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.

我们提出了一个基于人工神经网络（GAN）的模型，用于在粒子探测器的电子发展过程中生成脉冲信号，保留真实脉冲信号的特性。我们描述了网络架构、训练方法以及使用${}^{137}$Cs和${}^{22}$Na源的闪烁体接收辐射的真实脉冲信号来训练GAN的方法。通过将生成器安装在Xilinx的SoC上，我们展示了该网络能够生成与真实脉冲信号相同形状的脉冲信号，并且其数据分布与原始脉冲高度直方图数据相匹配。

利用生成对抗网络（GAN）从粒子探测器合成脉冲