An improved approach to derive the kappa distribution in polytropic plasmas

2021 ◽  
Vol 28 (4) ◽  
pp. 044502
Author(s):  
Ran Guo
Keyword(s):  
2021 ◽  
Author(s):  
Jan Benáček ◽  
Marian Karlický

<p>We study how hot plasma that is released during a solar flare can be confined in its source and interact with surrounding colder plasma. The X-ray emission of coronal flare sources is well explained using Kappa velocity distribution. Therefore, we compare the difference in the confinement of plasma with Kappa and Maxwellian distribution. We use a 3D Particle-in-Cell code, which is large along magnetic field lines, effectively one-dimensional, but contains all electromagnetic effects. In the case with Kappa distribution, contrary to Maxwellian distribution, we found formation of several thermal fronts associated with double-layers that suppress particle fluxes. As the Kappa distribution of electrons forms an extended tail, more electrons are not confined by the first front and cause formation of multiple fronts. A beam of electrons from the hot part is formed at each front; it generates return current, Langmuir wave density depressions, and a double layer with a higher potential step than in the Maxwellian case. We compare the Kappa and Maxwellian cases and discuss how these processes could be observed.</p>


Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 541
Author(s):  
Georgios Nicolaou ◽  
George Livadiotis

The velocities of space plasma particles often follow kappa distribution functions, which have characteristic high energy tails. The tails of these distributions are associated with low particle flux and, therefore, it is challenging to precisely resolve them in plasma measurements. On the other hand, the accurate determination of kappa distribution functions within a broad range of energies is crucial for the understanding of physical mechanisms. Standard analyses of the plasma observations determine the plasma bulk parameters from the statistical moments of the underlined distribution. It is important, however, to also quantify the uncertainties of the derived plasma bulk parameters, which determine the confidence level of scientific conclusions. We investigate the determination of the plasma bulk parameters from observations by an ideal electrostatic analyzer. We derive simple formulas to estimate the statistical uncertainties of the calculated bulk parameters. We then use the forward modelling method to simulate plasma observations by a typical top-hat electrostatic analyzer. We analyze the simulated observations in order to derive the plasma bulk parameters and their uncertainties. Our simulations validate our simplified formulas. We further examine the statistical errors of the plasma bulk parameters for several shapes of the plasma velocity distribution function.


Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 212 ◽  
Author(s):  
Georgios Nicolaou ◽  
George Livadiotis ◽  
Robert T. Wicks

The velocities of space plasma particles, often follow kappa distribution functions. The kappa index, which labels and governs these distributions, is an important parameter in understanding the plasma dynamics. Space science missions often carry plasma instruments on board which observe the plasma particles and construct their velocity distribution functions. A proper analysis of the velocity distribution functions derives the plasma bulk parameters, such as the plasma density, speed, temperature, and kappa index. Commonly, the plasma bulk density, velocity, and temperature are determined from the velocity moments of the observed distribution function. Interestingly, recent studies demonstrated the calculation of the kappa index from the speed (kinetic energy) moments of the distribution function. Such a novel calculation could be very useful in future analyses and applications. This study examines the accuracy of the specific method using synthetic plasma proton observations by a typical electrostatic analyzer. We analyze the modeled observations in order to derive the plasma bulk parameters, which we compare with the parameters we used to model the observations in the first place. Through this comparison, we quantify the systematic and statistical errors in the derived moments, and we discuss their possible sources.


2012 ◽  
Vol 117 (A6) ◽  
pp. n/a-n/a ◽  
Author(s):  
Qinghua Zhou ◽  
Fuliang Xiao ◽  
Jiankui Shi ◽  
Lijun Tang

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