Truth, beauty, and goodness in grand unification: a machine learning approach
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913617872420864 |
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| author | Kawai, Shinsuke Okada, Nobuchika |
| author_facet | Kawai, Shinsuke Okada, Nobuchika |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_06718 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Truth, beauty, and goodness in grand unification: a machine learning approach Kawai, Shinsuke Okada, Nobuchika High Energy Physics - Phenomenology Machine Learning High Energy Physics - Theory 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. |
| title | Truth, beauty, and goodness in grand unification: a machine learning approach |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Theory |
| url | https://arxiv.org/abs/2411.06718 |