Unveiling the Stars: Using Machine Learning to Map Stellar Parameters for 21 Million Stars

Astronomers used machine learning to estimate stellar parameters for 21 million stars from photometric data. Combining SAGES, Gaia, 2MASS, and WISE datasets, they achieved high precision in temperature, metallicity, and surface gravity measurements. This catalog offers new insights into the Milky Way and metal-poor stars, expanding future research possibilities.