Autoencoding and noise reduction of plasma diagnostics in Tokamak

Accurate measurement of plasma parameters is crucial for understanding and optimizing plasma behavior in Tokamaks. However, noise and disturbances often degrade the reliability of diagnostic data. This poster provides an overview of applying autoencoding and noise reduction techniques to enhance plasma diagnostics in Tokamaks. Autoencoders, a type of artificial neural network, effectively reduce noise and extract meaningful features from complex data. By training an autoencoder on a large dataset of measured plasma diagnostics, it learns to denoise signals and preserve essential plasma information. Specialized variants like variational autoencoders and denoising autoencoders address specific challenges in Tokamak plasma diagnostics. They reconstruct high-quality plasma parameters from noisy or incomplete measurements, enhancing accuracy and reliability. Integrating autoencoding techniques with noise reduction algorithms offers a comprehensive approach to address plasma diagnostics challenges. By leveraging machine learning and signal processing, these methods improve the quality of plasma parameter measurements, enabling precise analysis and modeling of plasma behavior. This research highlights the potential of autoencoding and noise reduction techniques to revolutionize plasma diagnostics in Tokamaks, advancing fusion research and sustainable energy sources. The poster serves as a foundation for further exploration and development of innovative methods to enhance plasma diagnostics in fusion devices.