A $\nu$ Approach to Analyzing Neutrino Data in the $\mathbf{R}$-Parity-Violating MSSM

TL;DR

This paper introduces a unified approach to analyze neutrino data within the RPV-MSSM, identifying minimal classes of models that can explain neutrino oscillations without restricting to specific submodels.

Contribution

It develops a general framework for analyzing neutrino masses in RPV-MSSM, identifying four minimal classes of models that fit oscillation data, enabling comprehensive phenomenological studies.

Findings

Identified four minimal classes of neutrino mass structures in RPV-MSSM.

Numerical fits demonstrate the models' compatibility with oscillation data.

The approach unifies the analysis of various RPV scenarios satisfying neutrino constraints.

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.