Statistical Decomposition and Machine Learning to Clean In Situ Spaceflight Magnetic Field Measurements

Abstract

High-quality in situ magnetic field measurements are essential to understanding the geophysical processes that couple mass, energy, and momentum throughout near-Earth space and the solar system. This often involves identifying comparably small perturbations due to field-aligned currents or plasma processes from a much larger background field that, unfortunately, is often contaminated by magnetic noise from the host satellite platform. Stray magnetic fields can emanate from the materials used in the construction of the host spacecraft, from attitude control systems such as reaction wheels and magnetorquers, or from the solar panels, batteries, and electrical systems that manage power for the spacecraft subsystems.To mitigate the impact of the interfering fields, magnetometers can be deployed on a boom, increasing the physical separation from the host spacecraft. Historically, very long booms (e.g., >5-m) have been implemented to achieve optimal interference mitigation (Miller, 1979;Smola et al., 1980). For additional interference mitigation potential, a pair of magnetometers has often been used, mounted at different distances along the boom. At a large distance from the source a simple dipole approximation can be fit to the field gradient and subtracted (Ness et al., 1971). However, many recent missions such as Van Allen Probes (Kletzing et al., 2013) and Dellingr (Clagett et al., 2017) have opted for shorter booms (3 and 0.52-m, respectively) to reduce complexity and implementation costs. This reduced boom length tends to place the sensors in the near-field of the magnetic noise

Enabling In-Situ Magnetic Interference Mitigation Algorithm Validation via a Laboratory-Generated Dataset

“…The desire for high-fidelity magnetic field data with limited boom length has recently led to the development of a variety of new approaches for the mitigation of local magnetic interference. These new techniques range from unique magnetometer configurations -such as the DAGR instrument on the Dellingr cube satellite (Clagett et al, 2017) and the NEMISIS instrument on the Lunar Gateway HERMES suite (Burt et al, 2022;Paterson et al, 2023) -to the development of 55 new algorithms for interference identification and removal (Bowen et al, 2020;Constantinescu et al, 2020;Finley, Bowen, et al, 2023;Finley, Broadfoot, et al, 2023;Hoffmann & Moldwin, 2022;Imajo et al, 2021;Sen Gupta & Miles, 2023). The performance of the interference mitigation offered by these techniques, however, is often difficult to rigorously quantify due to the unavailability of ground-truth data from in-situ measurements.…”