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

TL;DR

This paper uses machine learning to compare two modifications of the minimal SU(5) GUT model, finding that the 24-Higgs approach better reproduces observed fermion masses with minimal changes.

Contribution

It introduces a machine learning framework to evaluate and compare GUT model modifications, specifically analyzing the 45-Higgs and 24-Higgs approaches.

Findings

24-Higgs approach achieves observed fermion masses with fewer modifications

Machine learning optimizes the ratio of determinants of mass matrices

Minimal SU(5) model's fermion mass predictions are improved by the 24-Higgs method

Abstract

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.