As fusion pilot plants (FPPs) are increasingly viewed as within reach, many engineering challenges remain. Only a limited number of diagnostics are expected to be available in a reactor environment. Survivability, maintainability, and limited port space substantially restrict the number of FPP-relevant diagnostics. One remaining challenge is developing tools and devices to extract plasma state information necessary for controlling an FPP from a limited subset of diagnostics. This work is part of an overarching project to address this challenge. The specific diagnostic subset to be used in FPPs is still under debate. We take the approach of developing machine learning-based tools for different significant plasma state parameters, using already known FPP-relevant diagnostics. Previously, we developed a plasma confinement mode classifier using the electron cyclotron emission (ECE) diagnostic Clark et al (2026 Plasma Phys. Control. Fusion68 015022). Here, we expand on this by developing a classifier using the profile reflectometer (PR) data with 97% test accuracy, and an ensemble model that combines the ECE and PR models into a single model, achieving 99% test accuracy.
Diagnostics for Experimental Thermonuclear Fusion Reactors 2