Use of machine learning for a helium line intensity ratio method in Magnum-PSI

Abstract

Optical emission spectroscopy (OES) of helium (He) line intensities has been used to measure the electron density, n e , and temperature, T e , in various plasma devices. In this study, a neural network with five hidden layers is introduced to model the relation between the OES data and n e / T e from laser Thomson scattering in the linear plasma device Magnum-PSI and compared to multiple regression analysis. It is shown that the neural network reduces the residual errors of prediction values ( n e and T e ) less than half those of the multiple regression analysis in the ranges of 2 × 10 18 < n e < 8 × 1 0 20 m −3 and 0 . 1 < T e < 4 eV . We checked two different data splitting methods for training and validation data, i.e., with and without considering the unit of discharge. A comparison of the splitting methods suggests that the residual error will decrease to ∼ 10% even for a new discharge data when accumulating a sufficient data set. • The relation between the helium line emission and n e /T e from laser Thomson scattering in the linear plasma device Magnum-PSI are modeled. • A neural network (NN) with five hidden layers is introduced to model and compared to the multiple regression (MR) analysis. • The NN reduces the residual errors of prediction values less than half those of MR. • The residual error can be decreased to ̃10% by accumulating sufficient data set.