Random Forest Classification of Stars in the Galactic Centre

TL;DR

This paper develops a random forest classifier to identify faint early-type stars in the Galactic Centre using K-band photometry, achieving high accuracy without spectral models and enabling analysis of stellar populations.

Contribution

Introduces a machine learning approach for stellar classification in the Galactic Centre that simplifies calibration and improves scalability over traditional spectral methods.

Findings

Classifier achieves F1=0.85 in identifying stellar types.

Method reproduces known spatial distributions and luminosity functions.

Machine learning approach reduces calibration effort.

Abstract

Near-infrared high-angular resolution imaging observations of the Milky Way's nuclear star cluster have revealed all luminous members of the existing stellar population within the central parsec. Generally, these stars are either evolved late-type giants or massive young, early-type stars. We revisit the problem of stellar classification based on intermediate-band photometry in the K-band, with the primary aim of identifying faint early-type candidate stars in the extended vicinity of the central massive black hole. A random forest classifier, trained on a subsample of spectroscopically identified stars, performs similarly well as competitive methods (F1=0.85), without involving any model of stellar spectral energy distributions. Advantages of using such a machine-trained classifier are a minimum of required calibration effort, a predictive accuracy expected to improve as more training data becomes available, and the ease of application to future, larger data sets. By applying this classifier to archive data, we are also able to reproduce the results of previous studies of the spatial distribution and the K-band luminosity function of both the early- and late-type stars.