Neural networks for boosted di-$τ$ identification
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913420118327296 |
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| author | Tamir, Nadav Bessudo, Ilan Chen, Boping Raiko, Hely Barak, Liron |
| author_facet | Tamir, Nadav Bessudo, Ilan Chen, Boping Raiko, Hely Barak, Liron |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_08276 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Neural networks for boosted di-$τ$ identification Tamir, Nadav Bessudo, Ilan Chen, Boping Raiko, Hely Barak, Liron High Energy Physics - Experiment High Energy Physics - Phenomenology 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. |
| title | Neural networks for boosted di-$τ$ identification |
| topic | High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2312.08276 |