Variational Autoencoders for Generative Modelling of Water Cherenkov Detectors

TL;DR

This paper explores the use of variational autoencoders and normalizing flows to model and generate data for water Cherenkov detectors, aiding neutrino physics research.

Contribution

It introduces the application of advanced generative models to simulate water Cherenkov detector data, enhancing semi-supervised learning and data augmentation capabilities.

Findings

VAEs and normalizing flows effectively approximate detector data distributions.

Models enable high-quality synthetic data generation for physics experiments.

Potential to improve analysis in neutrino oscillation studies.

Abstract

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.