Calibrating Cosmological Simulations with Implicit Likelihood Inference Using Galaxy Growth Observables

Calibrating Cosmological Simulations with Implicit Likelihood Inference Using Galaxy Growth Observables

In a novel approach employing implicit likelihood inference (ILI), also known as likelihood-free inference, we calibrate the parameters of cosmological hydrodynamic simulations against observations, which has previously been unfeasible due to the high computational cost of these simulations. For com...

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Bibliographic Details
Main Authors: Yongseok Jo, Shy Genel, Benjamin Wandelt, Rachel S. Somerville, Francisco Villaescusa-Navarro, Greg L. Bryan, Daniel Anglés-Alcázar, Daniel Foreman-Mackey, Dylan Nelson, Ji-hoon Kim
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:The Astrophysical Journal
Subjects:
Nonparametric inference
Likelihood ratio test
Magnetohydrodynamical simulations
Cosmological parameters
Neural networks
Bayes’ Theorem
Online Access:https://doi.org/10.3847/1538-4357/aca8fe

Internet

https://doi.org/10.3847/1538-4357/aca8fe

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