标题： 基于强化学习的味模型轴子统计搜索策略 显示英文标题

标题： Reinforcement learning-based statistical search strategy for an axion model from flavor

摘要： 我们提出一种基于强化学习的搜索策略，以探索标准模型之外的新物理。 强化学习是机器学习方法之一，是一种强大的方法，用于寻找满足现象学约束的模型参数。 作为具体例子，我们关注一个具有全局$U(1)$集成对称性的最小轴子模型。 学习的代理成功找到了解决标准模型中味和宇宙学问题的夸克和轻子的$U(1)$电荷分配，并在考虑重整化效应的情况下，为夸克部分找到了超过150个现实解。 对于通过基于强化学习的分析找到的解，我们讨论了未来实验对检测轴子（即自发破缺的$U(1)$的Nambu-Goldstone玻色子）的敏感性。 我们还检查了基于强化学习的搜索方法在寻找最佳离散参数方面与传统优化方法相比的速度。 总之，基于强化学习策略的高效参数搜索使我们能够对与轴子模型相关的味参数空间进行统计分析。 显示英文摘要

摘要： We propose a reinforcement learning-based search strategy to explore new physics beyond the Standard Model. The reinforcement learning, which is one of machine learning methods, is a powerful approach to find model parameters with phenomenological constraints. As a concrete example, we focus on a minimal axion model with a global $U(1)$ flavor symmetry. Agents of the learning succeed in finding $U(1)$ charge assignments of quarks and leptons solving the flavor and cosmological puzzles in the Standard Model, and find more than 150 realistic solutions for the quark sector taking renormalization effects into account. For the solutions found by the reinforcement learning-based analysis, we discuss the sensitivity of future experiments for the detection of an axion which is a Nambu-Goldstone boson of the spontaneously broken $U(1)$. We also examine how fast the reinforcement learning-based searching method finds the best discrete parameters in comparison with conventional optimization methods. In conclusion, the efficient parameter search based on the reinforcement learning-based strategy enables us to perform a statistical analysis of the vast parameter space associated with the axion model from flavor.