no code implementations • 23 Oct 2023 • Natalí S. M. de Santi, Francisco Villaescusa-Navarro, L. Raul Abramo, Helen Shao, Lucia A. Perez, Tiago Castro, Yueying Ni, Christopher C. Lovell, Elena Hernandez-Martinez, Federico Marinacci, David N. Spergel, Klaus Dolag, Lars Hernquist, Mark Vogelsberger
In particular, de Santi et al. (2023) developed models that could accurately infer the value of $\Omega_{\rm m}$ from catalogs that only contain the positions and radial velocities of galaxies that are robust to uncertainties in astrophysics and subgrid models.
no code implementations • 27 Feb 2023 • Natalí S. M. de Santi, Helen Shao, Francisco Villaescusa-Navarro, L. Raul Abramo, Romain Teyssier, Pablo Villanueva-Domingo, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Elena Hernandez-Martinez, Ulrich P. Steinwandel, Christopher C. Lovell, Klaus Dolag, Tiago Castro, Mark Vogelsberger
We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project.
no code implementations • 14 Sep 2022 • 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 Angles-Alcazar, Tiago Castro, Elena Hernandez-Martinez, Klaus Dolag
More importantly, we find that our models are very robust: they can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses.