Machine Learning Techniques to Distinguish Giant Stars from Dwarf Stars Using Only Photometry—Pushing Redward

We present our photometric method, which combines Subaru/Hyper Suprime-Cam N B515, g-band, and i-band filters to distinguish giant stars in Local Group galaxies from Milky Way dwarf contamination. The N B515 filter is a narrowband filter that covers the MgI+MgH features at 5150 Å and is sensitive to stellar surface gravity. Using synthetic photometry derived from large empirical stellar spectral libraries, we model the N B515 filter’s sensitivity to stellar atmospheric parameters and chemical abundances. Our results demonstrate that the N B515 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, N B515 − 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.