SO(10) models with A4 modular symmetry

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作者
Gui-Jun Ding
Stephen F. King
Jun-Nan Lu
机构
[1] University of Science and Technology of China,Interdisciplinary Center for Theoretical Study and Department of Modern Physics
[2] Peng Huanwu Center for Fundamental Theory,Physics and Astronomy
[3] University of Southampton,undefined
关键词
Beyond Standard Model; CP violation; Discrete Symmetries; GUT;
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摘要
We combine SO(10) Grand Unified Theories (GUTs) with A4 modular symmetry and present a comprehensive analysis of the resulting quark and lepton mass matrices for all the simplest cases. We focus on the case where the three fermion families in the 16 dimensional spinor representation form a triplet of Γ3 ≃ A4, with a Higgs sector comprising a single Higgs multiplet H in the 10 fundamental representation and one Higgs field ∆¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta } $$\end{document} in the 126¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\mathbf{126}} $$\end{document} for the minimal models, plus one Higgs field Σ in the 120 for the non-minimal models, all with specified modular weights. The neutrino masses are generated by the type-I and/or type II seesaw mechanisms and results are presented for each model following an intensive numerical analysis where we have optimized the free parameters of the models in order to match the experimental data. For the phenomenologically successful models, we present the best fit results in numerical tabular form as well as showing the most interesting graphical correlations between parameters, including leptonic CP phases and neutrinoless double beta decay, which have yet to be measured, leading to definite predictions for each of the models.
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