Particle Physics Model Building with Reinforcement Learning

2021/03/08 by Thomas R. Harvey, André Lukas, Harvey, T. R. +1 · 1 citation
Computer Science · Physics and Astronomy · #Distributed and Parallel Computing Systems #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #High Energy Physics - Phenomenology (hep-ph) #High Energy Physics - Theory (hep-th) #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.2103.04759

openalex publication_date 2021/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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