Symbolic Regression for Beyond the Standard Model Physics

TL;DR

This paper demonstrates the use of symbolic regression to derive analytical expressions for key observables in a supersymmetric model, enabling rapid global fits of model parameters.

Contribution

It introduces symbolic regression as a novel approach for modeling beyond the Standard Model physics and provides analytical expressions for observables in the CMSSM.

Findings

Derived analytical expressions for Higgs mass, muon g-2, and dark matter density.

Enabled rapid global fits of model parameters.

Achieved faster parameter estimation compared to traditional methods.

Abstract

We propose symbolic regression as a powerful tool for studying Beyond the Standard Model physics. As a benchmark model, we consider the so-called Constrained Minimal Supersymmetric Standard Model, which has a four-dimensional parameter space defined at the GUT scale. We provide a set of analytical expressions that reproduce three low-energy observables of interest in terms of the parameters of the theory: the Higgs mass, the contribution to the anomalous magnetic moment of the muon, and the cold dark matter relic density. To demonstrate the power of the approach, we employ the symbolic expressions in a global fits analysis to derive the posterior probability densities of the parameters, which are obtained extremely rapidly in comparison with conventional methods.