Coincidence anomaly detection for unsupervised locating of edge localized modes in the DIII-D tokamak dataset

• Published in: Machine learning: science and technology 2024-08, Vol.5 (3)
• Main Authors: O’Shea, Finn H., Joung, Semin, Smith, David R., Ratner, Daniel, Coffee, Ryan
• Format: Article
• Language: English
• Subjects: 70 PLASMA PHYSICS AND FUSION TECHNOLOGY; anomaly detection; artificial intelligence; edge localized mode; ELM; machine learning
• ISSN: 2632-2153; 2632-2153

Abstract Using supervised learning to train a machine learning model to predict an on-coming Edge Localized Mode (ELM) requires a large number of labeled samples. Creating an appropriate data set from the very large database of discharges at a long-running tokamak, such as DIII-D, would be a very time-consuming process for a human. Considering this need and difficulty, we use coincidence anomaly detection, an unsupervised learning technique, to train an ELM-identifier to identify and label ELMs in the DIII-D discharge database. This ELM-identifier shows, simultaneously, a precision of 0.68 and a recall of 0.63 (AUC is 0.73) on identifying ELMs in example time series pulled from thousands of discharges spanning five years. In a test set of 50 discharges, the algorithm finds over 26 thousand ELM candidates, more than 5 times the existing catalog of ELMs labeled by humans.