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 |
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Hauptverfasser: | , , , , |
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. |
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ISSN: | 2331-8422 |
DOI: | 10.48550/arxiv.2312.08276 |