Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In this context, inverse beta decay represents the golden channel for antineutrino detection, providing a pair of correlated events, thus a strong experimental signature to distinguish the signal from a variety of backgrounds. However, given the low cross-section of antineutrino interactions, the development of a powerful event selection algorithm becomes imperative to achieve effective discrimination between signal and backgrounds. In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. We demonstrate, using the JUNO detector as an example, that, despite the already high efficiency of a cut-based approach, the presented ML model can further improve the overall event selection efficiency. Moreover, it allows for the retention of signal events at the detector edges that would otherwise be rejected because of the overwhelming amount of background events in that region. We also present the first interpretable analysis of the ML approach for event selection in reactor neutrino experiments. This method provides insights into the decision-making process of the model and offers valuable information for improving and updating traditional event selection approaches.

中文翻译：

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用于大型液体闪烁体探测器中电子反中微子选择的可解释机器学习方法


KamLAND、Daya Bay、Double Chooz、RENO 和即将推出的大规模 JUNO 等几个中微子探测器都依靠液体闪烁体来检测反应堆反中微子相互作用。在这种情况下，逆 β 衰变代表了反中微子检测的黄金通道，提供了一对相关事件，因此具有很强的实验特征，可以区分来自各种背景的信号。然而，鉴于反中微子相互作用的低横截面，开发强大的事件选择算法对于实现信号和背景之间的有效区分变得势在必行。在本研究中，我们引入了一个机器学习 （ML） 模型来实现这一目标：一个完全连接的神经网络作为大型液体闪烁体探测器的强大信号背景判别器。我们以 JUNO 检测器为例，证明尽管基于切割的方法已经很高的效率，但所提出的 ML 模型可以进一步提高整体事件选择效率。此外，它允许在检测器边缘保留信号事件，否则这些事件会因为该区域中的大量背景事件而被拒绝。我们还提出了对反应器中微子实验中事件选择的 ML 方法的第一个可解释分析。这种方法提供了对模型决策过程的见解，并为改进和更新传统的事件选择方法提供了有价值的信息。