  Galaxies co-evolve with their host dark matter halos. Models of the
galaxy-halo connection, calibrated using cosmological hydrodynamic simulations,
can be used to populate dark matter halo catalogs with galaxies. We present a
new method for inferring baryonic properties from dark matter subhalo
properties using message-passing graph neural networks (GNNs). After training
on subhalo catalog data from the Illustris TNG300-1 hydrodynamic simulation,
our GNN can infer stellar mass from the host and neighboring subhalo positions,
kinematics, masses, and maximum circular velocities. We find that GNNs can also
robustly estimate stellar mass from subhalo properties in 2d projection. While
other methods typically model the galaxy-halo connection in isolation, our GNN
incorporates information from galaxy environments, leading to more accurate
stellar mass inference.
