Abstract
For given computational resources, one of the main limitations in the accuracy of plasma simulations
using particles comes from the noise due to limited statistical sampling in the reconstruction of
the particle distribution function. A method based on wavelet multiresolution analysis is proposed
and tested to reduce this noise. The method, known as wavelet based density estimation (WBDE),
was previously introduced in the statistical literature to estimate probability densities given a nite
number of independent measurements. Its novel application to plasma simulations can be viewed
as a natural extension of the nite size particles (FSP) approach, with the advantage of estimating
more accurately distribution functions that have localized sharp features. The proposed method
preserves the moments of the particle distribution function to a good level of accuracy, has no
constraints on the dimensionality of the system, does not require an a priori selection of a global
smoothing scale, and its able to adapt locally to the smoothness of the density based on the given
discrete particle data. Most importantly, the computational cost of the denoising stage is of the
same order as one timestep of a FSP simulation. The method is compared with a recently proposed
proper orthogonal decomposition based method, and it is tested with particle data corresponding
to strongly collisional, weakly collisional, and collisionless plasmas simulations.
