The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence
<jats:title>Abstract</jats:title> <jats:p>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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Astronomical Society
2022
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| Online Access: | https://hdl.handle.net/1721.1/142371 |
| _version_ | 1826200836575854592 |
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| author | Villaescusa-Navarro, Francisco Genel, Shy Anglés-Alcázar, Daniel Thiele, Leander Dave, Romeel Narayanan, Desika Nicola, Andrina Li, Yin Villanueva-Domingo, Pablo Wandelt, Benjamin Spergel, David N Somerville, Rachel S Zorrilla Matilla, Jose Manuel Mohammad, Faizan G Hassan, Sultan Shao, Helen Wadekar, Digvijay Eickenberg, Michael Wong, Kaze WK Contardo, Gabriella Jo, Yongseok Moser, Emily Lau, Erwin T Machado Poletti Valle, Luis Fernando Perez, Lucia A Nagai, Daisuke Battaglia, Nicholas Vogelsberger, Mark |
| author2 | MIT Kavli Institute for Astrophysics and Space Research |
| author_facet | MIT Kavli Institute for Astrophysics and Space Research Villaescusa-Navarro, Francisco Genel, Shy Anglés-Alcázar, Daniel Thiele, Leander Dave, Romeel Narayanan, Desika Nicola, Andrina Li, Yin Villanueva-Domingo, Pablo Wandelt, Benjamin Spergel, David N Somerville, Rachel S Zorrilla Matilla, Jose Manuel Mohammad, Faizan G Hassan, Sultan Shao, Helen Wadekar, Digvijay Eickenberg, Michael Wong, Kaze WK Contardo, Gabriella Jo, Yongseok Moser, Emily Lau, Erwin T Machado Poletti Valle, Luis Fernando Perez, Lucia A Nagai, Daisuke Battaglia, Nicholas Vogelsberger, Mark |
| author_sort | Villaescusa-Navarro, Francisco |
| collection | MIT |
| description | <jats:title>Abstract</jats:title>
<jats:p>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 <jats:italic>N</jats:italic>-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 <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://camels-multifield-dataset.readthedocs.io" xlink:type="simple">https://camels-multifield-dataset.readthedocs.io</jats:ext-link>.</jats:p> |
| first_indexed | 2024-09-23T11:42:13Z |
| format | Article |
| id | mit-1721.1/142371 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:42:13Z |
| publishDate | 2022 |
| publisher | American Astronomical Society |
| record_format | dspace |
| spelling | mit-1721.1/1423712023-07-28T19:55:37Z The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence Villaescusa-Navarro, Francisco Genel, Shy Anglés-Alcázar, Daniel Thiele, Leander Dave, Romeel Narayanan, Desika Nicola, Andrina Li, Yin Villanueva-Domingo, Pablo Wandelt, Benjamin Spergel, David N Somerville, Rachel S Zorrilla Matilla, Jose Manuel Mohammad, Faizan G Hassan, Sultan Shao, Helen Wadekar, Digvijay Eickenberg, Michael Wong, Kaze WK Contardo, Gabriella Jo, Yongseok Moser, Emily Lau, Erwin T Machado Poletti Valle, Luis Fernando Perez, Lucia A Nagai, Daisuke Battaglia, Nicholas Vogelsberger, Mark MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology. Department of Physics <jats:title>Abstract</jats:title> <jats:p>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 <jats:italic>N</jats:italic>-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 <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://camels-multifield-dataset.readthedocs.io" xlink:type="simple">https://camels-multifield-dataset.readthedocs.io</jats:ext-link>.</jats:p> 2022-05-05T18:41:26Z 2022-05-05T18:41:26Z 2022-04-01 2022-05-05T18:31:37Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142371 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). en 10.3847/1538-4365/ac5ab0 The Astrophysical Journal Supplement Series Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0 application/pdf American Astronomical Society American Astronomical Society |
| spellingShingle | Villaescusa-Navarro, Francisco Genel, Shy Anglés-Alcázar, Daniel Thiele, Leander Dave, Romeel Narayanan, Desika Nicola, Andrina Li, Yin Villanueva-Domingo, Pablo Wandelt, Benjamin Spergel, David N Somerville, Rachel S Zorrilla Matilla, Jose Manuel Mohammad, Faizan G Hassan, Sultan Shao, Helen Wadekar, Digvijay Eickenberg, Michael Wong, Kaze WK Contardo, Gabriella Jo, Yongseok Moser, Emily Lau, Erwin T Machado Poletti Valle, Luis Fernando Perez, Lucia A Nagai, Daisuke Battaglia, Nicholas Vogelsberger, Mark The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence |
| title | The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence |
| title_full | The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence |
| title_fullStr | The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence |
| title_full_unstemmed | The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence |
| title_short | The CAMELS Multifield Data Set: Learning the Universe’s Fundamental Parameters with Artificial Intelligence |
| title_sort | camels multifield data set learning the universe s fundamental parameters with artificial intelligence |
| url | https://hdl.handle.net/1721.1/142371 |
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