用扩散模型反推符合实验数据的粒子物理参数，找到新可能。
Diffusion-model approach to flavor models: A case study for $S_4^\prime$ modular flavor model
- 用扩散生成模型逆向寻找满足实验约束的参数
- 在$S_4^\prime$模型中复现夸克质量与混合矩阵
- 发现多个可观测物理区域，支持自发CP破缺
我们提出一种基于扩散模型的数值方法，用于在一般味模型中搜索满足实验约束的参数，扩散模型属于生成式人工智能。以$S_4^\prime$模味模型为例，构建神经网络，将味模型中的自由参数作为生成目标，重现夸克质量、CKM矩阵及Jarlskog不变量。通过训练后的网络生成新参数并结合局部优化，发现多个具有物理意义的参数区域。此外，确认$S_4^\prime$模型中存在自发CP破坏。该方法实现了逆问题求解，使机器可从给定实验数据生成一系列合理模型参数。
原文摘要 · Abstract (English)
We propose a numerical method of searching for parameters with experimental constraints in generic flavor models by utilizing diffusion models, which are classified as a type of generative artificial intelligence (generative AI). As a specific example, we consider the $S_4^\prime$ modular flavor model and construct a neural network that reproduces quark masses, the CKM matrix, and the Jarlskog invariant by treating free parameters in the flavor model as generating targets. By generating new parameters with the trained network and local optimization, we find various phenomenologically interesting parameter regions. Additionally, we confirm that the spontaneous CP violation occurs in the $S_4^\prime$ model. The diffusion model enables an inverse problem approach, allowing the machine to provide a series of plausible model parameters from given experimental data.
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