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

[2504.00944] Diffusion-model approach to flavor models: A case study for $S_4^\prime$ modular flavor model Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search High Energy Physics - Phenomenology arXiv:2504.00944 (hep-ph) [Submitted on 1 Apr 2025 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Diffusion-model approach to flavor models: A case study for $S_4^\prime$ modular flavor model Authors: Satsuki Nishimura , Hajime Otsuka , Haruki Uchiyama View a PDF of the paper titled Diffusion-model approach to flavor models: A case study for $S_4^\prime$ modular flavor model, by Satsuki Nishimura and 2 other authors View PDF HTML (experimental) 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. Comments: 23 pages, 5 figures, v2: published version Subjects: High Energy Physics - Phenomenology (hep-ph) ; Machine Learning (cs.LG); High Energy Physics - Theory (hep-th) Report number: KYUSHU-HET-316 Cite as: 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 Journal reference: Prog. Theor. Exp. Phys. 2026 (2026) 5, 053B08 Related DOI : https://doi.org/10.1093/ptep/ptag069 Focus to learn more DOI(s) linking to related resources Submission history From: Satsuki Nishimura [ view email ] [v1] Tue, 1 Apr 2025 16:29:03 UTC (131 KB) [v2] Sat, 15 Aug 2026 05:43:44 UTC (680 KB) Full-text links: Access Paper: View a PDF of the paper titled Diffusion-model approach to flavor models: A case study for $S_4^\prime$ modular flavor model, by Satsuki Nishimura and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: hep-ph < prev | next > new | recent | 2025-04 Change to browse by: cs cs.LG hep-th References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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