Investigation of the J-TEXT plasma events by k-means clustering algorithm

Abstract

Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals, which can be applied to identify these events. A semi-supervised machine learning algorithm, the k-means clustering algorithm, is utilized to investigate and identify plasma events in the J-TEXT plasma. This method can cluster diverse plasma events with homogeneous features, and then these events can be identified if given few manually labeled examples based on physical understanding. A survey of clustered events reveals that the k-means algorithm can make plasma events (rotating tearing mode, sawtooth oscillations, and locked mode) gathering in Euclidean space composed of multi-dimensional diagnostic data, like soft x-ray emission intensity, edge toroidal rotation velocity, the Mirnov signal amplitude and so on. Based on the cluster analysis results, an approximate analytical model is proposed to rapidly identify plasma events in the J-TEXT plasma. The cluster analysis method is conducive to data markers of massive diagnostic data.