Speaker
Description
Unraveling the building blocks of the Milky Way’s stellar halo is typically achieved using either stellar dynamics or chemical abundances. While abundances are generally considered more robust tracers, clustering in this high-dimensional chemical space is highly non-trivial: measurement noise at low [Fe/H], the intrinsic scatter of distinct systems, and, most importantly, the heavy overlap of accreted populations make reliable partitioning a significant challenge, even for state-of-the-art density-based clustering algorithms.
To overcome these limitations, I present a framework based on Graph Attention Networks (GATs) that jointly leverages 13 chemical abundances from GALAH DR4 alongside integrals of motion (energy, E, and angular momentum, Lz) from Gaia. The graph is constructed such that stars represent nodes, abundances serve as node features, and edges are formed based on proximity in the E-Lz space. This graph then serves as input to a GAT autoencoder trained strictly to reconstruct the node features, yielding a dynamics-informed, denoised chemical space optimized for density-based clustering. I show that clustering the reconstructed space recovers globular clusters with a homogeneity and completeness that significantly improves upon the performance of PCA or standard autoencoders. Additionally, it successfully separates the in situ from the accreted halo and recovers stars from the Gaia-Sausage-Enceladus system that have lost their dynamical coherence (but not the chemical one), thereby resolving previously unrecognized chemical substructures within it.
While the astrophysical interpretation of newly discovered substructures retrieved from these learned spaces must still be carefully validated against the original abundances, this graph-based representation learning approach effectively addresses some of the limitations of clustering in raw observational spaces. By inherently embedding kinematic context and simultaneously denoising the chemical signatures of overlapping systems, this methodology provides a powerful tool to unravel the complex merger history of our Galaxy in present and upcoming spectroscopic surveys.