Improving heavy Dirac neutrino prospects at future hadron colliders using machine learning
arXiv:2112.15312 · doi:10.1007/JHEP09(2022)141
Abstract
In this work, by using the machine learning methods, we study the sensitivities of heavy pseudo-Dirac neutrino in the inverse seesaw at the high-energy hadron colliders. The production process for the signal is , while the dominant background is . 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 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 (with ) are estimated by using machine learning at the hadron colliders with TeV, 27 TeV, and 100 TeV, and it is found that can be improved up to for heavy neutrino mass GeV and for TeV.
arXiv comment33 pages, 14 figures, 4 tables, more details and more references added, version published in JHEP
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