Volume 632, December 2019
|Number of page(s)||18|
|Section||Catalogs and data|
|Published online||27 November 2019|
II. Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars⋆
Institute of Astronomy, V. N. Karazin Kharkiv National University, 35 Sumska Str., Kharkiv, Ukraine
2 Institute of Radio Astronomy of the National Academy of Sciences of Ukraine, 4 Mystetstv Str., Kharkiv, Ukraine
3 INAF – Osservatorio Astronomico di Capodimonte, Salita Moiariello, 16, 80131 Napoli, Italy
4 European Southern Observatory, Karl-Schwarschild-Str. 2, 85748 Garching, Germany
5 INAF – Osservatorio Astrofisico di Arcetri, Largo Enrico Fermi 5, 50125 Firenze, Italy
6 School of Physics and Astronomy, Sun Yat-sen University, 2 Daxue Road, Tangjia, Zhuhai, Guangdong 519082, PR China
7 DARK, Niels Bohr Institute, Copenhagen University, Lyngbyvej 2, 2100 Copenhagen, Denmark
8 Kapteyn Astronomical Institute, University of Groningen, PO Box 800, 9700 AV Groningen, The Netherlands
9 Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Netherlands
10 INAF – Osservatorio Astronomico di Padova, Via dell’Osservatorio 5, 35122 Padova, Italy
11 Shanghai Astronomical Observatory (SHAO), Nandan Road 80, Shanghai 200030, PR China
12 College of Physics of Jilin University, Qianjin Street 2699, Changchun 130012, PR China
Accepted: 8 October 2019
Context. The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) is aimed at finding as many previously undiscovered gravitational lensed quasars as possible in the Kilo Degree Survey. This is the second paper of this series where we present a new, automatic object-classification method based on the machine learning technique.
Aims. The main goal of this paper is to build a catalogue of bright extragalactic objects (galaxies and quasars) from the KiDS Data Release 4, with minimum stellar contamination and preserving the completeness as much as possible. We show here that this catalogue represents the perfect starting point to search for reliable gravitationally lensed quasar candidates.
Methods. After testing some of the most used machine learning algorithms, decision-tree-based classifiers, we decided to use CatBoost, which was specifically trained with the aim of creating a sample of extragalactic sources that is as clean of stars as possible. We discuss the input data, define the training sample for the classifier, give quantitative estimates of its performances, and finally describe the validation results with Gaia DR2, AllWISE, and GAMA catalogues.
Results. We built and made available to the scientific community the KiDS Bright EXtraGalactic Objects catalogue (KiDS-BEXGO), specifically created to find gravitational lenses but applicable to a wide number of scientific purposes. The KiDS-BEXGO catalogue is made of ≈6 million sources classified as quasars (≈200 000) and galaxies (≈5.7 M) up to r < 22m. To demonstrate the potential of the catalogue in the search for strongly lensed quasars, we selected ≈950 “Multiplets”: close pairs of quasars or galaxies surrounded by at least one quasar. We present cutouts and coordinates of the 12 most reliable gravitationally lensed quasar candidates. We showed that employing a machine learning method decreases the stellar contaminants within the gravitationally lensed candidates, comparing the current results to the previous ones, presented in the first paper from this series.
Conclusions. Our work presents the first comprehensive identification of bright extragalactic objects in KiDS DR4 data, which is, for us, the first necessary step towards finding strong gravitational lenses in wide-sky photometric surveys, but has also many other more general astrophysical applications.
Key words: gravitational lensing: strong / methods: data analysis / surveys / catalogs / quasars: general / galaxies: general
The KiDS Bright EXtraGalactic Objects (KiDS-BEXGO) catalogue is also available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (126.96.36.199) or via http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/632/A56
© ESO 2019
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