DA$\Phi$NE束流测试设施上CZT探测器的机器学习脉冲形状甄别
Pulse-shape discrimination with machine learning for CZT detectors at the DA$Φ$NE beam test facility
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中文总结 AI 辅助
本研究利用机器学习对CZT探测器脉冲形状进行甄别，在DA$\Phi$NE束流测试设施上，XGBoost分类器以约97%的准确率区分光子与异常事件，并显著降低光谱本底，提升对撞机环境下的CZT光谱测量性能。
中文摘要 AI 辅助
碲锌镉（CZT）探测器具有多功能性、操作简单以及室温X射线和伽马射线光谱测量能力，使其对对撞机应用具有吸引力，但其在高通量条件下的使用仍然受限。在此，我们介绍一项基于特征的初步脉冲形状分析，采用机器学习方法，对在INFN弗拉斯卡蒂国家实验室DA$\Phi$NE束流测试设施使用准半球形CZT探测器获取的数据进行处理。一束300-MeV电子束轰击铅靶，产生了特征性的Pb X射线以及延伸至正负电子湮灭区域的宽背景。从记录的波形中提取了具有物理意义的时序和形态特征，用于区分标称光子类脉冲与异常事件。一个在10,000个标记波形上训练和验证的XGBoost分类器达到了约97%的准确率，且其大部分分类性能仅使用几百个标记样本即可实现。训练后的模型应用于超过700,000个事件，显著减少了光谱连续本底和符合峰，同时保留了直至511-keV湮灭峰的特征性Pb X射线谱线。这些初步结果展示了机器学习辅助脉冲形状甄别在改善对撞机环境中CZT光谱测量方面的潜力。
英文摘要
Cadmium zinc telluride (CZT) detectors offer versatility, operational simplicity, and room-temperature X- and gamma-ray spectroscopy, making them attractive for collider applications, yet their use under high-flux conditions remains limited. Here, we present a preliminary feature-based pulse-shape analysis employing machine learning, on data acquired with a quasi-hemispherical CZT detector at the DA$\Φ$NE beam test facility of the National Laboratory of Frascati of INFN. A 300-MeV electron beam impinging on a lead target produced characteristic Pb X-rays together with a broad background extending up to the electron-positron annihilation region. Physically motivated temporal and morphological features were extracted from the recorded waveforms and used to distinguish nominal photon-like pulses from anomalous events. An XGBoost classifier trained and validated on 10,000 labeled waveforms achieved an accuracy of approximately 97%, with most of its classification performance reached using only a few hundred labeled examples. The trained model was applied to more than 700,000 events, substantially reducing the spectral continuum and coincidence peaks, while preserving the characteristic Pb X-ray lines up to the 511-keV annihilation peak. These preliminary results demonstrate the potential of machine-learning-assisted pulse-shape discrimination for improving CZT spectroscopy in collider environments.
