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May 3, 20260 citationsOpen Access

ICC XVIII: Dynamical Phase Correlations and Structural Constraints on Quantitative CP Prediction

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AGAlik Gimranov

Key Points

  • This research aims to explore how dynamical phase correlations affect the predictivity of the CP-violating phase δCP in the PMNS matrix.
  • Introduced a targeted effective potential to improve predictivity for δCP.
  • Analyzed the connections between phase correlations and the eigenvectors of the unitarization map’s Hessian.
  • Outlined a schematic expression for the targeted potential to suppress specific modes.
  • Predicted broad distributions for δCP are testable with upcoming experiments like DUNE and Hyper-Kamiokande.
  • The framework identifies minimal conditions necessary for achieving quantitative precision in predictions.
  • Achieved > 3σ significance for testing the hypothesis on the uncertainty of δCP.

Abstract

Building on the refined structural constraint identified in ICC XVII, we investigate whether dynamical phase correlations can suppress unitarization-induced scrambling and restore quantitative predictivity for the CP-violating phase δCP in the PMNS matrix. We in troduce a simple effective potential V (θi) ∝ i 3σ significance. This work identifies the minimal conditions for quantitative precision and establishes targeted dynamical correlations as the next necessary ingredient.

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Cite This Study

Alik Gimranov (2026) studied this question.

synapsesocial.com/papers/69f6e62e8071d4f1bdfc6dbdhttps://doi.org/10.5281/zenodo.19954979
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