KiDS-SQuaD

Abstract

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.

Machine learning technique for morphological classification of galaxies from the SDSS

“…The accuracy of each applied machine learning method for the two-parameter classification is written in the left corner of each panel. photometric redshift estimation (Mu et al 2020), prediction of galaxy halo masses (Calderon & Berlind 2019), gravitational lenses search (Khramtsov et al 2019b), automating discovery and classification of variable stars (Bloom et al 2012), and for analyzing huge observational surveys, for example the Zwicky Transient Facility (Mahabal et al 2019), or finding planets and exocomets from the Kepler and TESS surveys (Kohler 2018).…”

“…photometric redshift estimation(Mu et al 2020), prediction of galaxy halo masses(Calderon & Berlind 2019), gravitational lenses search(Khramtsov et al 2019b), automating discovery and classification of variable stars(Bloom et al 2012), and for analyzing huge observational surveys, for example the Zwicky Transient Facility(Mahabal et al 2019), or finding planets and exocomets from the Kepler and TESS surveys(Kohler 2018).Among other recent examples, we also note the determination of physical properties of galaxies (density, metallicity, column density, ionization) from their emission-line spectra with support-vector machine algorithms employed and developed in a new GAME numerical code byUcci et al (2017); prediction of the HI content of massive galaxies at z < 2 based on optical photometry data (SDSS) and environmental parameters, which was performed byRafieferantsoa et al (2018) with regressors and deep neural network (see also examples of applications of the AstroML Python module 5 for the large-scale observational extragalactic surveys 3 ). In addition to the traditional approach for classifying the galaxy types automatically in the optical range, the machine learning methods also demonstrate a strong utility for classifying the radio galaxy types and peculiarities(Aniyan & Thorat (2017);Alger et al (2018); Wagner et al (2019);Lukic et al (2019), andRalph et al (2019)).…”

Verification of Machine Learning Methods for Binary Morphological Classification of Galaxies From SDSS

“…Bottom: Late type (Spirals). work (DL) to the images of redshift-limited (z < 0.1) sample of ∼ 300 000 galaxies from the SDSS DR9 by [Khramtsov et al (2019)] with the same aim of binary morphological classification has been shown, for example, that DL method can classify rounded sources as Ellipticals but it can not catch the spectral energy distribution properties of galaxies more clearly than SVM, trained on the photometric features of galaxies.…”

VEXAS: VISTA EXtension to Auxiliary Surveys

“…The VISTA-Kilo-Degree Infrared Galaxy Survey (VIKING, Sutherland 2012;Edge et al 2013) covers 1500 square degrees overlapping the Kilo Degree Survey (KiDS, Kuijken et al 2015) for the optical counterpart, ensuring a uniform coverage on ∼1350 deg 2 with intermediate depth in nine bands (grizY JHK s, with limiting magnitude J = 21 in the Vega system), enabling accurate photometric redshift measurements for weak-lensing studies (Hildebrandt et al 2017). The science goals of the KiDS and VIKING include the following: the characterisation of galaxy clusters up to z ∼ 1 (Maturi et al 2019), the search for high-z objects (Venemans et al 2015) and new strong gravitational lenses (Spiniello et al 2018;Petrillo et al 2019;Khramtsov et al 2019), and the study of galaxy structural parameters and stellar masses for a statistically large sample of galaxies (Roy et al 2018).…”