Mapping the Stars: A Catalog of Over 50 Million Stars from SMSS and Gaia
Yang Huang and Timothy C. Beers have compiled a catalog of stellar parameters for over 50 million stars using data from SMSS DR4 and Gaia DR3. This dataset provides accurate metallicity, temperature, and distance estimates, significantly expanding previous surveys. Their work is part of SPORTS, a project to catalog as many Milky Way stars as possible. The results will help astronomers study galactic evolution and the early universe.
Stellar Secrets: Mapping M Dwarfs with SAPP
The adapted Stellar Abundances and atmospheric Parameters Pipeline (SAPP) successfully analyzes M dwarf stars, focusing on temperature, surface gravity, and metallicity using near-infrared spectra. Validated with APOGEE data, it shows good accuracy and prepares for missions like ESA’s Plato. Future updates aim to enhance precision and include full chemical abundance analysis.
Mapping the Milky Way's DNA: Stellar Parameters and Chemical Abundances Unveiled with S-PLUS
The S-PLUS survey analyzed 5 million Milky Way stars, estimating atmospheric parameters and chemical abundances using machine learning on multi-band photometric data. Neural networks outperformed random forests in accuracy, revealing trends like [Mg/Fe] bimodality and robustly mapping stellar properties. This cost-effective, scalable approach complements spectroscopy, offering new insights into Galactic evolution and paving the way for broader stellar population studies.