In the nuclear sciences and radiation detection fields, the differentiation between gamma-ray and neutron interactions inside a detector volume continues to be an area of active research. Historically, the primary mechanism for conducting particle identification has been pulse shape discrimination (PSD). However, almost all variations of this technique rely on only two factors: the area of the tail and the total area of the pulse. In the last decade, the emergence of advanced machine learning techniques, most specifically artificial neural networks (ANNs), offers a unique opportunity to capitalize on the entirety of the waveform. But such techniques appear highly reliant on the quality of datasets used for training. Our research addresses this challenge to quantify the relative performances of networks trained on a variety of datasets and subjected to the same test. Furthermore, we offer an analysis of the portability of a network trained on one detector to a similar detector.

中文翻译：

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训练用于颗粒类型识别的人工神经网络的注意事项


在核科学和辐射探测领域，探测器体积内伽马射线和中子相互作用之间的区别仍然是一个活跃的研究领域。从历史上看，进行颗粒识别的主要机制是脉冲形状辨别（PSD）。然而，该技术的几乎所有变体仅依赖于两个因素：尾部面积和脉冲总面积。在过去的十年中，先进的机器学习技术，特别是人工神经网络 (ANN) 的出现，为利用整个波形提供了独特的机会。但此类技术似乎高度依赖于用于训练的数据集的质量。我们的研究解决了这一挑战，以量化在各种数据集上训练并接受相同测试的网络的相对性能。此外，我们还分析了在一个检测器上训练的网络到类似检测器的可移植性。