Finding universal relations in Subhalo properties with artificial intelligence

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dc.contributor.authorDavé, Romeel
dc.date.accessioned2022-08-15T09:00:00Z
dc.date.available2022-08-15T09:00:00Z
dc.date.issued2022
dc.description.abstractWe 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.en_US
dc.identifier.citationShao, Villaescusa-Navarro, F., Genel, S., Spergel, D. N., Anglés-Alcázar, D., Hernquist, L., Davé, R., Narayanan, D., Contardo, G., & Vogelsberger, M. (2022). Finding Universal Relations in Subhalo Properties with Artificial Intelligence. The Astrophysical Journal, 927(1), 85–. https://doi.org/10.3847/1538-4357/ac4d30en_US
dc.identifier.urihttps://doi.org/10.3847/1538-4357/ac4d30
dc.identifier.urihttp://hdl.handle.net/10566/7729
dc.language.isoenen_US
dc.publisherThe Astrophysical Journalen_US
dc.subjectLarge-scale structure of the universeen_US
dc.subjectAstrostatisticsen_US
dc.subjectGalactic and extragalactic astronomyen_US
dc.subjectComputational methodsen_US
dc.subjectCosmology and Astrophysicsen_US
dc.subjectSubhaloen_US
dc.subjectMass Subhaloen_US
dc.titleFinding universal relations in Subhalo properties with artificial intelligenceen_US
dc.typeArticleen_US

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