Trees versus Neural Networks for enhancing tau lepton real-time selection in proton-proton collisions
This work addresses the problem of enhancing sensitivity in particle physics searches for new phenomena, but it is incremental as it applies existing ML methods to a specific domain.
The paper tackled real-time selection of tau leptons in proton-proton collisions by implementing decision trees and neural networks, resulting in visible performance improvements and potential lower energy thresholds compared to standard triggers.
This paper introduces supervised learning techniques for real-time selection (triggering) of hadronically decaying tau leptons in proton-proton colliders. By implementing classic machine learning decision trees and advanced deep learning models, such as Multi-Layer Perceptron or residual neural networks, visible improvements in performance compared to standard threshold tau triggers are observed. We show how such an implementation may lower selection energy thresholds, thus contributing to increasing the sensitivity of searches for new phenomena in proton-proton collisions classified by low-energy tau leptons. Moreover, we analyze when it is better to use neural networks versus decision trees for tau triggers with conclusions relevant to other problems in physics.