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 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.
