Loading non-Maxwellian Velocity Distributions in Particle Simulations
Seiji Zenitani, Shunsuke Usami, Shuichi Matsukiyo
TL;DR
The paper addresses the challenge of generating non-Maxwellian velocity distribution functions (VDFs) for particle-in-cell simulations in heliophysics. It develops dedicated, algorithmically explicit sampling strategies for nine VDFs, including the $(r,q)$ distribution, regularized and subtracted kappa distributions, ring and shell components (and their Maxwellian variants), as well as super-Gaussian and filled-shell forms, all using only three elemental variates: uniform, normal, and gamma. Key contributions are the beta-prime and piecewise rejection sampling methods, explicit efficiency analyses, and validated Monte Carlo tests demonstrating faithful reproduction of target distributions. The practical impact lies in enabling accurate, efficient kinetic modeling of diverse, scientifically relevant VDFs in the solar wind and planetary magnetospheres, with ready-to-implement recipes and clear guidance on method selection.
Abstract
Numerical procedures for generating non-Maxwellian velocity distributions in particle simulations are presented. First, Monte Carlo methods for an $(r,q)$ distribution that generalizes flattop and kappa distributions are discussed. Then, two rejection methods for the regularized kappa distribution are presented, followed by a comparison in the $κ$ space. A simple recipe is proposed for the subtracted kappa distribution. Properties and numerical recipes for the ring and shell distributions with a finite Gaussian width are discussed. The ring and shell Maxwellians are further introduced as alternatives to the ring and shell distributions. Finally, the super-Gaussian and the filled-shell distributions are presented.
