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, ne, and temperature, Te, 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 ne/Te 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 (ne and Te) less than half those of the multiple regression analysis in the ranges of 2 × 1018<ne<8×1020 m−3 and 0.1<Te<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.

Abstract licence: CC BY-NC

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.