Understanding of parameter dependence among the radiative collapse data in LHD plasmas with a causal discovery approach

Abstract

A causal discovery code, IEDS, has been developed and applied to data obtained from the Large Helical Device. IEDS can identify the dependence among variables quantitatively and construct a directed acyclic graph to represent their relations. The graph can be used to make graphical models, such as Bayesian networks, which can predict plasma behavior. The data used in this study include discharges with a radiative collapse and have been collected in a previous study to predict and control the radiative collapse. IEDS has demonstrated that the variables selected to predict the radiative collapse in the previous study are strongly connected to an indicator of the radiative collapse. The directed acyclic graph generated by IEDS also suggests that the relation between the line-averaged oxygen impurity emission intensity (OV) and the line-averaged carbon impurity emission intensity (CIV), which are included in the variables used to predict the radiative collapse, could be consistent with the experimental observation that shows OV increases before the increase in CIV