activity
20132023
most citedThe CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations

305 citations · 962 across the 30 of their papers we have counts for

collaborators
Showing 2022Show all

7 papers · 1 filter

astro-ph.CO2022★ 4 cited

HIGlow: Conditional Normalizing Flows for High-Fidelity HI Map Modeling

Roy Friedman, Sultan Hassan

Extracting the maximum amount of cosmological and astrophysical information from upcoming large-scale surveys remains a challenge. This includes evaluating the exact likelihood, pa…

astro-ph.CO2022★ 4 cited

Emulating cosmological multifields with generative adversarial networks

Sambatra Andrianomena, Francisco Villaescusa-Navarro, Sultan Hassan

We explore the possibility of using deep learning to generate multifield images from state-of-the-art hydrodynamic simulations of the CAMELS project. We use a generative adversaria…

astro-ph.CO2022★ 1 cited

Towards a non-Gaussian Generative Model of large-scale Reionization Maps

Yu-Heng Lin, Sultan Hassan, Bruno Régaldo-Saint Blancard +2

High-dimensional data sets are expected from the next generation of large-scale surveys. These data sets will carry a wealth of information about the early stages of galaxy formati…

astro-ph.CO2022★ 1 cited

Bridging the Gap between Cosmic Dawn and Reionization favors Faint Galaxies-dominated Models

Ankita Bera, Sultan Hassan, Aaron Smith +4

It has been claimed that traditional models struggle to explain the tentative detection of the 21\,cm absorption trough centered at measured by the EDGES collaboration. O…

astro-ph.CO2022★ 5 cited

Predictive uncertainty on improved astrophysics recovery from multifield cosmology

Sambatra Andrianomena, Sultan Hassan

We investigate how the constraints on cosmological and astrophysical parameters (, , , ) vary when exploiting information from multiple…

astro-ph.CO2022★ 25 cited

Wavelet Moments for Cosmological Parameter Estimation

Michael Eickenberg, Erwan Allys, Azadeh Moradinezhad Dizgah +10

Extracting non-Gaussian information from the non-linear regime of structure formation is key to fully exploiting the rich data from upcoming cosmological surveys probing the large-…

astro-ph.CO2022★ 67 cited

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…