Comparing machine learning models for tau triggers
Maayan Yaary Uriel Barron Luis Pascual Domínguez Boping Chen Liron Barak Erez Etzion Raja Giryes
This paper introduces novel 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 NN, visible
improvements in performance compared to standard tau triggers are observed. We
show how such an implementation may lower the current energy thresholds, thus
contributing to increasing the sensitivity of searches for new phenomena in
proton-proton collisions classified by low-energy tau leptons.