Searches for heavy neutral leptons with machine learning at the CMS experiment
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917887516606464 |
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| author | Knolle, Joscha |
| author_facet | Knolle, Joscha |
| contents | Two recent searches for heavy neutral leptons (HNLs) performed with proton-proton collision data recorded at 13 TeV by the CMS experiment are presented. A prompt search in the trilepton final state analyses events with exactly three charged leptons originating from the primary proton-proton interaction vertex, targeting HNL masses between 10 GeV and 1.5 TeV. A displaced search in the dilepton final state analyses events with exactly one prompt charged lepton and a second nonprompt charged lepton associated with a jet and a secondary vertex, targeting HNL masses between 1 and 20 GeV. In both searches, machine-learning methods are applied to separate the HNL signal from the standard model background. Exclusion limits are set on the HNL coupling strength as a function of the HNL mass, covering different mass ranges and HNL scenarios. In several cases, the results exceed previous limits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06298 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Searches for heavy neutral leptons with machine learning at the CMS experiment Knolle, Joscha High Energy Physics - Experiment Two recent searches for heavy neutral leptons (HNLs) performed with proton-proton collision data recorded at 13 TeV by the CMS experiment are presented. A prompt search in the trilepton final state analyses events with exactly three charged leptons originating from the primary proton-proton interaction vertex, targeting HNL masses between 10 GeV and 1.5 TeV. A displaced search in the dilepton final state analyses events with exactly one prompt charged lepton and a second nonprompt charged lepton associated with a jet and a secondary vertex, targeting HNL masses between 1 and 20 GeV. In both searches, machine-learning methods are applied to separate the HNL signal from the standard model background. Exclusion limits are set on the HNL coupling strength as a function of the HNL mass, covering different mass ranges and HNL scenarios. In several cases, the results exceed previous limits. |
| title | Searches for heavy neutral leptons with machine learning at the CMS experiment |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2412.06298 |