Automatic Identification of Edge Localized Modes in the DIII-D Tokamak

Abstract: Fusion power production in tokamaks uses discharge configurations that risk producing strong Type I Edge Localized Modes. The largest of these modes will likely increase impurities in the plasma and potentially damage plasma facing components such as the protective heat and waste divertor. Machine learning-based prediction and control may provide for online mitigation of these damaging modes before they grow too large to suppress. To that end, large labeled datasets are required for supervised training of machine learning models. We present an algorithm that achieves 97.7% precision when automatically labeling Edge Localized Modes in the large DIII-D tokamak discharge database. The algorithm has no user controlled parameters and is largely robust to tokamak and plasma configuration changes. This automatically-labeled database of events can subsequently feed future training of machine learning models aimed at autonomous Edge Localized Mode control and suppression.