Feasibility analysis of machine learning applied to magnetized plasma diagnosis

In the paper, machine learning is demonstrated for the diagnostic enhancement of magnetized plasma probes. In the plasma experiment, the magnetic field greatly affects the properties of plasma and the performance of the probe, which limits the range of measurement parameters and reduces the accuracy of probe diagnostic results. Existing probe correction methods based on improved theory and mechanics cannot completely eliminate the influence of magnetic field and often introduce additional errors. In this paper, a novel machine learning method is proposed to improve magnetized plasma probe diagnostic based on existing methods and traditional probe correction theory. The pressure of the plasma, the total voltage of the circuit, and the magnetic induction intensity are used as input parameters, and the electron density obtained from the probe diagnostics are used as output parameters. The paper presents experiments to analyse the original probe results and the probe results revised by magnetic field probe theory through the machine learning algorithm and compare them with the results of theoretical simulations. The experimental results prove that the machine learning model based on revised data has better learning efficiency and prediction results, which can expand the application scope of traditional probe correction theory and predict results closer to the theoretical value.