  The next generation of searches for neutrinoless double beta decay
(0{\nu}\b{eta}\b{eta}) are poised to answer deep questions on the nature of
neutrinos and the source of the Universe's matter-antimatter asymmetry. They
will be looking for event rates of less than one event per ton of instrumented
isotope per year. To claim discovery, accurate and efficient simulations of
detector events that mimic 0{\nu}\b{eta}\b{eta} is critical. Traditional Monte
Carlo (MC) simulations can be supplemented by machine-learning-based generative
models. In this work, we describe the performance of generative models designed
for monolithic liquid scintillator detectors like KamLAND to produce highly
accurate simulation data without a predefined physics model. We demonstrate its
ability to recover low-level features and perform interpolation. In the future,
the results of these generative models can be used to improve event
classification and background rejection by providing high-quality abundant
generated data.
