Performance analysis of extragalactic classifications in Gaia Data Release 4

Abstract

The Discrete Source Classifier (DSC) provides probabilistic classifications of sources in Gaia Data Release 4 (GDR4) based on empirically-trained Bayesian classifiers. Using Gaia astrometry, photometry, and low-resolution spectra (XP), DSC classifies all sources as quasars, galaxies, or stars. DSC comprises three trained neural networks and three combinations of their probabilities. When evaluated as a function of brightness and sky position on a test set excluding the Magellanic Clouds, the DSC purity in GDR4 has improved for a small loss in completeness. The average performance of the best classifiers at magnitudes brighter than G=20 is at least 88% completeness and 96% purity for the extragalactic classes, namely the quasar and galaxy classes. At fainter magnitudes, performance is lower due to increased noise. The average performance at magnitudes of 20$\leq$G<20.5 is a minimum of 55% completeness and 71% purity for the extragalactic classes. At G>20.5 mag, completeness is considerably reduced, primarily for the models that depend on the XP spectra. Furthermore, we train additional models on Gaia optical data together with mid-infrared photometry from the CatWISE2020 catalogue. Inclusion of infrared photometry increases the completeness of extragalactic samples at G>20 mag between 9 and 29 percentage points, at the cost of reducing purity between 1 and 9 percentage points. In GDR4, the best DSC-combined classifier prioritising completeness identifies three million quasars and two million galaxies, but with expected high contamination among fainter sources. In contrast, the combined classifiers prioritising purity identify approximately two million quasars and 1.3 million galaxies with an expected lower level of contamination. Finally, we provide recommendations for enhancing the purity of the DSC extragalactic selection by applying quality cuts to the Gaia photometry and astrometry.