In this notebook the cross-matching procedure applied by LaMassa et al. 2016 (LM hereafter) to multi-wavelength catalogs will be reproduced. Particularly, we will cross-match the x-ray data from XMM-Newton AO 13 cycle to UKIDSS near-infrared. The goal here is to verify whether we can recover the very same results therein, showing us our algorithm for Maximum Likelihood Estimator to be correct.
The UKIDSS catalog here in use is the same used in LM: UKIDSS-LAS Data
Release 8, primary objects, cleaned from spurious/noisy detections;
"?apermag3" magnitude measurements and accordingly errors were
retrieved. The table file ukidss.fits here used can be taken from
my github repository.
The LaMassa catalog --from where we’ll take the x-ray sources-- was downloaded from CDS.
MLE is applied by LM to find the correct --or most possible-- counterpart to their x-ray sources. MLE was first proposed by Sutherland & Saunders in 1992 and is being adopted as a better alternative to the simplistic nearest-neighbour algorithm.
What MLE does is to estimate how probable a given counterpart candidate is to be real counterpart from a source in its vicinity. The method was developed having in mind that multiple candidates can be nearby in the (RA,Dec) sky-projected plan. Accordingly, the method includes the ancillary magnitudes as a third component to help differentiating background objects from candidate(s).
Consider the situation where there is a source "S" (which was observed by instrument "A") and in the vicinities, within a distance "da", of S there are \(N\) objects ("N1", "N2", …, "NN") that were observed by a different instrument ("B"). Also observed using "B", but distant a bit further from "S". there are \(M\) objects ("M1", "M2", …, "MM") that can not be related to "S", but will be of our help further on. The "M" objects lie beyond the distance "da" and before distance "db", and \(db > da\). We want now to answer the following question: which of the objects observed by "B" is in fact "S" (observed by a different instrument)? Before coming with any answer, we are taught that instruments "A" and "B" suffer from different physical effects that lead to uncorrelated errors and different image resolutions when registering the pictures; which means that "S" and its (true) counterpart may not be one over the other, but shifted by some amount.
The distance "da" from "S" is considered to be "vicinity", and objects
inside this distance are considered, a priori, candidates to the
(true) counterpart. Such objects will be called ancillary objects. The
objects from sample M will be called background objects, they
compose the sample of objects observed by "B" definitely not candidate
to be "S" counterpart.
The MLE method will eventually give a score called Reliability (\(R\)) to each of the candidates. Such score --reliability-- is the probability of being the true counterpart, and is given by: \(\[ R_j = \frac{LR_j}{\sum_j{LR_j}+(1-Q)} ]\)
The central figure in MLE is the likelihood ratio, \(LR\): \(\[ LR_j = \frac{q(m) f(r)}{n(m)} ]\) .
\(f(r)\) is the prior regarding the position of the candidate object relative to the source. Typically, \(f(r)\) is modelled as a bidimensional Gaussian with \(\sigma\) being the quadrature sum of source’s positional error and objects' average positional error: \(\[ f(r) = \frac{1}{2 \pi \sigma} exp^{-r^2/2\sigma^2} ; ]\) \(\[ \sigma = \frac{1}{2}\Big[\sqrt{\sigma^2_{\alpha_S} + \sigma^2_{\delta_S}} + \sqrt{\sigma^2_{\alpha_O} + \sigma^2_{\delta_O}}\Big\) \]]
\(q(m)\) is the likelihood of the object being a (good) candidate regarding its magnitude. It is computed by drawing the ancillary objects normalized magnitude distribution and subtracting from it the background objects normalized magnitude distribution.
Finally, \(n(m)\) is the surface density of background objects with magnitude \(m\).It is computed by counting the number of background objects per magnitude bin per square-degree; normalized by the number of objects.
Let’s put it all together to build an algorithm.
To compute MLE quantities we need to define the background and ancillary samples. To do that we have to define the search radius (\(r_s\)) --from where the ancillary sample will come out-- and the inner & outer radii (\(r_i\), \(r_o\)) for the background sample.
There are different ways to estimate the (best) search radius. Typically, the instrument’s (nominal) error radius, systematic plus statistical, is used, as [LaMassa et al. 2016]. Timlin et al. 2016 have used the Rayleigh Criterion to estimate such radius, considering then a physical limitation on resolving close by objects; similarly, the overall PSF (FWHM) is a valid estimator. Another way of estimating \(r_s\), data driven, is by directly estimating the typical distance between the objects in each catalog.
Analogously, we have to define the inner and outer radii, from the primary source, of the annulus defining the background region. Trully speaking, the background region does not need to be drawn as an annulus centered centered in the source, but that is a straightforward, generic choice for sampling background sources. It is important to notice that the background region should avoid other sources' ancillary sample, which is to say that the (annulus) region should not intersect with another source’s search area.
-
Estimate samples radii
-
(ancillary) search radius: \(r_{s}\)
-
background (annulus) radii: \(r_{i} \lt r_{o}\)
Once we have the radii defined we cross-match the catalogs to define the ancillary and background samples; At this point, each source has two lists of objects linked to it:
-
Source
-
ancillary sample (within Rs)
-
background sample (between Ri and Ro)
But before looping through each primary source, we may define \(q(m)\) and \(n(m)\) as they are rather globally defined functions. And after we have \(q(m)\) we may estimate \(Q\).
-
Estimate magnitude distributions
-
\(n(m)\): background surface brightness distribution
-
\(q(m)\): ancillary brightness distribution
-
\(Q\): expected counterpart recover rate
The radial profile \(f(r) \propto \sigma^{-1} \exp^{-r^2/\sigma^2}\) is ideally defined for each source, for \(\sigma\) is a function of the source' and ancillary objects' positional errors, \(\sigma_s\) and \(\sigma_o\), resp.: \(\[ \sigma = \sqrt{\frac{\sigma_s^2 + \sigma_o^2}{2}} ]\)
If the positional errors are well behaved --i.e, their dispersion is small--, we may approximate \(f(r)\) as a global function. We may consider \(\sigma_s\) and \(\sigma_o\) as the mean of the respective positional errors.
-
Compute mean positional errors
-
primary sources catalog
-
ancillary objects
-
define \(f(r)\)
The LR-threshold, \(LR_{th}\), is the minimum value an ancillary object may score to be considered a counterpart candidate. There are different ways to compute \(LR_{th}\), the simplest one is based on the reliability parameter in a assintotic case: consider there is only one ancillary object within the search radius around a source; in this case we would expect such object to be the true source' counterpart. Considering the Reliability parameter, \(R\) a probability score, \(R_j=0.5\) is the minimal (reasonable) value for such parameter so that the object can be considered a candidate. Using the definition of \(R\) above we should have: \(\[ 0.5 = \frac{LR_{th}}{LR_{th} + (1-Q)} ]\)
\(\[ LR_{th} = \frac{0.5(Q-1)}{-0.5} ]\)
\(\[ LR_{th} = 1-Q ]\)
Now that we have all the ingredients in place we may visit each primary source' neighbourhood and evaluate each ancillary object.
For each source, * Loop over the respective ancillary sample: * evaluate each object’s \(LR\) * remove objects with \(LR_j < LR_{th}\) * Sum all ancillaries' \(LR_j\) * Loop over all candidates: * compute \(R_j\)
The highest \(R_j\) is said to be the true counterpart.
*In[1]:*
!ls **Out[1]:*
Untitled.ipynb s82x: chandra.dat ReadMe xmmao10.dat xmmao13.dat uks82: Readme.md ukidss_results3_2_34_51_409.fits v0.1 ukidss.fits ukidss_results3_2_34_51_409.fits.gz v0.2
*In[2]:*
import booq*In[3]:*
from booq.table import ATable*In[4]:*
cat_lm = ATable.read('s82x/xmmao13.dat',readme='s82x/ReadMe',format='ascii.cds')*In[5]:*
cat_lm*Out[5]:*
<ATable masked=True length=2862> Seq ObsID RAdeg DEdeg e_Pos DistNN ExtFlag InXMM InChandra FSoft e_FSoft CtSoft SoftDetml FHard e_FHard CtHard HardDetml FFull e_FFull CtFull FullDetml logLSoft logLHard logLFull RejS SDSS RASdeg DESdeg RelS rS CoaddS umag e_umag gmag e_gmag rmag e_rmag imag e_imag zmag e_zmag SpecID Class zsp r_zsp WISE RAWdeg DEWdeg e_RAWdeg e_DEWdeg RelW rW W1mag e_W1mag W1SNR W2mag e_W2mag W2SNR W3mag e_W3mag W3SNR W4mag e_W4mag W4SNR ExtW RejW UKIDSS RAUdeg DEUdeg RelU rU Ymag e_Ymag Jmag e_Jmag Hmag e_Hmag Kmag e_Kmag RejU VHS RAVdeg DEVdeg RelV rV JVmag e_JVmag HVmag e_HVmag KVmag e_KVmag RejV GALEX RAGdeg DEGdeg e_NUVPos e_FUVPos RelG rG NUV e_NUV FUV e_FUV FIRST RAFdeg DEFdeg rF F1.4GHz e_F1.4GHz RAHdeg DEHdeg rH F250 e_F250 F350 e_F350 F500 e_F500 XMMAO10CP ChCP CPCoord deg deg arcsec arcsec 1e-17 W / m2 1e-17 W / m2 ct 1e-17 W / m2 1e-17 W / m2 ct 1e-17 W / m2 1e-17 W / m2 ct [10-7W] [10-7W] [10-7W] deg deg arcsec mag mag mag mag mag mag mag mag mag mag deg deg arcsec arcsec arcsec mag mag mag mag mag mag mag mag deg deg arcsec mag mag mag mag mag mag mag mag deg deg arcsec mag mag mag mag mag mag deg deg arcsec arcsec arcsec mag mag mag mag deg deg arcsec mJy mJy deg deg arcsec mJy mJy mJy mJy mJy mJy int64 int64 float64 float64 float64 float64 int64 str8 str19 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 str3 str19 float64 float64 float64 float64 str3 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 str19 str6 float64 int64 str19 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 str3 str3 str12 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 str2 str12 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 str2 str19 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 str22 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 str1 str2 int64 2359 742830101 14.097 0.166 4.7 515.8 0 no no 1.61 0.52 14.92 16.53 0.0 0.0 0.0 0.0 5.5 1.66 22.32 14.48 -999.0 -999.0 -999.0 no 1237663784203584096 14.097 0.166 0.82 1.97 no 22.98 0.3 22.76 0.12 22.94 0.19 22.49 0.18 22.67 0.57 -- -- -999.0 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- no no -- -- -- -- -- -- -- -- -- -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 2360 742830101 14.115 -0.353 3.5 171.7 0 no no 1.68 0.38 30.6 33.04 0.0 0.0 0.0 0.0 6.39 1.26 51.67 33.41 -999.0 -999.0 -999.0 no 1237663783666712774 14.115 -0.352 0.98 3.42 no 21.87 0.24 20.39 0.03 18.93 0.02 18.33 0.01 17.9 0.03 -- -- -999.0 -- J005627.57-002108.1 14.11 -0.35 0.08 0.08 0.9857 3.66 14.82 0.04 31.3 -- -- -- -- -- -- -- -- -- no no 433834423123 14.115 -0.352 0.936 3.51 17.92 0.02 17.25 0.02 16.42 0.03 15.64 0.02 no 472469436506 14.115 -0.352 0.964 3.39 17.2 0.02 16.41 0.02 15.6 0.02 no -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 2361 742830101 14.115 -0.16 5.8 291.7 0 no no 2.02 0.5 26.23 26.34 7.29 2.78 15.92 7.4 8.61 1.74 49.88 33.0 -999.0 -999.0 -999.0 no 1237666339187917237 14.115 -0.16 0.81 1.01 no 23.41 0.69 22.61 0.16 21.97 0.13 21.49 0.13 20.63 0.24 -- -- -999.0 -- J005627.65-000937.0 14.12 -0.16 0.13 0.13 0.9522 1.27 15.68 0.05 22.7 -- -- -- -- -- -- -- -- -- no no 433834423565 14.115 -0.16 0.826 1.08 -- -- 19.49 0.15 18.64 0.2 17.88 0.15 no 472469432977 14.115 -0.16 0.831 1.24 19.9 0.17 19.06 0.2 17.93 0.16 no 2915238670318438835 14.115 -0.16 1.31 -- 0.76 1.61 22.64 0.32 -- -- FIRST J005627.5-000936 14.115 -0.16 1.17 8.64 0.11 -- -- -- -- -- -- -- -- -- -- -- 0 2362 742830101 14.142 -0.442 3.4 49.1 0 no no 0.95 0.26 22.03 20.45 0.0 0.0 0.0 0.0 3.48 0.83 36.45 20.87 -999.0 -999.0 -999.0 no -- -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- -- -999.0 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- no no -- -- -- -- -- -- -- -- -- -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- no 2918862660643598899 14.141 -0.441 0.95 -- 0.79 6.21 22.09 0.17 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 2363 742830101 14.154 -0.448 2.1 49.1 0 no no 7.25 0.63 177.33 389.08 17.33 2.73 73.38 62.06 24.68 1.83 273.08 402.95 43.8 44.17 44.33 no 1237666338651046108 14.155 -0.447 1.0 1.79 no 20.39 0.05 19.99 0.02 19.91 0.02 19.41 0.02 19.11 0.04 779144212938516480 QSO 0.474 11 J005637.10-002649.8 14.15 -0.45 0.07 0.07 0.9952 1.87 14.69 0.03 33.0 -- -- -- -- -- -- -- -- -- no no 433836362494 14.155 -0.447 0.977 1.81 18.22 0.04 17.87 0.04 17.17 0.05 16.25 0.03 no 472469438358 14.155 -0.447 0.987 1.84 17.81 0.03 16.96 0.03 16.06 0.03 no -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 2364 742830101 14.162 -0.357 5.2 145.3 0 no no 0.69 0.2 19.99 17.08 0.0 0.0 0.0 0.0 2.16 0.59 28.08 14.19 44.25 0.0 0.0 no 1237663783666712923 14.163 -0.357 0.97 2.12 no 21.3 0.08 21.12 0.03 21.29 0.05 20.9 0.05 20.78 0.16 -- QSO 1.858 1 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- no no -- -- -- -- -- -- -- -- -- -- -- -- -- no 472469436589 14.163 -0.357 0.878 1.98 20.23 0.23 -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 2365 742830101 14.162 0.038 4.0 224.1 0 no no 1.64 0.44 22.75 21.26 0.0 0.0 0.0 0.0 3.95 1.15 25.07 12.93 44.44 0.0 0.0 no 1237663784203583728 14.162 0.039 0.97 6.14 no 19.76 0.03 19.45 0.01 19.41 0.01 19.19 0.01 19.19 0.05 780344880525240320 QSO 1.577 11 J005638.88+000222.2 14.16 0.04 0.17 0.18 0.934 6.24 16.2 0.07 15.7 -- -- -- -- -- -- -- -- -- no no 433832563084 14.162 0.04 0.843 6.21 18.88 0.05 18.5 0.06 17.78 0.1 17.45 0.1 no 472447126149 14.162 0.04 0.907 6.28 18.28 0.05 17.6 0.05 17.44 0.11 no 6476213785057036472 14.162 0.04 0.64 -- 0.92 6.44 21.25 0.08 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 2366 742830101 14.163 -0.4 3.8 29.8 0 no no 1.24 0.38 17.36 17.26 0.0 0.0 0.0 0.0 2.96 0.98 20.61 9.98 -999.0 -999.0 -999.0 no -- -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- -- -999.0 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- no no 433836363120 14.164 -0.399 0.877 2.61 -- -- -- -- -- -- 18.11 0.19 no 472469437466 14.164 -0.399 0.888 2.46 20.4 0.27 -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 2367 742830101 14.17 -0.244 2.7 283.6 0 no no 0.89 0.2 33.01 26.73 0.0 0.0 0.0 0.0 2.68 0.61 44.18 19.81 -999.0 -999.0 -999.0 no 1237663783666713322 14.17 -0.244 0.97 0.88 no 23.92 0.64 22.77 0.14 22.15 0.12 22.05 0.15 21.94 0.48 -- -- -999.0 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- no no -- -- -- -- -- -- -- -- -- -- -- -- -- no 472469459857 14.17 -0.244 0.969 0.72 -- -- 19.3 0.24 -- -- no -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 5211 747440101 27.985 -0.049 3.5 298.5 0 no no 0.94 0.17 42.03 47.44 1.8 0.63 14.24 9.81 2.99 0.48 60.55 58.79 43.87 0.0 44.38 no 1237666407917813801 27.985 -0.049 0.99 0.22 no 19.57 0.03 19.6 0.01 19.46 0.01 19.49 0.02 19.58 0.07 787013142797379584 QSO 1.166 11 J015156.46-000255.6 27.99 -0.05 0.09 0.09 0.9779 0.41 15.61 0.04 25.2 14.26 0.04 26.1 11.35 0.17 6.5 8.26 0.22 5.0 no no 433832463003 27.985 -0.049 0.948 0.56 18.77 0.06 18.81 0.12 18.07 0.13 17.28 0.1 no 472468136661 27.985 -0.049 0.953 0.55 19.07 0.06 18.33 0.06 17.49 0.08 no 3779155742589725600 27.985 -0.049 0.52 0.73 0.99 1.66 20.05 0.03 22.94 0.18 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 5212 747440101 27.987 -0.564 3.6 289.2 0 no no 1.02 0.36 11.78 13.05 0.0 0.0 0.0 0.0 4.57 1.28 22.74 17.28 -999.0 -999.0 -999.0 no -- -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- -- -999.0 -- J015156.95-003350.2 27.99 -0.56 0.14 0.14 0.9854 1.84 15.98 0.05 21.4 15.37 0.1 11.1 12.85 0.54 2.0 9.18 -- -2.0 no no 433836104298 27.987 -0.563 0.917 3.39 19.31 0.11 -- -- 18.05 0.12 17.41 0.09 no -- -- -- -- -- -- -- -- -- -- -- no 3779155742587621381 27.988 -0.564 0.68 -- 0.95 3.21 21.54 0.09 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 5213 747440101 27.994 0.063 3.3 138.7 0 no no 0.99 0.26 22.0 20.86 0.0 0.0 0.0 0.0 3.06 0.73 30.38 20.14 -999.0 -999.0 -999.0 no 1237663784209613117 27.994 0.064 0.97 2.22 no 22.22 0.18 21.43 0.04 20.81 0.03 20.23 0.03 19.78 0.08 -- -- -999.0 -- J015158.57+000348.8 27.99 0.06 0.13 0.13 0.9856 2.31 15.93 0.05 22.2 15.64 0.11 9.6 12.09 0.3 3.6 9.39 -- -0.8 no no 433832462768 27.994 0.064 0.945 2.19 19.05 0.08 18.48 0.09 17.79 0.1 16.93 0.07 no 472447967913 27.994 0.064 0.971 2.12 18.54 0.04 17.74 0.04 17.03 0.06 no 3784609320263485369 27.994 0.064 0.7 1.06 0.89 2.26 22.77 0.16 24.21 0.38 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 5214 747440101 27.997 0.134 2.7 167.0 0 no no 0.9 0.2 30.32 38.29 0.0 0.0 0.0 0.0 2.44 0.49 36.95 35.57 44.55 0.0 44.98 no 1237663784209678565 27.997 0.134 0.96 3.21 no 22.35 0.19 21.74 0.05 21.7 0.06 21.67 0.08 21.06 0.21 4765066332620910592 QSO 2.223 11 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- no no -- -- -- -- -- -- -- -- -- -- -- -- -- no 472447965676 27.997 0.134 0.911 3.2 20.56 0.22 -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 5215 747440101 28.0 -0.415 2.5 174.1 0 no no 1.15 0.25 28.81 43.1 0.0 0.0 0.0 0.0 3.03 0.64 34.1 33.55 -999.0 -999.0 -999.0 no 1237663783672807520 28.0 -0.415 0.99 2.38 no 19.81 0.03 17.28 0.01 16.0 0.01 15.27 0.01 14.8 0.01 -- -- -999.0 -- J015200.06-002454.0 28.0 -0.42 0.04 0.04 0.9934 2.37 12.6 0.02 46.3 12.55 0.02 45.0 11.7 0.21 5.1 9.14 -- -0.2 no no 433836104605 28.0 -0.415 0.978 2.29 14.06 0.0 13.49 0.0 13.04 0.0 12.8 0.0 no 472468147807 28.0 -0.415 0.991 2.25 13.52 0.0 12.94 0.0 12.72 0.0 no 3779155742587623450 28.0 -0.415 0.74 -- 0.93 1.55 22.7 0.17 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 5216 747440101 28.005 -0.137 3.0 83.6 0 no no 0.87 0.21 24.67 29.19 0.0 0.0 0.0 0.0 2.31 0.53 29.77 27.15 -999.0 -999.0 -999.0 no 1237666407917813942 28.005 -0.137 0.97 0.6 no 22.17 0.2 22.28 0.09 21.81 0.09 21.58 0.1 21.19 0.26 -- -- -999.0 -- J015201.15-000813.2 28.0 -0.14 0.25 0.26 0.978 0.62 16.91 0.09 12.0 16.13 0.17 6.6 12.48 -- 0.2 9.14 -- -0.4 no no -- -- -- -- -- -- -- -- -- -- -- -- -- no 472468139171 28.005 -0.137 0.968 0.3 19.91 0.12 19.04 0.12 18.27 0.15 no 3779155742587627301 28.005 -0.137 1.65 -- 0.75 1.1 23.85 0.39 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 5217 747440101 28.007 -0.463 3.5 141.3 0 no no 0.77 0.25 15.35 14.8 0.0 0.0 0.0 0.0 2.29 0.64 20.32 17.07 -999.0 -999.0 -999.0 no 1237666407380943336 28.007 -0.464 0.96 2.13 no 23.05 0.48 22.84 0.16 22.13 0.13 21.66 0.13 21.01 0.25 -- -- -999.0 -- J015201.74-002748.6 28.01 -0.46 0.13 0.13 0.9201 1.87 15.94 0.05 21.4 15.13 0.08 13.9 12.74 0.54 2.0 8.65 -- 1.8 no no 433836104787 28.007 -0.464 0.931 2.2 -- -- 19.28 0.19 18.34 0.15 17.64 0.12 no 472468149254 28.007 -0.464 0.786 1.94 19.41 0.07 18.61 0.08 17.67 0.09 no 3779155742587622758 28.007 -0.463 1.27 -- 0.88 1.62 22.82 0.24 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 5218 747440101 28.011 0.371 3.6 309.4 0 no no 0.53 0.16 15.05 16.46 0.0 0.0 0.0 0.0 1.41 0.43 18.01 13.26 -999.0 -999.0 -999.0 no -- 28.012 0.371 0.86 4.22 yes 25.09 0.44 24.3 0.11 23.89 0.1 22.89 0.07 22.81 0.27 -- -- -999.0 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- no no -- -- -- -- -- -- -- -- -- -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- no -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 5219 747440101 28.024 -0.263 3.1 242.2 0 no no 0.85 0.25 16.62 18.57 0.0 0.0 0.0 0.0 2.53 0.71 22.43 15.99 -999.0 -999.0 -999.0 no 1237663783672807718 28.024 -0.263 0.99 1.88 no 21.31 0.08 21.39 0.04 21.02 0.04 20.89 0.05 20.3 0.11 -- -- -999.0 -- J015205.80-001547.2 28.02 -0.26 0.15 0.15 0.9823 2.3 16.14 0.06 19.4 15.57 0.11 10.2 12.18 -- 1.5 9.08 0.52 2.1 no no 433834205134 28.024 -0.263 0.757 1.84 -- -- 18.93 0.14 18.42 0.18 17.62 0.15 no 472468142881 28.024 -0.263 0.787 1.72 19.15 0.06 18.65 0.08 17.74 0.09 no 3779155742589722957 28.024 -0.262 0.67 0.64 0.98 2.78 21.28 0.07 22.19 0.11 -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0 5220 747440101 28.044 0.134 4.1 167.0 0 no no 0.79 0.22 20.02 17.82 0.0 0.0 0.0 0.0 2.14 0.57 24.03 17.67 -999.0 -999.0 -999.0 no 1237663784209678589 28.043 0.135 0.97 3.59 no 20.58 0.12 19.8 0.03 18.78 0.02 18.32 0.02 18.05 0.05 -- -- -999.0 -- J015210.30+000804.8 28.04 0.13 0.11 0.12 0.9773 3.51 15.69 0.04 25.2 15.45 0.1 10.6 12.16 -- 1.4 8.9 -- 0.5 no no 433830737812 28.043 0.135 0.911 3.94 18.55 0.05 17.98 0.05 17.29 0.06 16.63 0.06 no 472447965668 28.043 0.135 0.946 3.74 17.88 0.02 17.22 0.03 16.56 0.04 no -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- 0
*In[6]:*
cat_uk = ATable.read('uks82/ukidss.fits',columns=['RA','DEC'])*In[15]:*
cat_uk.metatable*Out[15]:*
description unit ucd dtype nil colname RA unknown () float64 None DEC unknown () float64 None
*In[17]:*
from astropy.coordinates import Angle
ra = Angle(cat_uk['RA'],'rad').to('deg')
dec= Angle(cat_uk['DEC'],'rad').to('deg')*In[18]:*
cat_uk['RA'] = ra
cat_uk['DEC'] = dec*In[21]:*
cat_uk*Out[21]:*
<ATable length=3501552> RA DEC deg deg float64 float64 344.989744243 1.2399620675 345.072243834 1.24008030105 345.02833517 1.240398902 345.02904828 1.24094234799 345.045858353 1.24102154323 345.009139562 1.24146907595 344.901459725 1.24213322914 344.993263786 1.24286858721 344.881007618 1.24328732306 ... ... 316.424155675 1.24810153286 316.430048762 1.24855215438 316.444278524 1.24909158423 316.457856242 1.24973021976 316.288146807 1.24395895055 316.478105197 1.24376225754 316.351084815 1.24526706361 316.333799484 1.24668968512 316.28118499 1.24718442192 316.311320316 1.24787590201
*In[22]:*
from booq import plot*Out[22]:*
/home/chbrandt/.conda/envs/booq/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: The bokeh.charts API has moved to a separate 'bkcharts' package. This compatibility shim will remain until Bokeh 1.0 is released. After that, if you want to use this API you will have to install the bkcharts package explicitly. warn(message)
*In[ ]:*