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LEOPARD: A grid-based dispersion relation solver for arbitrary gyrotropic distributions

机译:豹:一个基于网格的色散关系的能手对于任意旋转回归线分布

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Particle velocity distributions measured in collisionless space plasmas often show strong deviations from idealized model distributions. Despite this observational evidence, linear wave analysis in space plasma environments such as the solar wind or Earth’s magnetosphere is still mainly carried out using dispersion relation solvers based on Maxwellians or other parametric models. To enable a more realistic analysis, we present the new grid-based kinetic dispersion relation solver LEOPARD (Linear Electromagnetic Oscillations in Plasmas with Arbitrary Rotationally-symmetric Distributions) which no longer requires prescribed model distributions but allows for arbitrary gyrotropic distribution functions. In this work, we discuss the underlying numerical scheme of the code and we show a few exemplary benchmarks. Furthermore, we demonstrate a first application of LEOPARD to ion distribution data obtained from hybrid simulations. In particular, we show that in the saturation stage of the parallel fire hose instability, the deformation of the initial bi-Maxwellian distribution invalidates the use of standard dispersion relation solvers. A linear solver based on bi-Maxwellians predicts further growth even after saturation, while LEOPARD correctly indicates vanishing growth rates. We also discuss how this complies with former studies on the validity of quasilinear theory for the resonant fire hose. In the end, we briefly comment on the role of LEOPARD in directly analyzing spacecraft data, and we refer to an upcoming paper which demonstrates a first application of that kind.
机译:粒子速度分布测量无碰撞的空间等离子体通常显示强劲偏离理想模型分布。尽管观测证据,线性波分析在空间等离子体环境等太阳风和地球的磁气圈主要使用色散关系解决基于麦克斯韦或其他参数模型。现在新的基于网格的动态色散解算器豹(线性电磁关系在等离子体振荡任意的旋转对称分布),没有不再需要规定模型分布但允许任意旋转回归线分布功能。底层的数值方案,我们的代码显示几的基准。演示豹的第一个应用离子从混合分布数据模拟。的饱和阶段并行消防水带不稳定,初始的变形bi-Maxwellian分布的使用将会失效标准解决色散关系。解算器基于bi-Maxwellians进一步预测增长甚至在饱和,而豹正确显示消失的增长率。还讨论如何符合前研究拟线性理论的有效性共振消防水带。直接评论豹的作用分析航天器数据,我们参考的以后的文章中演示了一个第一次这类的应用。

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