Mechanism for Collective Energy Transfer from Neutral Beam-Injected Ions to Fusion-Born Alpha Particles on Cyclotron Timescales in a Plasma.

Helium ash alpha particles at ∼100 keV in magnetically confined fusion plasmas may have the same Larmor radius, as well as cyclotron frequency, as the energetic beam-injected deuterons that heat the plasma. While the velocity-space distribution of the helium ash is monotonically decreasing, that of the energetic deuterons is a delta function in the edge plasma. Here we identify, by means of first principles particle-in-cell computations, a new physical process by which Larmor radius matching enables collective gyroresonant energy transfer between these two colocated minority energetic ion populations, embedded in majority thermal plasma. This newly identified nonlinear phenomenon rests on similar underlying physics to widely observed ion cyclotron emission from suprathermal minority ion populations.

Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks.

The relationship between simulated ion cyclotron emission (ICE) signals s and the corresponding 1D velocity distribution function fv⊥ of the fast ions triggering the ICE is modeled using a two-layer deep neural network. The network architecture (number of layers and number of computational nodes in each layer) and hyperparameters (learning rate and number of learning iterations) are fine-tuned using a bottom-up approach based on cross-validation. Thus, the optimal mapping gs;θ of the neural network in terms of the number of nodes, the number of layers, and the values of the hyperparameters, where θ is the learned model parameters, is determined by comparing many different configurations of the network on the same training and test set and choosing the best one based on its average test error. The training and test sets are generated by computing random ICE velocity distribution functions f and their corresponding ICE signals s by modeling the relationship as the linear matrix equation Wf = s. The simulated ICE signals are modeled as edge ICE signals at LHD. The network predictions for f based on ICE signals s are on many simulated ICE signal examples closer to the true velocity distribution function than that obtained by 0th-order Tikhonov regularization, although there might be qualitative differences in which features one technique is better at predicting than the other. Additionally, the network computations are much faster. Adapted versions of the network can be applied to future experimental ICE data to infer fast-ion velocity distribution functions.