A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2
Qian Yang, Yue Shen

TL;DR
This paper introduces a comprehensive catalog of 1.4 million photometrically-selected quasar candidates in the southern hemisphere from the Dark Energy Survey, utilizing multi-band photometry and probabilistic classification methods.
Contribution
The study develops a novel multivariate Skew-t distribution model for classifying quasars, galaxies, and stars, and provides photometric redshifts with high accuracy, enhancing quasar candidate identification.
Findings
94.7% quasar classification accuracy with all IR bands
89% estimated completeness for quasars at r<21.5
96.7% of candidates with spectra are confirmed quasars
Abstract
We present a catalog of 1.4 million photometrically-selected quasar candidates in the southern hemisphere over the Dark Energy Survey (DES) wide survey area. We combine optical photometry from the DES second data release (DR2) with available near-infrared (NIR) and the all-sky unWISE mid-infrared photometry in the selection. We build models of quasars, galaxies, and stars with multivariate Skew-t distributions in the multi-dimensional space of relative fluxes as functions of redshift (or color for stars) and magnitude. Our selection algorithm assigns probabilities for quasars, galaxies, and stars, and simultaneously calculates photometric redshifts (photo-) for quasar and galaxy candidates. Benchmarking on spectroscopically confirmed objects, we successfully classify (with photometry) 94.7% of quasars, 99.3% of galaxies, and 96.3% of stars when all IR bands…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Remote Sensing in Agriculture · Gaussian Processes and Bayesian Inference
