Improving heavy Dirac neutrino prospects at future hadron colliders using machine learning

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 $pp\to \ell N\to 3\ell +E_T^{\mathrm{miss}}$, while the dominant background is $pp\to WZ\to 3\ell +E_T^{\mathrm{miss}}$. 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 $|V_{\ell N}{|}^2$ (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 $\mathcal{O}(10^{-6})$ for heavy neutrino mass $m_N=100$ GeV and $\mathcal{O}(10^{-4})$ for $m_N=1$ TeV.

33 pages, 14 figures, 4 tables, more details and more references added, version published in JHEP

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