Bayesian global analysis of neutrino oscillation data
read the original 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.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.