  Since the discovery of the Higgs boson, testing the many possible extensions
to the Standard Model has become a key challenge in particle physics. This
paper discusses a new method for predicting the compatibility of new physics
theories with existing experimental data from particle colliders. Using machine
learning, the technique obtained comparable results to previous methods (>90%
precision and recall) with only a fraction of their computing resources (<10%).
This makes it possible to test models that were impossible to probe before, and
allows for large-scale testing of new physics theories.
