基于强化学习的粒子物理模型建立
Particle Physics Model Building with Reinforcement Learning
摘要：
本文将强化学习应用于粒子物理模型的建立。作为一个例子，我们使用的空间Froggatt-Nielsen型模型的夸克质量。使用一个基本的基于策略的算法，我们表明，神经网络可以成功地训练，以构建Froggatt-Nielsen模型，这是与所观察到的夸克质量和混合。经过训练的政策网络导致从随机到现象学上可接受的模型，超过90%的事件，平均事件长度约为20步。我们还表明，该网络能够找到在文献中提出的模型时，从附近的配置。
英文摘要：
In this paper, we apply reinforcement learning to particle physics model building. As an example environment, we use the space of Froggatt-Nielsen type models for quark masses. Using a basic policy-based algorithm we show that neural networks can be successfully trained to construct Froggatt-Nielsen models which are consistent with the observed quark masses and mixing. The trained policy networks lead from random to phenomenologically acceptable models for over 90% of episodes and after an average episode length of about 20 steps. We also show that the networks are capable of finding models proposed in the literature when starting at nearby configurations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
