Surface events that exhibit incomplete charge collection are an essential background source in the light dark matter detection experiments with p-type point-contact germanium detectors. We propose a machine learning-based algorithm to identify bulk and surface events according to their pulse shape features. We construct the training and test set with part of the -source calibration data and use the rising edge of the waveform as the model input. This method is verified with the test set and another part of the -source calibration data. Results show that this method performs well on both datasets, and presents robustness against the bulk events’ proportion and the dataset size. Compared with the previous approach, the uncertainty is reduced by 16% near the energy threshold on the physics data of CDEX-1B. In addition, the key pattern identified in the waveform is verified to be consistent with its physical nature by digging into this algorithm.

中文翻译：

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基于机器学习的光暗物质检测用锗探测器体和表面事件的辨别

表现出不完全电荷收集的表面事件是 p 型点接触锗探测器的光暗物质探测实验中的重要背景源。我们提出了一种基于机器学习的算法，根据脉冲形状特征识别体事件和表面事件。我们使用部分源校准数据构建训练和测试集，并使用波形的上升沿作为模型输入。该方法通过测试集和另一部分源校准数据进行验证。结果表明，该方法在两个数据集上都表现良好，并且对批量事件的比例和数据集大小具有鲁棒性。与之前的方法相比，CDEX-1B物理数据在能量阈值附近的不确定性降低了16%。此外，通过深入研究该算法，波形中识别的关键模式被验证与其物理性质一致。