Finding structure in large datasets of particle distribution functions using unsupervised machine learning

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 in the data itself can be learned and discovered, to give researchers further insight into areas of interest. This has the potential benefit of discovering kinetic structure which would be lost by some physics based reductions. We utilize the 5D, gyrokinetic distribution function in simulations from the full-f 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.