Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos

Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos

We train graph neural networks on halo catalogs from Gadget N -body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogs contain ≲5000 halos with masses ≳10 ^10 h ^−1 M _⊙ in a periodic volume of ${(25\,{h}^{-1}\,\mathrm{Mpc})}^{3}$ ; every halo in th...

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Bibliographic Details
Main Authors: Helen Shao, Francisco Villaescusa-Navarro, Pablo Villanueva-Domingo, Romain Teyssier, Lehman H. Garrison, Marco Gatti, Derek Inman, Yueying Ni, Ulrich P. Steinwandel, Mihir Kulkarni, Eli Visbal, Greg L. Bryan, Daniel Anglés-Alcázar, Tiago Castro, Elena Hernández-Martínez, Klaus Dolag
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:The Astrophysical Journal
Subjects:
Cosmology
Sigma8
Density parameters
Cosmological parameters from large-scale structure
Online Access:https://doi.org/10.3847/1538-4357/acac7a

Internet

https://doi.org/10.3847/1538-4357/acac7a

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