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 \r{A}, 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.
@article{arxiv.2510.07005,
title = {Machine Learning Techniques to Distinguish Giant Stars from Dwarf Stars Using Only Photometry -- Pushing Redwards},
author = {Keyi Ding and Carrie Filion and Rosemary F. G. Wyse and Evan N. Kirby and Itsuki Ogami and Masashi Chiba and Yutaka Komiyama and László Dobos and Alexander S. Szalay},
journal= {arXiv preprint arXiv:2510.07005},
year = {2025}
}