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

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-alpha line emitted by hydrogen and deuterium, but unlike the standard method, it does not consist of fitting the H alpha/D alpha 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 H alpha/D alpha 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 H alpha/D alpha line profiles, which we have synthetized with pre-determined 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.