A nonholomorphic T' modular model realizes the T4-2-i one-loop topology for radiative Majorana neutrino masses, forbids tree-level seesaws via modular assignments, stabilizes DM with residual Z2, and fits oscillation data plus DM constraints for both hierarchies with fermionic DM.
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Two-zero textures in the neutrino mass matrix produce distinctive, testable correlations among charged lepton flavor violation processes, with some patterns suppressing muon-to-electron transitions while permitting tau decays at observable rates down to a 5-6 TeV cutoff.
Machine learning optimization is applied to find parameters yielding neutrino mass matrices with target textures in BSM leptonic models.
citing papers explorer
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Radiative Neutrino Mass in a Nonholomorphic $T'$ Modular Invariant Model
A nonholomorphic T' modular model realizes the T4-2-i one-loop topology for radiative Majorana neutrino masses, forbids tree-level seesaws via modular assignments, stabilizes DM with residual Z2, and fits oscillation data plus DM constraints for both hierarchies with fermionic DM.
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Hunting for Neutrino Texture Zeros with Muon and Tau Flavor Violation
Two-zero textures in the neutrino mass matrix produce distinctive, testable correlations among charged lepton flavor violation processes, with some patterns suppressing muon-to-electron transitions while permitting tau decays at observable rates down to a 5-6 TeV cutoff.
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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.