基于CNN的pnCCD卫星系统软X射线探测带电粒子事件排除算法的开发与评估
Development and Evaluation of a CNN-Based Charged-Particle Event Rejection Algorithm for Soft X-ray Detection in a pnCCD-Based Satellite System
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中文总结 AI 辅助
本研究针对pnCCD卫星系统软X射线探测的带电粒子干扰问题，开发并评估了一种CNN事件排除算法，其误分类率显著降低，为星上事件筛选提供了有效方案。
中文摘要 AI 辅助
软X射线波段的全天巡天对探测高红移伽马射线暴（GRBs）等暂现天体至关重要，这类天体为早期宇宙研究提供关键线索。HiZ-GUNDAM是未来用于探测和定位高红移GRBs的卫星任务，其宽场X射线监测仪EAGLE将龙虾眼光学系统与0.4-4 keV波段工作的pnCCD成像探测器相结合。受卫星遥测带宽限制，无法下传pnCCD的全帧图像，需在星上进行事件筛选。空间环境中的带电粒子会产生背景事件，可能被误判为X射线光子，降低探测灵敏度并可能触发虚假警报。本研究开发了pnCCD读出系统，采用传统分级方法和卷积神经网络（CNN）评估带电粒子排除性能；使用Fe-55源的X射线事件和Sr-90β源的电子事件对性能进行评估。CNN将传统分级方法的误分类率从10.2%-11.9%降至3.1%，同时保持对X射线事件的高接受率，在更高沉积能量下的改进尤为显著，这反映出CNN能通过捕捉电荷分布的详细空间特征，区分径迹状粒子事件与X射线事件。与薄耗尽层CMOS传感器的对比进一步表明，pnCCD更厚的耗尽层提升了鉴别性能。这些结果证明，基于CNN的事件分类可大幅减少带电粒子污染，同时保持高X射线接受率，有望成为未来基于pnCCD的宽场X射线任务星上事件筛选的有效方法。
英文摘要
All-sky surveys in the soft X-ray band are essential for detecting transient objects such as high-redshift gamma-ray bursts (GRBs), which provide key insights into the early universe. HiZ-GUNDAM is a future satellite mission designed to detect and localize high-redshift GRBs. Its wide-field X-ray monitor, EAGLE, combines Lobster Eye Optics with a pnCCD imaging detector operating in the 0.4-4 keV band. Because of limited satellite telemetry, full-frame pnCCD images cannot be downlinked, requiring onboard event selection. Charged particles in the space environment produce background events that can be misidentified as X-ray photons, degrading detection sensitivity and potentially triggering false alerts. In this study, we developed a pnCCD readout system and evaluated charged-particle rejection using conventional grade methods and a convolutional neural network (CNN). Performance was evaluated using X-ray events from an Fe-55 source and electron events from a Sr-90 beta source. The CNN reduced the misclassification rate from 10.2-11.9% for conventional grade methods to 3.1% while maintaining a high acceptance rate for X-ray events. The improvement is particularly pronounced at higher deposited energies, reflecting the CNN's ability to distinguish track-like particle events from X-ray events by capturing detailed spatial features of charge distributions. Comparison with a thin-depletion-layer CMOS sensor further indicates that the thicker depletion layer of the pnCCD enhances discrimination performance. These results demonstrate that CNN-based event classification can substantially reduce charged-particle contamination while maintaining high X-ray acceptance, making it a promising approach for onboard event selection in future pnCCD-based wide-field X-ray missions.
