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Precision neutrino experiments vs the Littlest Seesaw

Ballett, Peter; King, Stephen F.; Pascoli, Silvia; Prouse, Nick; Wang, TseChun

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Authors

Peter Ballett

Stephen F. King

Nick Prouse

TseChun Wang



Abstract

We study to what extent upcoming precision neutrino oscillation experiments will be able to exclude one of the most predictive models of neutrino mass and mixing: the Littlest Seesaw. We show that this model provides a good fit to current data, predicting eight observables from two input parameters, and provide new assessments of its predictions and their correlations. We then assess the ability to exclude this model using simulations of upcoming neutrino oscillation experiments including the medium-distance reactor experiments JUNO and RENO-50 and the long-baseline accelerator experiments DUNE and T2HK. We find that an accurate determination of the currently least well measured parameters, namely the atmospheric and solar angles and the CP phase δ, provide crucial independent tests of the model. For θ13 and the two mass-squared differences, however, the model’s exclusion requires a combination of measurements coming from a varied experimental programme. Our results show that the synergy and complementarity of future experiments will play a vital role in efficiently discriminating between predictive models of neutrino flavour, and hence, towards advancing our understanding of neutrino oscillations in the context of the flavour puzzle of the Standard Model.

Citation

Ballett, P., King, S. F., Pascoli, S., Prouse, N., & Wang, T. (2017). Precision neutrino experiments vs the Littlest Seesaw. Journal of High Energy Physics, 2017(03), Article 110. https://doi.org/10.1007/jhep03%282017%29110

Journal Article Type Article
Acceptance Date Mar 12, 2017
Online Publication Date Mar 21, 2017
Publication Date Mar 21, 2017
Deposit Date Jan 13, 2017
Publicly Available Date Mar 28, 2024
Journal Journal of High Energy Physics
Print ISSN 1126-6708
Publisher Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Peer Reviewed Peer Reviewed
Volume 2017
Issue 03
Article Number 110
DOI https://doi.org/10.1007/jhep03%282017%29110
Related Public URLs https://arxiv.org/pdf/1612.01999.pdf

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
Open Access, © The Author(s) 2017. Article funded by SCOAP3. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.





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