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...

Full description

Bibliographic Details
Main Authors: Shao, Helen, Villaescusa-Navarro, Francisco, Genel, Shy, Spergel, David N, Anglés-Alcázar, Daniel, Hernquist, Lars, Davé, Romeel, Narayanan, Desika, Contardo, Gabriella, Vogelsberger, Mark
Other Authors: Massachusetts Institute of Technology. Department of Physics
Format: Article
Language:English
Published: American Astronomical Society 2022
Online Access:https://hdl.handle.net/1721.1/142369
_version_ 1826214989062471680
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
work_keys_str_mv AT shaohelen findinguniversalrelationsinsubhalopropertieswithartificialintelligence
AT villaescusanavarrofrancisco findinguniversalrelationsinsubhalopropertieswithartificialintelligence
AT genelshy findinguniversalrelationsinsubhalopropertieswithartificialintelligence
AT spergeldavidn findinguniversalrelationsinsubhalopropertieswithartificialintelligence
AT anglesalcazardaniel findinguniversalrelationsinsubhalopropertieswithartificialintelligence
AT hernquistlars findinguniversalrelationsinsubhalopropertieswithartificialintelligence
AT daveromeel findinguniversalrelationsinsubhalopropertieswithartificialintelligence
AT narayanandesika findinguniversalrelationsinsubhalopropertieswithartificialintelligence
AT contardogabriella findinguniversalrelationsinsubhalopropertieswithartificialintelligence
AT vogelsbergermark findinguniversalrelationsinsubhalopropertieswithartificialintelligence