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Traditional Martian bow shock and magnetic pileup (magnetopause) boundary models are based on the fitting of free parameters in a prescribed formula. The form of the formula, fitted data, and considered controlling parameters distinguish the individual models from each other. However, all these models have one thing in common: the shape of the boundary and the parametric dependence assumed are fixed by the prescribed formula. The fitted data set typically consists of individual identified boundary crossings. This approach can suffer from a significant bias, as the boundary crossings are more likely to be identified in regions where a spacecraft spends more time. In this study, we use an automated region classification of the data measured by the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft to solar wind, magnetosheath, or magnetosphere. This is achieved by applying the Support Vector Machine method to individual spacecraft half-orbits (from periapsis to apoapsis or vice versa). Two different models of the locations of the bow shock and magnetic pileup boundaries are then constructed based on neural networks: i) a model trained using the classified data, and ii) a model trained using individual identified boundary crossings. As compared to formal empirical modeling efforts, the neural network models do not assume any prescribed shape/distance formula. Optimal model parameterization (considering the solar wind dynamic pressure, solar ionizing flux, crustal magnetic field magnitude, Alfvén Mach number, and interplanetary magnetic field magnitude) is discussed and the model performance evaluated. [ABSTRACT FROM AUTHOR]

Whistler mode quasiperiodic (QP) emissions are electromagnetic waves at frequencies between about 0.5 and 4 kHz observed in the Earth's inner magnetosphere, which are characterized by their nearly periodic time modulation of the wave intensity. Although they have been studied using both ground-based and satellite instruments for already a few decades, their origin remains unclear. Recent studies of these emissions revealed that, on top of the main modulation period, some of the events exhibit an additional fine inner intensity modulation. We use high-resolution electromagnetic wave measurements obtained by the low-altitude DEMETER spacecraft to systematically study the presence/absence of such fine inner structure and the corresponding fine inner modulation periods. Altogether, as many as 251 events are analyzed. Out of these, the clear fine inner modulation is observed for 71 events, only unclear fine inner modulation is observed for 63 events, and the fine inner modulation is completely absent for 117 events. We show that the fine inner structure tends to occur primarily for QP events with shorter modulation periods. The fine inner modulation periods are on the order of few seconds, corresponding to bounce times of whistler mode waves in between the hemispheres. They typically stay quite constant within a single event, and they tend to be lower at larger geomagnetic latitudes (L-shells). Interestingly, the fine inner modulation periods are positively correlated with the main QP modulation periods. The results obtained are of interest for understanding possible mechanisms responsible for the generation of QP events. [ABSTRACT FROM AUTHOR]

• The paper presents the electron fluxes measured by SATRAM on the Proba-V satellite in LEO. • A neural network has been apllied for particle identification and flux estimation at low fluxes. • A new simple, statistical method is provided to estimate fluxes at high fluxes. • The fluxes are compared with the fluxes measured by EPT on the Proba-V satellite. • Good agreement has been achieved.. We present the determination of electron fluxes measured by the Space Application of Timepix Radiation Monitor (SATRAM), a pixelated single-layer particle detector and the comparison with the Energetic Particle Telescope (EPT), a science-class radiation spectrometer. Both are attached to the Proba-V satellite of the European Space Agency. SATRAM hosts a Timepix chip with a 300 μ m thick silicon sensor divided into a 256 × 256 pixel matrix with 55 μ m pixel pitch. Simulations were conducted to determine the geometric factor of the sensor and the effective area that includes the shielding effects from all directions. The simulation was further used to study the influence of secondary particle production, track interruption, and backscattering on the number of detected particles. Particle identification is performed using two different methods. The first method requires that particle tracks are individually identifiable. A neural network was developed for this purpose, achieving an accuracy of 90.2% for particle identification. If individual particle tracks could not be identified, a statistical approach was used utilizing the energy deposition, average cluster energy, and the last known fraction of electrons. A comparison of the two instruments shows good agreement within one order of magnitude for the majority of the data. [ABSTRACT FROM AUTHOR]