Majorana phases in high-scale mixings unification hypotheses

TL;DR

This paper employs Bayesian algorithms to perform global fits of high-scale neutrino mixing unification hypotheses, including Majorana phases, providing a statistically robust analysis of their compatibility with experimental data.

Contribution

It introduces a Bayesian approach for exploring the parameter space of HSMU and HSMR models, including Majorana phases, improving upon previous limited-sample analyses.

Findings

Identified compatible parameter regions for HSMU and HSMR models.

Analyzed correlations between neutrino observables.

Provided insights for future experimental updates.

Abstract

For addressing the remarkable difference between neutrino and quark mixings, high-scale mixings relations (HSMR) or unification (HSMU) hypothe\ ses were proposed. These phenomenology frameworks have been explored with respect to bounds from neutrino oscillations and relevant cosmologic\ al data. However there are caveats with regards to assessing the hypotheses' compatibility with data in a statistically robust and convergent \ manner because most analysis employ a few sample of points in model parameters' space. A remedy could be achieved by using Bayesian algorithms\ for the parameters' space exploration. Using this approach, we made global fits of the HSMU and HSMR models to data and find compatible param\ eter regions, including for Majorana phases. The posterior samples could be used for studying correlations between neutrino observables and pr\ ospects for updates of related experiments.