Search for heavy Majorana neutrinos at electron-proton colliders
Haiyong Gu, Kechen Wang

TL;DR
This paper proposes a search strategy for heavy Majorana neutrinos at future electron-proton colliders, using machine learning to distinguish signals from background and setting new limits on neutrino mixing parameters.
Contribution
It introduces a novel search method combining multivariate analysis and machine learning for heavy neutrinos at electron-proton colliders, improving sensitivity over current experiments.
Findings
Limits on mixing parameter |V_{lN}|^2 are set for neutrino masses 10-1000 GeV.
Constraints on |V_{lN}|^2 are significantly stronger than current LHC limits for masses above 30 GeV.
The study evaluates the impact of long-lived neutrinos and positron final states on detection prospects.
Abstract
We develop the search strategy for a heavy Majorana neutrino via the lepton number violation signal process at future electron-proton colliders. The signal and dominant standard model background events are generated with the fast detector simulation. We apply the pre-selection criteria and perform the multi-variate analysis based on machine-learning to reject the background. Distributions of representative kinematic observables are presented for both signal and background processes and effects on final limits are compared by inputting two different set of observables when performing multi-variate analysis. The 2- and 5- limits on the mixing parameter are predicted for the heavy neutrino mass in the range of 101000 GeV. At the LHeC (FCC-eh) with an electron beam energy of 60 GeV, a proton beam energy of 7 (50) TeV and an…
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