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

Abstract

We present a catalog of quasars and corresponding redshifts in the Kilo-Degree Survey (KiDS) Data Release 4. We trained machine learning (ML) models, using optical ugri and near-infrared ZYJHK_s 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. We show that projections of the high-dimensional feature space can be successfully used to investigate the estimations. The model creation employs two test subsets: randomly selected and the faintest objects, which allows to 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 model for classification, while ANN performs the best for combined classification and redshift. The inference results are tested using number counts, Gaia parallaxes, and other quasar catalogs. Based on these tests, we derived the minimum classification probability which provides the best purity versus completeness trade-off: p(QSO_cand) > 0.9 for r < 22 and p(QSO_cand) > 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 modeled with Gaussian uncertainties. The redshift error (mean and scatter) equals 0.01 +/- 0.1 in the safe subset and -0.0004 +/- 0.2 in the extrapolation, in a redshift range of 0.14 < z < 3.63. Our success of the extrapolation challenges the way that models are optimized and applied at the faint data end. The catalog is ready for cosmology and active galactic nucleus (AGN) studies.

We publicly release the catalog at kids.strw.leidenuniv.nl/DR4/quasarcatalog.php , and the code at github.com/snakoneczny/kids-quasars

References in corpus (21)

• The DESI Experiment Part I: Science,Targeting, and Survey Design
• The Sloan Digital Sky Survey Quasar Catalog: Sixteenth Data Release
• Photometric redshift and classification for the XMM-COSMOS sources
• The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees
• X-CIGALE: fitting AGN/galaxy SEDs from X-ray to infrared
• Constraining the properties of AGN host galaxies with Spectral Energy Distribution modeling
• The WISE AGN Catalog
• Think Outside the Color Box: Probabilistic Target Selection and the SDSS-XDQSO Quasar Targeting Catalog
• The 2dF-SDSS LRG and QSO Survey: The spectroscopic QSO catalogue
• Clustering of quasars in SDSS-IV eBOSS : study of potential systematics and bias determination
• Luminous K-band Selected Quasars from UKIDSS
• No new cosmological concordance with massive sterile neutrinos
• The Angular Clustering of Infrared-Selected Obscured and Unobscured Quasars
• KiDS+VIKING-450: Improved cosmological parameter constraints from redshift calibration with self-organising maps
• Unsupervised star, galaxy, qso classification: Application of HDBSCAN
• Automated physical classification in the SDSS DR10. A catalogue of candidate Quasars
• The characteristic halo masses of half-a-million WISE-selected quasars
• Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4
• Quasar Photometric Redshifts and Candidate Selection: A New Algorithm Based on Optical and Mid-Infrared Photometric Data
• Tomographic imaging of the Fermi-LAT gamma-ray sky through cross-correlations: A wider and deeper look
• QSO photometric redshifts from SDSS, WISE and GALEX colours

Cited by in corpus (24)

• Bright galaxy sample in the Kilo-Degree Survey Data Release 4: selection, photometric redshifts, and physical properties
• The fifth data release of the Kilo Degree Survey: Multi-epoch optical/NIR imaging covering wide and legacy-calibration fields
• Photometric redshift-aided classification using ensemble learning
• Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4
• Photometric Redshifts from SDSS Images with an Interpretable Deep Capsule Network
• Random Forests as a viable method to select and discover high redshift quasars
• CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE
• Predicting the redshift of gamma-ray loud AGNs using supervised machine learning
• Predicting the redshift of gamma-ray loud AGNs using Supervised Machine Learning: Part 2
• The miniJPAS survey quasar selection II: Machine learning classification with photometric measurements and uncertainties
• Wide Area VISTA Extra-galactic Survey (WAVES): Unsupervised star-galaxy separation on the WAVES-Wide photometric input catalogue using UMAP and $\mathrm{HDBSCAN}$
• Active galactic nuclei catalog from the AKARI NEP Wide field
• The probabilistic random forest applied to the QUBRICS survey: improving the selection of high-redshift quasars with synthetic data
• Semi-supervised classification of stars, galaxies and quasars using K-means and random-forest approaches
• The Quasar Catalogue for S-PLUS DR4 (QuCatS) and the estimation of photometric redshifts
• Redshifts of radio sources in the Million Quasars Catalogue from machine learning
• Morpho-Photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star-Quasar-Galaxy Catalog
• Applying machine learning to Galactic Archaeology: how well can we recover the origin of stars in Milky Way-like galaxies?
• Exploring the Dependence of Gas Cooling and Heating Functions on the Incident Radiation Field with Machine Learning
• Automated algorithms to build Active Galactic Nuclei classifiers
• Photometric classification of QSOs from ALHAMBRA survey using random forest
• A Morphological Model to Separate Resolved-Unresolved Sources in the DESI Legacy Surveys: Application in the LS4 Alert Stream
• Photometric redshifts for quasars from WISE-PS1-STRM
• 4MOST ChANGES: Catalog of high-redshift quasar candidates (4.5 < $z$ < 7) selected with SED fitting