Classifying Magnetic Reconnection Regions Using k‐Means Clustering: Applications to Energy Partition Journal Article uri icon

Overview

abstract

  • AbstractMagnetic reconnection is a fundamental plasma process which facilitates the conversion of magnetic energy to particle energies. This local process both contributes to and is affected by a larger system, being dependent on plasma conditions and transporting energy around the system, such as Earth's magnetosphere. When studying the reconnection process with in situ spacecraft data, it can be difficult to determine where spacecraft are in relation to the reconnection structure. In this work, we use k‐means clustering, an unsupervised machine learning technique, to identify regions in a 2.5‐D PIC simulation of symmetric magnetic reconnection with conditions comparable to those observed in Earth's magnetotail. This allows energy flux densities to be attributed to these regions. The ion enthalpy flux density is the most dominant form of energy flux density in the outflows, agreeing with previous studies. Poynting flux density may be dominant at some points in the outflows and is only half that of the Poynting flux density in the separatrices. The proportion of outflowing particle energy flux decreases as guide field increases. We find that k‐means is beneficial for analyzing data and comparing between simulations and in situ data. This demonstrates an approach which may be applied to large volumes of data to determine statistically different regions within phenomena in simulations and could be extended to in situ observations, applicable to future multi‐point missions.

publication date

  • October 1, 2024

has restriction

  • hybrid

Date in CU Experts

  • October 30, 2024 10:58 AM

Full Author List

  • Waters CL; Eastwood JP; Fargette N; Newman DL; Goldman MV

author count

  • 5

Other Profiles

International Standard Serial Number (ISSN)

  • 2169-9380

Electronic International Standard Serial Number (EISSN)

  • 2169-9402

Additional Document Info

volume

  • 129

issue

  • 10