  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.
