A neural networks framework for real-time unfolding of neutron spectroscopic data at JET.

A determination of fast ion population parameters such as intensity and kinetic temperature is important for fusion reactors. This becomes more challenging with finer time resolution of the measurements, since the limited data in each time slice cause increasing statistical variations in the data. This paper describes a framework using Bayesian-regularized neural networks (NNs) designed for such a task. The method is applied to the TOFOR 2.5 MeV fusion neutron spectrometer at JET. NN training data are generated by random sampling of variables in neutron spectroscopy models. Ranges and probability distributions of the parameters are chosen to match the experimental data. Results have shown good performance both on synthetic and experimental data. The latter was assessed by statistical considerations and by examining the robustness and time consistency of the results. The regularization of the training algorithm allowed for higher time resolutions than simple forward methods. The fast execution time makes this approach suitable for real-time analysis with a time resolution limit in the microsecond time scale.