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.

翻译：基于模拟或真实事件样本训练的生成对抗网络，已被提出作为一种以较低计算成本生成大规模模拟数据集的方法。本研究展示了一种新颖方法，用于模拟EXO-200实验时间投影室中的光电探测器信号。该方法基于Wasserstein生成对抗网络——一种深度学习技术，能够对给定对象集合的总体分布进行隐式非参数估计。我们的网络以原始闪烁波形作为输入，在真实校准数据上训练。实验发现，该网络生成高质量模拟波形的速度比传统模拟方法快一个数量级，并且重要的是，它能够从训练样本中泛化，提取数据的显著高层特征。特别是，网络正确推断出探测器内闪烁光响应的位置依赖性，并准确识别出失效的光电探测通道。随后，将网络输出集成到EXO-200分析框架中，展示标准EXO-200重建流程处理模拟波形后产生的能量分布与真实波形结果相当。最后，本文指出了当前存在的偏差及进一步改进该方法的潜在途径。