Development of Reinforcement-Learning Based Search Strategy in Theoretical Particle Physics

Abstract

In theoretical particle physics, various hypothetical models are proposed to explain unsolved problems of particle phenomena. To validate them exhaustively, theoretical predictions should be compared with experimental data. However, the parameter space is large in general, so it is difficult to analyze the models numerically with low cost. In this work considering such a situation, we focus on matter particles that are called as quarks and leptons, and improve a method to explore their flavor structure with reinforcement learning. We utilize Deep Q-Network for one kind of the models, and train neural networks on the integer charges of quarks and leptons. The results show that there are indeed solutions that reproduce the experimental and renormalized masses of the quarks and leptons. On the other hand, the results suggest that appropriate parameters are very scarce when considering domain-wall problems, which are severely constrained by cosmological observations. Given its usefulness for such analysis, we expect that reinforcement learning can be applied to the verification of realistic particle models.