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.