Traditionally, electronics for pulse processing can be modeled as linear transfer functions. In contrast, due to the fact that artificial Neural Networks (NNs) are generally non-linear systems, their behavior against noise is significantly different as in linear systems. We take advantage of this non-linearity to achieve acceptable Signal-to-Noise Ratios (SNR) with a extremely short shaping time. This article shows an approach to a concrete NN named U-net as pulse shaper. It filters the pulses and return them unfolded solving the pile-up problem, and even estimates the height of the pulses when there has been saturation in the detector. In this article, the NN architecture and results using simulated pulses and real pulses from scintillators are shown. The results clearly show the effectiveness of the approach.

传统上，用于脉冲处理的电子设备可以建模为线性传递函数。相反，由于人工神经网络（NN）通常是非线性系统，因此它们的抗噪声行为与线性系统中的行为明显不同。我们利用这种非线性特性，以极短的整形时间实现了可接受的信噪比（SNR）。本文展示了一种具体的NN方法将U-net命名为脉冲整形器。它对脉冲进行滤波并返回展开状态，以解决堆积问题，甚至在检测器出现饱和时估计脉冲的高度。在本文中，显示了使用闪烁体的模拟脉冲和实际脉冲的NN架构和结果。结果清楚地表明了该方法的有效性。