利用人工智能与高级事例重建算法提升下一代X射线成像探测器的灵敏度
Enhancing the sensitivity of next-generation X-ray imaging detectors with artificial intelligence and advanced event reconstruction algorithms
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中文总结 AI 辅助
该研究研发了基于物理模型的AI/ML原型算法，可减少X射线探测器的粒子背景，提升低能段灵敏度与能量分辨率，其在MIT-LL CCID-93 CCD探测器上的性能已通过实验室数据验证，可满足未来X射线旗舰任务需求。
中文摘要 AI 辅助
结合人工智能与机器学习（AI/ML）的高级算法可提升X射线成像探测器的灵敏度，增强未来X射线任务的科学能力。当前在轨仪器的灵敏度受限于两点：（1）仪器背景，由宇宙射线产生的信号可与真实天体物理X射线信号混淆；（2）探测到的光子事例重建能力不足，导致最低能量段的量子效率与能量分辨率下降，而该段蕴含大量发现空间。本文报告了原型算法的研发，这些算法针对原始帧级数据运行，旨在改进粒子诱导背景事例的识别并提升能量重建性能。算法考虑帧内所有信号的上下文信息，基于探测器内电荷扩散与信号产生的物理驱动模型构建。通过高保真模拟，我们表明，结合最新进展，原型ML算法在适用于成像巡天源探测的激进模式下，相比传统滤波方法可减少最多68%的未被拒绝的粒子背景；在优先保证光谱精确测量的保守模式下，可减少最多40%的粒子背景。我们发现，下一代事例重建算法可提升类CCD探测器在1keV以下事例能量的灵敏度与能量分辨率，还可辅助背景滤波，降低光子堆积的影响。本文提供了新的实验室数据，展示了该算法在MIT-LL CCID-93 CCD探测器上的性能。结合下一代高速低噪声探测器的能力，这些算法可满足未来X射线旗舰任务的要求。
英文摘要
Advanced algorithms incorporating artificial intelligence and machine learning (AI/ML) enhance the sensitivity of X-ray imaging detectors and the scientific capabilities of future X-ray missions. In orbit, current instruments are limited in their sensitivity by (1) the instrumental background, induced by cosmic rays which produce signals that can be confused for genuine, astrophysical X-rays, and (2) the ability to reconstruct the detected photon events, degrading the quantum efficiency and energy resolution at the lowest energies, where much discovery space resides. We report on the development of prototype algorithms designed to operate on the raw frame-level data to provide improved identification of particle-induced background events and enhanced energy reconstruction. These algorithms consider the contextual information from all signals in a frame, and are built upon physics-motivated models of charge diffusion and signal generation within the detector. Using high fidelity simulations, we show that following recent developments, prototype ML algorithms can reduce the unrejected particle background by up to 68 per cent compared with traditional filtering methods when operated in an aggressive mode suitable for source detection in imaging surveys, or up to 40 per cent in a conservative mode designed to prioritize accurate measurements of the spectrum. We find that next-generation event reconstruction algorithms improve the sensitivity and energy resolution of CCD-like detectors at event energies below 1keV, and can aid in background filtering, and reduce the impact of photon pile-up. We present new laboratory data that demonstrates the performance of the algorithm on the MIT-LL CCID-93 CCD detector. Together with the capabilities of next-generation high-speed, low-noise detectors, these algorithms can satisfy the requirements for future X-ray flagship missions.
