On the sensitivity of different galaxy properties to warm dark matter. Costanza, B., Wang, B. Y., Villaescusa-Navarro, F., Garcia, A. M., Rose, J. C., Vogelsberger, M., Torrey, P., Farahi, A., Shen, X., & Leisher, I. October, 2025. arXiv:2510.05037 [astro-ph]
On the sensitivity of different galaxy properties to warm dark matter [link]Paper  doi  abstract   bibtex   
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 R2 = 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.
@misc{costanza_sensitivity_2025,
	title = {On the sensitivity of different galaxy properties to warm dark matter},
	url = {http://arxiv.org/abs/2510.05037},
	doi = {10.3847/1538-4357/ae0e6c},
	abstract = {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 R2 = 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.},
	language = {en},
	urldate = {2025-10-09},
	author = {Costanza, Belén and Wang, Bonny Y. and Villaescusa-Navarro, Francisco and Garcia, Alex M. and Rose, Jonah C. and Vogelsberger, Mark and Torrey, Paul and Farahi, Arya and Shen, Xuejian and Leisher, Ilem},
	month = oct,
	year = {2025},
	note = {arXiv:2510.05037 [astro-ph]},
	keywords = {Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics},
}
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