Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders

[2608.15042] Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search High Energy Physics - Phenomenology arXiv:2608.15042 (hep-ph) [Submitted on 15 Aug 2026] Title: Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders Authors: Haruto Kitagawa , Satsuki Nishimura , Hajime Otsuka View a PDF of the paper titled Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders, by Haruto Kitagawa and 2 other authors View PDF HTML (experimental) Abstract: We perform a global search for values of the Yukawa matrices and Majorana masses in the Type-I seesaw mechanism. Using flow matching, which is a generative artificial intelligence (generative AI) method, we generate a broad set of solutions reproducing the experimentally measured values of the neutrino mass-squared differences and the mixing angles. Then, a machine learning method known as an autoencoder is applied to uncover non-trivial correlations among physical quantities in the lepton sector. Our analysis reveals new non-linear relations involving neutrino masses and CP phases. These findings may contribute to elucidating the origins of the mass hierarchies and mixing patterns among generation structure. Comments: 32 pages, 6 figures Subjects: High Energy Physics - Phenomenology (hep-ph) ; Machine Learning (cs.LG); High Energy Physics - Theory (hep-th) Report number: KYUSHU-HET-368, KUNS-3124 Cite as: arXiv:2608.15042 [hep-ph] (or arXiv:2608.15042v1 [hep-ph] for this version) https://doi.org/10.48550/arXiv.2608.15042 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Haruto Kitagawa [ view email ] [v1] Sat, 15 Aug 2026 05:04:52 UTC (1,328 KB) Full-text links: Access Paper: View a PDF of the paper titled Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders, by Haruto Kitagawa and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: hep-ph < prev | next > new | recent | 2026-08 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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