Cosmology with Multiple Galaxies

Abstract

Recent works have discovered a relatively tight correlation between Ωm and the properties of individual simulated galaxies. Because of this, it has been shown that constraints on Ωm can 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 (Ωm and σ 8) 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 Ωm inference. 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 Ωm from 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.

Can We Constrain Warm Dark Matter Masses with Individual Galaxies?

“…In our case, for simplicity, we decided to optimize it. 17 https://optuna.org 18 We note that there are more powerful statistics tests than the ones presented here, but we employ them as a way to compare with previous works (Villaescusa-Navarro et al 2022;Chawak et al 2024;Echeverri-Rojas et al 2023) that used these metrics due to the different inference method.…”

“…Recent works have identified the existence of a tight correlation between the fraction of matter in the total matter density of our Universe Ω m and the properties of individual galaxies in state-of-the-art hydrodynamic simulations (Villaescusa-Navarro et al 2022;Chawak et al 2024;Echeverri-Rojas et al 2023).…”

“…Villaescusa-Navarro et al (2022) indicated that the existence of the above correlation may be due to the fact that changing the value of Ω m affects the overall dark matter content of galaxies and their assembly history, changing internal galaxy properties in a way that is not degenerate with feedback. In other words, injecting dark matter into galaxies (or changing their assembly history) has an impact on galaxy properties that is different (when looking at the data in high dimensions) from the one induced by feedback (Chawak et al 2024;Echeverri-Rojas et al 2023).…”

Cosmology from One Galaxy in a Void?

“…Recently, relying on machine learning, Villaescusa-Navarro et al (2022) extracted cosmological constraints from the properties of one galaxy. Subsequently, Chawak et al (2024) showed that constraints can be improved when using multiple galaxies. This method has also been tested in Echeverri-Rojas et al (2023) using four different simulations, including IllustrisTNG (Marinacci et al 2018;Naiman et al 2018;Springel et al 2018;Nelson et al 2019), SIMBA (Davé et al 2019), Astrid (Bird et al 2022;Ni et al 2022Ni et al , 2023, and Magneticum (Dolag 2015).…”