Correlations and Kappa Distributions: Numerical Experiment and Physical Understanding

David J. McComas, George Livadiotis, Nicholas V. Sarlis

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Kappa distributions, their statistical framework, and their thermodynamic origin describe systems with correlations among their particle energies, residing in stationary states out of classical thermal equilibrium/space plasmas, from solar wind to the outer heliosphere, are such systems. We show how correlations from long-range interactions compete with collisions to define the specific shape of particle velocity distributions, using a simple numerical experiment with collisions and a variable amount of correlation among the particles. When the correlations are turned off, collisions drive any initial distribution to evolve toward equilibrium and a Maxwell–Boltzmann (MB) distribution. However, when some correlation is introduced, the distribution evolves toward a different stationary state defined by a kappa distribution with some finite value of the thermodynamic kappa (Formula presented.) (where (Formula presented.) corresponds to a MB distribution). Furthermore, the stronger the correlations, the lower the (Formula presented.) value. This simple numerical experiment illuminates the role of correlations in forming stationary state particle distributions, which are described by kappa distributions, as well as the physical interpretation of correlations from long-range interactions and how they are related to the thermodynamic kappa.

Original languageEnglish (US)
Article number375
JournalEntropy
Volume27
Issue number4
DOIs
StatePublished - Apr 2025

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)
  • General Physics and Astronomy
  • Electrical and Electronic Engineering

Keywords

  • correlations
  • heliosphere
  • kappa distributions
  • numerical experiment
  • solar wind
  • space plasmas

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