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Abstract Galaxies can be characterized by many internal properties such as stellar mass, gas metallicity, and star formation rate. We quantify the amount of cosmological and astrophysical information that the internal properties of individual galaxies and their host dark matter halos contain. We train neural networks using hundreds of thousands of galaxies from 2000 state-of-the-art hydrodynamic simulations with different cosmologies and astrophysical models of the CAMELS project to perform likelihood-free inference on the value of the cosmological and astrophysical parameters. We find that knowing the internal properties of a single galaxy allows our models to infer the value of Ω m , at fixed Ω b , with a ∼10% precision, while no constraint can be placed on σ 8 . Our results hold for any type of galaxy, central or satellite, massive or dwarf, at all considered redshifts, z ≤ 3, and they incorporate uncertainties in astrophysics as modeled in CAMELS. However, our models are not robust to changes in subgrid physics due to the large intrinsic differences the two considered models imprint on galaxy properties. We find that the stellar mass, stellar metallicity, and maximum circular velocity are among the most important galaxy properties to determine the value of Ω m . We believe that our results can be explained by considering that changes in the value of Ω m , or potentially Ω b /Ω m , affect the dark matter content of galaxies, which leaves a signature in galaxy properties distinct from the one induced by galactic processes. Our results suggest that the low-dimensional manifold hosting galaxy properties provides a tight direct link between cosmology and astrophysics.
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This content will become publicly available on July 1, 2025
Cosmology with Multiple Galaxies
Abstract Recent works have discovered a relatively tight correlation between Ωmand the properties of individual simulated galaxies. Because of this, it has been shown that constraints on Ωmcan be placed using the properties of individual galaxies while accounting for uncertainties in astrophysical processes such as feedback from supernovae and active galactic nuclei. In this work, we quantify whether using the properties of multiple galaxies simultaneously can tighten those constraints. For this, we train neural networks to perform likelihood-free inference on the value of two cosmological parameters (Ωmandσ8) and four astrophysical parameters using the properties of several galaxies from thousands of hydrodynamic simulations of the CAMELS project. We find that using properties of more than one galaxy increases the precision of the Ωminference. Furthermore, using multiple galaxies enables the inference of other parameters that were poorly constrained with one single galaxy. We show that the same subset of galaxy properties are responsible for the constraints on Ωmfrom one and multiple galaxies. Finally, we quantify the robustness of the model and find that without identifying the model range of validity, the model does not perform well when tested on galaxies from other galaxy formation models.
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- Award ID(s):
- 2108944
- PAR ID:
- 10538578
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 969
- Issue:
- 2
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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