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Register a new userCross-Scale: Multi-Scale Coupling in Space Plasma, Assessment Study Report
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Driven by the support and interest of the international space plasma community to examine simultaneous physical plasma scales and their interactions, the Cross-Scale Mission concept was submitted and accepted as an ESA Cosmic Vision M-class candidate mission. This report presents an overview of the assessment study phase of the 7 ESA spacecraft Cross-Scale mission. Where appropriate, discussion of the benefit of international collaboration with the SCOPE mission, as well as other interested parties, is included.
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A higher-order multiscale analysis of the dissipation range of collisionless plasma turbulence is presented using in-situ high-frequency magnetic field measurements from the Cluster spacecraft in a stationary interval of fast ambient solar wind. The observations, spanning five decades in temporal scales, show a crossover from multifractal intermittent turbulence in the inertial range to non-Gaussian monoscaling in the dissipation range. This presents a strong observational constraint on theories of dissipation mechanisms in turbulent collisionless plasmas.
It is well-known that the resonance phenomena can destroy the adiabatic invariance and cause chaos and mixing. In the present paper we show that the nonlinear wave-particle resonant interaction may cause the emergence of large-scale coherent structures in the phase space. The combined action of the drift due to nonlinear scattering on resonance and trapping (capture) into resonance create a vortex-like structure, where the areas of particle acceleration and deceleration are macroscopically separated. At the same time, nonlinear scattering also creates a diffusion that causes mixing and uniformization in around the vortex.
Plasma turbulence at scales of the order of the ion inertial length is mediated by several mechanisms, including linear wave damping, magnetic reconnection, formation and dissipation of thin current sheets, stochastic heating. It is now understood that the presence of localized coherent structures enhances the dissipation channels and the kinetic features of the plasma. However, no formal way of quantifying the relationship between scale-to-scale energy transfer and the presence of spatial structures has so far been presented. In this letter we quantify such relationship analyzing the results of a two-dimensional high-resolution Hall-MHD simulation. In particular, we employ the technique of space-filtering to derive a spectral energy flux term which defines, in any point of the computational domain, the signed flux of spectral energy across a given wavenumber. The characterization of coherent structures is performed by means of a traditional two-dimensional wavelet transformation. By studying the correlation between the spectral energy flux and the wavelet amplitude, we demonstrate the strong relationship between scale-to-scale transfer and coherent structures. Furthermore, by conditioning one quantity with respect to the other, we are able for the first time to quantify the inhomogeneity of the turbulence cascade induced by topological structures in the magnetic field. Taking into account the low filling-factor of coherent structures (i.e. they cover a small portion of space), it emerges that 80% of the spectral energy transfer (both in the direct and inverse cascade directions) is localized in about 50% of space, and 50% of the energy transfer is localized in only 25% of space.
Reduced fluid models for collisionless plasmas including electron inertia and finite Larmor radius corrections are derived for scales ranging from the ion to the electron gyroradii. Based either on pressure balance or on the incompressibility of the electron fluid, they respectively capture kinetic Alfven waves (KAWs) or whistler waves (WWs), and can provide suitable tools for reconnection and turbulence studies. Both isothermal regimes and Landau fluid closures permitting anisotropic pressure fluctuations are considered. For small values of the electron beta parameter $beta_e$, a perturbative computation of the gyroviscous force valid at scales comparable to the electron inertial length is performed at order $O(beta_e)$, which requires second-order contributions in a scale expansion. Comparisons with kinetic theory are performed in the linear regime. The spectrum of transverse magnetic fluctuations for strong and weak turbulence energy cascades is also phenomenologically predicted for both types of waves. In the case of moderate ion to electron temperature ratio, a new regime of KAW turbulence at scales smaller than the electron inertial length is obtained, where the magnetic energy spectrum decays like $k_perp^{-13/3}$, thus faster than the $k_perp^{-11/3}$ spectrum of WW turbulence.
The proper classification of plasma regions in near-Earth space is crucial to perform unambiguous statistical studies of fundamental plasma processes such as shocks, magnetic reconnection, waves and turbulence, jets and their combinations. The majority of available studies have been performed by using human-driven methods, such as visual data selection or the application of predefined thresholds to different observable plasma quantities. While human-driven methods have allowed performing many statistical studies, these methods are often time-consuming and can introduce important biases. On the other hand, the recent availability of large, high-quality spacecraft databases, together with major advances in machine-learning algorithms, can now allow meaningful applications of machine learning to in-situ plasma data. In this study, we apply the fully convolutional neural network (FCN) deep machine-leaning algorithm to the recent Magnetospheric Multi Scale (MMS) mission data in order to classify ten key plasma regions in near-Earth space for the period 2016-2019. For this purpose, we use available intervals of time series for each such plasma region, which were labeled by using human-driven selective downlink applied to MMS burst data. We discuss several quantitative parameters to assess the accuracy of both methods. Our results indicate that the FCN method is reliable to accurately classify labeled time series data since it takes into account the dynamical features of the plasma data in each region. We also present good accuracy of the FCN method when applied to unlabeled MMS data. Finally, we show how this method used on MMS data can be extended to data from the Cluster mission, indicating that such method can be successfully applied to any in situ spacecraft plasma database.
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