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

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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Cited by in corpus (5)

• Exploration of Parameter Spaces Assisted by Machine Learning
• Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning
• Active learning BSM parameter spaces
• Quantum Active Learning
• Active Learning in Physics: From 101, to Progress, and Perspective