Unsupervised classification of fully kinetic simulations of plasmoid instability using Self-Organizing Maps (SOMs)

The growing amount of data produced by simulations and observations of space physics processes encourages the use of methods rooted in Machine Learning for data analysis and physical discovery. We apply a clustering method based on Self-Organizing Maps (SOM) to fully kinetic simulations of plasmoid instability, with the aim of assessing its suitability as a reliable analysis tool for both simulated and observed data. We obtain clusters that map well, a posteriori, to our knowledge of the process: the clusters clearly identify the inflow region, the inner plasmoid region, the separatrices, and regions associated with plasmoid merging. SOM-specific analysis tools, such as feature maps and Unified Distance Matrix, provide one with valuable insights into both the physics at work and specific spatial regions of interest. The method appears as a promising option for the analysis of data, both from simulations and from observations, and could also potentially be used to trigger the switch to different simulation models or resolution in coupled codes for space simulations

Particle-in-cell simulations of Alfv\'en wave parametric decay in a low-beta plasma

We study the parametric decay instability of parallel propagating Alfv\'en wave in a low-beta plasma using one-dimensional fully kinetic simulations. We focus for the first time on the conversion of the energy stored in the initial Alfv\'en wave into particle internal energy, and on its partition between particle species. We show that compressible fluctuations generated by the decay of the pump wave into a secondary ion-acoustic mode and a reflected Alfv\'en wave contribute to the gain of internal energy via two distinct mechanisms. First, the ion-acoustic mode leads nonlinearly to proton trapping and proton phase space mixing, in agreement with previous work based on hybrid simulations. Second, during the nonlinear stage, a compressible front of the fast type develops at the steepened edge of the backward Alfv\'en wave leading to a field-aligned proton beam propagating backwards at the Alfv\'en speed. We find that parametric decay heats preferentially protons, which gain about 50% of the pump wave energy in the form of internal energy. However, we find that electrons are also energized and that they contribute to the total energy balance by gaining 10% of the pump wave energy. By investigating energy partition and particle heating during parametric decay, our results contribute to determine the role of compressible and kinetic effects in wave-driven models of the solar wind

Generation of sub-ion scale magnetic holes from electron shear flow instabilities in plasma turbulence

Magnetic holes (MHs) are coherent structures associated with strong magnetic field depressions in magnetized plasmas. They are observed in many astrophysical environments at a wide range of scales but their origin is still under debate. In this work we investigate the formation of sub-ion scale MHs using a fully kinetic 2D simulation of plasma turbulence initialized with parameters typical of the Earth's magnetosheath. Our analysis shows that the turbulence is capable of generating sub-ion scale MHs from large scale fluctuations via the following mechanism: first, the nonlinear large scale dynamics spontaneously leads to the development of thin and elongated electron velocity shears; these structures then become unstable to the electron Kelvin-Helmholtz instability and break up into small scale electron vortices; the electric current carried by these vortices locally reduces the magnetic field, inducing the formation of sub-ion scale MHs. The MHs thus produced exhibit features consistent with satellite observations and with previous numerical studies. We finally discuss the kinetic properties of the observed sub-ion scale MHs, showing that they are characterized by complex non-Maxwellian electron velocity distributions exhibiting anisotropic and agyrotropic features.Comment: Submitted to AP

Clustering of Global Magnetospheric Observations

The use of supervised methods in space science have demonstrated powerful capability in classification tasks, but unsupervised methods have been less utilized for the clustering of spacecraft observations. We use a combination of unsupervised methods, being principal component analysis, self-organizing maps, and hierarchical agglomerative clustering, to make predictions on if THEMIS and MMS observations occurred in the magnetosphere, magnetosheath, or the solar wind. The resulting predictions are validated visually by analyzing the distribution of predictions and studying individual time series. Particular nodes in the self organizing map are studied to see what data they represent. The capability of deeper hierarchical analysis using this model is briefly explored. Finally, the changes in region prediction can be used to infer magnetopause and bow shock crossings, which can act as an additional method of validation, and are saved for their utility in solar wind validation, understanding magnetopause processes, and the potential to develop a bow shock model.Comment: 36 pages, 22 figure

Domain of Influence analysis: implications for Data Assimilation in space weather forecasting

Solar activity, ranging from the background solar wind to energetic coronal mass ejections (CMEs), is the main driver of the conditions in the interplanetary space and in the terrestrial space environment, known as space weather. A better understanding of the Sun-Earth connection carries enormous potential to mitigate negative space weather effects with economic and social benefits. Effective space weather forecasting relies on data and models. In this paper, we discuss some of the most used space weather models, and propose suitable locations for data gathering with space weather purposes. We report on the application of \textit{Representer analysis (RA)} and \textit{Domain of Influence (DOI) analysis} to three models simulating different stages of the Sun-Earth connection: the OpenGGCM and Tsyganenko models, focusing on solar wind - magnetosphere interaction, and the PLUTO model, used to simulate CME propagation in interplanetary space. Our analysis is promising for space weather purposes for several reasons. First, we obtain quantitative information about the most useful locations of observation points, such as solar wind monitors. For example, we find that the absolute values of the DOI are extremely low in the magnetospheric plasma sheet. Since knowledge of that particular sub-system is crucial for space weather, enhanced monitoring of the region would be most beneficial. Second, we are able to better characterize the models. Although the current analysis focuses on spatial rather than temporal correlations, we find that time-independent models are less useful for Data Assimilation activities than time-dependent models. Third, we take the first steps towards the ambitious goal of identifying the most relevant heliospheric parameters for modelling CME propagation in the heliosphere, their arrival time, and their geoeffectiveness at Earth.Comment: Accepted for publication at Frontiers in Astronomy and Space Science

Collisionless Heat Flux Regulation via the Electron Firehose Instability in the Presence of a Core and Suprathermal Population in the Expanding Solar Wind

The evolution of the electron heat flux in the solar wind is regulated by the interplay between several effects: solar wind expansion, which can potentially drive velocity–space instabilities, turbulence, wave–particle interactions, and, possibly, collisions. Here we address the respective role played by the solar wind expansion and the electron firehose instability (EFI), developing in the presence of multiple electron populations, in regulating the heat flux. We carry out fully kinetic, expanding box model simulations and separately analyze the enthalpy, bulk, and velocity distribution function skewness contributions for each of the electron species. We observe that the key factor determining electron energy flux evolution is the reduction of the drift velocity of the electron populations in the rest frame of the solar wind. In our simulations, redistribution of the electron thermal energy from the parallel to the perpendicular direction after the onset of the EFI is observed. However, this process seems to impact energy flux evolution only minimally. Hence, reduction of the electron species drift velocity in the solar wind frame appears to directly correlate with efficiency for heat flux instabilities