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Truth, beauty, and goodness in grand unification: a machine learning approach

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arxiv 2411.06718 v2 pith:NRLQ2FSH submitted 2024-11-11 hep-ph cs.LGhep-th

Truth, beauty, and goodness in grand unification: a machine learning approach

classification hep-ph cs.LGhep-th
keywords higgsmodelapproachapproachesfermionfieldgrandlearning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We investigate the flavour sector of the supersymmetric $SU(5)$ Grand Unified Theory (GUT) model using machine learning techniques. The minimal $SU(5)$ model is known to predict fermion masses that disagree with observed values in nature. There are two well-known approaches to address this issue: one involves introducing a 45-representation Higgs field, while the other employs a higher-dimensional operator involving the 24-representation GUT Higgs field. We compare these two approaches by numerically optimising a loss function, defined as the ratio of determinants of mass matrices. Our findings indicate that the 24-Higgs approach achieves the observed fermion masses with smaller modifications to the original minimal $SU(5)$ model.

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Cited by 4 Pith papers

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  4. Optimizing Yukawa couplings to suppress Dimension-five Proton Decay in $SU(5)$ GUT

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