Massive young stellar objects in the Local Group spiral galaxy M 33 identified using machine learning

Abstract

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, utilizes 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 disc 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 [Ha]/[24?µm] and [250?µm]/[500?µm] to estimate the relative evolutionary status of all M?33 SFRs. Masses were estimated for each YSO ranging from 6–27M?. Using these masses, we estimate star formation rates based on direct YSO counts of 0.63M??yr-1 in M?33’s SFRs, 0.79?±?0.16M??yr-1 in its centre and 1.42?±?0.16M??yr-1 globally.