Electron density profile reconstruction with convolutional neural networks

Abstract

Convolutional Neural Networks (CNN) are introduced into reconstructing electron density profiles from the line-integrated density measurements of inter-ferometers in EAST tokamak. Diagnostic data from the POlarimeter/INTerferometer (POINT) and the HCN interferometer diagnostic systems are integrated to improve the reconstruction performance. By training and optimization with unreliable measurements in the dataset, the robustness of this algorithm is enhanced. The established model can predict the probability distribution of density profiles accurately, fast, and robustly to noise and interference. This algorithm is not restricted to specific equilibrium configurations and can be transferred easily between different fusion devices.