Year: 2025
Type: article
Abstract: We study the impact of warm dark matter (WDM) particle mass on galaxy properties using 1024 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.
Source: The Astrophysical Journal
Institutions: Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de La Plata, Carnegie Mellon University, Princeton University, Flatiron Institute, NSF-Simons AI Institute for Cosmic Origins, University of Virginia, The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Massachusetts Institute of Technology, The University of Texas at Austin, Grinnell College
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
OpenAlex ID: W4414973284