Massive young stellar objects in the Local Group spiral galaxy M33 identified using machine learning
David A. Kinson, Joana M. Oliveira, Jacco Th. van Loon

TL;DR
This study applies machine learning to classify stellar populations in galaxy M33, identifying young stellar objects and star-forming regions, and estimating star formation rates with high accuracy using IR data.
Contribution
It introduces a supervised machine learning approach with Probabilistic Random Forest to classify diverse stellar objects and quantify star formation in M33, expanding methods used in extragalactic stellar population studies.
Findings
Classified 162,746 sources with 86% accuracy.
Identified 4,985 young stellar objects and 68 star-forming regions.
Estimated global star formation rate of 1.42 solar masses per year.
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, 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…
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