  Matter-antimatter asymmetry is one of the major unsolved problems in physics
that can be probed through precision measurements of charge-parity symmetry
violation at current and next-generation neutrino oscillation experiments. In
this work, we demonstrate the capability of variational autoencoders and
normalizing flows to approximate the generative distribution of simulated data
for water Cherenkov detectors commonly used in these experiments. We study the
performance of these methods and their applicability for semi-supervised
learning and synthetic data generation.
