Identification and Reconstruction of Cut-off Regions in Electron Cyclotron Emission (ECE) Measurements

In the evolving landscape of fusion research, precise measurement and analysis of Electron Cyclotron Emission (ECE) play a pivotal role such as Alfven eigenmode detection [1]. However, the integrity of these measurements is often compromised by cut-off regions [2], posing significant challenges in data interpretation and fusion plasma diagnostics. This study introduces a novel approach utilizing Generative Adversarial Networks (GANs) to identify and reconstruct these cut-off regions in ECE measurements.



Our methodology leverages anomaly detection to find the cutoff channels from the dataset and uses the advanced capabilities of GANs to generate realistic, physics-consistent reconstructions of the missing or distorted data in ECE measurements. The adversarial nature of GANs aids in refining the reconstruction process, ensuring high fidelity to the underlying physical processes of fusion plasma. We demonstrate the effectiveness of our approach through extensive simulations and comparison with traditional methods. Our results show a marked improvement in accuracy and reliability, substantially enhancing the quality of ECE data interpretation.



This research not only addresses a critical challenge in fusion diagnostics but also opens avenues for applying advanced machine-learning techniques in the field of plasma physics. The successful application of GANs as proposed in this study promises to enhance our understanding of fusion plasma behavior, contributing significantly to the advancement of fusion energy research.