Massive young stellar objects in the Local Group irregular galaxy NGC6822 identified using machine learning

We present a supervised machine learning methodology to classify stellar populations in the Local Group dwarf-irregular galaxy NGC6822. Near-IR colours (J - H, H - K, and J - K), K-band magnitudes and far-IR surface brightness (at 70 and 160 mu m) measured from Spitzer and Herschel images are the features used to train a Probabilistic Random Forest (PRF) classifier. Point-sources are classified into eight target classes: young stellar objects (YSOs), oxygen- and carbon-rich asymptotic giant branch stars, red giant branch and red supergiant stars, active galactic nuclei, massive main-sequence stars, and Galactic foreground stars. The PRF identifies sources with an accuracy of similar to 90 per cent across all target classes rising to similar to 96 per cent for YSOs. We confirm the nature of 125 out of 277 literature YSO candidates with sufficient feature information, and identify 199 new YSOs and candidates. Whilst these are mostly located in known star-forming regions, we have also identified new star formation sites. These YSOs have mass estimates between similar to 15 and 5 0M(circle dot), representing the most massive YSO population in NGC6822. Another 82 out of 277 literature candidates are definitively classified as non-YSOs by the PRF analysis. We characterize the star formation environment by comparing the spatial distribution of YSOs to those of gas and dust using archival images. We also explore the potential of using (unsupervised) t-distributed stochastic neighbour embedding maps for the identification of the same stellar population classified by the PRF.

Automated summary

A supervised machine learning methodology was used to classify stellar populations in NGC6822, achieving high accuracy in identifying different target classes. The study confirmed the nature of known YSO candidates, identified new YSOs, and characterized the star formation environment in the galaxy. The PRF analysis also definitively classified non-YSO candidates and explored the potential of unsupervised methods for stellar population identification.