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
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2312.08276