peakfit: improve numerical convolution treatment

configs/peakfit/tune0.yml

@@ -12,12 +12,12 @@
 input_path:   './actions-stash/input/icefit/dqcd_2018_test/flat/muon/generalTracks/JPsi'
 output_name:  'tune0'
 
-years:    [2018] #[2016, 2017, 2018]
-fitrange: [2.91, 3.29] # Nominal fit window (GeV)
+years:    [2018]         # [2016, 2017, 2018]
+fitrange: [2.91, 3.29]   # Nominal fit window (GeV)
 systematics: ['Nominal'] # ['Nominal', 'nVertices_Up', 'nVertices_Down']
 
 # List of systematic variations active
-variations: ['DEFAULT'] #, 'SYMMETRIC-SIGNAL_CB_asym_RBW', 'MASS-WINDOW-DOWN', 'MASS-WINDOW-UP']
+variations: ['DEFAULT']  #, 'SYMMETRIC-SIGNAL_CB_asym_RBW', 'MASS-WINDOW-DOWN', 'MASS-WINDOW-UP']
 
 num_cpus: 0 # put 0 for automatic, set manually to 1 if problems (ray not used with 1)
 
@@ -30,9 +30,9 @@ fit:
     func:    'generic_conv_pdf'
     args:
       norm:     True
-      Nmin:     256 # minimum number of function samples for convolution integral
-      xfactor:  0.2 # extra domain extension factor for convolution integral (0.2 gives 20%)
-
+      Nmin:     128   # minimum number of function samples for convolution integral
+      xfactor:  0.3   # extra domain extension factor for convolution integral (0.2 gives +-20%)
+      
       pdf_pair:   ['CB_pdf', 'asym_RBW_pdf']  # name of pdfs to be convoluted
       par_index:  [[0,1,2,3], [0,4,5]]        # parameter indices per pdf (use same index for shared parameters)
 
@@ -130,5 +130,5 @@ techno:
   tol: 0.1               # Default 0.1, https://iminuit.readthedocs.io/en/stable/reference.html#iminuit.Minuit.tol
 
   cov_eps: 0.0           # Covariance matrix post-regularization (added to diagonal) (set to 0 for none, can bias!)
-
+  
   draw_mnmatrix: False   # Draw MINOS profile likelihood scan 2D contours (very slow)

configs/peakfit/tune2.yml

@@ -12,12 +12,12 @@
 input_path:   './actions-stash/input/icefit/dqcd_2018_test/flat/muon/generalTracks/JPsi'
 output_name:  'tune2'
 
-years:    [2018] #[2016, 2017, 2018]
-fitrange: [2.91, 3.29] # Nominal fit window (GeV)
+years:    [2018]         # [2016, 2017, 2018]
+fitrange: [2.91, 3.29]   # Nominal fit window (GeV)
 systematics: ['Nominal'] # ['Nominal', 'nVertices_Up', 'nVertices_Down']
 
 # List of systematic variations active
-variations: ['DEFAULT'] #, 'MASS-WINDOW-DOWN', 'MASS-WINDOW-UP']
+variations: ['DEFAULT']  #, 'MASS-WINDOW-DOWN', 'MASS-WINDOW-UP']
 
 num_cpus: 0 # put 0 for automatic, set manually to 1 if problems (ray not used with 1)
 
@@ -121,5 +121,5 @@ techno:
   tol: 0.1              # Default 0.1, https://iminuit.readthedocs.io/en/stable/reference.html#iminuit.Minuit.tol
 
   cov_eps: 0.0          # Covariance matrix post-regularization (added to diagonal) (set to 0 for none, can bias!)
-
+  
   draw_mnmatrix: False  # Draw MINOS profile likelihood scan 2D contours (very slow)

icefit/icepeak.py

@@ -287,7 +287,9 @@ def generic_conv_pdf(x: np.ndarray, par: np.ndarray, pdf_pair: List[str],
     xp = highres_x(x=x, xfactor=xfactor, Nmin=Nmin)
     f0 = func0(x=xp, par=f0_param, norm=False)
     f1 = func1(x=xp, par=f1_param, norm=False)
-    yp = np.convolve(a=f0, v=f1, mode='same')
+
+    dx = xp[1] - xp[0]
+    yp = np.convolve(a=f0, v=f1, mode='same') * dx
     y  = interpolate.interp1d(xp, yp)(x)
 
     if norm:
@@ -296,14 +298,14 @@ def generic_conv_pdf(x: np.ndarray, par: np.ndarray, pdf_pair: List[str],
     return y
 
 @numba.njit
-def highres_x(x: np.ndarray, xfactor: float=0.2, Nmin: int=256):
+def highres_x(x: np.ndarray, xfactor: float=0.5, Nmin: int=256):
     """
     Extend range and sampling of x.
     
     Args:
-        x:       Array of values.
-        xfactor: Domain extension factor (default is 0.2).
-        Nmin:    Minimum number of samples (default is 256).
+        x:       Array of values
+        xfactor: Domain extension factor (e.g. 0.2 gives +- 20%)
+        Nmin:    Minimum number of samples
     
     Returns:
         np.ndarray: Extended high-resolution array.

icefit/peakfit.py

@@ -38,7 +38,7 @@
 
 import ray
 
-__VERSION__ = 0.06
+__VERSION__ = 0.07
 __AUTHOR__  = '[email protected]'