Trees versus Neural Networks for enhancing tau lepton real-time selection in proton-proton collisions
arxiv(2023)
摘要
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.
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