Statistics of Solar Wind Electron Breakpoint Energies Using Machine Learning Techniques
Mayur R. Bakrania, I. Jonathan Rae, Andrew P. Walsh, Daniel, Verscharen, Andy W. Smith, T\'eo Bloch, Clare E. J. Watt

TL;DR
This study uses machine learning to classify and analyze solar wind electron populations, revealing how their energies vary with solar wind parameters and core temperature, providing insights into scattering processes.
Contribution
It introduces machine learning techniques for robust classification of solar wind electron populations and analyzes their energy variations over ten years.
Findings
Halo and strahl breakpoint energies increase with core temperature.
Halo breakpoint energy correlates more positively with core temperature than strahl.
Breakpoint energies decrease with increasing solar wind speed, with distinct profiles above and below 500 km/s.
Abstract
Solar wind electron velocity distributions at 1 au consist of a thermal "core" population and two suprathermal populations: "halo" and "strahl". The core and halo are quasi-isotropic, whereas the strahl typically travels radially outwards along the parallel and/or anti-parallel direction with respect to the interplanetary magnetic field. With Cluster-PEACE data, we analyse energy and pitch angle distributions and use machine learning techniques to provide robust classifications of these solar wind populations. Initially, we use unsupervised algorithms to classify halo and strahl differential energy flux distributions to allow us to calculate relative number densities, which are of the same order as previous results. Subsequently, we apply unsupervised algorithms to phase space density distributions over ten years to study the variation of halo and strahl breakpoint energies with solar…
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