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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…
Inferring halo masses with Graph Neural Networks
Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar +7
Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work we build a model that infers the…
Revisiting the [C II] line-intensity mapping power spectrum from the EoR using non-uniform line-luminosity scatter
Chandra Shekhar Murmu, Karen P. Olsen, Thomas R. Greve +9
Detecting the line-intensity mapping (LIM) signal from the galaxies of the Epoch of Reionization is an emerging tool to constrain their role in reionization. Ongoing and upcoming e…
HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows
Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt +11
A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys. It is crucial to prepare for these surveys now and develop to…
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…
Multifield Cosmology with Artificial Intelligence
Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel +10
Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly und…