用强化学习找轴子模型的粒子荷分配，高效发现150多个可行解。
Reinforcement learning-based statistical search strategy for an axion model from flavor
- 用强化学习自动搜索轴子模型中满足味对称的粒子荷分配
- 考虑重整化效应后找到超过150种符合标准模型的夸克解
- 适合研究轴子暗物质和未来实验探测的物理学家
我们提出一种基于强化学习的新物理参数搜索策略。以具有全局U(1)味对称性的最小轴子模型为例，该方法成功找到了满足标准模型味与宇宙学难题的夸克和轻子电荷分配方案，并在考虑重整化效应后发现了超过150个夸克扇区的现实解。针对这些解，我们讨论了未来实验对轴子（U(1)自发对称性破缺产生的奈姆布尔-戈尔登玻色子）探测的灵敏度。同时对比了强化学习与传统优化方法在寻找最优离散参数时的效率。结果表明，基于强化学习的高效参数搜索可实现对轴子模型味相关参数空间的统计分析。
原文摘要 · Abstract (English)
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.
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