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.
We measured the emission lines in the spaxel spectra of MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) galaxies in order to determine the abundance distributions therein. It has been suggested that the strength of the low-ionization lines, R2, N2, and S2, may be increased (relative to Balmer lines) in (some) spaxel spectra of the MaNGA survey due to a contribution of the radiation of the diffuse ionized gas. Consequently, the abundances derived from the spaxel spectra through strong-line methods may suffer from large errors. We examined this expectation by comparing the behaviour of the line intensities and the abundances estimated through different calibrations for slit spectra of H II regions in nearby galaxies, for fibre spectra from the Sloan Digital Sky Survey (SDSS), and for spaxel spectra of the MaNGA survey. We found that the S2 strength is increased significantly in the fibre and spaxel spectra. The mean enhancement changes with metallicity and can be as large as a factor of ∼ 2. The mean distortion of R2 and N2 is less than a factor of ∼ 1.3. This suggests that Kaufmann et al.'s demarcation line between active galactic nuclei (AGNs) and H II regions in the Baldwin, Phillips, & Terlevich (BPT) diagram is a useful criterion to reject spectra with significantly distorted strengths of the N2 and R2 lines. We find that the three-dimensional R calibration, which uses the N2 and R2 lines, produces reliable abundances in the MaNGA galaxies. The one-dimensional N2 calibration produces either reliable or wrong abundances depending on whether excitation and N/O abundance ratio in the target region (spaxel) are close to or differ from those parameters in the calibrating points located close to the calibration relation. We then determined abundance distributions within the optical radii in the discs of 47 MaNGA galaxies. The optical radii of the galaxies were estimated from the surface brightness profiles constructed based on the MaNGA observations.
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