用机器学习比较两种方法，让大统一模型更符合实际粒子质量。
Truth, beauty, and goodness in grand unification: a machine learning approach
- 用损失函数比对两种修正方式的优劣
- 24-希格斯方案只需小幅调整即可匹配观测质量
- 适合对粒子物理与机器学习交叉感兴趣的读者
我们采用机器学习技术研究超对称SU(5)大统一理论中的味结构。最小SU(5)模型预测的费米子质量与自然界观测值不符。现有两种改进方法：引入45表示的希格斯场，或使用涉及24表示大统一希格斯场的高维算符。本文通过数值优化定义为质量矩阵行列式比值的损失函数，对比两种方案。结果表明，24-希格斯方案仅需较小修改即可实现与观测一致的费米子质量，优于45-希格斯方案。
原文摘要 · Abstract (English)
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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