Measuring the electron temperature and identifying plasma detachment using machine learning and spectroscopy

C. M. Samuell, A. G. McLean, C. A. Johnson, F. Glass, Aaro Järvinen

Research output: Contribution to journalArticleScientificpeer-review

17 Citations (Scopus)


A machine learning approach has been implemented to measure the electron temperature directly from the emission spectra of a tokamak plasma. This approach utilized a neural network (NN) trained on a dataset of 1865 time slices from operation of the DIII-D tokamak using extreme ultraviolet/vacuum ultraviolet emission spectroscopy matched with high-accuracy divertor Thomson scattering measurements of the electron temperature, Te. This NN is shown to be particularly good at predicting Te at low temperatures (Te < 10 eV) where the NN demonstrated a mean average error of less than 1 eV. Trained to detect plasma detachment in the tokamak divertor, a NN classifier was able to correctly identify detached states (Te < 5 eV) with a 99% accuracy (an F1 score of 0.96) at an acquisition rate 10× faster than the Thomson scattering measurement. The performance of the model is understood by examining a set of 4800 theoretical spectra generated using collisional radiative modeling that was also used to predict the performance of a low-cost spectrometer viewing nitrogen emission in the visible wavelengths. These results provide a proof-of-principle that low-cost spectrometers leveraged with machine learning can be used to boost the performance of more expensive diagnostics on fusion devices and be used independently as a fast and accurate Te measurement and detachment classifier.

Original languageEnglish
Article number043520
Pages (from-to)043520
JournalReview of Scientific Instruments
Issue number4
Publication statusPublished - 1 Apr 2021
MoE publication typeA1 Journal article-refereed


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