J/A+A/624/A13 KiDS DR3 QSO catalog (Nakoneczny+, 2019)
Catalog of quasars from the Kilo-Degree Survey Data Release 3.
Nakoneczny S., Bilicki M., Soalrz A., Pollo A., Maddox N., Spiniello C.,
Brescia M., Napolitano N.R.
<Astron. Astrophys. 624, A13 (2019)>
=2019A&A...624A..13N 2019A&A...624A..13N (SIMBAD/NED BibCode)
ADC_Keywords: Surveys ; Active gal. nuclei ; QSOs ; Galaxy catalogs ;
Galaxies, photometry ; Colors
Keywords: catalogues - surveys - quasars: general -
large-scale structure of Universe - methods: data anlysis -
methods: observational
Abstract:
We present a catalog of quasars selected from broad-band photometric
ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The
QSOs are identified by the random forest (RF) supervised machine
learning model, trained on Sloan Digital Sky Survey (SDSS) DR14
spectroscopic data. We first cleaned the input KiDS data of entries
with excessively noisy, missing or otherwise problematic measurements.
Applying a feature importance analysis, we then tune the algorithm and
identify in the KiDS multiband catalog the 17 most useful features for
the classification, namely magnitudes, colors, magnitude ratios, and
the stellarity index. We used the t-SNE algorithm to map the
multidimensional photometric data onto 2D planes and compare the
coverage of the training and inference sets. We limited the inference
set to r<22 to avoid extrapolation beyond the feature space covered
by training, as the SDSS spectroscopic sample is considerably
shallower than KiDS. This gives 3.4 million objects in the final
inference sample, from which the random forest identified 190,000
quasar candidates. Accuracy of 97% (percentage of correctly classified
objects), purity of 91% (percentage of true quasars within the objects
classified as such), and completeness of 87% (detection ratio of all
true quasars), as derived from a test set extracted from SDSS and not
used in the training, are confirmed by comparison with external
spectroscopic and photometric QSO catalogs overlapping with the KiDS
footprint. The robustness of our results is strengthened by number
counts of the quasar candidates in the r band, as well as by their
mid-infrared colors available from the Wide-field Infrared Survey
Explorer (WISE). An analysis of parallaxes and proper motions of our
QSO candidates found also in Gaia DR2 suggests that a probability cut
of pQSO>0.8 is optimal for purity, whereas pQSO>0.7 is
preferable for better completeness. Our study presents the first
comprehensive quasar selection from deep high-quality KiDS data and
will serve as the basis for versatile studies of the QSO population
detected by this survey. We publicly release the resulting catalog at
http://kids.strw.leidenuniv.nl/DR3/quasarcatalog.php, and the code at
https://github.com/snakoneczny/kids-quasars
Description:
The released catalog includes all the KiDS DR3 objects which were
subject to the classification (the inference set). Apart from basic
columns taken from KiDS DR3 (IDs and coordinates), we add the
resulting probabilities for each class in QSO, STAR and GALAXY
columns. The final classification, given by the class with the highest
probability, is provided in the CLASS column. To obtain the full
catalog of all the 190k quasars candidates, one has to query
CLASS ≡ "QSO", while the high purity catalog is accessible by taking
QSO>0.8 (see the paper for more details on quality cuts).
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
kids3qso.dat 81 3392580 Objects from KiDS DR3 limited at R<22,
with 3-class (QSO-STAR-GALAXY) photometric
classification results
kids3qso.fits 2880 69505 fits version of the catalog
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See also:
II/347 : KiDS-ESO-DR3 multi-band source catalog (de Jong+, 2017)
VII/279 : SDSS quasar catalog: twelfth data release (Paris+, 2017)
Byte-by-byte Description of file: kids3qso.dat
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Bytes Format Units Label Explanations
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1- 25 A25 --- ID Source identifier (KiDS JHHMMSS.ss+DDMMSS.ss)
27- 36 F10.6 deg RAdeg Right ascension (J2000.0)
38- 47 F10.6 deg DEdeg Declination (J2000.0)
49- 54 A6 --- Class Source classification (GALAXY - QSO - STAR)
56- 63 F8.6 --- Pgal Source probability of being a galaxy
65- 72 F8.6 --- PQSO Source probability of being a quasar
74- 81 F8.6 --- Pstar Source probability of being a star
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Acknowledgements:
Szymon Nakoneczny [NCBJ, Poland], szymon.nakoneczny(at)ncbj.gov.pl
(End) Szymon Nakoneczny [NCBJ, Poland], Patricia Vannier [CDS] 23-Feb-2019