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 θBn and the Alfvénic Mach number MA. We find no clear correlation between the acceleration efficiency and MA; however, we find that quasi‐parallel shocks are more efficient at accelerating ions than quasi‐perpendicular shocks.

Over the recent decades, missions such as Cluster, THEMIS, and Magnetospheric Multiscale Mission (MMS), have provided the space physics community with an abundance of in situ measurements across the magnetosphere (MSP) and the solar wind. These regions contain internally distinct plasma and field characteristics, which correspond to important regions and boundaries (e.g., bow shock, magnetopause, foreshock) that are of high scientific interest. On many occasions, scientific investigations are centered explicitly on the physical processes operating at these regions/boundaries. However, before that, they must be manually identified in the data. The current state of available measurements encompasses decades of observations, and manually surveying these data and choosing regions of interest is labor-intensive and often ineffective. The combination of an improvement in the sophistication of machine learning techniques and the more immediately available computational resources, afford a means to classify and sort massive quantities of data. In this paper, we describe a machine learning methodology that can automatically identify separate plasma regions across the upstream solar wind and dayside MSP using MMS data.The principal objective of the MMS (Burch et al., 2016) is to understand the physical processes and the fundamental sequence of events causing magnetic reconnection since it is the central driver of space weather events at Earth and a fundamental plasma process across diverse plasma environments. However, MMS

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° and 42°, 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.