Kavli Affiliate: Mark Vogelsberger
| First 5 Authors: , , , ,
| Summary:
We study the impact of warm dark matter (WDM) particle mass on galaxy
properties using 1,024 state-of-the-art cosmological hydrodynamical simulations
from the DREAMS project. We begin by using a Multilayer Perceptron (MLP)
coupled with a normalizing flow to explore global statistical descriptors of
galaxy populations, such as the mean, standard deviation, and histograms of 14
galaxy properties. We find that subhalo gas mass is the most informative
feature for constraining the WDM mass, achieving a determination coefficient of
R^2 = 0.9. We employ symbolic regression to extract simple, interpretable
relations with the WDM particle mass. Finally, we adopt a more localized
approach by selecting individual dark matter halos and using a Graph Neural
Network (GNN) with a normalizing flow to infer the WDM mass, incorporating
subhalo properties as node features and global simulation statistics as
graph-level features. The GNN approach yields only a residual improvement over
MLP models based solely on global features, indicating that most of the
predictive power resides in the global descriptors, with only marginal gains
from halo-level information.
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