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

Understand

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.