Speaker
Description
Stellar streams, the remnants of tidally disrupted dwarf galaxies and globular clusters, are powerful probes of galaxy assembly and dark matter. While many streams have been discovered in the Milky Way, comparisons with cosmological simulations suggest that both the census of streams and the identification of their constituent stars remain incomplete due to overwhelming stellar backgrounds, diverse stream morphologies, and partial or uncertain observational data. We present a machine-learning framework for stellar stream identification based on graph neural networks (GNNs) tuned for hyperparameter transfer, a strategy for parametrizing NNs so that near-optimal hyperparameters identified in small, cheap-to-optimize models, are the same for their larger counterparts. Unlike traditional cut-based analyses or those that rely on explicit models of the Galactic potential, GNNs learn local structures by message passing across stars embedded as graph nodes. This relational inductive bias makes them well suited to the detection of faint tidal features, diffuse streams, and irregular substructures. We apply this framework to stellar populations in the Dark Energy Survey, with the goal of improving membership identification for known streams and establishing a scalable foundation for future stream discovery efforts with upcoming surveys such as LSST and Roman in the Milky Way and nearby galaxies.