To address the possible lack or total absence of pulses from particle detectors during the development of its associate electronics, we propose a model that can generate them without losing the features of the real ones. This model is based on artificial neural networks, namely Generative Adversarial Networks (GAN). We describe the proposed network architecture, its training methodology and the approach to train the GAN with real pulses from a scintillator receiving radiation from sources of ${}^{137}$Cs and ${}^{22}$Na. The Generator was installed in a Xilinx's System-On-Chip (SoC). We show how the network is capable of generating pulses with the same shape as the real ones that even match the data distributions in the original pulse-height histogram data.

翻译：为解决粒子探测器在配套电子学开发过程中可能出现的脉冲缺失或完全缺失问题，我们提出一种既能生成脉冲又不丢失真实脉冲特征的模型。该模型基于人工神经网络，即生成对抗网络（GAN）。我们描述了所提出的网络架构、训练方法，以及利用来自 ${}^{137}$Cs 和 ${}^{22}$Na 放射源的闪烁体接收的真实脉冲训练该 GAN 的方案。生成器部署在赛灵思系统级芯片（SoC）上。我们展示了该网络如何生成与真实脉冲具有相同形状的脉冲，甚至能与原始脉冲幅度直方图数据的数据分布相匹配。