Pulse shape discrimination plays a key role in improving the signal-to-background ratio in NEOS analysis by removing fast neutrons. Identifying particles by looking at the tail of the waveform has been an effective and plausible approach for pulse shape discrimination, but has the limitation in sorting low energy particles. As a good alternative, the convolutional neural network can scan the entire waveform as they are to recognize the characteristics of the pulse and perform shape classification of NEOS data. This network provides a powerful identification tool for all energy ranges and helps to search unprecedented phenomena of low-energy, a few MeV or less, neutrinos.

中文翻译：

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使用 NEOS II 的卷积神经网络对快中子背景的脉冲形状判别

通过去除快中子，脉冲形状鉴别在提高 NEOS 分析中的信号背景比方面发挥着关键作用。通过查看波形尾部来识别粒子是一种有效且合理的脉冲形状鉴别方法，但在对低能量粒子进行分类方面存在局限性。作为一个很好的替代方案，卷积神经网络可以原样扫描整个波形，以识别脉冲特征并对 NEOS 数据进行形状分类。该网络为所有能量范围提供了强大的识别工具，并有助于搜索前所未有的低能量、几 MeV 或更少的中微子现象。