  Generative Adversarial Networks trained on samples of simulated or actual
events have been proposed as a way of generating large simulated datasets at a
reduced computational cost. In this work, a novel approach to perform the
simulation of photodetector signals from the time projection chamber of the
EXO-200 experiment is demonstrated. The method is based on a Wasserstein
Generative Adversarial Network - a deep learning technique allowing for
implicit non-parametric estimation of the population distribution for a given
set of objects. Our network is trained on real calibration data using raw
scintillation waveforms as input. We find that it is able to produce
high-quality simulated waveforms an order of magnitude faster than the
traditional simulation approach and, importantly, generalize from the training
sample and discern salient high-level features of the data. In particular, the
network correctly deduces position dependency of scintillation light response
in the detector and correctly recognizes dead photodetector channels. The
network output is then integrated into the EXO-200 analysis framework to show
that the standard EXO-200 reconstruction routine processes the simulated
waveforms to produce energy distributions comparable to that of real waveforms.
Finally, the remaining discrepancies and potential ways to improve the approach
further are highlighted.
