One-loop corrections in minimal SO(10) GUTs cause 30-40% shifts in neutrino observables from tree-level fits, requiring their inclusion for reliable parameter space exploration.
Running of Fermion Observables in Non-Supersymmetric SO(10) Models
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abstract
We investigate the complete renormalization group running of fermion observables in two different realistic non-supersymmetric models based on the gauge group $\textrm{SO}(10)$ with intermediate symmetry breaking for both normal and inverted neutrino mass orderings. Contrary to results of previous works, we find that the model with the more minimal Yukawa sector of the Lagrangian fails to reproduce the measured values of observables at the electroweak scale, whereas the model with the more extended Yukawa sector can do so if the neutrino masses have normal ordering. The difficulty in finding acceptable fits to measured data is a result of the added complexity from the effect of an intermediate symmetry breaking as well as tension in the value of the leptonic mixing angle $\theta^\ell_{23}$.
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Corrected reality conditions on the 10 and 120 Higgs representations in minimal SO(10) allow the Yukawa sector to fit all fermion masses and mixings while predicting a hierarchical right-handed neutrino spectrum and proton decay modes testable in upcoming experiments.
Machine learning optimization of a generalized SU(5) parameter y finds y ≈ 0.8 produces the closest match to the original model while resolving the fermion mass discrepancy.
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Towards Precision Neutrino Fits in GUTs: Relevance of One-Loop Finite Corrections
One-loop corrections in minimal SO(10) GUTs cause 30-40% shifts in neutrino observables from tree-level fits, requiring their inclusion for reliable parameter space exploration.
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Reality-constrained Minimal Yukawa Structure in SO(10) GUT
Corrected reality conditions on the 10 and 120 Higgs representations in minimal SO(10) allow the Yukawa sector to fit all fermion masses and mixings while predicting a hierarchical right-handed neutrino spectrum and proton decay modes testable in upcoming experiments.
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Good flavor search in SU(5): a machine learning approach
Machine learning optimization of a generalized SU(5) parameter y finds y ≈ 0.8 produces the closest match to the original model while resolving the fermion mass discrepancy.