Machine Learning Techniques to Distinguish Giant Stars from Dwarf Stars Using Only Photometry -- Pushing Redwards

Abstract

We present our photometric method, which combines Subaru/HSC $NB515$, g, and i band filters to distinguish giant stars in Local Group galaxies from Milky Way dwarf contamination. The $NB515$ filter is a narrow-band filter that covers the MgI+MgH features at $5150$ Ã , and is sensitive to stellar surface gravity. Using synthetic photometry derived from large empirical stellar spectral libraries, we model the $NB515$ filter's sensitivity to stellar atmospheric parameters and chemical abundances. Our results demonstrate that the $NB515$ filter effectively separates dwarfs from giants, even for the reddest and coolest M-type stars. To further enhance this separation, we develop machine learning models that improve the classification on the two-color ($g-i$, $NB515-g$) diagram. We apply these models to photometric data from the Fornax dwarf spheroidal galaxy and two fields of M31, successfully identifying red giant branch stars in these galaxies.

21 pages, 13 figures, accepted to AJ