name of the output video file.

Optional Arguments

  • barsize_phi=0.1: The size of the bars in the phi direction.

  • barsize_y=0.1: The size of the bars in the y direction.

  • colormap=:glasbey_category10_n256: The colormap to use for coloring the jets.

  • perspective=0.5: The perspective of the plot.

  • azimuth=2.7: The azimuth angle of the plot.

  • elevation=0.5: The elevation angle of the plot.

  • framerate=5: The framerate of the output video.

  • end_frames=0: The number of static frames to show at the end of the animation. This can be useful to show the final state of the jets for a longer time.

  • title=nothing: The title to add to the plot.

  • ancestors=false: Whether to include ancestors of the jets in the animation. When true the ancestors of the jets will be plotted as well, as height zero bars, with the same colour as the jet they are ancestors of.

  • Module: The plotting module to use. Default is Makie.

For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.

Returns

  • fig: The figure object representing the final frame.

source

JetReconstruction.jetsplot Method

jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
+            Module = Makie)

Animate the jet reconstruction process and save it as a video file.

Arguments

  • cs::ClusterSequence: The cluster sequence object containing the jets.

  • filename: The name of the output video file.

Optional Arguments

  • barsize_phi=0.1: The size of the bars in the phi direction.

  • barsize_y=0.1: The size of the bars in the y direction.

  • colormap=:glasbey_category10_n256: The colormap to use for coloring the jets.

  • perspective=0.5: The perspective of the plot.

  • azimuth=2.7: The azimuth angle of the plot.

  • elevation=0.5: The elevation angle of the plot.

  • framerate=5: The framerate of the output video.

  • end_frames=0: The number of static frames to show at the end of the animation. This can be useful to show the final state of the jets for a longer time.

  • title=nothing: The title to add to the plot.

  • ancestors=false: Whether to include ancestors of the jets in the animation. When true the ancestors of the jets will be plotted as well, as height zero bars, with the same colour as the jet they are ancestors of.

  • Module: The plotting module to use. Default is Makie.

For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.

Returns

  • fig: The figure object representing the final frame.

source

JetReconstruction.jetsplot Method

jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
 using CairoMakie # use any other Makie that you have here
 
 jetsplot([object1, object2, object3], [[1], [2, 3]])

The example above plots object1 as a separate jet in one colour and object2 and object3 together in another colour.

This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:

import CairoMakie
@@ -36,7 +36,7 @@
 jetsplot(my_objects, my_colour_arrays, Module=GLMakie)
 
 using WGLMakie
-jetsplot(my_objects, my_colour_arrays, Module=Main) #default

source

JetReconstruction.jetsplot Method
julia
jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
+jetsplot(my_objects, my_colour_arrays, Module=Main) #default

source

JetReconstruction.jetsplot Method
julia
jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)

Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.

Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);

# example
 using CairoMakie # use any other Makie that you have here
 jetsplot([object1, object2, object3], cluster_sequence_I_got_from_jet_reconstruct; Module=CairoMakie)

This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:

import CairoMakie
 jetsplot(my_objects, cs, Module=CairoMakie)
@@ -45,8 +45,8 @@
 jetsplot(my_objects, cs, Module=GLMakie)
 
 using WGLMakie
-jetsplot(my_objects, cs, Module=Main) #default

source

- +jetsplot(my_objects, cs, Module=Main) #default

source

+ \ No newline at end of file