Fast inverse transform sampling of non-Gaussian distribution functions in space plasmas

TL;DR

This paper introduces Chebsampling, a fast and robust method for sampling non-Gaussian distributions in space plasmas, enhancing particle-in-cell simulation initialization.

Contribution

The paper presents Chebsampling, a novel inverse transform sampling technique using Chebyshev polynomial approximation for efficient sampling of non-Gaussian distributions in space plasmas.

Findings

Chebsampling efficiently samples 1D and 2D non-Gaussian distributions.

It improves the initialization process for plasma simulations.

The method is robust and computationally efficient.

Abstract

Non-Gaussian distributions are commonly observed in collisionless space plasmas. Generating samples from non-Gaussian distributions is critical for the initialization of particle-in-cell simulations that investigate their driven and undriven dynamics. To this end, we report a computationally efficient, robust tool, Chebsampling, to sample general distribution functions in one and two dimensions. This tool is based on inverse transform sampling with function approximation by Chebyshev polynomials. We demonstrate practical uses of Chebsampling through sampling typical distribution functions in space plasmas.