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doi:10.1093/mnrasl/slab095

Cited by 2 publications

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“…Consequently, QSOs are used to study astrophysical problems in various fields. For instance, the spectrum of QSOs is a powerful tracer of the formation and evolution of black holes, the spins of black holes, and the co-evolution of black holes and host galaxies (Kormendy & Richstone 1995;Silk & Rees 1998;Kaspi et al 2000;Di Matteo et al 2005;Springel et al 2005;Kormendy & Ho 2013;Chen 2021;Valentini et al 2021); taking advantage of microlensing, astronomers study the feature of accretion disks with the light curves of QSOs (Agol & Krolik 1999;Morgan et al 2010;Blackburne et al 2011;Dexter & Agol 2011); the absorption lines of quasars are unique tracers of the interstellar media along the line of sight (Scaringi et al 2009;Hall et al 2013;Chen et al 2020;Mishra et al 2021;Zabl et al 2021). Besides, high-redshift quasars are valuable for understanding the reionization of the universe and the formation of galaxies (McLure & Jarvis 2002;Wang et al 2019;Lupi et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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

He¹,

Li²

2022

Res. Astron. Astrophys.

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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.

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