The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence
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Villaescusa-Navarro_2022_ApJS_259_61.pdf
Description
Published version
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730.33 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Villaescusa-Navarro, Francisco
Genel, Shy
Anglés-Alcázar, Daniel
Thiele, Leander
Dave, Romeel
Narayanan, Desika
Nicola, Andrina
Li, Yin
Villanueva-Domingo, Pablo
Wandelt, Benjamin
Date Issued
April 1, 2022
Journal
The Astrophysical Journal Supplement Series
Publisher
American Astronomical Society
Citation
Villaescusa-Navarro, Francisco, Genel, Shy, Anglés-Alcázar, Daniel, Thiele, Leander, Dave, Romeel et al. 2022. "The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence." The Astrophysical Journal Supplement Series, 259 (2).
Version
Final published version
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.
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.
MIT Department
MIT Kavli Institute for Astrophysics and Space Research
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3847/1538-4365/ac5ab0
