Particle Physics Model Building with Reinforcement Learning

Understand

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.