Denoising Particle-In-Cell Data via Smoothness-Increasing Accuracy-Conserving Filters with Application to Bohm Speed Computation

TL;DR

This paper explores the use of SIAC convolution filters to effectively denoise Particle-In-Cell simulation data, improving accuracy and reducing computational requirements in plasma physics applications.

Contribution

It introduces the application of SIAC filters to PIC data, demonstrating enhanced denoising and scale capturing in physical and Fourier spaces, and shows reduced data needs for computing plasma quantities.

Findings

SIAC filters effectively denoise PIC data.

SIAC filtering captures relevant scales in Fourier space.

Reduced data requirements for Bohm speed computation.

Abstract

The simulation of plasma physics is computationally expensive because the underlying physical system is of high dimensions, requiring three spatial dimensions and three velocity dimensions. One popular numerical approach is Particle-In-Cell (PIC) methods owing to its ease of implementation and favorable scalability in high-dimensional problems. An unfortunate drawback of the method is the introduction of statistical noise resulting from the use of finitely many particles. In this paper we examine the application of the Smoothness-Increasing Accuracy-Conserving (SIAC) family of convolution kernel filters as denoisers for moment data arising from PIC simulations. We show that SIAC filtering is a promising tool to denoise PIC data in the physical space as well as capture the appropriate scales in the Fourier space. Furthermore, we demonstrate how the application of the SIAC technique reduces the amount of information necessary in the computation of quantities of interest in plasma physics such as the Bohm speed.