用扩散模型生成中微子质量矩阵，探索轻子味结构
Exploring the flavor structure of leptons via diffusion models
- 用扩散模型生成符合中微子质量平方差和混合角的解
- 发现CP相位和中微子总质量有非平凡分布趋势
- 可帮助未来实验验证中微子无双贝塔衰变有效质量
我们提出一种基于扩散模型的方法，探索轻子味结构。在引入类型I seesaw机制的简化标准模型框架下，训练神经网络生成中微子质量矩阵。通过迁移学习，扩散模型生成了104个符合中微子质量平方差和轻子混合角的解。尽管CP相位和中微子质量之和未作为条件标签输入，但其分布呈现出非平凡特征。此外，中微子无双贝塔衰变的有效质量集中在现有置信区间的边界附近，为未来实验验证提供了可检验的预测。该逆向生成方法有望从不同于传统解析手段的角度，推动味模型的实验验证。
原文摘要 · Abstract (English)
We propose a method to explore the flavor structure of leptons using diffusion models, which are known as one of generative artificial intelligence (generative AI). We consider a simple extension of the Standard Model with the type I seesaw mechanism and train a neural network to generate the neutrino mass matrix. By utilizing transfer learning, the diffusion model generates 104 solutions that are consistent with the neutrino mass squared differences and the leptonic mixing angles. The distributions of the CP phases and the sums of neutrino masses, which are not included in the conditional labels but are calculated from the solutions, exhibit non-trivial tendencies. In addition, the effective mass in neutrinoless double beta decay is concentrated near the boundaries of the existing confidence intervals, allowing us to verify the obtained solutions through future experiments. An inverse approach using the diffusion model is expected to facilitate the experimental verification of flavor models from a perspective distinct from conventional analytical methods.
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