用新型机器学习算法识别X射线成像探测器中的带电粒子背景事件
Identifying charged particle background events in X-ray imaging detectors with novel machine learning algorithms
摘要：
天基X射线探测器会受到轨道上大量带电粒子流的影响，特别是高能宇宙射线质子，从而产生大量本底。我们开发了新的机器学习算法来检测下一代X射线CCD和DEPFET探测器中的带电粒子事件，初步研究集中在Athena宽场成像仪（WFI）DEPFET探测器上。我们训练和测试原型卷积神经网络算法，发现带电粒子和X射线事件被识别具有高度的准确性，利用像素之间的相关性，以提高现有事件检测算法的性能。99%的包含宇宙射线的帧被识别，神经网络能够正确识别高达40%的被当前事件分类标准遗漏的宇宙射线，显示出显着减少仪器背景的潜力，并释放未来X射线任务的全部科学潜力，如雅典娜，山猫和AXIS。
英文摘要：
Space-based X-ray detectors are subject to significant fluxes of charged particles in orbit, notably energetic cosmic ray protons, contributing a significant background. We develop novel machine learning algorithms to detect charged particle events in next-generation X-ray CCDs and DEPFET detectors, with initial studies focusing on the Athena Wide Field Imager (WFI) DEPFET detector. We train and test a prototype convolutional neural network algorithm and find that charged particle and X-ray events are identified with a high degree of accuracy, exploiting correlations between pixels to improve performance over existing event detection algorithms. 99 per cent of frames containing a cosmic ray are identified and the neural network is able to correctly identify up to 40 per cent of the cosmic rays that are missed by current event classification criteria, showing potential to significantly reduce the instrumental background, and unlock the full scientific potential of future X-ray missions such as Athena, Lynx and AXIS.
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