Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks
This addresses the problem of inferring invisible dark matter halo masses for astrophysicists, but it is incremental as it applies an existing GNN method to new simulation data.
The paper tackled estimating dark matter halo masses in simulated galaxy clusters by developing a graph neural network (GNN) model that uses stellar mass data, achieving superior predictive performance compared to baseline models.
Galaxies grow and evolve in dark matter halos. Because dark matter is not visible, galaxies' halo masses ($\rm{M}_{\rm{halo}}$) must be inferred indirectly. We present a graph neural network (GNN) model for predicting $\rm{M}_{\rm{halo}}$ from stellar mass ($\rm{M}_{*}$) in simulated galaxy clusters using data from the IllustrisTNG simulation suite. Unlike traditional machine learning models like random forests, our GNN captures the information-rich substructure of galaxy clusters by using spatial and kinematic relationships between galaxy neighbour. A GNN model trained on the TNG-Cluster dataset and independently tested on the TNG300 simulation achieves superior predictive performance compared to other baseline models we tested. Future work will extend this approach to different simulations and real observational datasets to further validate the GNN model's ability to generalise.