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A $\nu$ Approach to Analyzing Neutrino Data in the $\mathbf{R}$-Parity-Violating MSSM
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abstract
The $R$-parity-violating Minimal Supersymmetric Standard Model (RPV-MSSM) can naturally accommodate massive neutrinos as required by the oscillation data. However, studying the phenomenology is complicated due to the large number of undetermined parameters involved. Thus, studies are usually restricted to specific submodels. In this work, we develop an approach that allows us to be less restrictive. Working in (almost) the completely general RPV-MSSM setting, we analyze the structure of the neutrino mass matrix, and identify -- for the case of two massive neutrinos -- only four minimal classes of structures that can solve the neutrino data; we call these Minimal Oscillation Models (MOMs). We study the general features of each MOM class, and present numerical fits to the oscillation data. Our approach allows us to study all RPV models satisfying the neutrino data in a unified manner, as long as they satisfy the MOM criteria. Through several examples, we show that this indeed holds for many interesting scenarios.
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Markov Chain Monte Carlo analysis to probe trilinear $R$-parity violating SUSY scenarios and possible LHC signatures
A Bayesian MCMC fit to neutrino, Higgs, and flavor data constrains the trilinear R-parity-violating couplings λ_i33 and λ'_i33 to at most ~10^-4, with tanβ below 15, in bino- and stop-LSP supersymmetric scenarios.
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