69 citations · 249 across the 10 of their papers we have counts for
5 papers · 1 filter
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
Natalí S. M. de Santi, Francisco Villaescusa-Navarro, L. Raul Abramo +11
It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood…
Quijote-PNG: The Information Content of the Halo Mass Function
Gabriel Jung, Andrea Ravenni, Marco Baldi +8
We study signatures of primordial non-Gaussianity (PNG) in the redshift-space halo field on non-linear scales, using a combination of three summary statistics, namely the halo mass…
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
Yueying Ni, Shy Genel, Daniel Anglés-Alcázar +12
We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets tha…
A universal equation to predict from halo and galaxy catalogues
Helen Shao, Natalí S. M de Santi, Francisco Villaescusa-Navarro +13
We discover analytic equations that can infer the value of from the positions and velocity moduli of halo and galaxy catalogues. The equations are derived by combining…
Robust Field-level Likelihood-free Inference with Galaxies
Natalí S. M. de Santi, Helen Shao, Francisco Villaescusa-Navarro +12
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. Our mode…