Neural networks for boosted di-τ identification

Nadav Tamir, Ilan Bessudo, Boping Chen, Hely Raiko, Liron Barak

Research output: Contribution to journalArticlepeer-review

Abstract

We train several neural networks and boosted decision trees to discriminate fully-hadronic boosted di-τ topologies against background QCD jets, using calorimeter and tracking information. Boosted di-τ topologies consisting of a pair of highly collimated τ-leptons, arise from the decay of a highly energetic Standard Model Higgs or Z boson or from particles beyond the Standard Model. We compare the tagging performance for different neural-network models and a boosted decision tree, the latter serving as a simple benchmark machine learning model. The code used to obtain the results presented in this paper is available on GitHub.

Original languageEnglish
Article numberP07004
JournalJournal of Instrumentation
Volume19
Issue number7
DOIs
StatePublished - 1 Jul 2024

Keywords

  • Analysis and statistical methods
  • Particle identification methods

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Mathematical Physics

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