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

Pennock, Clara M; van Loon, Jacco Th; Cioni, Maria-Rosa L; Maitra, Chandreyee; Oliveira, Joana M; Craig, Jessica E M; Ivanov, Valentin D; Aird, James; Anih, Joy O; Cross, Nicholas J G; Dresbach, Francesca; de Grijs, Richard; Groenewegen, Martin A T

Authors

Clara M Pennock

Maria-Rosa L Cioni

Chandreyee Maitra

Jessica E M Craig

Valentin D Ivanov

James Aird

Joy O Anih

Nicholas J G Cross

Francesca Dresbach

Richard de Grijs

Martin A T Groenewegen



Abstract

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 multi-wavelength photometry from optical to far-infrared as features to be trained on, and spectra of Active Galactic Nuclei (AGN), galaxies and a range of stellar classes including from new observations with the Southern African Large Telescope (SALT) and SAAO 1.9m telescope. We also retain a label for sources that remain unknown. This yielded average classifier accuracies of ∼79% (SMC) and ∼87% (LMC). Restricting to the 56,696,719 sources with class probabilities (Pclass) > 80% yields accuracies of ∼90% (SMC) and ∼98% (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 datasets, 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.

Citation

Pennock, C. M., van Loon, J. T., Cioni, M.-R. L., Maitra, C., Oliveira, J. M., Craig, J. E. M., …Groenewegen, M. A. T. (2025). The VMC Survey – LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm. Monthly Notices of the Royal Astronomical Society, 537(2), 1028-1055. https://doi.org/10.1093/mnras/staf080

Journal Article Type Article
Acceptance Date Jan 10, 2025
Online Publication Date Jan 15, 2025
Publication Date 2025-02
Deposit Date Feb 3, 2025
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Publisher Oxford University Press
Peer Reviewed Peer Reviewed
Volume 537
Issue 2
Pages 1028-1055
DOI https://doi.org/10.1093/mnras/staf080
Keywords methods: data analysis, galaxies: active, Magellanic Clouds, galaxies: photometry
Public URL https://keele-repository.worktribe.com/output/1053698
Publisher URL https://academic.oup.com/mnras/article/537/2/1028/7954762?login=true