Integrated data analysis on the electron temperature profile of HL-2A with the Bayesian probability inference method

Abstract

Abstract Data analysis on tokamak plasmas is mainly based on various diagnostic systems, which are usually modularized and independent of each other. This leads to a large amount of data not being fully and effectively exploited so that it is not conducive to revealing the deep physical mechanism. In this work, Bayesian probability inference with machine learning methods have been applied to the electron cyclotron emission and Thomson scattering diagnostic systems on HL-2A/2M, and the effects of integrated data analysis (IDA) on the electron temperature of HL-2A with Bayesian probability inference are demonstrated. A program is developed to infer the whole electron temperature profile with a confidence interval, and the program can be applied in online analysis. The IDA results show that the full profile of the electron temperature can be obtained and the diagnostic information is more comprehensive and abundant with IDA. The inference models for electron temperature analysis are established and the developed programs will serve as an experimental data analysis tool for HL-2A/2M in the near future.