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

Juan Rocamonde

Abstract:

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.