Learning the galaxy-environment connection with graph neural networks

TL;DR

This paper introduces a graph neural network approach to predict galaxy properties from dark matter subhalo data, leveraging environmental information for improved accuracy over traditional methods.

Contribution

It presents a novel GNN-based method that infers galaxy stellar mass from dark matter subhalo properties, including environmental context, trained on hydrodynamic simulation data.

Findings

GNN accurately predicts stellar mass from subhalo data

Environmental information improves inference accuracy

Method effective in 2D projection scenarios

Abstract

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.