仅利用测光数据区分巨星与矮星的机器学习技术——向更红端推进
Machine Learning Techniques to Distinguish Giant Stars from Dwarf Stars Using Only Photometry -- Pushing Redwards
AI总结：
本研究利用Subaru/HSC窄带滤光片结合机器学习，在双色图上区分巨星与矮星，成功识别Fornax和M31中的红巨星分支恒星。
摘要：
我们提出了结合Subaru/HSC $NB515$、g和i波段滤光片的测光方法，用以在本星系群星系中区分巨星与来自银河系的矮星污染。$NB515$滤光片是一个窄带滤光片，覆盖了位于$5150$ Å的MgI+MgH特征，对恒星表面重力敏感。利用从大型经验恒星光谱库导出的合成测光数据，我们模拟了$NB515$滤光片对恒星大气参数和化学丰度的敏感性。我们的结果表明，$NB515$滤光片能有效区分矮星和巨星，即使对于最红最冷的M型恒星也是如此。为进一步增强这种区分能力，我们开发了机器学习模型，以改进在双色（$g-i$，$NB515-g$）图上的分类。我们将这些模型应用于Fornax矮椭球星系和M31两个天区的测光数据，成功识别了这些星系中的红巨星分支恒星。
英文摘要：
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 $5150$ Å, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
