用AI寻找中微子质量与相位的隐藏关联
Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders
- 用流匹配生成符合实验数据的中微子参数解集
- 发现中微子质量与CP相位间的非线性新关系
- 适合关注粒子物理深层规律的研究者
我们在型-I seesaw机制中对Yukawa矩阵和Majorana质量进行了全局搜索。采用流匹配（flow matching）这一生成式人工智能方法，生成了大量能复现实验测量的中微子质量平方差和混合角的解。随后利用自编码器（autoencoder）机器学习方法，揭示了轻子区物理量间的非平凡关联。分析发现新的非线性关系，涉及中微子质量与CP相位，可能有助于理解生成结构中的质量层级与混合模式起源。
原文摘要 · Abstract (English)
We perform a global search for values of the Yukawa matrices and Majorana masses in the Type-I seesaw mechanism. Using flow matching, which is a generative artificial intelligence (generative AI) method, we generate a broad set of solutions reproducing the experimentally measured values of the neutrino mass-squared differences and the mixing angles. Then, a machine learning method known as an autoencoder is applied to uncover non-trivial correlations among physical quantities in the lepton sector. Our analysis reveals new non-linear relations involving neutrino masses and CP phases. These findings may contribute to elucidating the origins of the mass hierarchies and mixing patterns among generation structure.
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