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

We investigate the flavour sector of the supersymmetric S U ( 5 ) Grand Unified Theory (GUT) model using machine learning techniques. The minimal S U ( 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 S U ( 5 ) model.