Compressing the time series of five dimensional distribution function data from gyrokinetic simulation using principal component analysis

Asahi, Yuichi   ; Fujii, Keisuke*; Heim, D. M.*; Maeyama, Shinya*; Garbet, X.*; Grandgirard, V.*; Sarazin, Y.*; Dif-Pradalier, G.*; Idomura, Yasuhiro   ; Yagi, Masatoshi*

This article demonstrates a data compression technique for the time series of five dimensional distribution function data based on Principal Component Analysis (PCA). Phase space bases and corresponding coefficients are constructed by PCA in order to reduce the data size and the dimensionality. It is shown that about 83% of the variance of the original five dimensional distribution can be expressed with 64 components. This leads to the compression of the degrees of freedom from to . One of the important findings - resulting from the detailed analysis of the contribution of each principal component to the energy flux - deals with avalanche events, which are found to be mostly driven by coherent structures in the phase space, indicating the key role of resonant particles.