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A Bayesian analysis of sneutrino DM in the NMSSM with Type-I seesaw mechanism

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arxiv 1807.03762 v3 pith:QLKH5VCH submitted 2018-07-10 hep-ph

classification hep-ph
keywords massmechanismhiggsinosneutrinonmssmparameterscenariosseesaw
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abstract

In the Next-to-Minimal Supersymmetric Standard Model (NMSSM) with extra heavy neutrino superfields, neutrino may acquire its mass via a seesaw mechanism and sneutrino may act as a viable dark matter (DM) candidate. Given the strong tension between the naturalness for $Z$ boson mass and the DM direct detection experiments for customary neutralino DM candidate, we augment the NMSSM with Type-I seesaw mechanism, which is the simplest extension of the theory to predict neutrino mass, and study the scenarios of sneutrino DM. We construct likelihood function with LHC Higgs data, B-physics measurements, DM relic density and its direct and indirect search limits, and perform a comprehensive scan over the parameter space of the theory by Nested Sampling method. We adopt both Bayesian and frequentist statistical quantities to illustrate the favored parameter space of the scenarios, the DM annihilation mechanism as well as the features of DM-nucleon scattering. We find that the scenarios are viable over broad parameter regions, especially the Higgsino mass $\mu$ can be below about $250 {\rm GeV}$ for a significant part of the region, which predicts $Z$ boson mass in a natural way. We also find that the DM usually co-annihilated with the Higgsinos to get the measured relic density, and consequently the DM-nucleon scattering rate is naturally suppressed to coincide with the recent XENON-1T results even for light Higgsinos. Other issues, such as the LHC search for the Higgsinos, are also addressed.

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  1. The 96 GeV Diphoton Excess in the Seesaw Extensions of the Natural NMSSM

    hep-ph 2019-08 conditional novelty 5.0 of 10

    In the seesaw-extended NMSSM, a ~96 GeV scalar can reproduce both the LEP bbbar and CMS diphoton excesses while the sneutrino dark matter passes all current constraints, in three parameter regions.

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