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Since the discovery of the Higgs boson, testing the many possible extensionsto the Standard Model has become a key challenge in particle physics. Thispaper discusses a new method for predicting the compatibility of new physicstheories with existing experimental data from particle colliders. Using machinelearning, 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, andallows for large-scale testing of new physics theories.

Picking the low-hanging fruit: testing new physics at scale with active learning