The VMC Survey - LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm

We used a supervised machine learning algorithm (probabilistic random forest) to classify 130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multiwavelength photometry from optical to far-infrared as features to be trained on, and spectra of active galactic nuclei (AGNs), galaxies and a range of stellar classes including from new observations with the Southern African Large Telescope (SALT) and South African Astronomical Observatory (SAAO) 1.9-m telescope. We also retain a label for sources that remain unknown. This yielded average classifier accuracies of 79 per cent [Small Magellanic Cloud (SMC)] and 87 per cent [Large Magellanic Cloud (LMC)]. Restricting to the 56 696 719 sources with class probabilities (P_class) > 80 per cent yields accuracies of 90 per cent (SMC) and 98 per cent (LMC). After removing sources classed as 'Unknown', we classify a total of 707 939 (SMC) and 397 899 (LMC) sources, including >77 600 extragalactic sources behind the Magellanic Clouds. The extragalactic sources are distributed evenly across the field, whereas the Magellanic sources concentrate at the centres of the Clouds, and both concentrate in optical/IR colour-colour/magnitude diagrams as expected. We also test these classifications using independent data sets, finding that, as expected, the majority of X-ray sources are classified as AGN (554/883) and the majority of radio sources are classed as AGN (1756/2694) or galaxies (659/2694), where the relative AGN-galaxy proportions vary substantially with radio flux density. We have found >49 500 hitherto unknown AGN candidates, likely including more AGN dust dominated sources which are in a critical phase of their evolution; >26 500 new galaxy candidates and >2800 new young stellar object (YSO) candidates.