Finding universal relations in subhalo properties with artificial intelligence

TL;DR

This paper demonstrates that neural networks can predict subhalo total mass from internal properties with high accuracy, revealing a potential universal relation that generalizes across various cosmological simulations and is connected to the virial theorem.

Contribution

The study introduces a formalism using neural networks to discover universal relations in subhalo properties, and derives new analytic expressions for mass prediction.

Findings

Neural networks predict subhalo mass with over 99% accuracy within 0.2 dex.

The models generalize across different cosmologies and simulation parameters.

Analytic expressions can outperform neural networks in certain regimes.

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 (CAMELS) 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. We interpret the relation found by the neural network and approximated by the analytic equation as being connected to the virial theorem.