Metallicity and α-abundance for 48 Million Stars in Low-extinction Regions in the Milky Way

Abstract

We estimate ([M/H], [ α /M]) for 48 million giants and dwarfs in low-dust extinction regions from the Gaia DR3 XP spectra by using tree-based machine learning models trained on APOGEE DR17 and a metal-poor star sample from Li et al. The root mean square error of our estimation is 0.0890 dex for [M/H] and 0.0436 dex for [ α /M], when we evaluate our models on the test data that are not used in training the models. Because the training data is dominated by giants, our estimation is most reliable for giants. The high-[ α /M] stars and low-[ α /M] stars selected by our ([M/H], [ α /M]) show different kinematical properties for giants and low-temperature dwarfs. We further investigate how our machine learning models extract information on ([M/H], [ α /M]). Intriguingly, we find that our models seem to extract information on [ α /M] from Na D lines (589 nm) and Mg i line (516 nm). This result is understandable given the observed correlation between Na and Mg abundances in the literature. The catalog of ([M/H], [ α /M]) as well as their associated uncertainties is publicly available.

Keywords

• Spectroscopy
• Stellar abundances
• Milky Way disk
• Milky Way stellar halo
• Astroinformatics