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<p>This paper introduces supervised learning techniques for real-time selection (triggering) of hadronically decaying tau leptons in proton-proton colliders. By implementing traditional 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 rule-based tau triggers are observed. We show how such an implementation may lower selection energy thresholds, thus 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 vs decision trees for tau triggers with conclusions relevant to other problems in physics.</p>

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Trees vs neural networks for enhancing tau lepton real-time selection in proton-proton collisions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Maayan Yaari,
  • Uriel Barron,
  • Luis Pascual Domínguez,
  • Boping Chen,
  • Liron Barak,
  • Erez Etzion,
  • Raja Giryes

摘要

This paper introduces supervised learning techniques for real-time selection (triggering) of hadronically decaying tau leptons in proton-proton colliders. By implementing traditional 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 rule-based tau triggers are observed. We show how such an implementation may lower selection energy thresholds, thus 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 vs decision trees for tau triggers with conclusions relevant to other problems in physics.