A catalog of candidate double and lensed quasars from Gaia and WISE data
Valeri V. Makarov, Nathan J. Secrest

TL;DR
This paper presents a catalog of 3140 candidate double and lensed quasars and AGNs identified using Gaia and WISE data, employing machine learning to estimate redshifts and filter out contaminants, with high estimated accuracy.
Contribution
The study introduces a new catalog of candidate double and lensed quasars using Gaia and WISE data, incorporating machine learning for redshift prediction and advanced filtering techniques.
Findings
Estimated 4.9% interloper rate among high-redshift candidates.
Catalog contains over 3000 candidates with an estimated 98% true positive rate.
More than half of the candidates are likely to be physically related quasars.
Abstract
Making use of strong correlations between closely separated multiple or double sources and photometric and astrometric metadata in Gaia EDR3, we generate a catalog of candidate double and multiply imaged lensed quasars and AGNs, comprising 3140 systems. It includes two partially overlapping parts, a sample of distant (redshifts mostly greater than 1) sources with perturbed data, and systems resolved into separate components by Gaia at separations less than . For the first part, which is roughly one third of the published catalog, we synthesized 0.617 million redshifts by multiple machine learning prediction and classification methods, using independent photometric and astrometric data from Gaia EDR3 and WISE with accurate spectroscopic redshifts from SDSS as a training set. Using these synthetic redshifts, we estimate a rate of 4.9\% of interlopers with spectroscopic redshift…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Gamma-ray bursts and supernovae · Stellar, planetary, and galactic studies
