Heavy Neutrinos across the Electroweak-to-Multi-TeV Frontier via Novel ML-Enhanced Probes

TL;DR

This paper introduces a machine learning-enhanced method to search for heavy neutrinos at the LHC, covering a broad mass range and improving sensitivity to their mixing parameters through novel production channels.

Contribution

It presents a new strategy combining machine learning and novel production mechanisms to improve heavy neutrino detection at the LHC across a wide mass spectrum.

Findings

Heavy neutrinos with masses 50 GeV to 10 TeV can be probed.

Sensitivity to mixing parameter |V_{lN}|^2 ranges from 10^{-5} to 1.

Vector boson fusion dominates at higher neutrino masses.

Abstract

We propose a new strategy to probe heavy neutrinos with non-universal fermion couplings at the Large Hadron Collider (LHC) using a novel production mechanism and machine-learning algorithms. Focusing on proton--proton collisions at $\sqrt{s} = 13.6~\mathrm{TeV}$, we investigate final states containing a charged lepton, missing transverse energy, and two jets. For heavy neutrino masses below $\mathcal{O}(1~\mathrm{TeV})$, production is dominated by the $s$ channel process. At higher masses, vector boson fusion becomes the dominant production mechanism, with cross sections that decrease slowly as the heavy neutrino mass increases. We simulate both signal and Standard Model background events and employ gradient-boosted decision trees to optimize event classification. Assuming an integrated luminosity of $3000~\mathrm{fb^{-1}}$, expected for the high-luminosity, and considering realistic statistical and systematic uncertainties, we find that heavy neutrinos in the mass range $50~\mathrm{GeV}$--$10~\mathrm{TeV}$ can be probed with sensitivity to the mixing parameter $|V_{\ell N}|^2$ spanning from $\mathcal{O}(10^{-5})$ to 1. This approach enhances the discovery potential for heavy neutrinos and provides a complementary pathway to existing search strategies.