Automatic Particle Trajectory Classification in Plasma Simulations

TL;DR

This paper presents an unsupervised machine learning workflow combining FFT, PCA, and clustering to classify particle trajectories in plasma simulations, revealing new insights into acceleration mechanisms across various plasma phenomena.

Contribution

It introduces a general, physics-model independent workflow for automatic classification of particle trajectories in plasma simulations using unsupervised learning methods.

Findings

Successfully classifies electron trajectories during magnetic reconnection

Recovers known results without prior system knowledge

Identifies previously undetected particle acceleration mechanisms

Abstract

Numerical simulations of plasma flows are crucial for advancing our understanding of microscopic processes that drive the global plasma dynamics in fusion devices, space, and astrophysical systems. Identifying and classifying particle trajectories allows us to determine specific on-going acceleration mechanisms, shedding light on essential plasma processes. Our overall goal is to provide a general workflow for exploring particle trajectory space and automatically classifying particle trajectories from plasma simulations in an unsupervised manner. We combine pre-processing techniques, such as Fast Fourier Transform (FFT), with Machine Learning methods, such as Principal Component Analysis (PCA), k-means clustering algorithms, and silhouette analysis. We demonstrate our workflow by classifying electron trajectories during magnetic reconnection problem. Our method successfully recovers existing results from previous literature without a priori knowledge of the underlying system. Our workflow can be applied to analyzing particle trajectories in different phenomena, from magnetic reconnection, shocks to magnetospheric flows. The workflow has no dependence on any physics model and can identify particle trajectories and acceleration mechanisms that were not detected before.