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

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 × 10¹⁸<n_e<8×10²⁰ m⁻³ and 0.1<T_e<4eV. 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.