The GALAH Survey: Neutron-Capture Elemental Abundances for 350,000 Gaia-RVS Spectra and the Chemodynamics of Accreted Structures

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; $R\approx11,500$), aimed at deriving precise 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 GALAH DR4, adopting GALAH labels ($R\approx28,000$) for training. The resulting model predicts eleven stellar labels for RVS giants: effective temperature ($T_{\rm eff}$), surface gravity ($\log g$), projected rotational velocity ($v\sin i$), 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, optimised 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 power 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.