69 citations · 249 across the 10 of their papers we have counts for
9 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…
Robust field-level inference with dark matter halos
Helen Shao, Francisco Villaescusa-Navarro, Pablo Villanueva-Domingo +13
We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain…
The CAMELS project: public data release
Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar +44
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynami…
Robust marginalization of baryonic effects for cosmological inference at the field level
Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +11
We train neural networks to perform likelihood-free inference from 2D maps containing the total mass surface density from thousands of hydrodynamic simula…
Finding universal relations in subhalo properties with artificial intelligence
Helen Shao, Francisco Villaescusa-Navarro, Shy Genel +7
We use a generic formalism designed to search for relations in high-dimensional spaces to determine if the total mass of a subhalo can be predicted from other internal properties s…