Abstract
For addressing the remarkable difference between neutrino and quark mixings, high-scale mixing relations (HSMR) or unification (HSMU) hypotheses were proposed. These phenomenology frameworks have been explored with respect to bounds from neutrino oscillations and relevant cosmological 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 parameter regions, including for Majorana phases. The posterior samples could be used for studying correlations between neutrino observables and prospects for updates of related experiments.
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