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

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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.

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Notes

  1. This is an unproven hypothesis because we don’t consider the denominator TP, but we suppose that this hypothesis is valid in our numerical range. Here what we want is to borrow some techniques (will be introduced shortly) that help to improve ξ for achieving a satisfied η in training ML model process.

  2. These methods are not applicable for random forest model.

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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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Authors and Affiliations

  1. Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131, Padova, Italy

    Haitao Cao & Alessandro Paccagnella

  2. Istituto Nazionale di Fisica Nucleare, Via Marzolo 8, 35131, Padova, Italy

    Haitao Cao, Denis Bastieri & Riccardo Rando

  3. School of Physics and Electronic Engineering, Guangzhou University, 510006, Guangzhou, China

    Haitao Cao & Gaoyong Luo

  4. Department of Physics and Astronomy “G. Galilei”, University of Padova, Via Marzolo 8, 35131, Padova, Italy

    Denis Bastieri, Riccardo Rando & Giorgio Urso

  5. Center for Astrophysics, Guangzhou University, 510006, Guangzhou, China

    Denis Bastieri

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  1. Haitao Cao
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  2. Denis Bastieri
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Correspondence to Haitao Cao.

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Cao, H., Bastieri, D., Rando, R. et al. Machine learning on compton event identification for a nano-satellite mission. Exp Astron 47, 129–144 (2019). https://doi.org/10.1007/s10686-019-09620-4

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