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Abstract We are entering an era in which we will be able to detect and characterize hundreds of dwarf galaxies within the Local Volume. It is already known that a strong dichotomy exists in the gas content and star formation properties of field dwarf galaxies versus satellite dwarfs of larger galaxies. In this work, we study the more subtle differences that may be detectable in galaxies as a function of distance from a massive galaxy, such as the Milky Way. We compare smoothed particle hydrodynamic simulations of dwarf galaxies formed in a Local Volume-like environment (several megaparsecs away from a massive galaxy) to those formed nearer to Milky Way–mass halos. We find that the impact of environment on dwarf galaxies extends even beyond the immediate region surrounding Milky Way–mass halos. Even before being accreted as satellites, dwarf galaxies near a Milky Way–mass halo tend to have higher stellar masses for their halo mass than more isolated galaxies. Dwarf galaxies in high-density environments also tend to grow faster and form their stars earlier. We show observational predictions that demonstrate how these trends manifest in lower quenching rates, higher Hifractions, and bluer colors for more isolated dwarf galaxies.
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Inferring Halo Masses with Graph Neural Networks
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 ).
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- Award ID(s):
- 2108944
- PAR ID:
- 10349264
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 935
- Issue:
- 1
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 30
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3847/1538-4357/ac7aa3
