Machine learning optimization is applied to find parameters yielding neutrino mass matrices with target textures in BSM leptonic models.
Nearly Tri-bimaximal Neutrino Mixing and CP Violation from mu-tau Symmetry Breaking
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abstract
Assuming the Majorana nature of massive neutrinos, we generalize the Friedberg-Lee neutrino mass model to include CP violation in the neutrino mass matrix $M^{}_\nu$. We show that a favorable neutrino mixing pattern (with $\theta^{}_{12} \approx 35.3^\circ$, $\theta^{}_{23} = 45^\circ$, $\theta^{}_{13} \neq 0^\circ$ and $\delta = 90^\circ$) can naturally be derived from $M^{}_\nu$, if it has an approximate or softly-broken $\mu$-$\tau$ symmetry. We point out a different way to obtain the nearly tri-bimaximal neutrino mixing with $\delta = 0^\circ$ and non-vanishing Majorana phases. The most general case, in which all the free parameters of $M^{}_\nu$ are complex and the resultant neutrino mixing matrix contains both Dirac and Majorana phases of CP violation, is also discussed.
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hep-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Rolling Down the Leptonic BSM Landscape Using Machine Learning Techniques
Machine learning optimization is applied to find parameters yielding neutrino mass matrices with target textures in BSM leptonic models.