Variational Autoencoders for Generative Modelling of Water Cherenkov Detectors

Understand

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.