Random Forest Classification of Stars in the Galactic Centre

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.

accepted for publication in MNRAS

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Cited by in corpus (8)

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• White dwarf Random Forest classification through Gaia spectral coefficients
• A machine-learning photometric classifier for massive stars in nearby galaxies I. The method
• Membership analysis and 3D kinematics of the star-forming complex around Trumpler 37 using Gaia-DR3
• Random Forest classification of Gaia DR3 white dwarf-main sequence spectra: a feasibility study
• Photometric Classification of Stars Around the Milky Way's Central Black Hole: I. Central Parsec
• A spectroscopic map of the Galactic centre -- Observations and resolved stars