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Application of Machine Learning to Investigation of Arcing on Geosynchronous Satellites

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Abstract

The harsh space environment at geosynchronous orbit (GEO) induces differential charging of spacecraft surfaces due to fluxes of high energy electrons onto and through them. Thus, satellite surfaces can charge thousands of volts with respect to each other whereas entire satellites can charge tens of thousands of volts negative of their surrounding space plasma. The ensuing electric fields can cause local discharges (arcs), endangering the normal operation of the satellite. Remote detection of spacecraft arcing is important for the satellite operators in order to properly respond to anomalies caused by spacecraft charging due to the space weather conditions. However, analysis of satellite data is laborious due to the amount of data generated. In this work, we explored the application of machine learning for analysis of GEO satellite arcing behavior using the radio frequency observations by the Arecibo 305 m telescope.

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Acknowledgements

This work was partially supported by Air Force Office of Scientific Research, Remote Sensing and Imaging Physics Portfolio (Dr. Arje Nachmann) Grant 17RVCOR414.

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Authors and Affiliations

  1. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA

    Sergey M. Plis

  2. Air Force Research Laboratory, Space Vehicles Directorate, Kirtland AFB, Albuquerque, NM, 87117, USA

    Dale C. Ferguson

  3. Georgia Tech Research Institute, 925 Dalney St NW, Atlanta, GA, 30318, USA

    Elena A. Plis

  4. Assurance Technology Corporation, Carlisle, MA, 01741, USA

    Elena A. Plis

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  1. Sergey M. Plis
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Correspondence to Elena A. Plis.

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This article belongs to the Topical Collection: Emerging Techniques in Space Domain Awareness

Guest Editors: Elena Plis, Daniel P Engelhart, Ryan C Hoffmann, Vishnu Reddy, Roberto Furfaro, James Frith

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Plis, S.M., Ferguson, D.C. & Plis, E.A. Application of Machine Learning to Investigation of Arcing on Geosynchronous Satellites. J Astronaut Sci 69, 570–580 (2022). https://doi.org/10.1007/s40295-022-00314-2

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