Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders
This work provides a new tool for exploring parameter spaces in particle physics, potentially aiding in understanding lepton flavor structure, though the immediate impact is domain-specific.
The authors used flow matching and autoencoders to search for Type-I seesaw parameters consistent with neutrino oscillation data, uncovering new non-linear correlations among neutrino masses and CP phases.
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.