Physics

Space plasma data analysis and mission operations are aided by the categorization of plasma data between different regions of the magnetosphere and identification of the boundary regions between them. Without computerized automation this means sorting large amounts of data to hand-pick regions. Using hand-labeled data created to support calibration of the Fast Plasma Instrument, this task was automated for the MMS mission with 99.9% accuracy. The method partitions the number density and ion temperature plane into sub-planes for each region, fitting boundaries between the sub-planes using a machine learning technique known as the support vector machine. This method presented in this paper is novel because it offers both statistical automation power and interpretability that yields scientific insight into how the task is performed.

Cosmic rays (CRs) have been studied extensively in the last century to understand the processes in the universe as well as in the solar system. In today's satellite era, although many observations are made from space, CR observations from the ground are still viewed as an important tool. These observations, however, mostly detect the secondary cosmic rays (SCRs) such as neutron, muon, and gamma. It is well known that the atmospheric pressure plays an important role in the SCR flux observed on the ground. Barometric pressure correction is standard practice for neutron monitor (NM) data. However, no such correction is applied to gamma-ray, being massless. But the pressure affects the particles such as e ± , μ ± , which produce gamma rays in the cascade. Subsequently, the indirect pressure dependence of the gamma-ray flux can be anticipated. We examine this aspect in detail by studying the gamma-ray counts detected by the NaI (Tl) detector. The present study confirms that there is no correlation between the atmospheric pressure and the total gamma-ray counts collected from all energies. However, the scenario differs when the gamma-ray fluxes of different energies are investigated separately. The gamma rays of energy below ∼ 3 MeV are primarily due to the radioactivity originating from the ground, whereas gamma rays above 3 MeV are mainly produced in the CR cascade. It is observed that the counts of energy above 3 MeV are well anti-correlated with the atmospheric pressure and hence need to be corrected. It is demonstrated that applying the barometric correction formula successfully removes the pressure dependence in the gamma-ray flux above 3 MeV. Therefore, we suggest that the gamma-ray data above 3 MeV needs to be corrected for the local atmospheric pressure variations.

The Van Allen radiation belts in the magnetosphere have been extensively studied using models based on radial diffusion theory, which is based on a quasi-linear approach with prescribed inner and outer boundary conditions. The 1-d diffusion model requires the knowledge of a diffusion coefficient and an electron loss timescale, which are typically parameterized in terms of various quantities such as the spatial ( L ) coordinate or a geomagnetic index (for example, Kp ). These terms are empirically derived, not directly measurable, and hence are not known precisely, due to the inherent non-linearity of the process and the variable boundary conditions. In this work, we demonstrate a probabilistic approach by inferring the values of the diffusion and loss term parameters, along with their uncertainty, in a Bayesian framework, where identification is obtained using the Van Allen Probe measurements. Our results show that the probabilistic approach statistically improves the performance of the model, compared to the parameterization employed in the literature.

Space weather indices are commonly used to drive operational forecasts of various geospace systems, including the thermosphere for mass density and satellite drag. The drivers serve as proxies for various processes that cause energy flow and deposition in the geospace system. Forecasts of neutral mass density is a major uncertainty in operational orbit prediction and collision avoidance for objects in low earth orbit (LEO). For the strongly driven system, accuracy of space weather driver forecasts is crucial for operations. The High Accuracy Satellite Drag Model (HASDM) currently employed by the United States Air Force in an operational environment is driven by four (4) solar and two (2) geomagnetic proxies. Space Environment Technologies (SET) is contracted by the space command to provide forecasts for the drivers. This work performs a comprehensive assessment for the performance of the driver forecast models. The goal is to provide a benchmark for future improvements of the forecast models. Using an archived data set spanning six (6) years and 15,000 forecasts across solar cycle 24, we quantify the temporal statistics of the model performance.

We draw a comparison between a solar energetic particle event associated with the release of a slow coronal mass ejection close to the sun, and the energetic particle population produced in high current density field-aligned current structures associated with auroral phenomena in planetary magnetospheres. We suggest that this process is common in CME development and lift-off in the corona, and may account for the electron populations that generate Type III radio bursts, as well as for the prompt energetic ion and electron populations typically observed in interplanetary space.

We present Rosetta observations from comet 67P/Churyumov-Gerasimenko during the impact of a coronal mass ejection (CME). The CME impacted on 5-6 Oct 2015, when Rosetta was about 800 km from the comet nucleus, \textcolor{black}{and 1.4 AU from the Sun}. Upon impact, the plasma environment is compressed to the level that solar wind ions, not seen a few days earlier when at 1500 km, now reach Rosetta. In response to the compression, the flux of suprathermal electrons increases by a factor of 5-10 and the background magnetic field strength increases by a factor of ∼ 2.5. The plasma density increases by a factor of 10 and reaches 600 cm −3 , due to increased particle impact ionisation, charge exchange and the adiabatic compression of the plasma environment. We also observe unprecedentedly large magnetic field spikes at 800 km, reaching above 200 nT, which are interpreted as magnetic flux ropes. We suggest that these could possibly be formed by magnetic reconnection processes in the coma as the magnetic field across the CME changes polarity, or as a consequence of strong shears causing Kelvin-Helmholtz instabilities in the plasma flow. Due to the \textcolor{black}{limited orbit of Rosetta}, we are not able to observe if a tail disconnection occurs during the CME impact, which could be expected based on previous remote observations of other CME-comet interactions.

We present a statistical analysis for the characteristics and radial evolution of linear magnetic holes (LMHs) in the solar wind from 0.166 to 0.82 AU using Parker Solar Probe observations of the first two orbits. It is found that the LMHs mainly have a duration less than 25 s and the depth is in the range from 0.25 to 0.7. The durations slightly increase and the depths become slightly deeper with the increasing heliocentric distance. Both the plasma temperature and the density for about 50% of all events inside the holes are higher than the ones surrounding the holes. The average occurrence rate is 8.7 events/day, much higher than that of the previous observations. The occurrence rate of the LMHs has no clear variation with the heliocentric distance (only a slight decreasing trend with the increasing heliocentric distance), and has several enhancements around ~0.525 AU and ~0.775 AU, implying that there may be new locally generated LMHs. All events are segmented into three parts (i.e., 0.27, 0.49 and 0.71 AU) to investigate the geometry evolution of the linear magnetic holes. The results show that the geometry of LMHs are prolonged both across and along the magnetic field direction from the Sun to the Earth, while the scales across the field extend a little faster than along the field. The present study could help us to understand the evolution and formation mechanism of the LMHs in the solar wind.

The terrestrial magnetopause is the boundary that shields the Earth's magnetosphere on one side from the shocked solar wind and its embedded interplanetary magnetic field on the other side. In this paper, we show observations from two of the Time History of Events and Macroscales Interactions during Substorms (THEMIS) satellites, comparing dayside magnetopause crossings with flank crossings near the terminator. Macroscopic properties such as current sheet thickness, motion, and current density are examined for a large number of magnetopause crossings. The results show that the flank magnetopause is typically thicker than the dayside magnetopause and has a lower current density. Consistent with earlier results from Cluster observations, we also find a persistent dawn-dusk asymmetry with a thicker and more dynamic magnetopause at dawn than at dusk.

Hot flow anomalies (HFAs) are studied using observations of the FGM magnetometer and the CIS plasma detector aboard the four Cluster spacecraft. Previously we studied several specific features of tangential discontinuities on the basis of Cluster measurements in February-April 2003 and discovered a new condition for forming HFAs that is the solar wind speed is higher than the average. However during the whole spring season of 2003, the solar wind speed was higher than average. In this study we analyse HFAs detected in 2007, the year of solar cycle minimum. Our earlier result was confirmed: the higher solar wind speed is a real condition for HFA formation; furthermore this constraint is independent of Schwartz et al. s condition for HFA formation.

During the Parker Solar Probe's (PSP) first perihelion pass, the spacecraft reached within a heliocentric distance of \(\sim 37~R_\odot\) and observed numerous magnetic and flow structures characterized by sharp gradients. To better understand these intermittent structures in the young solar wind, an important property to examine is their degree of correlation in time and space. To this end, we use the well-tested Partial Variance of Increments (PVI) technique to identify intermittent events in FIELDS and SWEAP observations of magnetic and proton-velocity fields (respectively) during PSP's first solar encounter, when the spacecraft was within 0.25 au from the Sun. We then examine distributions of waiting times between events with varying separation and PVI thresholds. We find power-law distributions for waiting times shorter than a characteristic scale comparable to the correlation time, suggesting a high degree of correlation that may originate in a clustering process. Waiting times longer than this characteristic time are better described by an exponential, suggesting a random memory-less Poisson process at play. These findings are consistent with near-Earth observations of solar wind turbulence. The present study complements the one by Dudok de Wit et al. (2020, present volume), which focuses on waiting times between observed "switchbacks" in the radial magnetic field.