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Using data from NASA's Magnetospheric Multiscale mission captured in a reconnection inflow on the magnetospheric side of Earth's dayside magnetopause, we find a region where the heat flux density gradient term balances the parallel compression term in the electron parallel temperature equation. Combining these observations with analysis of the generalized fluid equations indicates that such a behavior represents a quasi‐isothermal region, where cold magnetosheath beams that have transported across the magnetopause introduce non‐zero gradients in parallel heat flux density. This region should prevail near dayside reconnection X‐lines in inflows on the magnetospheric side due to the formation of mixed electron distributions and increased parallel temperatures that arise from three‐dimensional boundary dynamics. Plain Language Summary The equation that describes how electron temperature evolves with space and time includes several terms that account for the mixing of different plasmas across a boundary. NASA's Magnetospheric Multiscale mission can measure these terms and has found that under certain circumstances, the terms in the equations balance to result in a more constant electron temperature than expected. This phenomena should be common near similar interfaces and requires a three‐dimensional model of the boundary physics to explain the observed behavior. Key Points The transport of cold magnetosheath electrons across the magnetopause impacts the dominant terms in the electron temperature equation Parallel heat flux density gradient terms can balance those of parallel bulk velocity gradients Three‐dimensional boundary effects are key to understanding electron properties in the magnetospheric inflows

Alfven waves are fundamental plasma wave modes that permeate the universe. At small kinetic scales they provide a critical mechanism for the transfer of energy between electromagnetic fields and charged particles. These waves are important not only in planetary magnetospheres, heliospheres, and astrophysical systems, but also in laboratory plasma experiments and fusion reactors. Through measurement of charged particles and electromagnetic fields with NASAs Magnetospheric Multiscale (MMS) mission, we utilize Earths magnetosphere as a plasma physics laboratory. Here we confirm the conservative energy exchange between the electromagnetic field fluctuations and the charged particles that comprise an undamped kinetic Alfven wave. Electrons confined between adjacent wave peaks may have contributed to saturation of damping effects via non-linear particle trapping. The investigation of these detailed wave dynamics has been unexplored territory in experimental plasma physics and is only recently enabled by high-resolution MMS observations.

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. Plain Language Summary Measurements captured by spacecraft are necessary to our understanding the space environment near Earth and throughout our solar system. However, spacecraft can often only transmit a small portion of the data they capture back to Earth. This means that many spacecraft must prioritize intervals of data that have the highest probability of helping to further our understanding of these environments. Some missions utilize humans, on Earth, to help select these scientifically important intervals. This solution, called the Scientist‐In‐The‐Loop, can be too expensive or programmatically complex for many small missions to implement. This manuscript presents a technique for the detection of anomalous events in spaceflight measurements using statistical analysis and machine learning. These detected anomalies can be used to prioritize data that has a high probability of scientific relevance. Further, the proposed technique is highly generalizable and computationally lightweight, making it suitable for a variety of missions. Several case studies from multiple existing missions will be analyzed throughout this paper. Key Points Spacecraft often cannot transmit all measurements to Earth at full cadence due to telemetry bandwidth limitations Many missions must implement complex data prioritization schemes to ensure only the highest‐priority data is transmitted at high cadence The proposed data prioritization technique is highly generic, compatible with inexpensive hardware, and suitable for low‐cost missions

We use a newly developed global Hall magnetohydrodynamic (MHD) code to investigate how reconnection drives magnetotail asymmetries in small, ion-scale magnetospheres. Here, we consider a magnetosphere with a similar aspect ratio to Earth but with the ion inertial length (δi) artificially inflated by a factor of 70: δi is set to the length of the planetary radius. This results in a magnetotail width on the order of 30 δi, slightly smaller than Mercury's tail and much smaller than Earth's with respect to δi. At this small size, we find that the Hall effect has significant impact on the global flow pattern, changing from a symmetric, Dungey-like convection under resistive MHD to an asymmetric pattern similar to that found in previous Hall MHD simulations of Ganymede's subsonic magnetosphere as well as other simulations of Mercury's using multi-fluid or embedded kinetic physics. We demonstrate that the Hall effect is sufficient to induce a dawnward asymmetry in observed dipolarization front locations and find quasi-periodic global-scale dipolarizations under steady, southward solar wind conditions. On average, we find a thinner current sheet dawnward; however, the measured thickness oscillates with the dipolarization cycle. During the flux-pileup stage, the dawnward current sheet can be thicker than the duskward sheet. This could be an explanation for recent observations that suggest Mercury's current sheet is actually thicker on the duskside: a sampling bias due to a longer lasting “thick” state in the sheet.

We present high resolution Hall MHD simulations of Ganymede's magnetosphere demonstrating that Hall electric fields in ion-scale magnetic reconnection layers have significant global effects not captured in resistive MHD simulations. Consistent with local kinetic simulations of magnetic reconnection, our global simulations show the development of intense field-aligned currents along the magnetic separatrices. These currents extend all the way down to the moon's surface, where they may contribute to Ganymede's aurora. Within the magnetopause and magnetotail current sheets, Hall currents in the reconnection plane accelerate ions to the local Alfvén speed in the out-of-plane direction, producing a global system of ion drift belts that circulates Jovian magnetospheric plasma throughout Ganymede's magnetosphere. We discuss some observable consequences of these Hall-induced currents and ion drifts: the appearance of a sub-Jovian \"double magnetopause\" structure, an Alfvénic ion jet extending across the upstream magnetopause and an asymmetric pattern of magnetopause Kelvin-Helmholtz waves.

We describe a systematic development of kinetic entropy as a diagnostic in fully kinetic particle-in-cell (PIC) simulations and use it to interpret plasma physics processes in heliospheric, planetary, and astrophysical systems. First, we calculate kinetic entropy in two forms -- the ``combinatorial'' form related to the logarithm of the number of microstates per macrostate and the ``continuous'' form related to \\(f \\ln f\\), where \\(f\\) is the particle distribution function. We discuss the advantages and disadvantages of each and discuss subtleties about implementing them in PIC codes. Using collisionless PIC simulations that are two-dimensional in position space and three-dimensional in velocity space, we verify the implementation of the kinetic entropy diagnostics and discuss how to optimize numerical parameters to ensure accurate results. We show the total kinetic entropy is conserved to three percent in an optimized simulation of anti-parallel magnetic reconnection. Kinetic entropy can be decomposed into a sum of a position space entropy and a velocity space entropy, and we use this to investigate the nature of kinetic entropy transport during collisionless reconnection. We find the velocity space entropy of both electrons and ions increases in time due to plasma heating during magnetic reconnection, while the position space entropy decreases due to plasma compression. This project uses collisionless simulations, so it cannot address physical dissipation mechanisms; nonetheless, the infrastructure developed here should be useful for studies of collisional or weakly collisional heliospheric, planetary, and astrophysical systems. Beyond reconnection, the diagnostic is expected to be applicable to plasma turbulence and collisionless shocks.

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.

We describe our experience using NVIDIA's CUDA (Compute Unified Device Architecture) C programming environment to implement a two-dimensional second-order MUSCL-Hancock ideal magnetohydrodynamics (MHD) solver on a GTX 480 Graphics Processing Unit (GPU). Taking a simple approach in which the MHD variables are stored exclusively in the global memory of the GTX 480 and accessed in a cache-friendly manner (without further optimizing memory access by, for example, staging data in the GPU's faster shared memory), we achieved a maximum speed-up of approx. = 126 for a sq 1024 grid relative to the sequential C code running on a single Intel Nehalem (2.8 GHz) core. This speedup is consistent with simple estimates based on the known floating point performance, memory throughput and parallel processing capacity of the GTX 480.

The most common instrument for low energy plasmas consists of a top-hat electrostatic analyzer geometry coupled with a microchannel-plate (MCP)-based detection system. While the electrostatic optics for such sensors are readily simulated and parameterized during the laboratory calibration process, the detection system is often less well characterized. Furthermore, due to finite resources, for large sensor suites such as the Fast Plasma Investigation (FPI) on NASA's Magnetospheric Multiscale (MMS) mission, calibration data are increasingly sparse. Measurements must be interpolated and extrapolated to understand instrument behavior for untestable operating modes and yet sensor inter-calibration is critical to mission success. To characterize instruments from a minimal set of parameters we have developed the first comprehensive mathematical description of both sensor electrostatic optics and particle detection systems. We include effects of MCP efficiency, gain, scattering, capacitive crosstalk, and charge cloud spreading at the detector output. Our parameterization enables the interpolation and extrapolation of instrument response to all relevant particle energies, detector high voltage settings, and polar angles from a small set of calibration data. We apply this model to the 32 sensor heads in the Dual Electron Sensor (DES) and 32 sensor heads in the Dual Ion Sensor (DIS) instruments on the 4 MMS observatories and use least squares fitting of calibration data to extract all key instrument parameters. Parameters that will evolve in flight, namely MCP gain, will be determined daily through application of this model to specifically tailored in-flight calibration activities, providing a robust characterization of sensor suite performance throughout mission lifetime. Beyond FPI, our model provides a valuable framework for the simulation and evaluation of future detection system designs and can be used to maximize instrument understanding with minimal calibration resources.