Symbolic Regression for Beyond the Standard Model Physics

Institute for Particle Physics Phenomenology, Department of Physics, Durham University, Durham DH1 3LE, U.K. 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. The chief test of any proposal for Beyond the Standard To avoid this bottleneck, it is natural to turn to Model (BSM) physics is to confront it with experimental machine learning to bypass the computation chain or data.