Neural networks for boosted di-\(\tau\) identification

Neural networks for boosted di-\(\tau\) identification

We train several neural networks and boosted decision trees to discriminate fully-hadronic boosted di-\(\tau\) topologies against background QCD jets, using calorimeter and tracking information. Boosted di-\(\tau\) topologies consisting of a pair of highly collimated \(\tau\)-leptons, arise from the...

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Veröffentlicht in:arXiv.org 2024-03
Hauptverfasser: Tamir, Nadav, Bessudo, Ilan, Chen, Boping, Raiko, Hely, Barak, Liron
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Decision trees
Leptons
Machine learning
Network topologies
Neural networks
Quantum chromodynamics
Standard model (particle physics)
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creator Tamir, Nadav
Bessudo, Ilan
Chen, Boping
Raiko, Hely
Barak, Liron
description We train several neural networks and boosted decision trees to discriminate fully-hadronic boosted di-\(\tau\) topologies against background QCD jets, using calorimeter and tracking information. Boosted di-\(\tau\) topologies consisting of a pair of highly collimated \(\tau\)-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.
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subjects Decision trees
Leptons
Machine learning
Network topologies
Neural networks
Quantum chromodynamics
Standard model (particle physics)
title Neural networks for boosted di-\(\tau\) identification
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