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

DALL·E - A high-energy particle collision at the Large Hadron Collider, as simulated by an AI


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. By 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.

In SciPost Physics
Juan Rocamonde
Juan Rocamonde
Research Fellow

My interests include AI, human language, knowledge systems, physics.