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In this work, we assess the combined use of Gaia photometry and astrometrywith infrared data from CatWISE in improving the identification ofextragalactic sources compared to the classification obtained using Gaia data.We evaluate different input feature configurations and prior functions, withthe aim of presenting a classification methodology integrating prior knowledgestemming from realistic class distributions in the universe. In our work, wecompare different classifiers, namely Gaussian Mixture Models (GMMs), XGBoostand CatBoost, and classify sources into three classes - star, quasar, andgalaxy, with the target quasar and galaxy class labels obtained from SDSS16 andthe star label from Gaia EDR3. In our approach, we adjust the posteriorprobabilities to reflect the intrinsic distribution of extragalactic sources inthe universe via a prior function. We introduce two priors, a global priorreflecting the overall rarity of quasars and galaxies, and a mixed prior thatincorporates in addition the distribution of the these sources as a function ofGalactic latitude and magnitude. Our best classification performances, in termsof completeness and purity of the galaxy and quasar classes, are achieved usingthe mixed prior for sources at high latitudes and in the magnitude range G =18.5 to 19.5. We apply our identified best-performing classifier to threeapplication datasets from Gaia DR3, and find that the global prior is moreconservative in what it considers to be a quasar or a galaxy compared to themixed prior. In particular, when applied to the pure quasar and galaxycandidates samples, we attain a purity of 97% for quasars and 99.9% forgalaxies using the global prior, and purities of 96% and 99% respectively usingthe mixed prior. We conclude our work by discussing the importance of applyingadjusted priors portraying realistic class distributions in the universe.

Quasar and galaxy classification using Gaia EDR3 and CatWise2020