Magnetometer Data Research Articles

A Subsurface Magma Ocean on Io: Exploring the Steady State of Partially Molten Planetary Bodies

Intense tidal heating within Io produces active volcanism on the surface, and its internal structure has long been a subject of debate. A recent reanalysis of the Galileo magnetometer data suggested the presence of a high-melt-fraction layer with >50 km thickness in the subsurface region of Io. Whether this layer is a “magmatic sponge” with interconnected solid or a rheologically liquid “magma ocean” would alter the distribution of tidal heating and would also influence the interpretation of various observations. To this end, we explore the steady state of a magmatic sponge and estimate the amount of internal heating necessary to sustain such a layer with a high degree of melting. Our results show that the rate of tidal dissipation within Io is insufficient to sustain a partial-melt layer of ϕ > 0.2 for a wide range of parameters, suggesting that such a layer would swiftly separate into two phases. Unless melt and/or solid viscosities are at the higher end of the estimated range, a magmatic sponge would be unstable, and thus a high-melt-fraction layer suggested in Khurana et al. is likely to be a subsurface magma ocean.

Worldwide study of the Sporadic E (Es) layer development during a space weather event

This work analyzes the worldwide development of Sporadic E (Es) layers in different regions during a High-Speed Solar Wind Stream (HSS) event on March 18, 2018. We used Digisonde data from auroral, equatorial, and low-middle latitude stations as the primary source for the analysis. We also examined a set of data from selected geomagnetic quiet days to compare the Es layer behavior in such regions. The preliminary results agreed with the understanding that the wind shear mechanism is the principal agent causing the Es layer behavior around the globe, except for the auroral region. Magnetometer and Total Electron Content (TEC) data showed that an undershielding electric field could also influence the Es layer formation over equatorial regions. Further, we observed auroral-type Es layer (Esa) occurrence in the South American Magnetic Anomaly (SAMA). The particle precipitation dynamics between the auroral site and SAMA region have different ways of acting in the Esa layer formation. Finally, our analysis provided a deep understanding of the Es layer behavior in different latitudes during a specific space weather event.

Addressing Power Issues in Biologging: An Audio/Inertial Recorder Case Study.

In the past decades, biologging, i.e., the development and deployment of animal-borne loggers, has revolutionized ecology. Despite recent advances, power consumption and battery size however remain central issues and limiting factors, constraining the quantity of data that can be collected and the size of the animals that can be studied. Here, we present strategies to achieve ultra-low power in biologging, using the design of a lightweight audio-inertial logger as an example. It is designed with low-power MEMS sensors, and a firmware based on a small embedded RTOS. Both methodologies for power reduction and experimental results are detailed. With an average power consumption reduced to 5.3 mW, combined with a battery of 1800 mAh, the logger provides 900 h of continuous 8 kHz audio, 50 Hz accelerometer and 10 Hz magnetometer data.

Use of spacecraft solar panels and Sun sensors for estimation of the Sun-pointing direction in the UPMSat-2 mission

Solar panels are a key component for most spacecraft as they provide energy to the vehicle. However, their possible use is not limited only to power generation, as they can also be used as solar sensors for attitude determination. To calculate the direction of the Sun, photovoltaic models (two of them explicit, and the well-known 1-D/2-R equivalent circuit) that simulate the performance of the solar panel (including environmental conditions such as temperature, irradiance and Earth’s albedo, to obtain accurate results) are described in this paper. These models are used to estimate the Sun incidence angle in relation to a spacecraft based on solar panel generation data. This methodology is checked with data from the UPMSat-2 mission and compared with independent measurements derived from this satellite magnetometer data. The Sun direction results are finally compared to the results obtained from the low-cost solar sensors on board the UPMSat-2. The results included in this paper hint at the possibility of using spacecraft solar panels as an additional attitude determination instrument to improve the accuracy of the estimation of the sun direction.

On the green isolated proton auroras during Canada thanksgiving geomagnetic storm

The existence of detached/isolated auroral structures in the subauroral ionosphere has been recognized and studied for decades. One major subset of such detached auroras is the so-called “isolated proton aurora” (IPA). IPA is characterized by substantial hydrogen emissions and thus inferred to be proton aurora, but is also accompanied by other emission lines. In particular, IPA is usually dominated by 557.7 nm green-line emissions in optical luminosity. To date, there is still a lack of dedicated spectrographic study and detailed comparison among structures in different emission lines of IPA. The intensity ratios between the 557.7 nm and hydrogen emissions in IPA have not been well established from concurrent observations or theoretical models. In this study, we report an IPA event during ∼0245–0345 UT on 12 October 2021, the Canada Thanksgiving storm night. Using multi-station, multi-wavelength optical instruments, including the Transition Region Explorer (TREx) spectrograph, we investigate the evolution and spectrographic properties of the IPA. In-situ and ground magnetometer data show evidence of electromagnetic ion cyclotron (EMIC) waves associated with the passage of IPA, supporting a causal link between the EMIC wave and the proton precipitation. The precipitating proton energies are estimated to range between a few keV and a few tens of keV according to the IPA emission heights inferred from triangulation analyses. Via careful examination of the spectral intensities and the elevation-angle profiles of the 557.7, 427.8, and 486.1 nm emissions based on the spectrograph data, we conclude that the 557.7 nm emissions contained in the IPA were unlikely to owe their source to energetic electron precipitation from the magnetosphere, but were the byproduct of the proton precipitation. The intensity ratio between the 557.7 nm (427.8 nm) and the 486.1 nm emissions of the IPA are confined within a relatively narrow range around ∼26 (∼4), which may serve as validation tests for existing and developing proton transport models.

Patterns of geomagnetic Pc1 pulsations in different solar cycles in the near-equatorial region from the Indian subcontinent

Pc1 geomagnetic pulsations have been investigated at very low latitudes using 13 years of archived records covering solar cycle 20–21 from the equatorial site Choutuppal (CPL, L = 1.03), and 5 years of digital induction coil magnetometer data covering descending phase of solar cycle 24 from the low latitude site Desalpar (DSP, L = 1.07). At CPL, the maximum number of Pc1 events is seen in the declining phase of the solar cycle 20 and minimum occurrence is observed in the ascending phase of the solar cycle 21. In contrast, at DSP, the Pc1 occurrence shows a maximum during solar maximum and a minimum in the declining phase of solar cycle 24. The annual and seasonal patterns of Pc1 occurrence show an inverse relation with sunspot numbers at both stations. The seasonal variation of Pc1 occurrence has a maximum in December solstice at CPL and in June solstice at DSP, respectively. The diurnal variations of Pc1 activity show similar trends at both locations with maximum Pc1 occurrence during pre- and post-midnight sectors. The superposed epoch analysis shows minimum number of Pc1s in the initial phase, followed by Pc1 occurrence in the main phase, increasing significantly in the recovery phase of geomagnetic storms. The role of ionospheric plasma density and attenuation of Pc1 waves upon propagation via the ionospheric waveguide is presented.

A deep learning model to discern indoor from outdoor environments based on data recorded by a tri-axial digital magnetic sensor

The increased use of wearable devices (WDs) for monitoring daily-life activities has led to the development of different location-driven applications. The first fundamental distinction is between indoor and outdoor environments. The most intuitive approach is the analysis of GPS coordinates or Wi-Fi signals [1] but both solutions are power consuming. In this study, we proposed and tested the use of deep learning techniques for indoor/outdoor discrimination based on local magnetic field properties of the specific environment during free-living activities. Eight participants were recruited in four different centres (Turin, Italy; Sheffield, UK; Newcastle upon Tyne, UK; Tel Aviv, Israel) and were equipped with the INDIP system [2] (including four magneto-inertial units attached to each foot, lower back, and non-dominant wrist), a smartphone (running the Aeqora mobile application) and were monitored during 2.5-hours of daily free-living activities. Magnetometer data was used to train a deep learning model, while indoor/outdoor probability based on GPS coordinates was provided by the Aeqora app and used as a reference. For each WD, the following features were extracted: x, y, z components and norm of the magnetometer and the 10-sample moving average (0.1 s window) of the latter features as a “contextual” rating. A bi-layer long short-term memory structure with a linear layer as a tail and with a gaussian error linear unit as activation unit has been implemented [3]. To achieve a lower-bias training and a more robust model, the network has been validated by exploiting a leave one subject out validation approach. In addition, the classification is based on two different observation timeframes: windows of length equal to the magnetometer data acquisition period (0.01 s) and 1 s windows. The average accuracy of the model, across participants, in the classification of indoor/outdoor environments while using as input one WD at a time and all WDs together is reported in Table 1. Based on this preliminary analysis, the model seems suitable for discerning indoor from outdoor environments with an average accuracy score higher than 88.3% in participants spread through three countries (different morphology of the territory, culture, lifestyle, etc.). With this respect, considering a longer observation time (1 s vs. 0.01 s) has resulted in increasing of the accuracy for all conditions. Overall, the best performances were obtained by using the whole INDIP system, with a 94.1% score. However, very similar performances were obtained when only one WD is considered. The effect of the experimental setup (e.g., the number and position of the WDs) and the input of the model (e.g., the length of the time-observation window) on the performance metrics require further investigations.

A Simplified Magnetic Positioning Approach Based on Analytical Method and Data Fusion for Automated Guided Vehicles

Magnetic positioning approach (MPA) is a reliable solution for automated guided vehicles (AGVs) under relatively fixed route conditions. Ferrite magnets are usually buried in the ground and employed as magnetic nails (MNs) for the parking and steering of AGVs. Our previous studies implemented a novel MPA based on super-strong NdFeB magnets and the minimize iterations between magnetometer data and predicted values via the magnetic dipole model, with the advantage of more exceptional positioning performance than traditional MPAs. Nevertheless, the iterative optimization algorithms depend on the initial guess setting; besides, the complicated calculation based on multiple magnetometer data makes it difficult to be executed on a microcontroller in real-time. In this article, we propose a simplified MPA based on the analytical method. The analytical expression for calculating the two-dimensional position of an MN is derived from the magnetic dipole model. Even a single tri-axis magnetometer can localize the MN, whereas multiple magnetometers tend to achieve higher positioning performance. Thus, positioning results of multiple magnetometers are fused based on the confidence criterion. Comparing with the MPA based on the Levenberg–Marquardt algorithm, the proposed MPA could achieve comparable positioning accuracy but with far lower computational complexity and greater robustness. Thereby, this article introduces a new concept of high-precision MPA for AGV applications without the complicated visual perception.

Modeling the Effects of Drift Shell Splitting in Two Case Studies of Simultaneous Observations of Substorm‐Driven Pi1B and IPDP‐Type EMIC Waves

AbstractIntervals of pulsations of diminishing periods (IPDPs) are a subtype of electromagnetic ion cyclotron (EMIC) waves that can be triggered by substorm onset. Pi1B waves are ultralow frequency (ULF) broadband bursts that are well correlated with substorm onset. IPDPs are associated with increased fluxes of 40–60 keV substorm‐injected protons which undergo gradient‐curvature drifting and interact with the cold plasmasphere population. While particle trajectories and the generation of IPDPs have been modeled in the past, those models neglect the role that drift shell splitting plays in the process. This research investigates the different pathways that Pi1B and IPDPs take from their shared origin in substorm onset to their distinct observations on the ground, including the effects of drift shell splitting en route. This paper presents two case studies using data from an array of four ground‐based Antarctic magnetometers that cover the evening sector, as well as in situ magnetometer data, proton fluxes, and proton pitch angles from the Van Allen Probes spacecraft. These observations identify a separation in geomagnetic latitude between Pi1Bs and IPDPs, and pinpoint a separation in magnetic local time (MLT). From these observations we model the drift shell splitting which injected particles undergo post‐onset. This study shows that simulations that incorporate drift shell splitting across a full injection front are dominated by injection boundary effects, and that the inclusion of drift shell splitting introduces a slight horizontal component to the time axis of the time–frequency dependence of the IPDPs.

Gait Analysis Based on Magnetometer and Inertial Sensors Data Fusion

Gait analysis mainly contains gait phase detection and step counting, providing effective standards for health status and setting appropriate limits for the amount of physical activity. A method based on magnetometer and inertial sensors data fusion is presented for gait phase detection and step counting conveniently and accurately. For gait phase detection, the transient state (turning points) can be detected using the amplitude of magnetic field generated by a permanent attached to the ankle. We can calculate accurate gait phase with gait event in a gait cycle, of which adaptive threshold is estimated based on turning points. For step counting, we can pick out preselected peaks by detecting the peak of the magnetic field intensity amplitude, processing inertial sensor data corresponding to continuous peaks of magnetic field intensity to realize adaptive step counting. The comparative experiment results show that for gait phase detection, average relative errors of foot-flat, push-off, swing, and foot-flat phase are 11.07%, 9.95%, 13.04%, and 10.92%, respectively, based on the proposed method to detect four phases; for step counting, the average accuracy of the presented method is 96.04% in walking without interference and 94.46% in walking with interference. Overall, our proposed method has an improvement in the anti-interference ability and accuracy.

Machine learning-based calibration of the GOCE satellite platform magnetometers

Additional datasets from space-based observations of the Earth’s magnetic field are of high value to space physics and geomagnetism. The use of platform magnetometers from non-dedicated satellites has recently successfully provided additional spatial and temporal coverage of the magnetic field. The Gravity and steady-state Ocean Circulation Explorer (GOCE) mission was launched in March 2009 and ended in November 2013 with the purpose of measuring the Earth’s gravity field. It also carried three platform magnetometers onboard. Careful calibration of the platform magnetometers can remove artificial disturbances caused by other satellite payload systems, improving the quality of the measurements. In this work, a machine learning-based approach is presented that uses neural networks to achieve a calibration that can incorporate a variety of collected information about the satellite system. The evaluation has shown that the approach is able to significantly reduce the calibration residual with a mean absolute residual of about 6.47nT for low- and mid-latitudes. In addition, the calibrated platform magnetometer data can be used for reconstructing the lithospheric field, due to the low altitude of the mission, and also observing other magnetic phenomena such as geomagnetic storms. Furthermore, the inclusion of the calibrated platform magnetometer data also allows improvement of geomagnetic field models. The calibrated dataset is published alongside this work.Graphical

Convolutional Neural Networks for Automated ULF Wave Classification in Swarm Time Series

Ultra-low frequency (ULF) magnetospheric plasma waves play a key role in the dynamics of the Earth’s magnetosphere and, therefore, their importance in Space Weather phenomena is indisputable. Magnetic field measurements from recent multi-satellite missions (e.g., Cluster, THEMIS, Van Allen Probes and Swarm) are currently advancing our knowledge on the physics of ULF waves. In particular, Swarm satellites, one of the most successful missions for the study of the near-Earth electromagnetic environment, have contributed to the expansion of data availability in the topside ionosphere, stimulating much recent progress in this area. Coupled with the new successful developments in artificial intelligence (AI), we are now able to use more robust approaches devoted to automated ULF wave event identification and classification. The goal of this effort is to use a popular machine learning method, widely used in Earth Observation domain for classification of satellite images, to solve a Space Physics classification problem, namely to identify ULF wave events using magnetic field data from Swarm. We construct a Convolutional Neural Network (ConvNet) that takes as input the wavelet spectrum of the Earth’s magnetic field variations per track, as measured by Swarm, and whose building blocks consist of two alternating convolution and pooling layers, and one fully connected layer, aiming to classify ULF wave events within four different possible signal categories: (1) Pc3 wave events (i.e., frequency range 20–100 MHz), (2) background noise, (3) false positives, and (4) plasma instabilities. Our preliminary experiments show promising results, yielding successful identification of more than 97% accuracy. The same methodology can be easily applied to magnetometer data from other satellite missions and ground-based arrays.

A Gyroless Attitude Estimation for Ground Vehicle Under Severe Dynamic Conditions

In this paper, we propose an inertial measurement unit (IMU) based accurate attitude estimation algorithm for land vehicles using only accelerometer and magnetometer data along with a set of nonholonomic constraints on vehicle motion. Currently, most of the IMU based attitude estimation methods deploy gyroscopes along with accelerometer measurements which work efficiently only either for static or quasi-static conditions. However, during prolonged dynamic phases of vehicle motion, the accelerometer measurements are corrupted due to external acceleration which deteriorates the attitude estimation accuracy. In this paper, we illustrate that vehicle and sensor constraints allow the removal of external acceleration from accelerometer measurement in a magnetically clean environment. This, in turn, enables to design and use a linear Kalman filter (KF) using only accelerometer and magnetometer data for accurate attitude estimation. The proposed algorithm is tested and compared with three different state-of-the-art algorithms in simulation and real-world experiments under various dynamic conditions. The results illustrate that vehicle attitude information can be obtained accurately and reliably during long term fast dynamic conditions, using the designed KF.

A Statistical Study of Modulation of Electromagnetic Ion Cyclotron Waves Observed on Ground

AbstractThe modulation of electromagnetic ion cyclotron (EMIC) waves by different geomagnetic pulsations is known to us from both ground and satellite observations. However, their dependence on the EMIC wave characteristics is not well explored. We report a statistical analysis of modulation of EMIC waves by short and long periodicities at the Indian Antarctic station, Maitri (L ≈ 5). We have analyzed the induction coil magnetometer data for the period of 2011–2017 and identified 6,845 EMIC wave events, out of which 5,502 events (80%) clearly showed the presence of short period modulation. These short period modulations are associated with repetitive rising tone EMIC wave emissions. Among these 5,502 EMIC wave events only 2,413 events showed presence of long period modulation, in addition to the short period modulation. Next we have examined the characteristics like start time, end time, peak frequency, frequency extent, maximum power, and dominant short and long periodicities present in each EMIC wave event. Based on the statistical analysis, we found that the dominant short and long periodicities in the EMIC waves are in the range of 1.5–3 min and 10–60 min, respectively. These short period decreases with an increase in the peak frequency of the EMIC wave. It is attributed to the decrease in magnetic field line oscillation period at lower L‐shells. Additionally, we noticed that the stronger EMIC wave events are likely to have a higher peak frequency. All these observed tendencies are examined in light of nonlinear theory, and they are found to be in good agreement.

Plasmoids in the Jovian Magnetotail: Statistical Survey of Ion Acceleration Using Juno Observations

AbstractTransient magnetic reconnection plays an important role in energetic particle acceleration in planetary magnetospheres. Jupiter's magnetosphere provides a unique natural laboratory to study processes of energy transport and transformation. Strong electric fields in spatially confined structures such as plasmoids can be responsible for ion acceleration to high energies. In this study we focus on the effectiveness of ion energization and acceleration in plasmoids. Therefore, we present a statistical study of plasmoid structures in the predawn magnetotail, which were identified in the magnetometer data of the Juno spacecraft from 2016 to 2018. We additionally use the energetic particle observations from the Jupiter Energetic Particle Detector Instrument which discriminates between different ion species. We are particularly interested in the analysis of the acceleration and energization of oxygen, sulfur, helium, and hydrogen ions. We investigate how the event properties, such as the radial distance and the local time of the observed plasmoids in the magnetotail, affect the ion intensities close to the current sheet center. Furthermore, we analyze if ion acceleration is influenced by magnetic field turbulence inside the plasmoids. We find significant heavy ion acceleration in plasmoids close to the current sheet center which is in line with the previous statistical results based on Galileo observations conducted by Kronberg et al. (2019, https://doi.org/10.1029/2019JA026553). The observed effectiveness of the acceleration is dependent on the position of Juno in the magnetotail during the plasmoid event observation. Our results show no correlation between magnetic field turbulence and nonadiabatic acceleration for heavy ions during plasmoids.

Flying State Sensing and Estimation Method of Large-Scale Bionic Flapping Wing Flying Robot

A large bionic flapping wing robot has unique advantages in flight efficiency. However, the fluctuation of fuselage centroid during flight makes it difficult for traditional state sensing and estimation methods to provide stable and accurate data. In order to provide stable and accurate positioning and attitude information for a flapping wing robot, this paper proposes a flight state sensing and estimation method integrating multiple sensors. Combined with the motion characteristics of a large flapping wing robot, the autonomous flight, including the whole process of takeoff, cruise and landing, is realized. An explicit complementary filtering algorithm is designed to fuse the data of inertial sensor and magnetometer, which solves the problem of attitude divergence. The Kalman filter algorithm is designed to estimate the spatial position and speed of a flapping wing robot by integrating inertial navigation with GPS (global positioning system) and barometer measurement data. The state sensing and estimation accuracy of the flapping wing robot are improved. Finally, the flying state sensing and estimation method is integrated with the flapping wing robot, and the flight experiments are carried out. The results verify the effectiveness of the proposed method, which can provide a guarantee for the flapping wing robot to achieve autonomous flight beyond the visual range.

HYSGUITS: A MATLAB Graphical User Interface (GUI) for hysteresis loop simulation in Vibrating-Sample Magnetometer (VSM) data

In rock magnetism, Vibrating-Sample Magnetometer (VSM) data displays magnetic moment in specific magnetic field applied in descending and ascending magnitude, which results in a pattern called hysteresis loop. This loop characterizes different magnetic materials depending on its shape. In recent years, the usage of computer software to analyze hysteresis loop has become necessary due to its precision. Easily executable, intuitive, and user-friendly open-source programs for analyzing VSM data are still not widely available despite their necessary utilization. HYSGUITS was designed with this issue in mind to further improve the development of tools in this field. HYSGUITS is a Graphic User Interface (GUI) to analyze the hysteresis loop of VSM data. MATLAB is a suitable base for producing this GUI compared to other programming language due to its sophisticated features and clean data visualization. This software is able to visualize hysteresis loop in different ways, mainly through the difference of ascending with descending magnetization value and its 1st derivative. The GUI displays the graph as an interactive plot window which provides detailed observation on each data points, supported by features such as interpolation and smoothing. This article introduces of the functionalities of HYSGUITS and demonstrates its utilization with example use case.

Solar and lunar daily geomagnetic variations and their equivalent current systems observed by Swarm

This paper describes solar and lunar daily variations of the geomagnetic field over low- and mid-latitude regions, using vector magnetometer data from Swarm satellites at altitudes of sim500 km during the solar minimum years of 2017–2020. The average solar variation of the geomagnetic field is within the range of ±14 nT, while the lunar variation is within ±2 nT. The latter is comparable to the ocean tidal field. A spherical harmonic analysis is performed on the solar and lunar variations to evaluate their internal and external equivalent current systems. The results show that both the solar and lunar variations are mainly of internal origin, which can be attributed to combined effects of ionospheric dynamo currents and induced underground currents. Global patterns of the internal solar and lunar current systems are consistent with the corresponding external current systems previously reported based on ground observations. The Swarm external currents are mainly in the meridional direction, and are likely associated with interhemispheric field-aligned currents. Both the internal and external current systems depend on the season and longitude.Graphical

Validation of Non-Restrictive Inertial Gait Analysis of Individuals with Incomplete Spinal Cord Injury in Clinical Settings.

Inertial Measurement Units (IMUs) have gained popularity in gait analysis and human motion tracking, and they provide certain advantages over stationary line-of-sight-dependent Optical Motion Capture (OMC) systems. IMUs appear as an appropriate alternative solution to reduce dependency on bulky, room-based hardware and facilitate the analysis of walking patterns in clinical settings and daily life activities. However, most inertial gait analysis methods are unpractical in clinical settings due to the necessity of precise sensor placement, the need for well-performed calibration movements and poses, and due to distorted magnetometer data in indoor environments as well as nearby ferromagnetic material and electronic devices. To address these limitations, recent literature has proposed methods for self-calibrating magnetometer-free inertial motion tracking, and acceptable performance has been achieved in mechanical joints and in individuals without neurological disorders. However, the performance of such methods has not been validated in clinical settings for individuals with neurological disorders, specifically individuals with incomplete Spinal Cord Injury (iSCI). In the present study, we used recently proposed inertial motion-tracking methods, which avoid magnetometer data and leverage kinematic constraints for anatomical calibration. We used these methods to determine the range of motion of the Flexion/Extension (F/E) hip and Abduction/Adduction (A/A) angles, the F/E knee angles, and the Dorsi/Plantar (D/P) flexion ankle joint angles during walking. Data (IMU and OMC) of five individuals with no neurological disorders (control group) and five participants with iSCI walking for two minutes on a treadmill in a self-paced mode were analyzed. For validation purposes, the OMC system was considered as a reference. The mean absolute difference (MAD) between calculated range of motion of joint angles was , , , and for hip F/E, hip A/A, knee F/E, and ankle D/P flexion angles, respectively. In addition, relative stance, swing, double support phases, and cadence were calculated and validated. The MAD for the relative gait phases (stance, swing, and double support) was %, and the average cadence error was steps/min. The MAD values for RoM and relative gait phases can be considered as clinically acceptable. Therefore, we conclude that the proposed methodology is promising, enabling non-restrictive inertial gait analysis in clinical settings.

Spherical harmonic based noise rejection and neuronal sampling with multi-axis OPMs

In this study we explore the interference rejection and spatial sampling properties of multi-axis Optically Pumped Magnetometer (OPM) data. We use both vector spherical harmonics and eigenspectra to quantify how well an array can separate neuronal signal from environmental interference while adequately sampling the entire cortex. We found that triaxial OPMs have superb noise rejection properties allowing for very high orders of interference (L=6) to be accounted for while minimally affecting the neural space (2dB attenuation for a 60-sensor triaxial system). We show that at least 11th order (143 spatial degrees of freedom) irregular solid harmonics or 95 eigenvectors of the lead field are needed to model the neural space for OPM data (regardless of number of axes measured). This can be adequately sampled with 75–100 equidistant triaxial sensors (225–300 channels) or 200 equidistant radial channels. In other words, ordering the same number of channels in triaxial (rather than purely radial) configuration may give significant advantages not only in terms of external noise rejection but also by minimizing cost, weight and cross-talk.