Plasma beta is an important parameter characterizing dynamics of various systems such as the solar wind, planetary magnetospheres, and accretion disks. Dependence of some plasma properties such as spectral break, relative proton-electron heating, and intermittency has been studied using observations as well as simulations [1,2,3]. In this study, we use particle-in-cell (PIC) simulations of turbulence to study various, yet unexplored, aspects of this beta variation. We analyze kinetic range electric fields, the variation of scale-to-scale energy transfer, and higher order statistics with respect to plasma beta. Systematic trends in the behavior of various quantities are discussed, and their implications for kinetic plasma dissipation are examined. Finally, we extend this approach to laminar reconnection, which shows turbulence like properties of magnetic spectrum and energy cascade [4,5]. References: Chen, et. al., Geophy. Res. Lett. 41.22 (2014); (https://doi.org/10.1002/2014GL062009) Franci, et. al., ApJ 833 91 (2016); (https://doi.org/10.3847/1538-4357/833/1/91) Parashar, et. al., ApJ Lett. 864 L21 (2018);(https://doi.org/10.3847/2041-8213/aadb8b ) Adhikari, et. al., Phys. of Plasmas 27,042305 (2020); (https://doi.org/10.1063/1.5128376) Adhikari, et. al.(2021); (arXiv:2104.12013)

Time series datasets often have missing or corrupted entries, which need to be ignored in subsequent data analysis. For example, in the context of space physics, calibration issues, satellite telemetry issues, and unexpected events can make parts of a time series unusable. Various approaches exist to tackle this problem, including mean/median imputation, linear interpolation, and autoregressive modeling. Here we study the utility of artificial neural networks (ANNs) to predict statistics, particularly second-order structure functions, of turbulent time series concerning the solar wind. Using a dataset with artificial gaps, a neural network is trained to predict second-order structure functions and then tested on an unseen dataset to quantify its performance. A small feedforward ANN, with only 20 hidden neurons, can predict the large-scale fluctuation amplitudes better than mean imputation or linear interpolation when the percentage of missing data is high. Although they perform worse than the other methods when it comes to capturing both the shape and fluctuation amplitude together, their performance is better in a statistical sense for large fractions of missing data. Caveats regarding their utility, the optimisation procedure, and potential future improvements are discussed.

Decomposing the electric field (E) into the contributions from generalized Ohm’s law provides key insight into both nonlinear and dissipative dynamics across the full range of scales within a plasma. Using high-resolution, multi-spacecraft measurements of three intervals in Earth’s magnetosheath from the Magnetospheric Multiscale mission, the influence of the magnetohydrodynamic, Hall, electron pressure, and electron inertia terms from Ohm’s law, as well as the impact of a finite electron mass, on the turbulent spectrum are examined observationally for the first time. The magnetohydrodynamic, Hall, and electron pressure terms are the dominant contributions to over the accessible length scales, which extend to scales smaller than the electron inertial length at the greatest extent, with the Hall and electron pressure terms dominating at sub-ion scales. The strength of the non-ideal electron pressure contribution is stronger than expected from linear kinetic Alfvén waves and a partial anti-alignment with the Hall electric field is present, linked to the relative importance of electron diamagnetic currents in the turbulence. The relative contribution of linear and nonlinear electric fields scale with the turbulent fluctuation amplitude, with nonlinear contributions playing the dominant role in shaping for the intervals examined in this study. Overall, the sum of the Ohm’s law terms and measured agree to within ~20% across the observable scales. These results both confirm general expectations about the behavior of in turbulent plasmas and highlight features that should be explored further theoretically.