发表机构
Kyushu University; Kyoto University; Quantum and Spacetime Research Institute (QuaSR), Kyushu University（九州大学; 京都大学; 九州大学量子与时空研究所（QuaSR））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究结合流匹配与自编码器，对I型 seesaw 机制的 Yukawa 矩阵和 Majorana 质量做全局搜索，揭示了轻子领域中涉及中微子质量与 CP 相位的新非线性关联，为阐明质量等级和混合模式起源提供了线索。
AI 中文摘要
我们对I型 seesaw 机制中的 Yukawa 矩阵和 Majorana 质量值进行全局搜索。采用生成式人工智能方法流匹配（flow matching）生成大量解，重现实验测得的中微子质量平方差和混合角数值。随后应用名为自编码器（autoencoder）的机器学习方法，揭示轻子领域物理量间的非平凡关联。分析发现涉及中微子质量和 CP 相位的新非线性关系，这些发现或有助于阐明代结构中质量等级和混合模式的起源。
英文摘要
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.
Comments32 pages, 6 figures
