The Quasar Candidates Catalogs of DESI Legacy Imaging Survey Data Release 9
Zizhao He, Nan Li

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 positives and 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…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Gamma-ray bursts and supernovae
