Abstract
Collisionless space plasma environments are typically characterised by distinct particle populations. Although moments of their velocity distribution functions help in distinguishing different plasma regimes, the distribution functions themselves provide more comprehensive information about the plasma state, especially at times when the distribution function includes non-thermal effects. Unlike moments, however, distribution functions are not easily characterised by a small number of parameters, making their classification more difficult to achieve. In order to perform this classification, we propose to distinguish between the different plasma regions by applying dimensionality reduction and clustering methods to electron distributions in pitch angle and energy space. We utilise four separate algorithms to achieve our plasma classifications: autoencoders, principal component analysis, mean shift, and agglomerative clustering. We test our classification algorithms by applying our scheme to data from the Cluster-PEACE instrument measured in the Earth's magnetotail. Traditionally, it is thought that the Earth's magnetotail is split into three different regions (the plasma sheet, the plasma sheet boundary layer, and the lobes), that are primarily defined by their plasma characteristics. Starting with the ECLAT database with associated classifications based on the plasma parameters, we identify 8 distinct groups of distributions, that are dependent upon significantly more complex plasma and field dynamics. By comparing the average distributions as well as the plasma and magnetic field parameters for each region, we relate several of the groups to different plasma sheet populations, and the rest we attribute to the plasma sheet boundary layer and the lobes. We find clear distinctions between each of our classified regions and the ECLAT results.
Highlights
Particle populations in collisionless space plasma environments, such as the Earth’s magnetotail, are traditionally characterized by the moments of their distribution functions. 2D distribution functions in pitch angle and energy, provide the full picture of the state of each plasma environment, especially when non-thermal particle populations are present that are less characterized by a Maxwellian fit
We propose to apply dimensionality reduction and clustering methods to particle distributions in pitch angle and energy space as a new method to distinguish between the different plasma regions. 2D distributions functions in pitch angle and energy are derived from full 3D distributions in velocity space based on the magnetic field direction and the assumption of gyrotropy of electrons
(4) Evaluation: We estimate the probabilities of the agglomerative clustering (AC) labels and compare our clustering results to the original ECLAT labels in order to evaluate our method
Summary
Particle populations in collisionless space plasma environments, such as the Earth’s magnetotail, are traditionally characterized by the moments of their distribution functions. 2D distribution functions in pitch angle and energy, provide the full picture of the state of each plasma environment, especially when non-thermal particle populations are present that are less characterized by a Maxwellian fit. 2D distribution functions in pitch angle and energy, provide the full picture of the state of each plasma environment, especially when non-thermal particle populations are present that are less characterized by a Maxwellian fit. These non-thermal plasma populations are ubiquitous across the solar system. 2D distributions functions in pitch angle and energy are derived from full 3D distributions in velocity space based on the magnetic field direction and the assumption of gyrotropy of electrons With these novel methods, we robustly classify variations in particle populations to a high temporal and spatial resolution, allowing us to better identify the physical processes governing particle populations in nearEarth space. Our method has the advantage of being independent of the model applied, as these methods do not require prior assumptions of the distributions of each population
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