论文标题
使用新型机器学习算法识别X射线成像探测器中带电的粒子背景事件
Identifying charged particle background events in X-ray imaging detectors with novel machine learning algorithms
论文作者
论文摘要
基于空间的X射线检测器受到轨道中带电颗粒的显着通量，尤其是能量宇宙射线质子，从而造成了重要的背景。我们开发了新型的机器学习算法，以检测下一代X射线CCD和DEPFET探测器中的带电粒子事件，其初步研究集中在雅典娜广阔的田间成像器（WFI）DEPFET检测器上。我们训练和测试原型卷积神经网络算法，发现带电的粒子和X射线事件具有高度的精度，利用像素之间的相关性，以提高现有事件检测算法的性能。确定了99％的包含宇宙射线的框架，并且神经网络能够正确识别当前事件分类标准所遗漏的宇宙射线中多达40％的镜头，显示出显着降低仪器背景的潜力，并释放未来X射线的全部科学潜力，例如Athena，Lynx和Axis。
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.
