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.

通过机器学习模型，我们展示了在液体闪烁体探测器中，如何用全连接神经网络作为强大的事例选择工具，以区分信号和背景，并能在探测器边缘仍保留信号事件，提供了反应堆中微子实验中事件选择的解释性分析，从而可用于改进和更新传统的事件选择方法。