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Improving heavy Dirac neutrino prospects at future hadron colliders using machine learning
- Source :
- JHEP 09 (2022) 141
- Publication Year :
- 2021
-
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 $pp \to \ell N \to 3 \ell + E_T^{\rm miss}$, while the dominant background is $p p \to W Z \to 3 \ell + E_T^{\rm miss}$. 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 $|V_{\ell N}|^2$ (with $\ell = e,\,\mu$) are estimated by using machine learning at the hadron colliders with $\sqrt{s}=14$ TeV, 27 TeV, and 100 TeV, and it is found that $|V_{\ell N}|^2$ can be improved up to ${\cal O} (10^{-6})$ for heavy neutrino mass $m_N = 100$ GeV and ${\cal O} (10^{-4})$ for $m_N = 1$ TeV.<br />Comment: 33 pages, 14 figures, 4 tables, more details and more references added, version published in JHEP
- Subjects :
- High Energy Physics - Phenomenology
High Energy Physics - Experiment
Subjects
Details
- Database :
- arXiv
- Journal :
- JHEP 09 (2022) 141
- Publication Type :
- Report
- Accession number :
- edsarx.2112.15312
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1007/JHEP09(2022)141