Year: 2022
Type: article
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. The PyTorch Geometric implementation of the GNN is publicly available on GitHub ( https://github.com/PabloVD/HaloGraphNet ).
Source: The Astrophysical Journal
Institutions: Universitat de València, Instituto de Física Corpuscular, Princeton University, Flatiron Institute, University of Connecticut, Columbia University, University of Bologna, Center for Astrophysics Harvard & Smithsonian, Massachusetts Institute of Technology, Royal Observatory, South African Astronomical Observatory, University of the Western Cape, University of Edinburgh, University of Florida
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,499
Awards: The Interscale Galactic Nuclei Simulations (IGNIS): Hyper-refined black hole growth and feedback in cosmological environments, Collaborative Research: CDS&E: Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) to maximize the science return of next generation cosmological experiments
OpenAlex ID: W3214323773