  Galaxies are theorized to form and co-evolve with their dark matter halos,
such that their stellar masses and halo masses should be well-correlated.
However, it is not known whether other observable galaxy features, such as
their morphologies or large-scale environments, can be used to tighten the
correlation between galaxy properties and halo masses. In this work, we train a
baseline random forest model to predict halo mass using galaxy features from
the Illustris TNG50 hydrodynamical simulation, and compare with convolutional
neural networks (CNNs) and graph neural networks (GNNs) trained respectively
using galaxy image cutouts and galaxy point clouds. The best baseline model has
a root mean squared error (RMSE) of 0.310 and mean absolute error (MAE) of
0.220, compared to the CNN (RSME=0.359, MAE=0.238), GNN (RMSE=0.248,
MAE=0.158), and a novel combined CNN+GNN (RMSE=0.248, MAE=0.144). The CNN is
likely limited by our small data set, and we anticipate that the CNN and
CNN+GNN would benefit from training on larger cosmological simulations. We
conclude that deep learning models can leverage information from galaxy
appearances and environment, beyond commonly used summary statistics, in order
to better predict the halo mass.
