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 + \( {E}_T^{\mathrm{miss}} \), while the dominant background is pp → WZ → 3ℓ + \( {E}_T^{\mathrm{miss}} \). 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 |VℓN|2 (with ℓ = e, μ) 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 |VℓN|2 can be improved up to \( \mathcal{O} \)(10−6) for heavy neutrino mass mN = 100 GeV and \( \mathcal{O} \)(10−4) for mN = 1 TeV.
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Feng, J., Li, M., Yan, QS. et al. Improving heavy Dirac neutrino prospects at future hadron colliders using machine learning. J. High Energ. Phys. 2022, 141 (2022). https://doi.org/10.1007/JHEP09(2022)141
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DOI: https://doi.org/10.1007/JHEP09(2022)141