Good flavor search in $SU(5)$: a machine learning approach
This work addresses a specific discrepancy in particle physics models for theorists, but it is incremental as it builds on known remedies without broad new insights.
The paper tackles the fermion mass problem in the SU(5) grand unified theory by using machine learning to assess which modification (via a 45- or 24-dimensional field) is more natural based on proximity to the original model, finding that the 24-dimensional field is more natural and that a continuous parameter y ≈ 0.8 yields the closest match.
We revisit the fermion mass problem of the $SU(5)$ grand unified theory using machine learning techniques. The original $SU(5)$ model proposed by Georgi and Glashow is incompatible with the observed fermion mass spectrum. Two remedies are known to resolve this discrepancy, one is through introducing a new interaction via a 45-dimensional field, and the other via a 24-dimensional field. We investigate which modification is more natural, defining naturalness as proximity to the original Georgi-Glashow $SU(5)$ model. Our analysis shows that, in both supersymmetric and non-supersymmetric scenarios, the model incorporating the interaction with the 24-dimensional field is more natural under this criterion. We then generalise these models by introducing a continuous parameter $y$, which takes the value 3 for the 45-dimensional field and 1.5 for the 24-dimensional field. Numerical optimisation reveals that $y \approx 0.8$ yields the closest match to the original $SU(5)$ model, indicating that this value corresponds to the most natural model according to our definition.