Using Kernel-Based Statistical Distance to Study the Dynamics of Charged Particle Beams in Particle-Based Simulation Codes

TL;DR

This paper introduces kernel-based statistical distance measures as numerical diagnostics to analyze the complex dynamics of charged-particle beams in simulations, providing sensitive insights into nonlinear and high-intensity systems.

Contribution

It applies kernel-based statistical distances, like Maximum Mean Discrepancy, to charged-particle beam simulations, offering a novel diagnostic approach with broad applicability.

Findings

Effective detection of dynamical changes in beam simulations

Applicable to high-intensity and nonlinear systems

Potential extension to plasmas and gravitational systems

Abstract

Measures of discrepancy between probability distributions (statistical distance) are widely used in the fields of artificial intelligence and machine learning. We describe how certain measures of statistical distance can be implemented as numerical diagnostics for simulations involving charged-particle beams. Related measures of statistical dependence are also described. The resulting diagnostics provide sensitive measures of dynamical processes important for beams in nonlinear or high-intensity systems, which are otherwise difficult to characterize. The focus is on kernel-based methods such as Maximum Mean Discrepancy, which have a well-developed mathematical foundation and reasonable computational complexity. Several benchmark problems and examples involving intense beams are discussed. While the focus is on charged-particle beams, these methods may also be applied to other many-body systems such as plasmas or gravitational systems.