Characterizing magnetic reconnection regions using Gaussian mixture models on particle velocity distributions

TL;DR

This paper introduces an unsupervised machine learning method using Gaussian mixture models to automatically identify magnetic reconnection regions by analyzing particle velocity distributions in plasma simulations.

Contribution

The novel approach applies Gaussian mixture models with Bayesian information criterion for automatic detection of complex particle distributions indicating reconnection regions.

Findings

Successfully identifies non-Maxwellian features in particle distributions.

Demonstrates effectiveness on Harris sheet simulation data.

Potential for application to various simulation and observational datasets.

Abstract

We present a method based on unsupervised machine learning to identify regions of interest using particle velocity distributions as a signature pattern. An automatic density estimation technique is applied to particle distributions provided by PIC simulations to study magnetic reconnection. The key components of the method involve: i) a Gaussian mixture model determining the presence of a given number of subpopulations within an overall population, and ii) a model selection technique with Bayesian Information Criterion to estimate the appropriate number of subpopulations. Thus, this method identifies automatically the presence of complex distributions, such as beams or other non-Maxwellian features, and can be used as a detection algorithm able to identify reconnection regions. The approach is demonstrated for specific double Harris sheet simulations but it can in principle be applied to any other type of simulation and observational data on the particle distribution function.