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Abstract The Magnetospheric Multiscale (MMS) mission's extended phase introduced an “unbiased campaign” that provided the first high‐resolution data set of the terrestrial magnetosheath obtained without human selection, across complete crossings from the bow shock to the magnetopause. This is achieved by collecting 3 min of burst‐mode data every 9 min. Leveraging this novel data set, we report average plasma parameters and observe the emergence of a range in the magnetic field power spectrum. A distinct ordering of both bulk and turbulence quantities is demonstrated when the Alfvénic Mach number is used as a proxy for the distance from the bow shock. This ordering enables us to obtain clear insights into the occurrence of small‐scale current sheets throughout the magnetosheath by evaluating magnetic field kurtosis. Furthermore, the evolution of the correlation time is opposite to that of kurtosis and aligns with the principle of turbulence relaxation. This information is dispersed when quantities are sorted by geocentric distance. Crucially, striking differences emerge in turbulence‐related quantities when comparing unsupervised and supervised data sets. These findings provide valuable insights into magnetospheric turbulence, establishing the campaign as a critical resource for unbiased statistical analysis of this environment. 

Abstract Shocks in collisionless astrophysical plasmas redistribute some of the incident flow energy into both thermal and non‐thermal energy. Quantifying the partition of that energy amongst various particle species or their sub‐populations, and electromagnetic energy, represents a fundamental goal of shock physics. It embodies the role of the equation of state for the system. Here we apply a framework to assess all the incident and downstream energy fluxes at a crossing of Earth's bow shock for which the upstream magnetic field was roughly aligned with the shock normal direction. Such quasi‐parallel shocks are known to be non‐steady and to produce significant populations of suprathermal particles. We quantify the evolution of all the important carriers of energy flux through the shock region. We sub‐divide the proton population into thermal, suprathermal, and energetic components in order to investigate the shock's efficiency in energizing the nonthermal particles. While the largest energy fluxes are found in the incident proton ram energy and downstream proton thermal enthalpy fluxes, a significant suprathermal population pervades the regions both up‐ and downstream. We also evaluate the energy fluxes attributable to fluctuations in the fluid and field parameters. 

Abstract Magnetic reconnection is a fundamental plasma process that has been studied with analytical theory, numerical simulations, in situ observations, and laboratory experiments for decades. The models that have been established to describe magnetic reconnection often assume a reconnection plane normal to the current sheet in which an antiparallel magnetic field annihilates. The annihilation points, also known as the X-points, form an x -line, which is believed to be perpendicular to the reconnection plane. Recently, a new study using Magnetospheric Multiscale mission observations has challenged our understanding of magnetic reconnection by providing evidence that the x -line is not necessarily orthogonal to the reconnection plane. In this study we report a second nonorthogonal x -line event with similar features as that in the previous case study, supporting that the sheared x -line phenomenon is not an aberrant event. We employ a detailed directional derivative analysis to identify the x -line direction and show that the in-plane reconnection characteristics are well maintained even with a nonorthogonal x -line. In addition, we find the x -line tends to follow the magnetic field on one side of the current sheet, which suggests an asymmetry across the current sheet. We discuss the possibility that the nonorthogonal x -line arises from an interplay between the two aspects of reconnection: the macroscopic magnetic field topology and microscopic particle kinetics. 

Summary For large-scale testing with graph-associated data, we present an empirical Bayes mixture technique to score local false-discovery rates (FDRs). Compared to procedures that ignore the graph, the proposed Graph-based Mixture Model (GraphMM) method gains power in settings where non-null cases form connected subgraphs, and it does so by regularizing parameter contrasts between testing units. Simulations show that GraphMM controls the FDR in a variety of settings, though it may lose control with excessive regularization. On magnetic resonance imaging data from a study of brain changes associated with the onset of Alzheimer’s disease, GraphMM produces greater yield than conventional large-scale testing procedures.