Plasma confinement state classification via FPP relevant microwave diagnostics
We present a parsimonious and robust machine learning approach for identifying plasma confinement states in fusion power plants (FPPs) where reliable identification of the low-confinement and high-confinement regimes is critical for safe and efficient operation. Unlike research-oriented devices, FPPs must operate with a severely constrained set of diagnostics. To address this challenge, we demonstrate that a minimalist model, using only electron cyclotron emission (ECE) signals, can achieve accurate and reliable state classification. ECE provides electron temperature profiles without the engineering or survivability issues of in-vessel probes, making it a primary candidate for FPP-relevant diagnostics. Our framework employs ECE as input, extracts features using radial basis functions, and applies a gradient boosting classifier, achieving a test accuracy of 96% (correct predictions). Robustness analysis and feature importance analyzes confirm the approach’s reliability. These results demonstrate that state-of-the-art performance is attainable from a restricted diagnostic set, paving the way for minimalist yet resilient plasma control architectures for FPPs.