Picking the low-hanging fruit: testing new physics at scale with active learning
High Energy Physics - Phenomenology
2022-07-20 v2
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.
Cite
@article{arxiv.2202.05882,
title = {Picking the low-hanging fruit: testing new physics at scale with active learning},
author = {Juan Rocamonde and Louie Corpe and Gustavs Zilgalvis and Maria Avramidou and Jon Butterworth},
journal= {arXiv preprint arXiv:2202.05882},
year = {2022}
}