Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders

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.

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

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 (pending registration)

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