Author:

Keywords:

Science & Technology, Physical Sciences, Astronomy & Astrophysics, Solar magnetic reconnection, Gaussian mixture model, Plasma astrophysics, ELECTRON-DIFFUSION REGION, DIPOLARIZATION FRONTS, KAPPA DISTRIBUTIONS, PLASMA, ACCELERATION, ANISOTROPY, EVOLUTION, SCIENCE, ENERGY, NULLS, C14/19/089#55221731, 0201 Astronomical and Space Sciences, 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics, 0306 Physical Chemistry (incl. Structural), 5101 Astronomical sciences, 5107 Particle and high energy physics, 5109 Space sciences

Abstract:

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.