Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4

Abstract

We present a catalog of quasars with their corresponding redshifts derived from the photometric Kilo-Degree Survey (KiDS) Data Release 4. We achieved it by training machine learning (ML) models, using optical ugri and near-infrared ZYJHKs bands, on objects known from Sloan Digital Sky Survey (SDSS) spectroscopy. We define inference subsets from the 45 million objects of the KiDS photometric data limited to 9-band detections, based on a feature space built from magnitudes and their combinations. We show that projections of the high-dimensional feature space on two dimensions can be successfully used, instead of the standard color-color plots, to investigate the photometric estimations, compare them with spectroscopic data, and efficiently support the process of building a catalog. The model selection and fine-tuning employs two subsets of objects: those randomly selected and the faintest ones, which allowed us to properly fit the bias versus variance trade-off. We tested three ML models: random forest (RF), XGBoost (XGB), and artificial neural network (ANN). We find that XGB is the most robust and straightforward model for classification, while ANN performs the best for combined classification and redshift. The ANN inference results are tested using number counts, Gaia parallaxes, and other quasar catalogs that are external to the training set. Based on these tests, we derived the minimum classification probability for quasar candidates which provides the best purity versus completeness trade-off: p(QSOcand) > 0.9 for r < 22 and p(QSOcand) > 0.98 for 22 < r < 23.5. We find 158 000 quasar candidates in the safe inference subset (r < 22) and an additional 185 000 candidates in the reliable extrapolation regime (22 < r < 23.5). Test-data purity equals 97% and completeness is 94%; the latter drops by 3% in the extrapolation to data fainter by one magnitude than the training set. The photometric redshifts were derived with ANN and modeled with Gaussian uncertainties. The test-data redshift error (mean and scatter) equals 0.009 ± 0.12 in the safe subset and −0.0004 ± 0.19 in the extrapolation, averaged over a redshift range of 0.14 < z < 3.63 (first and 99th percentiles). Our success of the extrapolation challenges the way that models are optimized and applied at the faint data end. The resulting catalog is ready for cosmology and active galactic nucleus (AGN) studies.

Automated algorithms to build Active Galactic Nuclei classifiers

“…The photometric redshifts were obtained from the Kilo-Degree-Survey (KiDS) by Kuijken et al (2019) 7 . Crossmatching the sample of 25599 AGN plus 7262 galaxies with ∼ 2 million KiDS sources, which includes quasars from Nakoneczny et al (2021) and bright galaxies from Bilicki et al (2021), we obtained 260 galaxies and 710 AGN with photometric redshifts. The crossmatch was done between the optical SDSS coordinates and the KiDS coordinates using a 1 arcsec radius.…”

Photometric redshift-aided classification using ensemble learning

“…Machine learning offers a promising alternative to the colour-colour and template fitting methods, for two main reasons: (i) the full range of photometric information in a source SED can be made use of; (ii) once a machine learning model is trained, it can be computationally inexpensive to apply it to new samples. While there are various ways in which machine learning can be applied to astronomical data, the three most common applications are in source classification (e.g., Elting et al 2008;Kurcz et al 2016;Krakowski et al 2016;Bai et al 2019;Clarke et al 2020), physical property estimation (e.g., Ucci et al 2017;Bonjean et al 2019;Delli Veneri et al 2019;Simet et al 2019;Mucesh et al 2021), and photometric redshift estimation (e.g., Fotopoulou & Paltani 2018;Sadeh et al 2019;Salvato et al 2019;Carvajal et al 2021;Nakoneczny et al 2021).…”

Predicting the redshift of gamma-ray loud AGNs using Supervised Machine Learning: Part 2

“…This can, in turn, enhance our knowledge about the cosmic evolution of blazars (Singal et al 2012(Singal et al , 2014Singal 2015;Singal et al 2013a;Chiang et al 1995;Ackermann et al 2015;Singal et al 2013b;Ackermann et al 2012), the structure of the magnetic field in the intergalactic medium, (Marcotulli et al 2020;Venters & Pavlidou 2013;Fermi-LAT Collaboration et al 2018) as well as constraining cosmological parameters (Domínguez et al 2019;Petrosian 1976;Singal et al 2013b). In recent years the number of studies focusing on photometric redshift estimation of high redshift AGN, using a machine learning (MLapproach, has increased significantly (Jones & Singal 2017;Cavuoti et al 2014;Fotopoulou & Paltani 2018;Logan & Fotopoulou 2020;Yang et al 2017;Zhang et al 2019;Curran 2020;Nakoneczny et al 2020;Pasquet-Itam & Pasquet 2018;Jones & Singal 2017). This is primarily due to the availability of large data sets from all-sky surveys like the Sloan Digital Sky Survey (SDSS) (Aihara et al 2011) and Wide-field Infrared Survey Explorer (WISE) (Brescia et al 2019;Ilbert et al 2008;Hildebrandt et al 2010;Brescia et al 2013;Wright et al 2010;D'Isanto & Polsterer 2018).…”