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Register a new userUnsupervised classification of simulated magnetospheric regions
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Maria Elena Innocenti Dr
Publication date
2021
fields
Physics
and research's language is
English
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In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest. Our method is based on Self Organizing Maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The dimensionality of the data is reduced with Principal Component Analysis before classification. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well defined magnetospheric regions. We validate our classification results by plotting the classified data in the simulated space and by comparing with K-means classification. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.
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We report the observations of an electron vortex magnetic hole corresponding to a new type of coherent structures in the magnetosheath turbulent plasma using the Magnetospheric Multiscale (MMS) mission data. The magnetic hole is characterized by a magnetic depression, a density peak, a total electron temperature increase (with a parallel temperature decrease but a perpendicular temperature increase), and strong currents carried by the electrons. The current has a dip in the center of the magnetic hole and a peak in the outer region of the magnetic hole. The estimated size of the magnetic hole is about 0.23 r{ho}i (~ 30 r{ho}e) in the circular cross-section perpendicular to its axis, where r{ho}i and r{ho}e are respectively the proton and electron gyroradius. There are no clear enhancement seen in high energy electron fluxes, but an enhancement in the perpendicular electron fluxes at ~ 90{deg} pitch angles inside the magnetic hole is seen, implying that the electron are trapped within it. The variations of the electron velocity components Vem and Ven suggest that an electron vortex is formed by trapping electrons inside the magnetic hole in the circular cross-section (in the M-N plane). These observations demonstrate the existence of a new type of coherent structures behaving as an electron vortex magnetic hole in turbulent space plasmas as predicted by recent kinetic simulations.
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.
Alfven vortex is a multi-scale nonlinear structure which contributes to intermittency of turbulence. Despite previous explorations mostly on the spatial properties of the Alfven vortex (i.e., scale, orientation, and motion), the plasma characteristics within the Alfven vortex are unknown. Moreover, the connection between the plasma energization and the Alfven vortex still remains unclear. Based on high resolution in-situ measurement from the Magnetospheric Multiscale (MMS) mission, we report for the first time, distinctive plasma features within an Alfven vortex. This Alfven vortex is identified to be two-dimensional ($k_{bot} gg k_{|}$) quasi-monopole with a radius of ~10 proton gyroscales. Its magnetic fluctuations $delta B_{bot}$ are anti correlated with velocity fluctuations $delta V_{bot}$, thus the parallel current density $j_{|}$ and flow vorticity $omega_{|}$ are anti-aligned. In different part of the vortex (i.e., edge, middle, center), the ion and electron temperatures are found to be quite different and they behave in the reverse trend: the ion temperature variations are correlated with $j_{|}$, while the electron temperature variations are correlated with $omega_{|}$. Furthermore, the temperature anisotropies, together with the non-Maxwellian kinetic effects, exhibit strong enhancement at peaks of $|omega_{|}| (|j_{|}|)$ within the vortex. Comparison between observations and numerical/theoretical results are made. In addition, the energy-conversion channels and the compressibility associated with the Alfven vortex are discussed. These results may help to understand the link between coherent vortex structures and the kinetic processes, which determines how turbulence energy dissipate in the weakly-collisional space plasmas.
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