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

Abstract

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 + $$ {E}_T^{\mathrm{miss}} $$ E T miss , while the dominant background is pp → WZ → 3ℓ + $$ {E}_T^{\mathrm{miss}} $$ E T 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 $$ \sqrt{s} $$ s = 14 TeV, 27 TeV, and 100 TeV, and it is found that |VℓN|2 can be improved up to $$ \mathcal{O} $$ O (10−6) for heavy neutrino mass mN = 100 GeV and $$ \mathcal{O} $$ O (10−4) for mN = 1 TeV.