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One of the goals of current particle physics research is to obtain evidencefor new physics, that is, physics beyond the Standard Model (BSM), ataccelerators such as the Large Hadron Collider (LHC) at CERN. The searches fornew physics are often guided by BSM theories that depend on many unknownparameters, which, in some cases, makes testing their predictions difficult. Inthis paper, machine learning is used to model the mapping from the parameterspace of the phenomenological Minimal Supersymmetric Standard Model (pMSSM), aBSM theory with 19 free parameters, to some of its predictions. Bayesian neuralnetworks are used to predict cross sections for arbitrary pMSSM parameterpoints, the mass of the associated lightest neutral Higgs boson, and thetheoretical viability of the parameter points. All three quantities are modeledwith average percent errors of 3.34% or less and in a time significantlyshorter than is possible with the supersymmetry codes from which the resultsare derived. These results are a further demonstration of the potential formachine learning to model accurately the mapping from the high dimensionalspaces of BSM theories to their predictions.

Bayesian Neural Networks for Fast SUSY Predictions