Nano-satellite MeV telescope is becoming attractive nowadays. The dominant interaction mechanism of the electromagnetic spectrum around 1MeV is Compton scattering. However, the gamma-rays generated by primary particles hitting the atmosphere and the pair production events are the two significant background events when the satellite is operating in Low Earth Orbit. In this paper, we applied Machine Learning models to identify and reject the two troublesome background event types. Ensemble technique and imbalance solution are explored in order to obtain a better performance. Experiments demonstrated that the proposed methods can discriminate the pair events with a high accuracy, and the satellite’s sensitivity has also been improved dramatically.

中文翻译：

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用于纳米卫星任务的康普顿事件识别机器学习

如今，纳米卫星 MeV 望远镜正变得越来越有吸引力。1MeV 附近电磁波谱的主要相互作用机制是康普顿散射。然而，当卫星在低地球轨道运行时，初级粒子撞击大气层产生的伽马射线和对产生事件是两个重要的背景事件。在本文中，我们应用机器学习模型来识别和拒绝两种麻烦的背景事件类型。探索集成技术和不平衡解决方案以获得更好的性能。实验表明，所提出的方法能够以较高的精度区分配对事件，并且卫星的灵敏度也得到了显着提高。