  One of the goals of current particle physics research is to obtain evidence
for new physics, that is, physics beyond the Standard Model (BSM), at
accelerators such as the Large Hadron Collider (LHC) at CERN. The searches for
new physics are often guided by BSM theories that depend on many unknown
parameters, which, in some cases, makes testing their predictions difficult. In
this paper, machine learning is used to model the mapping from the parameter
space of the phenomenological Minimal Supersymmetric Standard Model (pMSSM), a
BSM theory with 19 free parameters, to some of its predictions. Bayesian neural
networks are used to predict cross sections for arbitrary pMSSM parameter
points, the mass of the associated lightest neutral Higgs boson, and the
theoretical viability of the parameter points. All three quantities are modeled
with average percent errors of 3.34% or less and in a time significantly
shorter than is possible with the supersymmetry codes from which the results
are derived. These results are a further demonstration of the potential for
machine learning to model accurately the mapping from the high dimensional
spaces of BSM theories to their predictions.
