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

  • Haitao CaoEmail author
  • Denis Bastieri
  • Riccardo Rando
  • Giorgio Urso
  • Gaoyong Luo
  • Alessandro Paccagnella
Original Article
  • 43 Downloads

Abstract

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.

Keywords

Machine learning Neural network Ensemble methods Imbalance problem MeV telescope Loss functions 

Notes

Acknowledgements

Author Haitao Cao would like to acknowledge the scholarship supported by Guangzhou University, China and the excellent research facilities provided by Istituto Nazionale di Fisica Nucleare, Padova, Italy.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Information EngineeringUniversity of PadovaPadovaItaly
  2. 2.Istituto Nazionale di Fisica NuclearePadovaItaly
  3. 3.School of Physics and Electronic EngineeringGuangzhou UniversityGuangzhouChina
  4. 4.Department of Physics and Astronomy “G. Galilei”University of PadovaPadovaItaly
  5. 5.Center for AstrophysicsGuangzhou UniversityGuangzhouChina

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