Finding Universal Relations in Subhalo Properties with Artificial Intelligence
Abstract
We use a generic formalism designed to search for relations in highdimensional 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.
 Publication:

The Astrophysical Journal
 Pub Date:
 March 2022
 DOI:
 10.3847/15384357/ac4d30
 arXiv:
 arXiv:2109.04484
 Bibcode:
 2022ApJ...927...85S
 Keywords:

 Largescale structure of the universe;
 Astrostatistics;
 Galactic and extragalactic astronomy;
 Computational methods;
 902;
 1882;
 563;
 1965;
 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Astrophysics of Galaxies;
 Astrophysics  Instrumentation and Methods for Astrophysics
 EPrint:
 23 pages, 15 figures