The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence
Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés‐Alcázar, Leander Thiele, Romeel Davé, Desika Narayanan, Andrina Nicola, Yin Li, Pablo Villanueva-Domingo, B. D. Wandelt, David N. Spergel, Rachel S. Somerville, José Manuel Zorrilla Matilla, Faizan G Mohammad, Sultan Hassan, Helen Shao, Digvijay Wadekar, Michael Eickenberg, Kaze W. K. Wong, Gabriella Contardo, Yongseok Jo, E. Barry Moser, Erwin T. Lau, Luis Fernando Machado Poletti Valle, Lucia A. Perez, Daisuke Nagai, Nicholas Battaglia, Mark Vogelsberger
Abstract
Abstract We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (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 more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span ∼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 data set of its kind containing more than 70 TB 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 .