Bayesian global analysis of neutrino oscillation data

TL;DR

This paper presents a Bayesian analysis of neutrino oscillation data, confirming results similar to traditional methods but highlighting subtleties in parameter correlations and finding no significant evidence for mass ordering, octant, maximal mixing, or CP-violation.

Contribution

It introduces a Bayesian framework for analyzing neutrino oscillation data, addressing parameter correlations and model comparison with no significant evidence for certain phenomena.

Findings

Results largely agree with chi-squared analysis

No significant evidence for mass ordering or CP-violation

Highlights subtleties in parameter correlations

Abstract

We perform a Bayesian analysis of current neutrino oscillation data. When estimating the oscillation parameters we find that the results generally agree with those of the $\chi^2$ method, with some differences involving $s_{23}^2$ and CP-violating effects. We discuss the additional subtleties caused by the circular nature of the CP-violating phase, and how it is possible to obtain correlation coefficients with $s_{23}^2$. When performing model comparison, we find that there is no significant evidence for any mass ordering, any octant of $s_{23}^2$ or a deviation from maximal mixing, nor the presence of CP-violation.