Finding Structure in Large Data Sets of Particle Distribution Functions Using Unsupervised Machine Learning

Abstract

The raw data generated by simulation codes on supercomputers can be so large that it requires data reduction methods to allow scientists to understand it. Physics-based reductions are often used, for example, taking moments of particle distribution functions. It must be realized, however, that there will be a loss of information in these reductions. Here, we explore the use of unsupervised machine learning algorithms to see if patterns and structure can be learned and discovered directly in the data itself, before any reductions, and to give researchers further insights into areas of interest. This has the potential benefit of discovering kinetic structure that would be lost by some physics-based reductions. We utilize the 5-D, gyrokinetic distribution function in simulations from the full- $f$ code X-point Gyrokinetic Code (XGC1). We find that in spatial regions of “blobby” turbulence in the edge, the electron distribution function has a very distinct signature, with higher energy regions varying across space separately from the lower energy component and higher energy regions showing a distinction near passed/trapped boundaries.