Application of Deep Learning to Spectroscopic Features of the Balmer-Alpha Line for Hydrogen Isotopic Ratio Determination in Tokamaks

M. Koubiti (1) , Malo Kerebel

1 PIIM - Physique des interactions ioniques et moléculaires

We propose in this paper the use of artificial intelligence, especially deep learning algorithms, for the isotopic ratio determination for hydrogen-deuterium mixtures. Our approach is based on the Balmer-α line emitted by hydrogen and deuterium, but unlike the standard method, it does not consist of fitting the Hα/Dα line spectra. Instead, only some basic spectroscopic features such as the line peak-to-dip wavelength separation, peak-to-peak and dip-to-peak intensity ratios of the Zeeman-Doppler-broadened Hα/Dα line spectra are used by the regression algorithm for training. We demonstrate the proof-of-principle of our approach by applying deep learning from the open-access machine-learning platform TensorFlow to Hα/Dα line profiles, which we have synthetized with predetermined parameters such as neutral temperatures, the magnetic field strength and the H/(H + D) isotopic ratio. The used regression algorithm allowed us to retrieve with a good accuracy the isotopic ratios used for the synthetized line profiles.

Keywords: plasma spectroscopy; isotopic ratio; machine learning; artificial intelligence; deep-learning; fusion plasmas; tokamaks; hydrogen isotopes; Zeeman-Doppler line profiles

Domains: Physics [physics]; Physics [physics]; Plasma Physics [physics.plasm-ph]; Computer Science [cs]; Artificial Intelligence [cs.AI]

M. Koubiti, Malo Kerebel. Application of Deep Learning to Spectroscopic Features of the Balmer-Alpha Line for Hydrogen Isotopic Ratio Determination in Tokamaks. Applied Sciences, 2022, 12, pp.9891. ⟨10.3390/app12199891⟩. ⟨hal-03798754⟩