Inferring halo masses with graph neural networks
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Physics
Abstract
Understanding 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.
Description
Keywords
Cosmology, Neural networks, Astrophysics, Astronomy, Halo
Citation
Villanueva-Domingo, P. et al. (2022). Inferring halo masses with graph neural networks. Astrophysical Journal, 935(1), 30. https://doi.org/10.3847/1538-4357/ac7aa3