69 citations · 249 across the 10 of their papers we have counts for
10 papers
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…
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +25
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containi…
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…