用深度学习区分并抑制液闪探测器中碳-14的干扰光子，提升能量分辨率。
Suppression of $^{14}\mathrm{C}$ photon hits in large liquid scintillator detectors via spatiotemporal deep learning
- 基于时空图神经网络和Transformer模型，识别碳-14光子信号
- 在重叠事件中实现25%-48%的碳-14召回率，误判率低于1%
- 适用于低能正电子与碳-14共存的中微子探测场景
液闪探测器因低能阈值和高能量分辨率被广泛用于中微子实验。尽管碳-14在液闪中丰度极低，其β衰变产生的光子仍会污染信号，影响能量分辨率。本文提出三种模型，在正电子事件中带有碳-14堆积的情况下，对碳-14光子击中进行标记，从而在击中层面抑制其影响：一种门控时空图神经网络，以及两种基于Transformer的模型，分别采用标量和向量电荷编码。在模拟数据集上，每个事件包含一个碳-14和一个动能低于5 MeV的正电子，模型实现了25%-48%的碳-14召回率，同时保持正电子与碳-14的误判率低于1%，显著改善了正电子与碳-14光子击中在空间和时间上高度重叠事件的能量分辨率。
原文摘要 · Abstract (English)
Liquid scintillator detectors are widely used in neutrino experiments due to their low energy threshold and high energy resolution. Despite the tiny abundance of $^{14}$C in LS, the photons induced by the $β$ decay of the $^{14}$C isotope inevitably contaminate the signal, degrading the energy resolution. In this work, we propose three models to tag $^{14}$C photon hits in $e^+$ events with $^{14}$C pile-up, thereby suppressing its impact on the energy resolution at the hit level: a gated spatiotemporal graph neural network and two Transformer-based models with scalar and vector charge encoding. For a simulation dataset in which each event contains one $^{14}$C and one $e^+$ with kinetic energy below 5 MeV, the models achieve $^{14}$C recall rates of 25%-48% while maintaining $e^+$ to $^{14}$C misidentification below 1%, leading to a large improvement in the resolution of total charge for events where $e^+$ and $^{14}$C photon hits strongly overlap in space and time.
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