{"ID":2843351,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08154","arxiv_id":"2511.08154","title":"Good flavor search in SU(5): a machine learning approach","abstract":"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 beautiful, defining the beauty 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 beautiful 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 beautiful model according to our definition.","short_abstract":"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 v...","url_abs":"https://arxiv.org/abs/2511.08154","url_pdf":"https://arxiv.org/pdf/2511.08154v2","authors":"[\"Fayez Abu-Ajamieh\",\"Shinsuke Kawai\",\"Nobuchika Okada\"]","published":"2025-11-11T12:06:14Z","proceeding":"hep-ph","tasks":"[\"hep-ph\",\"cs.LG\",\"hep-th\"]","methods":"[]","has_code":false}