Issue |
A&A
Volume 690, October 2024
|
|
---|---|---|
Article Number | A381 | |
Number of page(s) | 10 | |
Section | The Sun and the Heliosphere | |
DOI | https://doi.org/10.1051/0004-6361/202450714 | |
Published online | 24 October 2024 |
Derivation of a generalized Kappa distribution from the scaling properties of solar wind magnetic field fluctuations at kinetic scales
1
Department of Physics, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome 00133, Italy
2
INAF – Institute for Space Astrophysics and Planetology, Via del Fosso del Cavaliere 100, Rome 00133, Italy
⋆⋆ Corresponding author; simone.benella@inaf.it
Received:
14
May
2024
Accepted:
2
September
2024
Context. Kinetic-scale dynamics in weakly collisional space plasmas usually exhibits a self-similar statistics of magnetic field fluctuations. This implies the existence of an invariant probability density function (master curve).
Aims. We provide an analytical derivation of the master curve by assuming that perpendicular fluctuations can be modeled through a scale-dependent Langevin equation.
Methods. In our model, magnetic field fluctuations are the stochastic variable, and their scale-to-scale evolution is assumed to be a Langevin process. We propose a formal derivation of the master curve describing the statistics of the fluctuations at kinetic scales. The model predictions were tested on independent data samples of the fast solar wind measured near the Sun by Parker Solar Probe and near the Earth by Cluster.
Results. The master curve is a generalization of the Kappa distribution with two parameters: One parameter regulates the tails, and the other controls the asymmetry. The model predictions match the spacecraft observations up to 5σ and even beyond in the case of perpendicular magnetic field fluctuations.
Key words: magnetic fields / turbulence / waves / Sun: heliosphere / solar wind
© The Authors 2024
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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