English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Collisionless space plasma environments are typically characterised bydistinct particle populations. Although moments of their velocity distributionfunctions help in distinguishing different plasma regimes, the distributionfunctions themselves provide more comprehensive information about the plasmastate, especially at times when the distribution function includes non-thermaleffects. Unlike moments, however, distribution functions are not easilycharacterised by a small number of parameters, making their classification moredifficult to achieve. In order to perform this classification, we propose todistinguish between the different plasma regions by applying dimensionalityreduction and clustering methods to electron distributions in pitch angle andenergy space. We utilise four separate algorithms to achieve our plasmaclassifications: autoencoders, principal component analysis, mean shift, andagglomerative clustering. We test our classification algorithms by applying ourscheme to data from the Cluster-PEACE instrument measured in the Earth'smagnetotail. Traditionally, it is thought that the Earth's magnetotail is splitinto three different regions (the plasma sheet, the plasma sheet boundarylayer, and the lobes), that are primarily defined by their plasmacharacteristics. Starting with the ECLAT database with associatedclassifications based on the plasma parameters, we identify 8 distinct groupsof distributions, that are dependent upon significantly more complex plasma andfield dynamics. By comparing the average distributions as well as the plasmaand magnetic field parameters for each region, we relate several of the groupsto different plasma sheet populations, and the rest we attribute to the plasmasheet boundary layer and the lobes. We find clear distinctions between each ofour classified regions and the ECLAT results.

Using dimensionality reduction and clustering techniques to classify space plasma regimes