We devised a new data analysis technique to identify the threat level of solar active regions by processing a combined data set of magnetic field properties and flaring activity. The data set is composed of two elements: a reduced factorization of SHARP properties of the active regions, and information about the flaring activity at the time of measurement of the SHARP parameters. Machine learning is used to reduce the data and to subsequently classify the active regions. For this classification we used both supervised and unsupervised clustering. The following processing steps are applied to reduce and enhance the SHARP data: outlier detection, redundancy elimination with common factor analysis, addition of sparsity with autoencoders, and construction of a balanced data set with under- and over-sampling. Supervised clustering (based on K-nearest neighbors) produces very good results on the strong X- and M-flares, with TSS scores of respectively 93% and 75%. Unsupervised clustering (based on K-means and Gaussian Mixture Models) shows that non-flaring and flaring active regions can be distinguished, but there is not enough information in the data set for the technique to identify clear differences between the different flaring levels. This work shows that the SHARP database lacks information to accurately make flaring predictions: there is no clear hyperplane in the SHARP parameter space, even after a detailed cleaning procedure, that can separate active regions with different flaring activity. We propose instead, for future projects, to complement the magnetic field parameters with additional information, like images of the active regions.

Velocity distribution functions (VDFs) measured by the Magnetospheric Multiscale (MMS) mission are complex 3D datasets that can be represented as a superposition of multiple beams (M. V. Goldman et al., 2020). A recent work (Dupuis et al., 2020) proposed the use of the Gaussian Mixture Model (GMM). Here we investigate the approach by considering first synthetic distributions made by artificially creating beams of either Maxwellian distributions or kappa distributions with varying power law index. By varying the inter-beam average difference and the beam standard deviation we evaluate the ability of the GMM in recognizing correctly the beam. We then apply the method systematically to MMS data in the tail and in the dayside. In this case, the data need preparation before being processed by the GMM to account for the specifics of the instrument and in particular the lack of data at low energy and to account for the noise in the counts. The conclusion of the analysis is that the GMM is capable of detecting the presence of multiple beams when their distinction is significant. The GMM can define reliably the complexity of a measured dataset in terms of the number of optimal beams provided by information theory criteria. Visual inspection confirms this automatic definition of complexity.

We devised a new data analysis technique to identify the threat level of solar active regions by processing a combined data set of magnetic field parameters and flaring activity. The data set is composed of two elements: a reduced factorization of SHARP parameters of the active regions, and information about the flaring activity at the time of measurement of the SHARP parameters. Machine learning is used to reduce the data and to subsequently classify the active regions. For this classification we used both supervised and unsupervised methods. The following processing steps are applied to reduce and enhance the SHARP data: outlier detection, redundancy elimination with common factor analysis, addition of sparsity with autoencoders, and construction of a balanced data set with under- and over-sampling. The supervised method, K-nearest neighbors, produces very good results on the strong X- and M-flares, with TSS scores of respectively 0.94 and 0.75. As unsupervised methods we used clustering models, K-means and Gaussian Mixture Models. We find that both techniques are able to distinguish non-flaring and flaring active regions. Moreover, K-means is able to distinguish C-/M-flaring active regions from X-flaring active regions. For processing purposes an unsupervised method is more useful, since the type of flare will not be available. Therefore, we conclude that K-means provides the most promising results.

This White Paper outlines the importance of addressing the fundamental science theme “How are charged particles energized in space plasmas” through a future ESA mission. The White Paper presents five compelling science questions related to particle energization by shocks, reconnection, waves and turbulence, jets and their combinations. Answering these questions requires resolving scale coupling, nonlinearity, and nonstationarity, which cannot be done with existing multi-point observations. In situ measurements from a multi-point, multi-scale L-class Plasma Observatory consisting of at least seven spacecraft covering fluid, ion, and electron scales are needed. The Plasma Observatory will enable a paradigm shift in our comprehension of particle energization and space plasma physics in general, with a very important impact on solar and astrophysical plasmas. It will be the next logical step following Cluster, THEMIS, and MMS for the very large and active European space plasmas community. Being one of the cornerstone missions of the future ESA Voyage 2050 science programme, it would further strengthen the European scientific and technical leadership in this important field.

The vicinity of the electron diffusion region (EDR) at the core of magnetic reconnection is frequently characterized by agyrotropic electron velocity distributions such as perpendicular velocity-space crescents [J. L. Burch et al., Science, DOI:10.1126/science.aaf2939 (2016)]. Although the evolving EDR is not itself an equilibrium state, its evolution may be slow on electron-cyclotron time scales. When this is the case, homogeneous equilibrium models are limited in their ability to model dynamical processes, such as instabilities and wave generation, in the presence of agyrotropic populations. In order to better study these processes, we initiate implicit PIC simulations with inhomogeneous kinetic equilibria built upon agyrotropic electron velocity distributions measured by the FPI spectrometers on MMS. The methodology involves the following elements: 1. Modeling the observed agyrotropic (e.g., crescent) and background plasma distributions 2. Numerically evaluating self-consistent inhomogeneous equilibria – including anisotropy 3. Initializing 1D, 2D, and 3D PIC simulations with the equilibria 4. Evaluating the simulation output for instabilities and the persistence of the crescents Particle tracing and other visualization tools will be employed to illustrate the underlying dynamics of particles and fields – and their interactions.