Reinforcement learning-based statistical search strategy for an axion model from flavor
This work addresses the challenge of efficiently searching vast parameter spaces in theoretical physics models, particularly for researchers in particle physics and cosmology, though it is incremental as it applies an existing machine learning method to a specific domain.
The authors tackled the problem of exploring new physics beyond the Standard Model by developing a reinforcement learning-based search strategy to find model parameters, specifically applying it to a minimal axion model with a global U(1) flavor symmetry, resulting in the discovery of over 150 realistic solutions for the quark sector.
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.