Revisiting the Universal Texture Zero of Flavour: a Markov Chain Monte Carlo Analysis
Jordan Bernigaud, Ivo de Medeiros Varzielas, Levy, Miguel, Jim Talbert · arXiv (Cornell University) · 2022
We revisit the phenomenological predictions of the Universal Texture Zero (UTZ) model of flavour originally presented in arXiv:1710.01741, and update them in light of both improved experimental constraints and numerical analysis techniques. In particular, we have developed an in-house Markov Chain Monte Carlo (MCMC) algorithm to exhaustively explore the UTZ's viable parameter space, considering both leading- and next-to-leading contributions in the model's effective operator product expansion. We also extract -- for the first time -- reliable UTZ predictions for the (poorly constrained) leptonic CP-violating phases, and ratio observables that characterize neutrino masses probed by (e.g.) oscillation, $β$-decay, and cosmological processes. We therefore dramatically improve on the proof-in-principle phenomenological analysis originally presented in arXiv:1710.01741, and ultimately show that the UTZ remains a minimal, viable, and appealing theory of flavour. Our results also further demonstrate the potential of robustly examining multi-parameter flavour models with MCMC routines.