Finding Universal Relations in Subhalo Properties with Artificial Intelligence
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Shao_2022_ApJ_927_85.pdf
Description
Published version
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5.37 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Shao, Helen
Villaescusa-Navarro, Francisco
Genel, Shy
Spergel, David N
Anglés-Alcázar, Daniel
Hernquist, Lars
Davé, Romeel
Narayanan, Desika
Contardo, Gabriella
Vogelsberger, Mark
Date Issued
March 1, 2022
Journal
The Astrophysical Journal
Publisher
American Astronomical Society
Citation
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).
Version
Final published version
Abstract
Abstract
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.
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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
MIT Kavli Institute for Astrophysics and Space Research
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Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.3847/1538-4357/ac4d30
