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

We present a method based on unsupervised machine learning to identify and characterize regions of interest using particle velocity distributions as a signature pattern. An automatic density estimation technique is applied to particle distributions provided by particle-in-cell simulations to study magnetic reconnection regions. Its application to magnetic reconnection is new. 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 a Bayesian information criterion to estimate the appropriate number of subpopulations. Thus, this method automatically identifies 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 a specific double Harris sheet simulation, but it can in principle be applied to any other type of simulation data on the particle distribution function.

Jaar van publicatie:2020

BOF-keylabel:ja

IOF-keylabel:ja

BOF-publication weight:3

CSS-citation score:2

Auteurs:International

Authors from:Higher Education

Toegankelijkheid:Open