Automated signal classification in the C-2W fusion experiment.

Abstract

In TAE Technologies' current experimental fusion device, C-2W (also called "Norman"), record breaking, advanced beam-driven field-reversed configuration plasmas are produced and sustained in steady state utilizing variable-energy neutral beams, expander divertors, end-bias electrodes, and an active plasma control system. With a rapid shot-pace and an extensive number of data channels, the amount of data generated necessitates automated signal processing. To this end, a machine learning algorithm consisting of a multi-layered neural network as well as other data processing software has been designed for signal feature identification, allowing for accurate and fast signal classification, anomalous condition detection, and providing for signal pre-processing. With a small set of training data, the neural network can be "bootstrapped" to provide a robust classification system while minimizing human oversight. An example using data from the theta pinch plasma formation pulsed power system is presented. With an overall accuracy of ∼97%-having classified more than 5 × 106 pulsed power signals-the classification scheme is more than sufficient to fine-tune machine set points. However, this technique can be used for near-real-time preprocessing of any plasma physics signal and has wide ranging application in fusion experiments for the varied data produced by plasma diagnostics.