Reinforcement learning-based statistical search strategy for an axion model from flavor

TL;DR

This paper introduces a reinforcement learning approach to efficiently explore the parameter space of a minimal axion model with flavor symmetry, aiding in the search for new physics beyond the Standard Model.

Contribution

It presents a novel reinforcement learning-based method for parameter search in axion models, demonstrating its efficiency and ability to find numerous realistic solutions.

Findings

Successfully identified over 150 viable solutions for quark sector charges.

Compared the RL method's speed with conventional optimization, showing improved efficiency.

Analyzed experimental sensitivities for future axion detection based on the solutions.

Abstract

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.