R-Symmetric Flipped SU(5)

TL;DR

This paper develops a supersymmetric flipped SU(5) grand unified model with an R symmetry that naturally solves the doublet-triplet splitting problem, generates neutrino masses consistent with oscillation data, and predicts distinctive proton decay modes testable by future experiments.

Contribution

It introduces an R-symmetric flipped SU(5) model that addresses key GUT issues and makes unique predictions for proton decay signatures.

Findings

The model solves the doublet-triplet splitting problem.

It predicts a color-triplet Higgs at an intermediate scale.

Proton decay modes are distinct from conventional GUT models.

Abstract

We construct a supersymmetric flipped SU(5) grand unified model that possesses an $R$ symmetry. This $R$ symmetry forbids dangerous non-renormalizable operators suppressed by a cut-off scale up to sufficiently large mass dimensions so that the SU(5)-breaking Higgs field develops a vacuum expectation value of the order of the unification scale along the $F$- and $D$-flat directions, with the help of the supersymmetry-breaking effect. The mass terms of the Higgs fields are also forbidden by the $R$ symmetry, with which the doublet-triplet splitting problem is solved with the missing partner mechanism. The masses of right-handed neutrinos are generated by non-renormalizable operators, which then yield a light neutrino mass spectrum and mixing through the seesaw mechanism that are consistent with neutrino oscillation data. This model predicts one of the color-triplet Higgs multiplets to lie at an intermediate scale, and its mass is found to be constrained by proton decay experiments to be $\gtrsim 5 \times 10^{11}$ GeV. If it is $\lesssim 10^{12}$ GeV, future proton decay experiments at Hyper-Kamiokande can test our model in the $p \to \pi^0 \mu^+$ and $p \to K^0 \mu^+$ decay modes, in contrast to ordinary grand unified models where $p \to \pi^0 e^+ $ or $p \to K^+ \bar{\nu}$ is the dominant decay mode. This characteristic prediction for the proton decay branches enables us to distinguish our model from other scenarios.