Good flavor search in $SU(5)$: a machine learning approach

The $SU(5)$ grand unified theory, initially proposed by Georgi and Glashow, has faced challenges in explaining the observed fermion mass spectrum. A recent study employs machine learning techniques to explore potential modifications to the original model. It identifies two known remedies: introducing a 45-dimensional field or a 24-dimensional field. The analysis concludes that the latter modification is more natural, adhering closely to the original framework. By introducing a continuous parameter $y$, the study finds that a value of approximately 0.8 yields the best alignment with the original $SU(5)$ model. This finding is crucial as it not only addresses the discrepancies in the fermion mass problem but also enhances our understanding of grand unified theories in particle physics.