Gaia RVS stellar labels and GSE chemodynamics

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 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.