Catalogues of Active Galactic Nuclei From Gaia and unWISE Data
Yiping Shu, Sergey E. Koposov, N. Wyn Evans, Vasily Belokurov, Richard, G. McMahon, Matthew W. Auger, and Cameron A. Lemon

TL;DR
This paper presents two large, reliable catalogues of active galactic nucleus candidates derived from Gaia and unWISE data, using machine learning to classify and estimate redshifts, aiding future AGN studies and surveys.
Contribution
The authors develop a novel machine learning approach to identify AGN candidates from Gaia and unWISE data, creating extensive, reliable catalogues with photometric redshifts and potential lensing systems.
Findings
Catalogues contain over 2 million AGN candidates each.
Achieved 75% and 85% completeness and reliability thresholds.
Photometric redshift accuracy of 0.11.
Abstract
We present two catalogues of active galactic nucleus (AGN) candidates selected from the latest data of two all-sky surveys -- Data Release 2 (DR2) of the \emph{Gaia} mission and the unWISE catalogue of the \emph{Wide-field Infrared Survey Explorer} (\emph{WISE}). We train a random forest classifier to predict the probability of each source in the \emph{Gaia}-unWISE joint sample being an AGN, , based on \emph{Gaia} astrometric and photometric measurements and unWISE photometry. The two catalogues, which we designate C75 and R85, are constructed by applying different threshold cuts to achieve an overall completeness of 75\% (90\% at \emph{Gaia} mag) and reliability of 85\% respectively. The C75 (R85) catalogue contains 2,734,464 (2,182,193) AGN candidates across the effective 36,000 deg sky, of which 0.91 (0.52) million are new…
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