Massive young stellar objects in the Local Group spiral galaxy M33 identified using machine learning

We present a supervised machine learning classification of stellar populations in the Local Group spiral galaxy M 33. The Probabilistic Random Forest (PRF) methodology, previously applied to populations in NGC 6822, utilises both near and far-IR classification features. It classifies sources into nine target classes: young stellar objects (YSOs), oxygen- and carbon-rich asymptotic giant branch stars, red giant branch and red super-giant stars, active galactic nuclei, blue stars (e.g. O-, B- and A-type main sequence stars), Wolf-Rayet stars and Galactic foreground stars. Across 100 classification runs the PRF classified 162,746 sources with an average estimated accuracy of ∼ 86 per cent, based on confusion matrices. We identified 4985 YSOs across the disk of M 33, applying a density-based clustering analysis to identify 68 star forming regions (SFRs) primarily in the galaxy’s spiral arms. SFR counterparts to known H ii regions were recovered, with ∼ 91 per cent of SFRs spatially coincident with giant molecular clouds identified in the literature. Using photometric measurements, as well as SFRs in NGC 6822 with an established evolutionary sequence as a benchmark, we employed a novel approach combining ratios of [H 𝛼 ] / [24 𝜇 m] and [250 𝜇 m] / [500 𝜇 m] to estimate the relative evolutionary status of all M 33 SFRs. Masses were estimated for each YSO ranging from 6 − 27 M (cid:12) . Using these masses, we estimate star formation rates based on direct YSO counts of 0.63 M (cid:12) yr − 1 in M 33’s SFRs, 0.79 ± 0.16 M (cid:12) yr − 1 in its centre and 1.42 ± 0.16 M (cid:12) yr − 1 globally.