Towards Galactic Archaeology with Inferred Ages of Giant Stars From Gaia Spectra

TL;DR

This paper demonstrates that machine learning models trained on Gaia spectra can accurately infer ages of giant stars, enabling detailed mapping of the Milky Way's formation history.

Contribution

The study introduces SIDRA, a machine learning framework that infers stellar ages from Gaia spectra, achieving high precision and enabling large-scale Galactic archaeology.

Findings

SIDRA-XP achieves residuals of ~0.064 dex for age estimation.

Applied to over 2 million stars, revealing Galactic structure features.

Demonstrates the feasibility of spectral-based age inference for Galactic studies.

Abstract

In the era of Gaia, the accurate determination of stellar ages is transforming Galactic archaeology. We demonstrate the feasibility of inferring stellar ages from Gaia's RVS spectra and the BP/RP (XP) spectrophotometric data, specifically for red giant branch and high-mass red clump stars. We successfully train two machine learning models, dubbed SIDRA: Stellar age Inference Derived from Gaia spectRA to predict the age. The SIDRA-RVS model uses the RVS spectra and SIDRA-XP the stellar parameters obtained from the XP spectra. Both models use BINGO, an APOGEE-derived stellar age as the training data. SIDRA-RVS estimates ages of stars whose age is around $\tau_\mathrm{BINGO}=10$~Gyr with a standard deviation of residuals of $\sim$ 0.12 dex in the unseen test dataset, while SIDRA-XP achieves higher precision with residuals $\sim$ 0.064 dex for stars around $\tau_\mathrm{BINGO}=10$~ Gyr. Since SIDRA-XP outperforms SIDRA-RVS, we apply SIDRA-XP to analyse the ages for 2,218,154 stars. This allowed us to map the chronological and chemical properties of Galactic disc stars, reproducing the known distinct features such as the Gaia-Sausage-Enceladus merger and a potential gas-rich interaction event linked to the first infall of the Sagittarius dwarf galaxy. This study demonstrates that machine learning techniques applied to Gaia's spectra can provide valuable individual age information, particularly for giant stars, thereby enhancing our understanding of the Milky Way's formation and evolution.