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In this work, by using the machine learning methods, we study thesensitivities of heavy pseudo-Dirac neutrino $N$ in the inverse seesaw at thehigh-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 $p p \toW Z \to 3 \ell + E_T^{\rm miss}$. We use either the Multi-Layer Perceptron orthe Boosted Decision Tree with Gradient Boosting to analyse the kinematicobservables and optimize the discrimination of background and signal events. Itis found that the reconstructed $Z$ boson mass and heavy neutrino mass from thecharged leptons and missing transverse energy play crucial roles in separatingthe signal from backgrounds. The prospects of heavy-light neutrino mixing$|V_{\ell N}|^2$ (with $\ell = e,\,\mu$) are estimated by using machinelearning 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.

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