Magnetometers are essential instruments in space physics, but their measurements are often contaminated by various external interference sources. In this work, we present a comprehensive review of existing magnetometer interference removal methods and introduce MAGPRIME (MAGnetic signal PRocessing, Interference Mitigation, and Enhancement), an open-source Python library featuring a collection of state-of-the-art interference removal algorithms. MAGPRIME streamlines the process of interference removal in magnetic field data by providing researchers with an integrated, easy-to-use platform. We detail the design, structure, and functionality of the library, as well as its potential to facilitate future research by enabling rapid testing and customization of interference removal methods. Using the MAGPRIME Library, we present two Monte Carlo benchmark results to compare the efficacy of interference removal algorithms in different magnetometer configurations. In Benchmark A, the Underdetermined Blind Source Separation (UBSS) and traditional gradiometry algorithms surpass the uncleaned boom-mounted magnetometers, achieving improved correlation and reducing median error in each simulation. Benchmark B tests the efficacy of the suite of MAGPRIME algorithms in a boomless magnetometer configuration. In this configuration, the UBSS algorithm proves to significantly reduce median error, along with improvements in median correlation and signal to noise ratio. This study highlights MAGPRIME’s potential in enhancing magnetic field measurement accuracy in various spacecraft designs, from traditional gradiometry setups to compact, cost-effective alternatives like bus-mounted CubeSat magnetometers, thus establishing it as a valuable tool for researchers and engineers in space exploration and magnetism studies.

In-situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near-Earth space and beyond. However, on-orbit telemetry constraints can severely limit the capability of spacecraft to transmit high-cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission’s science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist-In-The-Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high-cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low-cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One-Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data’s potential for scientific utility. The technique’s performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e-POP/Swarm-Echo and MMS missions.

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.

In-situ magnetic field measurements are critical to our understanding of a variety of space physics phenomena including field-aligned currents and plasma waves. Unfortunately, high-fidelity magnetometer measurements are often degraded by stray magnetic fields from the host spacecraft, its subsystems, and other instruments. One dominant source of magnetic interference on many missions are reaction wheels - spinning platters of varying rates used to control spacecraft attitude. This manuscript presents a novel approach to the mitigation of reaction wheel interference on magnetometer measurements aboard spacecraft where multiple magnetometer sensors are deployed. Specifically, multichannel singular spectrum analysis is employed to decompose multiple time series simultaneously. A technique for automatic component selection is proposed that classifies the decomposed signals into common geophysical signals and disparate locally generated signals enabling the robust estimation and removal of the local interference without requiring any assumptions about its characteristics or source. The utility of this proposed method is demonstrated empirically using in-situ data from the CASSIOPE/Swarm-Echo mission, and a data interval with near-constant background field was shown to have its local reaction wheel interference reduced from 1.90 nT RMS, for the uncorrected outboard sensor, to 0.21 nT RMS (an 89.0\% reduction). This technique can be generalized to arrays of more than two sensors, and should apply to additional types of magnetic interference.