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Computer Science > Machine Learning

arXiv:2109.10915 (cs)
[Submitted on 22 Sep 2021]

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

Authors:Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar, Leander Thiele, Romeel Dave, Desika Narayanan, Andrina Nicola, Yin Li, Pablo Villanueva-Domingo, Benjamin Wandelt, David N. Spergel, Rachel S. Somerville, Jose Manuel Zorrilla Matilla, Faizan G. Mohammad, Sultan Hassan, Helen Shao, Digvijay Wadekar, Michael Eickenberg, Kaze W.K. Wong, Gabriella Contardo, Yongseok Jo, Emily Moser, Erwin T. Lau, Luis Fernando Machado Poletti Valle, Lucia A. Perez, Daisuke Nagai, Nicholas Battaglia, Mark 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 this https URL.
Comments: 17 pages, 1 figure. Third paper of a series of four. Hundreds of thousands of labeled 2D maps and 3D grids from thousands of simulated universes publicly available at this https URL
Subjects: Machine Learning (cs.LG); Cosmology and Nongalactic Astrophysics (astro-ph.CO); Astrophysics of Galaxies (astro-ph.GA); Instrumentation and Methods for Astrophysics (astro-ph.IM); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2109.10915 [cs.LG]
  (or arXiv:2109.10915v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.10915
Related DOI: https://doi.org/10.3847/1538-4365/ac5ab0

Submission history

From: Francisco Villaescusa-Navarro [view email]
[v1] Wed, 22 Sep 2021 18:00:01 UTC (2,539 KB)
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Francisco Villaescusa-Navarro
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