Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200

Understand

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.