Deep chemical tagging - Identifying open clusters and moving groups in chemical space with graph attention networks

Abstract: Reconstructing the formation history of the Milky Way is hindered by stellar migration, which erases kinematic birth signatures. In contrast, stellar chemical abundances remain stable and can be used to trace stars back to their birth environments through chemical tagging. This study aims to improve chemical tagging by developing a method that leverages kinematic and age information to enhance clustering in chemical space, while remaining grounded in chemistry. We implement a graph attention auto-encoder that encodes stars as nodes with chemical features and connects them via edges based on orbital similarity and age. The network learns an ``informed'' chemical space that accentuates coherent groupings.Applied to $\sim$47,000 APOGEE thin disk stars, the method identifies 282 stellar groups. Among them, five out of six open clusters are successfully recovered. Other groups align with the known moving groups Arch/Hat, Sirius, Hyades, and Hercules. Our approach enables chemically grounded yet kinematically and age informed chemical tagging. It significantly improves the identification of coherent stellar populations, offering a framework for future large-scale stellar archaeology efforts.