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The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence

22 September 2021
F. Villaescusa-Navarro
S. Genel
D. Anglés-Alcázar
L. Thiele
R. Davé
D. Narayanan
A. Nicola
Yin Li
Pablo Villanueva-Domingo
Benjamin Dan Wandelt
D. Spergel
R. Somerville
J. Z. Matilla
F. G. Mohammad
Sultan Hassan
Helen Shao
D. Wadekar
Michael Eickenberg
Kaze W. K. Wong
Gabriella Contardo
Yongseok Jo
E. Moser
E. Lau
Luis Fernando Machado Poletti Valle
L. A. Perez
D. Nagai
N. Battaglia
M. Vogelsberger
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Abstract

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 containing many different properties of cosmic gas, dark matter, and stars from 2,000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span ∼\sim∼100 million light years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine learning models, CMD is the largest dataset of its kind containing more than 70 Terabytes of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.

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