Diffusion-model approach to flavor models: A case study for $S_4^\prime$ modular flavor model

Abstract:We propose a numerical method of searching for parameters with experimental constraints in generic flavor models by utilizing diffusion models, which are classified as a type of generative artificial intelligence (generative AI). As a specific example, we consider the $S_4^\prime$ modular flavor model and construct a neural network that reproduces quark masses, the CKM matrix, and the Jarlskog invariant by treating free parameters in the flavor model as generating targets. By generating new parameters with the trained network and local optimization, we find various phenomenologically interesting parameter regions. Additionally, we confirm that the spontaneous CP violation occurs in the $S_4^\prime$ model. The diffusion model enables an inverse problem approach, allowing the machine to provide a series of plausible model parameters from given experimental data.

High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); High Energy Physics - Theory (hep-th)

arXiv:2504.00944 [hep-ph]

(or arXiv:2504.00944v2 [hep-ph] for this version)

https://doi.org/10.48550/arXiv.2504.00944 Focus to learn more arXiv-issued DOI via DataCite

Prog. Theor. Exp. Phys. 2026 (2026) 5, 053B08

https://doi.org/10.1093/ptep/ptag069 Focus to learn more DOI(s) linking to related resources

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