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

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.