Linking Warm Dark Matter to Merger Tree Histories via Deep Learning Networks. Leisher, I., Torrey, P., Garcia, A. M., Rose, J. C., Villaescusa-Navarro, F., Lubberts, Z., Farahi, A., O’Neil, S., Shen, X., Mostow, O., Kallivayalil, N., Zimmerman, D., Narayanan, D., & Vogelsberger, M. The Astrophysical Journal, 1005(2):186, July, 2026.
Linking Warm Dark Matter to Merger Tree Histories via Deep Learning Networks [link]Paper  doi  abstract   bibtex   
Dark matter (DM) halos form hierarchically in the Universe through a series of merger events. By pairing halo finder and postprocessing codes, merger series in cosmological simulations can be conveniently represented as a graph-like “tree” structure. Previous work has shown that these merger trees are sensitive to cosmological simulation parameters, but as structures comprised of DM halos, the outstanding question of their sensitivity to DM models remains unanswered. In this work, we investigate the feasibility of deep learning methods trained on merger trees to infer warm dark matter (WDM) particle masses from the DREAMS simulation suite. We organize the merger trees from 1024 zoom-in simulations into graphs with nodes representing halos at different epochs and edges denoting hereditary links. We vary the complexity of the node features included in the graphs, ranging from a single node feature up through an array of several galactic properties. We train a graph neural network (GNN) to predict the WDM mass using the graph representation of the merger tree as input. We find that the GNN can predict the mass of the WDM particle, with success depending on the graph complexity and node features. We extend the same methods to supernovae (SNe) and active galactic nuclei feedback parameters, successfully inferring the SNe parameters. With reduced accuracy, the GNN can even infer the WDM mass from merger tree histories without any node features, indicating that the structure of merger trees alone inherits information about the cosmological parameters of the simulations from which they form.
@article{leisher_linking_2026,
	title = {Linking {Warm} {Dark} {Matter} to {Merger} {Tree} {Histories} via {Deep} {Learning} {Networks}},
	volume = {1005},
	issn = {0004-637X, 1538-4357},
	url = {https://iopscience.iop.org/article/10.3847/1538-4357/ae7bec},
	doi = {10.3847/1538-4357/ae7bec},
	abstract = {Dark matter (DM) halos form hierarchically in the Universe through a series of merger events. By pairing halo finder and postprocessing codes, merger series in cosmological simulations can be conveniently represented as a graph-like “tree” structure. Previous work has shown that these merger trees are sensitive to cosmological simulation parameters, but as structures comprised of DM halos, the outstanding question of their sensitivity to DM models remains unanswered. In this work, we investigate the feasibility of deep learning methods trained on merger trees to infer warm dark matter (WDM) particle masses from the DREAMS simulation suite. We organize the merger trees from 1024 zoom-in simulations into graphs with nodes representing halos at different epochs and edges denoting hereditary links. We vary the complexity of the node features included in the graphs, ranging from a single node feature up through an array of several galactic properties. We train a graph neural network (GNN) to predict the WDM mass using the graph representation of the merger tree as input. We find that the GNN can predict the mass of the WDM particle, with success depending on the graph complexity and node features. We extend the same methods to supernovae (SNe) and active galactic nuclei feedback parameters, successfully inferring the SNe parameters. With reduced accuracy, the GNN can even infer the WDM mass from merger tree histories without any node features, indicating that the structure of merger trees alone inherits information about the cosmological parameters of the simulations from which they form.},
	language = {en},
	number = {2},
	urldate = {2026-07-15},
	journal = {The Astrophysical Journal},
	author = {Leisher, Ilem and Torrey, Paul and Garcia, Alex M. and Rose, Jonah C. and Villaescusa-Navarro, Francisco and Lubberts, Zachary and Farahi, Arya and O’Neil, Stephanie and Shen, Xuejian and Mostow, Olivia and Kallivayalil, Nitya and Zimmerman, Dhruv and Narayanan, Desika and Vogelsberger, Mark},
	month = jul,
	year = {2026},
	keywords = {WG: Explainable},
	pages = {186},
}
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