Machine learning on compton event identification for a nano-satellite mission

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.