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We present a method based on unsupervised machine learning to identifyregions of interest using particle velocity distributions as a signaturepattern. An automatic density estimation technique is applied to particledistributions provided by PIC simulations to study magnetic reconnection. Thekey components of the method involve: i) a Gaussian mixture model determiningthe presence of a given number of subpopulations within an overall population,and ii) a model selection technique with Bayesian Information Criterion toestimate the appropriate number of subpopulations. Thus, this method identifiesautomatically the presence of complex distributions, such as beams or othernon-Maxwellian features, and can be used as a detection algorithm able toidentify reconnection regions. The approach is demonstrated for specific doubleHarris sheet simulations but it can in principle be applied to any other typeof simulation and observational data on the particle distribution function.

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