000007663 001__ 7663
000007663 005__ 20251226131558.0
000007663 037__ $$aSCART-2025-0154
000007663 100__ $$aPennock, C.M.
000007663 245__ $$aThe VMC Survey : LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm 
000007663 260__ $$c2025
000007663 520__ $$aWe used a supervised machine learning algorithm (probabilistic random forest) to classify ∼130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multiwavelength photometry from optical to far-infrared as features to be trained on, and spectra of active galactic nuclei (AGNs), galaxies and a range of stellar classes including from new observations with the Southern African Large Telescope (SALT) and South African Astronomical Observatory (SAAO) 1.9-m telescope. We also retain a label for sources that remain unknown. This yielded average classifier accuracies of ∼79 per cent [Small Magellanic Cloud (SMC)] and ∼87 per cent [Large Magellanic Cloud (LMC)]. Restricting to the 56 696 719 sources with class probabilities (Pclass ) > 80 per cent yields accuracies of ∼90 per cent (SMC) and ∼98 per cent (LMC). After removing sources classed as ‘Unknown’, we classify a total of 707 939 (SMC) and 397 899 (LMC) sources, including >77 600 extragalactic sources behind the Magellanic Clouds. The extragalactic sources are distributed evenly across the field, whereas the Magellanic sources concentrate at the centres of the Clouds, and both concentrate in optical/IR colour–colour/magnitude diagrams as expected. We also test these classifications using independent data sets, finding that, as expected, the majority of X-ray sources are classified as AGN (554/883) and the majority of radio sources are classed as AGN (1756/2694) or galaxies (659/2694), where the relative AGN–galaxy proportions vary substantially with radio flux density. We have found >49 500 hitherto unknown AGN candidates, likely including more AGN dust dominated sources which are in a critical phase of their evolution; >26 500 new galaxy candidates and >2800 new young stellar object (YSO) candidates.
000007663 594__ $$aNO
000007663 6531_ $$amethods: data analysis
000007663 6531_ $$agalaxies: active
000007663 6531_ $$aMagellanic Clouds
000007663 6531_ $$agalaxies: photometry.
000007663 700__ $$avan Loon, J.Th. 
000007663 700__ $$aCioni, M.-R.L. 
000007663 700__ $$aMaitra, C. 
000007663 700__ $$aOliveira, J.M. 
000007663 700__ $$aCraig, J.E.M.
000007663 700__ $$aIvanov, V.D. 
000007663 700__ $$aAird, J. 
000007663 700__ $$aAnih, J.O.
000007663 700__ $$aCross, N.J.G.
000007663 700__ $$aDresbach, F. 
000007663 700__ $$ade Grijs, R.
000007663 700__ $$aGroenewegen, M.A.T.
000007663 773__ $$c1028-1055$$pMNRAS$$v537$$y2025
000007663 8560_ $$fmartin.groenewegen@ksb-orb.be
000007663 8564_ $$s3507423$$uhttp://publi2-as.oma.be/record/7663/files/PennockvanLoonCioni_ea_25MN537_1028_VMC_LI_extragalacticsources_randomforestsupervisedlearning.pdf
000007663 8564_ $$s4395$$uhttp://publi2-as.oma.be/record/7663/files/PennockvanLoonCioni_ea_25MN537_1028_VMC_LI_extragalacticsources_randomforestsupervisedlearning.gif?subformat=icon$$xicon
000007663 8564_ $$s6582$$uhttp://publi2-as.oma.be/record/7663/files/PennockvanLoonCioni_ea_25MN537_1028_VMC_LI_extragalacticsources_randomforestsupervisedlearning.jpg?subformat=icon-180$$xicon-180
000007663 905__ $$apublished in
000007663 980__ $$aREFERD