English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

A leptophobic $Z^\prime$ that does not couple with the Standard Model leptonscan evade the stringent bounds from the dilepton-resonance searches. In ourearlier paper [T. Arun et al., Search for the $Z'$ boson decaying to aright-handed neutrino pair in leptophobic $U(1)$ models, Phys. Rev. D, 106(2022) 095035; arXiv:2204.02949], we presented two gauge anomaly-free $U(1)$models -- one based on the Green-Schwarz (GS) anomaly cancellation mechanism,and the other on a grand unified theory (GUT) framework with gauge kineticmixing -- where a heavy leptophobic $Z'$ is present along with right-handedneutrinos ($N_R$). We pointed out the interesting possibility of a correlatedsearch for $Z'$ and $N_R$ at the LHC through the $pp\to Z'\to N_R N_R$ channel.This channel can probe a part of the $Z'$ parameter space beyond the reach ofthe standard dijet resonance searches. In this follow-up paper, we analyse thechallenging monolepton final state arising from the decays of the $N_R$ pairwith Deep Learning. We present the high-luminosity LHC discovery reaches forsix different GUT embeddings and a benchmark point in the GS setup. We alsoupdate our previous estimates in the dilepton channel with Deep Learning. Weidentify parameter regions that can be probed with the proposed channel butwill remain inaccessible to dijet searches at the HL-LHC.

Pinning down the leptophobic $Z^\prime$ in leptonic final states with Deep Learning