@article{bibcite_8825,
  author = {S. Kajita and I. Sho and H. Tanaka and D. Nishijima and K. Fujii and H. J. van der Meiden and N. Ohno},
  title = {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 {\texttimes} 10 18\<n e\<8{\texttimes}10 20m-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.
},
  year = {2022},
  journal = {Nuclear Materials and Energy},
  volume = {33},
  pages = {101281},
  month = {10/2022},
  doi = {10.1016/j.nme.2022.101281},
  language = {eng},
}