| Autor: |
Jie Feng, Mingqiu Li, Qi-Shu Yan, Yu-Pan Zeng, Hong-Hao Zhang, Yongchao Zhang, Zhijie Zhao |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Journal of High Energy Physics, Vol 2022, Iss 9, Pp 1-34 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1029-8479 |
| DOI: |
10.1007/JHEP09(2022)141 |
| Popis: |
Abstract In this work, by using the machine learning methods, we study the sensitivities of heavy pseudo-Dirac neutrino N in the inverse seesaw at the high-energy hadron colliders. The production process for the signal is pp → ℓ → 3ℓ + E T miss $$ {E}_T^{\mathrm{miss}} $$ , while the dominant background is pp → WZ → 3ℓ + E T miss $$ {E}_T^{\mathrm{miss}} $$ . We use either the Multi-Layer Perceptron or the Boosted Decision Tree with Gradient Boosting to analyse the kinematic observables and optimize the discrimination of background and signal events. It is found that the reconstructed Z boson mass and heavy neutrino mass from the charged leptons and missing transverse energy play crucial roles in separating the signal from backgrounds. The prospects of heavy-light neutrino mixing |V ℓN | 2 (with ℓ = e, μ) are estimated by using machine learning at the hadron colliders with s $$ \sqrt{s} $$ = 14 TeV, 27 TeV, and 100 TeV, and it is found that |V ℓN | 2 can be improved up to O $$ \mathcal{O} $$ (10 −6) for heavy neutrino mass m N = 100 GeV and O $$ \mathcal{O} $$ (10 −4) for m N = 1 TeV. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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