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

Abstract We present our photometric method, which combines Subaru/Hyper Suprime-Cam N B 515, g -band, and i- band filters to distinguish giant stars in Local Group galaxies from Milky Way dwarf contamination. The N B 515 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 B 515 filter’s sensitivity to stellar atmospheric parameters and chemical abundances. Our results demonstrate that the N B 515 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 B 515 − 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.