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

TL;DR

This paper develops a machine learning approach to classify stellar populations in NGC6822, successfully identifying massive young stellar objects and new star formation sites with high accuracy, enhancing understanding of star formation in dwarf irregular galaxies.

Contribution

The study introduces a supervised Probabilistic Random Forest classifier using multi-wavelength data to accurately identify and characterize stellar populations, including the most massive YSOs in NGC6822.

Findings

Achieved ~90% classification accuracy across stellar classes.

Confirmed 125 known YSOs and identified 199 new candidates.

Discovered new star formation regions in NGC6822.

Abstract

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 micron) 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 super-giant stars, active galactic nuclei, massive main-sequence stars and Galactic foreground stars. The PRF identifies sources with an accuracy of ~90 percent across all target classes rising to ~96 percent 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 ~15-50 Msun, representing the most massive YSO population in NGC 6822. Another 82 out of 277 literature candidates are definitively classified as non-YSOs by the PRF analysis. We characterise 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.