We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised silicon photomultiplier signals from a dual scintillator detector element made of 6 Li F:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset was created to train the network by exposing the detector to an AmBe source, and a data-driven method utilising a separate photomultiplier tube was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and a simpler artificial neural net. It is found to provide superior levels of discrimination, achieving an area under the curve of 0.996 ± 0.003. We find that the neural network is capable of extracting interpretable features directly from the raw data. In addition, by visualising the high-dimensional representations of the network with the t-SNE algorithm, we discov...

中文翻译：

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卷积神经网络的脉冲形状识别和闪烁信号探索

我们演示了使用卷积神经网络执行中子-伽马脉冲形状判别，其中网络的唯一输入是来自由6 Li F：ZnS（Ag）闪烁体和晶体组成的双闪烁体探测器元件的原始数字化硅光电倍增管信号。 PVT塑料。通过将检测器暴露于AmBe光源，创建了一个现实的带有标签的数据集来训练网络，并使用了使用独立光电倍增管的数据驱动方法为记录的信号分配标签。将该方法与电荷积分和连续小波变换方法以及更简单的人工神经网络进行了比较。发现可以提供出色的辨别水平，曲线下面积为0.996±0.003。我们发现神经网络能够直接从原始数据中提取可解释的特征。此外，通过使用t-SNE算法可视化网络的高维表示，我们发现...