Reconstruction of Poloidal Magnetic Field Profiles in Field-Reversed Configurations with Machine Learning in Laser-Driven Ion-Beam Trace Probe

The diagnostic of poloidal magnetic field（B p ） in field-reversed configuration（FRC）,promising for achieving efficient plasma confinement due to its high β,is a huge challenge because B p is small and reverses around the core region.The laser-driven ion-beam trace probe（LITP） has been proven to diagnose the B p profile in FRCs recently,whereas the existing iterative reconstruction approach cannot handle the measurement errors well.In this work,the machine learning approach,a fast-growing and powerful technology in automation and control,is applied to B p reconstruction in FRCs based on LITP principles and it has a better performance than the previous approach.The machine learning approach achieves a more accurate reconstruction of B p profile when 20% detector errors are considered,15% B p fluctuation is introduced and the size of the detector is remarkably reduced.Therefore,machine learning could be a powerful support for LITP diagnosis of the magnetic field in magnetic confinement fusion devices.