Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO2 interferometer on DIII-D

Abstract

A Machine-Learning (ML) based detection scheme that automatically detects Alfvén Eigenmodes (AE) in a labelled DIII-D database is presented here. Controlling AEs is important for the success of planned burning plasma devices such as ITER, since resonant fast ions can drive AEs unstable and degrade the performance of the plasma or damage the first walls of the machine vessel. Artificial Intelligence could be useful for real-time detection and control of AEs in steady-state plasma scenarios by implementing ML-based models into control algorithms that drive actuators for mitigation of AE impacts. Thus, the objective is to compare differences in performance between using two different recurrent neural network systems (Reservoir Computing Network and Long Short Term Memory Network) and two different representations of the ${\mathrm{CO_2}}$ phase data (simple and crosspower spectrograms). All ${\mathrm{CO_2}}$ interferometer chords are used to train both models, but only one is processed during each training step. The results from the model and data comparison show higher performance for the RCN model (True Positive Rate = 90% and False Positive Rate = 14%), and that using simple magnitude spectrograms is sufficient to detect AEs. Also, the vertical ${\mathrm{CO_2}}$ interferometer chord passing near the center is better for ML-based detection of AEs.

Keywords

• fast-ion physics
• machine learning
• Alfvén eigenmodes
• CO2 interferometer
• DIII-D