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We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the Magnetospheric Multiscale Mission (MMS) on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is >98%. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.

Plain Language Summary

Magnetospheric Multiscale Mission (MMS) has been traversing the Earth's magnetosphere to help scientists understand how the tremendous amounts of energy are released through the phenomenon known as magnetic reconnection. The spacecraft can transfer to the Earth only 4% of its measurements due to link limitations. The success of the mission relies on the selection of the most relevant measurement intervals to be sent down to the science operation center. We have trained a small deep convolutional neural network which identifies the kind of plasma the spacecraft is traversing at each measurement interval with an excellent accuracy >98%. We have used our model to identify some of the most interesting regions, bow shocks. It took only a day for the model to process all observations collected by the MMS within 3 years. The model can save a substantial amount of time for the scientists in the loop whose role is to locate such regions manually. The proposed model is suitable for the hierarchy of models being built to fully automate the on-ground data processing. Moreover, it is small enough to be embedded in the on-board software of future missions.

Identifying collisionless shock crossings in data sent from spacecraft has so far been done manually or using basic algorithms. It is a tedious job that shock physicists have to go through if they want to conduct case studies or perform statistical studies. We use a machine learning approach to automatically identify shock crossings from the Magnetospheric Multiscale (MMS) spacecraft. We compiled a database of 2,797 shock crossings, spanning a period from October 2015 to December 2020, including various spacecraft-related and shock-related parameters for each event. Furthermore, we show that the shock crossings in the database are spread out in space, from the subsolar point to the far flanks. On top of that, we show that they cover a wide range of parameter space. We also present a possible scientific application of the database by looking for correlations between ion acceleration efficiency at shocks with different shock parameters, such as the angle between the upstream magnetic field and the shock normal theta(Bn) and the Alfvenic Mach number M-A. We find no clear correlation between the acceleration efficiency and M-A; however, we find that quasi-parallel shocks are more efficient at accelerating ions than quasi-perpendicular shocks.

Whistler waves are thought to play an essential role in the dynamics of collisionless shocks. We use the magnetospheric multiscale spacecraft to study whistler waves around the lower hybrid frequency, upstream of 11 quasi-perpendicular supercritical shocks. We apply the 4-spacecraft timing method to unambiguously determine the wave vector k of whistler waves. We find that the waves are oblique to the background magnetic field with a wave-normal angle between 20 degrees and 42 degrees, and a wavelength of around 100 km, which is close to the ion inertial length. We also find that k is predominantly in the same plane as the magnetic field and the normal to the shock. By combining this precise knowledge of k with high-resolution measurements of the 3D ion velocity distribution, we show that a reflected ion beam is in resonance with the waves, opening up the possibility for wave-particle interaction between the reflected ions and the observed whistlers. The linear stability analysis of a system mimicking the observed distribution suggests that such a system can produce the observed waves.

Determination of the wave mode of short-wavelength electrostatic waves along with their generation mechanism requires reliable measurement of the wave electric field. We show that for such waves the electric field measured by Magnetospheric MultiScale becomes unreliable when the wavelength is close to the probe-to-probe separation. We develop a method, based on spin-plane interferometry, to reliably determine the full three-dimensional wave vector of the observed waves. We test the method on synthetic data and then apply it to ion acoustic wave bursts measured in the solar wind. By studying the statistical properties of ion acoustic waves in the solar wind, we retrieve the known results that the wave propagation is predominantly field aligned. We also determine the wavelength of the waves. We find that the nominal value is around 100 m, which when normalized to the Debye length corresponds to scales between 10 and 20 Debye lengths.

Adiabatic and non-adiabatic electron dynamics have been proposed to explain electron heating across collisionless shocks. We analyze the evolution of the suprathermal electrons across 310 quasi-perpendicular shocks with $1.7<M_A<48$ using in-situ measurements. We show that the electron heating mechanism shifts from predominantly adiabatic to non-adiabatic for the Alfv\'enic Mach number in the de Hoffman-Teller $\gtrsim 30$ with the latter constituting 48\% of the analyzed shocks. The observed non-adiabatic heating is consistent with the stochastic shock drift acceleration mechanism.