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arXiv · 2202.05882

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

Juan Rocamonde·Louie Corpe·Gustavs Zilgalvis·Maria Avramidou·Jon Butterworth
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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.

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Juan Rocamonde, Louie Corpe, Gustavs Zilgalvis, Maria Avramidou, Jon Butterworth. 2022-02-11. Picking the low-hanging fruit: testing new physics at scale with active learning. https://doi.org/10.21468/scipostphys.13.1.002

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