Symbolic regression for beyond the standard model physics

Abstract

We propose symbolic regression as a powerful tool for the numerical studies of proposed models of physics beyond the Standard Model. In this paper we demonstrate the efficacy of the method on a benchmark model, namely the constrained minimal supersymmetric Standard Model, which has a four-dimensional parameter space. 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 two orders of magnitude more rapidly than is possible using conventional methods. Published by the American Physical Society 2025