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

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.

Discovering strongly lensed quasar candidates with catalogue-based methods from DESI Legacy Surveys

“…In this work, we have designed a catalogue-based approach for mining multiply imaged lensed QSO candidates from large catalogue datasets. The approach adopts three stages: 1. identifying groups of candidate QSOs; 2. refining the groups using a statistical measure of the similarity of group member colours and the likelihood of members being QSOs according to He & Li (2022). This includes both removal of entire groups and removal of individual members for groups with more than two members.…”

“…Their g-band apparent magnitudes span the range 17 ≤ g ≤ 24, peaking at g ∼ 20.5. We have estimated the recovery rate of our mining from the 57 known lensed QSOs contained in the catalogue of He & Li (2022); our catalogue of 971 contains 53 of these, indicating a recovery rate of approximately 93%. We note that the use of these 57 known systems in optimising our mining strategy likely results in selection bias towards discovering similar systems.…”

“…As expected, the QGC includes all groups contained in the RLQ. The QGC provides the RA, Dec, the probability of being a QSO given by He & Li (2022), and the five-band magnitudes g, r, z, W1, W2 for each candidate of every group. (The QGC also gives the quantities S colour and S RF which we define below.…”

“…In this paper, we build a catalogue-based algorithm for finding the candidates of multiply imaged lensed QSOs from the QSO candidate catalogue of He & Li (2022) extracted from the 9th data release of the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys (DESI-LS; Dey et al 2019) which covers ∼ 20, 000 deg 2 of the extragalactic sky in g, r, z-bands. Specifically, we compile a catalogue containing 971 multiply imaged lensed systems, 620 of which are new candidates not contained in the known lensed QSO databases of Dawes et al (2022, D22 hereafter) or Lemon et al (2022, L22 hereafter).…”

Discovering strongly lensed quasar candidates with catalogue-based methods from DESI Legacy Surveys

“…In this paper, we describe our catalogue-based algorithm for finding the candidates of multiply-imaged lensed QSOs from the QSO candidate catalogue of He & Li (2022) extracted from the ninth data release of the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys (DESI-LS; Dey et al 2019), which covers ∼ 20, 000 deg 2 of the extragalactic sky in the g, r, z-bands. Specifically, we compiled a catalogue containing 971 multiply imaged lensed systems, 620 of which are new candidates not contained in the known lensed QSO databases of Dawes et al 2022 (D22 hereafter) or Lemon et al 2022 (L22 hereafter).…”