Statistical Understanding of Quark and Lepton Masses in Gaussian Landscapes
Lawrence J. Hall, Michael P. Salem, Taizan Watari

TL;DR
This paper explores how Gaussian landscape models can statistically explain the patterns of quark, lepton, and neutrino masses and mixings, suggesting these patterns are likely due to landscape statistics rather than fundamental symmetries.
Contribution
It introduces Gaussian landscapes as simplified models for flavor parameters, demonstrating their ability to reproduce key features of fermion masses and mixings with only five free parameters.
Findings
Reproduces quark and lepton mass hierarchies
Predicts neutrino mass ratios consistent with data
Distributions for mixing angles are peaked at observed values
Abstract
The fundamental theory of nature may allow a large landscape of vacua. Even if the theory contains a unified gauge symmetry, the 22 flavor parameters of the Standard Model, including neutrino masses, may be largely determined by the statistics of this landscape, and not by any symmetry. Then the measured values of the flavor parameters do not lead to any fundamental symmetries, but are statistical accidents; their precise values do not provide any insights into the fundamental theory, rather the overall pattern of flavor reflects the underlying landscape. We investigate whether random selection from the statistics of a simple landscape can explain the broad patterns of quark, charged lepton, and neutrino masses and mixings. We propose Gaussian landscapes as simplified models of landscapes where Yukawa couplings result from overlap integrals of zero-mode wavefunctions in…
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