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Direct Bounds on Electroweak Scale Pseudo-Dirac Neutrinos from $\sqrt s=8$ TeV LHC Data

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arxiv 1405.0177 v2 pith:LWT3VRX7 submitted 2014-05-01 hep-ph hep-ex

classification hep-phhep-ex
keywords neutrinoneutrinosdataheavyleptonnumberboundsbreaking
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Seesaw models with a small lepton number breaking can naturally accommodate electroweak-scale pseudo-Dirac neutrinos with a sizable mixing with the active neutrinos, while satisfying the light neutrino oscillation data. Due to the smallness of the lepton number breaking parameter, the 'smoking gun' collider signature of same-sign dileptons is suppressed, and the heavy neutrinos in these models would manifest at the LHC dominantly through lepton number conserving trilepton final states. Using the recent CMS results for anomalous production of multilepton events at $\sqrt{s}$=8 TeV LHC with an integrated luminosity of $19.5~{\rm fb}^{-1}$, we derive direct upper bounds on the light-heavy neutrino mixing parameter as a function of the heavy Dirac neutrino mass. These limits extend the collider sensitivity to higher heavy neutrino masses up to about 500 GeV. In the lower mass range, our limits are comparable to the existing indirect constraints derived from Higgs and electroweak precision data.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Heavy Neutral Leptons without Prejudice

    hep-ph 2024-12 conditional novelty 5.0 of 10

    HL-LHC and FCC-ee can probe HNL Yukawa couplings down to y^2 near 10^-4 to 10^-6 depending on mass; at zero mixing, Higgs width precision is the strongest constraint and the two colliders have comparable reach.

  2. Right-handed neutrinos: seesaw models and signatures

    hep-ph 2025-02 conditional novelty 1.0 of 10

    A pedagogical review that explains how adding right-handed neutrinos can generate small neutrino masses through seesaw mechanisms and what experimental signatures such models predict.

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