The GALAH Survey: Neutron-Capture Elemental Abundances for 350,000 Gaia-RVS spectra and the Chemodynamics of Accreted Structures
arXiv:2606.04567 · doi:10.1093/mnras/stag1142
Abstract
We present a comprehensive data-driven spectroscopic analysis of 357,415 red giant stars using Gaia DR3 Radial Velocity Spectrometer (RVS) spectra (8460-8700 A; ), aimed at deriving homogenous stellar parameters and elemental abundances (collectively referred to as stellar labels). We employ The Cannon, a generative model based on 2747 giants in common with GALactic Archaeology with HERMES (GALAH) DR4, adopting GALAH labels () for training. The resulting model predicts 11 stellar labels for RVS giants: effective temperature (), surface gravity (), projected rotational velocity (), and abundances of [Fe/H], [Ca/Fe], [Si/Fe], [Ni/Fe], [Ti/Fe], as well as the neutron-capture elements [Zr/Fe], [Ce/Fe], and [Nd/Fe]. Building on these results, we develop a probabilistic framework to chemically identify debris from the Gaia-Sausage-Enceladus (GSE) accretion event. A logistic regression classifier, optimized via Markov chain Monte Carlo sampling and trained on a small reference sample of GSE members and comparison stars, identifies stars with high GSE membership probabilities based solely on their chemical abundances, with the resulting candidates exhibiting distinctive abundance-ratio patterns, including [Ca/Ti], [Ti/Ce], and [Nd/Zr]. Applying independent kinematic constraints yields a robust sample of GSE candidates, demonstrating that the characteristic chemical signatures remain consistent after applying these constraints. This work demonstrates the potential of data-driven analysis techniques to extract detailed chemical information from medium-resolution spectra and establishes a framework for tracing Galactic accretion events using chemical abundances.
Published in MNRAS; 24 pages, 14 figures, 8 tables. The resulting catalogues are available via the VizieR catalogue service at https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/550/G1142
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