Finding Universal Relations in Subhalo Properties with Artificial Intelligence
<jats:title>Abstract</jats:title> <jats:p>We use a generic formalism designed to search for relations in high-dimensional spaces to determine if the total mass of a subhalo can be predicted from other internal properties such as velocity dispersion, radius, or star...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Astronomical Society
2022
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| Online Access: | https://hdl.handle.net/1721.1/142369 |
| _version_ | 1826214989062471680 |
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| author | Shao, Helen Villaescusa-Navarro, Francisco Genel, Shy Spergel, David N Anglés-Alcázar, Daniel Hernquist, Lars Davé, Romeel Narayanan, Desika Contardo, Gabriella Vogelsberger, Mark |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Shao, Helen Villaescusa-Navarro, Francisco Genel, Shy Spergel, David N Anglés-Alcázar, Daniel Hernquist, Lars Davé, Romeel Narayanan, Desika Contardo, Gabriella Vogelsberger, Mark |
| author_sort | Shao, Helen |
| collection | MIT |
| description | <jats:title>Abstract</jats:title>
<jats:p>We use a generic formalism designed to search for relations in high-dimensional spaces to determine if the total mass of a subhalo can be predicted from other internal properties such as velocity dispersion, radius, or star formation rate. We train neural networks using data from the Cosmology and Astrophysics with MachinE Learning Simulations project and show that the model can predict the total mass of a subhalo with high accuracy: more than 99% of the subhalos have a predicted mass within 0.2 dex of their true value. The networks exhibit surprising extrapolation properties, being able to accurately predict the total mass of any type of subhalo containing any kind of galaxy at any redshift from simulations with different cosmologies, astrophysics models, subgrid physics, volumes, and resolutions, indicating that the network may have found a universal relation. We then use different methods to find equations that approximate the relation found by the networks and derive new analytic expressions that predict the total mass of a subhalo from its radius, velocity dispersion, and maximum circular velocity. We show that in some regimes, the analytic expressions are more accurate than the neural networks. The relation found by the neural network and approximated by the analytic equation bear similarities to the virial theorem.</jats:p> |
| first_indexed | 2024-09-23T16:14:29Z |
| format | Article |
| id | mit-1721.1/142369 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:14:29Z |
| publishDate | 2022 |
| publisher | American Astronomical Society |
| record_format | dspace |
| spelling | mit-1721.1/1423692023-01-17T20:32:53Z Finding Universal Relations in Subhalo Properties with Artificial Intelligence Shao, Helen Villaescusa-Navarro, Francisco Genel, Shy Spergel, David N Anglés-Alcázar, Daniel Hernquist, Lars Davé, Romeel Narayanan, Desika Contardo, Gabriella Vogelsberger, Mark Massachusetts Institute of Technology. Department of Physics MIT Kavli Institute for Astrophysics and Space Research <jats:title>Abstract</jats:title> <jats:p>We use a generic formalism designed to search for relations in high-dimensional spaces to determine if the total mass of a subhalo can be predicted from other internal properties such as velocity dispersion, radius, or star formation rate. We train neural networks using data from the Cosmology and Astrophysics with MachinE Learning Simulations project and show that the model can predict the total mass of a subhalo with high accuracy: more than 99% of the subhalos have a predicted mass within 0.2 dex of their true value. The networks exhibit surprising extrapolation properties, being able to accurately predict the total mass of any type of subhalo containing any kind of galaxy at any redshift from simulations with different cosmologies, astrophysics models, subgrid physics, volumes, and resolutions, indicating that the network may have found a universal relation. We then use different methods to find equations that approximate the relation found by the networks and derive new analytic expressions that predict the total mass of a subhalo from its radius, velocity dispersion, and maximum circular velocity. We show that in some regimes, the analytic expressions are more accurate than the neural networks. The relation found by the neural network and approximated by the analytic equation bear similarities to the virial theorem.</jats:p> 2022-05-05T18:24:43Z 2022-05-05T18:24:43Z 2022-03-01 2022-05-05T18:19:37Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142369 Shao, Helen, Villaescusa-Navarro, Francisco, Genel, Shy, Spergel, David N, Anglés-Alcázar, Daniel et al. 2022. "Finding Universal Relations in Subhalo Properties with Artificial Intelligence." The Astrophysical Journal, 927 (1). en 10.3847/1538-4357/ac4d30 The Astrophysical Journal Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0 application/pdf American Astronomical Society American Astronomical Society |
| spellingShingle | Shao, Helen Villaescusa-Navarro, Francisco Genel, Shy Spergel, David N Anglés-Alcázar, Daniel Hernquist, Lars Davé, Romeel Narayanan, Desika Contardo, Gabriella Vogelsberger, Mark Finding Universal Relations in Subhalo Properties with Artificial Intelligence |
| title | Finding Universal Relations in Subhalo Properties with Artificial Intelligence |
| title_full | Finding Universal Relations in Subhalo Properties with Artificial Intelligence |
| title_fullStr | Finding Universal Relations in Subhalo Properties with Artificial Intelligence |
| title_full_unstemmed | Finding Universal Relations in Subhalo Properties with Artificial Intelligence |
| title_short | Finding Universal Relations in Subhalo Properties with Artificial Intelligence |
| title_sort | finding universal relations in subhalo properties with artificial intelligence |
| url | https://hdl.handle.net/1721.1/142369 |
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