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Title: 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.  more » « less
Award ID(s):
2108944
PAR ID:
10538578
Author(s) / Creator(s):
; ; ; ; ;
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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