Deep chemical tagging

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

¹ INAF – Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125 Firenze, Italy
² INAF – Padova Observatory, Vicolo dell'Osservatorio 5, 35122 Padova, Italy
³ Dipartimento di Fisica e Astronomia, Universitá di Padova, 35122 Padova, Italy
⁴ Dipartimento di Fisica e Astronomia, Università degli Studi di Firenze, Via Sansone 1, 50019 Sesto Fiorentino, Italy
⁵ University of Birmingham, School of Computer Science, Birmingham B15 2TT, UK

^★ Corresponding author: lorenzo.spina@inaf.it

Received: 3 June 2025
Accepted: 11 August 2025

Abstract

Context. 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.

Aims. 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.

Methods. 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.

Results. Applied to ~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.

Conclusions. 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.