Two faces of Gaia-Sausage-Enceladus: Mining the chemical abundance space with graph attention networks

TL;DR

This study uses graph attention networks to analyze high-dimensional chemical abundance data from the Milky Way halo, revealing substructures and distinct stellar populations related to Gaia-Sausage-Enceladus.

Contribution

It introduces a novel graph attention autoencoder approach for chemical tagging and substructure identification in stellar halo data.

Findings

Successfully recovers major globular clusters in the dataset.

Estimates the in-situ stellar fraction at approximately 41%.

Identifies two chemically distinct clusters within Gaia-Sausage-Enceladus stars.

Abstract

Recent studies suggest that chemical abundances hold the key to disentangling halo substructure, providing a more reliable tracer than dynamics alone. We aim to probe the Milky Way stellar halo using high-dimensional chemical abundances from GALAH DR4. By leveraging multiple nucleosynthesis channels in synergy with integrals of motion (IoM), we extract information hidden in the raw abundance space to perform chemical tagging. With a graph attention autoencoder, we reconstruct a dynamics-informed, denoised chemical space and identify coherent stellar substructures by applying ensemble clustering. Our method successfully recovers the three largest globular clusters hidden in the dataset, estimates the in-situ fraction to be approximately 41\%, and chemically characterizes several dynamical halo substructures. Strikingly, stars dynamically associated with Gaia-Sausage-Enceladus (GSE) separate into two chemically distinct clusters. By examining their abundances, energy ($E$) and angular momentum ($L_z$) distributions, together with the metallicity trend with $E$, we connect these clusters to their birthplace within the progenitor by proposing a simple infall scenario: one cluster traces the metal-poor, less evolved outskirts, while the other traces the metal-rich, chemically evolved core.