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• W4297183386 endingPage "160" @default.
• W4297183386 startingPage "140" @default.
• W4297183386 abstract "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 [Hα]/[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." @default.
• W4297183386 created "2022-09-27" @default.
• W4297183386 creator A5019066699 @default.
• W4297183386 creator A5037517484 @default.
• W4297183386 creator A5089033481 @default.
• W4297183386 date "2022-09-23" @default.
• W4297183386 modified "2023-10-16" @default.
• W4297183386 title "Massive young stellar objects in the Local Group spiral galaxy M 33 identified using machine learning" @default.
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