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Abstract We present a catalog of 1.4 million photometrically selected quasar candidates in the southern hemisphere over the ∼5000 deg2Dark 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-tdistributions in the multidimensional 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-z) 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 (NIRYJHKand WISE W1W2) are available. The classification and photo-zregression success rates decrease when fewer bands are available. Our quasar (galaxy) photo-zquality, defined as the fraction of objects with the difference between the photo-z zpand the spectroscopic redshiftzs, ∣Δz∣ ≡ ∣zs−zp∣/(1 +zs) ≤ 0.1, is 92.2% (98.1%) when all IR bands are available, decreasing to 72.2% (90.0%) using optical DES data only. Our photometric quasar catalog achieves an estimated completeness of 89% and purity of 79% atr< 21.5 (0.68 million quasar candidates), with reduced completeness and purity at 21.5 <r≲ 24. Among the 1.4 million quasar candidates, 87,857 have existing spectra, and 84,978 (96.7%) of them are spectroscopically confirmed quasars. Finally, we provide quasar, galaxy, and star probabilities for all (0.69 billion) photometric sources in the DES DR2 coadded photometric catalog.
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This content will become publicly available on April 17, 2026
Identifying Catastrophic Outlier Photometric Redshift Estimates in the COSMOS Field with Machine Learning Methods
Abstract We present the result of two binary classifier ensembled neural networks to identify catastrophic outliers for photo-zestimates within the COSMOS field utilizing only eight and five photometric bandpasses, respectively. Our neural networks can correctly classify 55.6% and 33.3% of the true positives with few to no false positives. These methods can be used to reduce the errors caused by the errors in redshift estimates, particularly at high redshift. When applied to a larger data set with only photometric data available, our eight bandpass network increased the number of objects with a photo-zgreater than five from 0.1% to 1.6%, and our five bandpass network increased the number of objects with a photo-zgreater than five from 0.2% to 1.8%.
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- Award ID(s):
- 1716093
- PAR ID:
- 10655847
- Publisher / Repository:
- IOP Science
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Edition / Version:
- 1
- Volume:
- 983
- Issue:
- 2
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 173
- Subject(s) / Keyword(s):
- High-redshift galaxies Galaxies Neural networks Classification
- Format(s):
- Medium: X Size: 1MB Other: PDF/A
- Size(s):
- 1MB
- Sponsoring Org:
- National Science Foundation
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