The Quasar Candidates Catalogs of DESI Legacy Imaging Survey Data Release 9

TL;DR

This paper presents a large catalog of quasar candidates identified using a machine learning approach from DESI Legacy Imaging Survey data, significantly aiding future spectroscopic confirmation efforts.

Contribution

The work introduces a novel quasar candidate catalog created with a Random Forest classifier trained on multi-survey photometric data, achieving high completeness and reducing follow-up workload.

Findings

Nearly 2 million Grade-A quasar candidates identified

Catalog covers approximately 99% of quasars in the dataset

High agreement with color-cut selection methods

Abstract

Quasars can be used to measure baryon acoustic oscillations at high redshift, which are considered as direct tracers of the most distant large-scale structures in the Universe. It is fundamental to select quasars from observations before implementing the above research. This work focuses on creating a catalog of quasar candidates based on photometric data to provide primary priors for further object classification with spectroscopic data in the future, such as The Dark Energy Spectroscopic Instrument (DESI) Survey. We adopt a machine learning algorithm (Random Forest, RF) for quasar identification. The training set includes $651,073$ positives and $1,227,172$ negatives, in which the photometric information are from DESI Legacy Imaging Surveys (DESI-LIS) \& Wide-field Infrared Survey Explore (WISE), and the labels are from a database of spectroscopically confirmed quasars based on Sloan Digital Sky Survey (SDSS) and the Set of Identifications \& Measurements and Bibliography for Astronomical Data (SIMBAD). The trained RF model is applied to point-like sources in DESI-LIS Data Release 9. To quantify the classifier's performance, we also inject a testing set into the to-be-applied data. Eventually, we obtained $1,953,932$ Grade-A quasar candidates and $22,486, 884$ Grade-B quasar candidates out of $425,540,269$ sources ($\sim 5.7\%$). The catalog covers $\sim 99\%$ of quasars in the to-be-applied data by evaluating the completeness of the classification on the testing set. The statistical properties of the candidates agree with that given by the method of color-cut selection. Our catalog can intensely decrease the workload for confirming quasars with the upcoming DESI data by eliminating enormous non-quasars but remaining high completeness. All data in this paper is publicly available online.