Revisiting the universal texture zero of flavour: a Markov chain Monte Carlo analysis
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AbstractWe revisit the phenomenological predictions of the Universal Texture Zero (UTZ) model of flavour originally presented in [1], 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, $$\beta $$ β -decay, and cosmological processes. We therefore dramatically improve on the proof-in-principle phenomenological analysis originally presented in [1], 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.
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Acknowledgements: JT and IdMV are indebted to Prof. Graham Garland Ross (1944–2021), whose supervision and collaboration greatly inspired our research interests, including the Universal Texture Zero theory we originally co-authored in [1] and further studied in this paper. We thank him for his mentorship, both personal and professional, and for the time we shared together over the years. Our collaboration also thanks Ben Risebrow for his helpful contributions over the course of his summer research project, and Andrew Fowlie for comments on our MCMC approach. JB is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant 396021762-TRR 257. IdMV acknowledges funding from Fundação para a Ciência e a Tecnologia (FCT) through the contract UID/FIS/00777/2020 and was supported in part by FCT through projects CFTP-FCT Unit 777 (UID/FIS/00777/2019), PTDC/FIS-PAR/29436/2017, CERN/FIS-PAR/0004/2019 and CERN/FIS-PAR/0008/2019 which are partially funded through POCTI (FEDER), COMPETE, QREN and EU. The work of ML is funded by FCT Grant no. PD/BD/150488/2019, in the framework of the Doctoral Programme IDPASC-PT. JT gratefully acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant agreement no. 101022203.
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1434-6052
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FCT (PTDC/FIS-P)
H2020 Marie Sklodowska-Curie Actions (101022203)
Deutsche Forschungsgemeinschaft (396021762-TRR 257)

