High precision measurement of fuel density profiles in nuclear fusion plasmas

This paper presents a method for deducing fuel density profiles of nuclear fusion plasmas in realtime during an experiment. A Multi Layer Perceptron (MLP) neural network is used to create a mapping between plasma radiation spectra and indirectly deduced hydrogen isotope densities. By combining different measurements a cross section of the density is obtained. For this problem, precision can be optimised by exploring the fact that both the input errors and target errors are known a priori. We show that a small adjustment of the backpropagation algorithm can take this into account during training. For subsequent predictions by the trained model, Bayesian posterior intervals will be derived, reflecting the known errors on inputs and targets both from the training set and current input pattern. The model is shown to give reliable estimates of the full fuel density profile in realtime, and could therefore be utilised for realtime feedback control of the fusion plasma.