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arXiv · 2608.15042

Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders

Haruto Kitagawa·Satsuki Nishimura·Hajime Otsuka
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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.

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Haruto Kitagawa, Satsuki Nishimura, Hajime Otsuka. 2026-08-15. Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders. https://arxiv.org/abs/2608.15042

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