Exploring the flavor structure of quarks and leptons with reinforcement learning
This work addresses the challenge of understanding flavor structure in particle physics for researchers, but it is incremental as it applies an existing reinforcement learning method to a new domain.
The researchers tackled the problem of exploring the flavor structure of quarks and leptons by proposing a reinforcement learning method, which found 21 models consistent with experimental data, predicting specific values for neutrinoless double beta decay and leptonic CP violation.
We propose a method to explore the flavor structure of quarks and leptons with reinforcement learning. As a concrete model, we utilize a basic value-based algorithm for models with $U(1)$ flavor symmetry. By training neural networks on the $U(1)$ charges of quarks and leptons, the agent finds 21 models to be consistent with experimentally measured masses and mixing angles of quarks and leptons. In particular, an intrinsic value of normal ordering tends to be larger than that of inverted ordering, and the normal ordering is well fitted with the current experimental data in contrast to the inverted ordering. A specific value of effective mass for the neutrinoless double beta decay and a sizable leptonic CP violation induced by an angular component of flavon field are predicted by autonomous behavior of the agent. Our finding results indicate that the reinforcement learning can be a new method for understanding the flavor structure.