Bayesian probability for leptonic CP phase with strong CP prior

TL;DR

This paper explores how the absence of axions and specific symmetries in certain models influence leptonic CP violation, using Bayesian methods to analyze current and future neutrino experiment data.

Contribution

It introduces a Bayesian prior with peaks at CP-conserving values for the leptonic CP phase and evaluates the posterior probability using current experimental data.

Findings

Significant probability that  is near  or pi.

Bayesian analysis enhances understanding of leptonic CP phase distribution.

Provides tables for posterior probabilities with current and future data.

Abstract

In a world devoid of axions, the smallness of the strong CP phase can have implications for leptonic CP violation being probed by neutrino experiments. For example, if nature adopted an axionless solution to the strong CP problem, the same symmetries that set the strong CP phase to zero at the tree-level, may also set the leptonic $CP$ phases to zero (mod \pi). This automatically happens in the left-right symmetric model when it is extended minimally to solve the strong CP problem by imposition of both P and CP. In the Nelson Barr solution the needed symmetries can be assigned so that leptonic CP violation is not generated. In the minimal left-right symmetric model with P (and not CP), where the strong CP problem remains, leptonic CP phases radiatively generate the strong CP phase in one loop, and therefore they may be absent (negligibly small) in large regions of parameter space. All these results motivate us to consider a Bayesian prior for leptonic Dirac CP phase \delta_{CP} of the PMNS matrix that has delta function like peaks at CP conserving values of 0 and \pi on top of a uniform distribution. We evaluate the posterior probability distribution for \delta_{CP} using the current global fit to neutrino experiments and find significant enhancements for the probability that \delta_{CP} is at or negligibly close to \pi. We also provide useful tables for the posterior probability considering present and future experimental sensitivities.