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Statistical Decomposition and Machine Learning to Clean In-Situ Spaceflight Magnetic Field Measurements
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  • Matthew G. Finley,
  • Trevor A Bowen,
  • Marc Pulupa,
  • Andriy Koval,
  • David Michael Miles
Matthew G. Finley
University of Iowa

Corresponding Author:[email protected]

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Trevor A Bowen
Space Sciences Laboratory
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Marc Pulupa
Space Sciences Laboratory, University of California at Berkeley
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Andriy Koval
Goddard Planetary Heliophysics Institute, University of Maryland Baltimore County
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David Michael Miles
University of Iowa
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Robust in-situ magnetic field measurements are critical to understanding the various mechanisms that couple mass, momentum, and energy throughout our solar system. However, the spacecraft on which magnetometers are often deployed contaminate the magnetic field measurements via onboard subsystems including reaction wheels and magnetorquers. Two magnetometers can be deployed at different distances from the spacecraft to determine an approximation of the interfering field for subsequent removal, but constant data streams from both magnetometers can be impractical due to power and telemetry limitations. Here we propose a method to identify and remove time-varying magnetic interference from sources such as reaction wheels using statistical decomposition and convolutional neural networks, providing high-fidelity magnetic field data even in cases where dual-sensor measurements are not constantly available. For example, a measurement interval from the Parker Solar Probe outboard magnetometer experienced a 95.1% reduction in reaction wheel interference following application of the proposed technique.
14 Mar 2023Submitted to ESS Open Archive
16 Mar 2023Published in ESS Open Archive