Inferring halo masses with graph neural networks

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dc.contributor.authorVillanueva-Domingo, Pablo
dc.contributor.authorVillaescusa-Navarro, Francisco
dc.contributor.authorDave, Romeel
dc.date.accessioned2022-08-30T09:10:41Z
dc.date.available2022-08-30T09:10:41Z
dc.date.issued2022
dc.description.abstractUnderstanding the halo–galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a ∼0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method.en_US
dc.identifier.citationVillanueva-Domingo, P. et al. (2022). Inferring halo masses with graph neural networks. Astrophysical Journal, 935(1), 30. https://doi.org/10.3847/1538-4357/ac7aa3en_US
dc.identifier.issn1538-4357
dc.identifier.urihttps://doi.org/10.3847/1538-4357/ac7aa3
dc.identifier.urihttp://hdl.handle.net/10566/7782
dc.language.isoenen_US
dc.publisherInstitute of Physicsen_US
dc.subjectCosmologyen_US
dc.subjectNeural networksen_US
dc.subjectAstrophysicsen_US
dc.subjectAstronomyen_US
dc.subjectHaloen_US
dc.titleInferring halo masses with graph neural networksen_US
dc.typeArticleen_US

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