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Updated: Jun 7, 2026

Photoelectron Imaging of Anions Illustrated by 310 Nm Detachment of F−
Published on: July 27, 2018
2D electron cyclotron emission imaging at ASDEX Upgrade (invited)
I G J Classen1, J E Boom, W Suttrop
1Max Planck Institut für Plasmaphysik, 85748 Garching, Germany. ivo.classen@ipp.mpg.de
A new electron cyclotron emission imaging diagnostic offers high-resolution 2D measurements of electron temperature dynamics. This tool aids in studying plasma phenomena like edge localized modes and reversed shear Alfvén eigenmodes.
Area of Science:
- Plasma Physics
- Fusion Energy Research
Background:
- Understanding electron temperature dynamics is crucial for magnetic confinement fusion.
- Existing diagnostics have limitations in spatial and temporal resolution for capturing fast plasma phenomena.
Purpose of the Study:
- To introduce and detail the new electron cyclotron emission imaging (ECEi) diagnostic on ASDEX Upgrade.
- To demonstrate the capabilities of ECEi for 2D electron temperature measurements.
- To showcase advanced data analysis techniques for ECEi data.
Main Methods:
- Installation and calibration of the ECEi diagnostic.
- Acquisition of 2D electron temperature data during plasma discharges.
- Analysis of edge localized modes (ELMs) and reversed shear Alfvén eigenmodes (RSAEs).
- Application of singular value decomposition (SVD) for data analysis and filtering.
Main Results:
- The ECEi diagnostic provides high spatial and temporal resolution 2D electron temperature measurements.
- Measurements successfully captured dynamics of ELMs and RSAEs, highlighting the advantages of 2D imaging.
- Limitations of ECE measurements were identified and illustrated.
- SVD proved effective for analyzing and filtering complex 2D ECEi data.
Conclusions:
- The ECEi diagnostic is a valuable tool for studying 2D electron temperature dynamics in fusion plasmas.
- ECEi enhances the understanding of plasma instabilities and transport phenomena.
- Advanced analysis methods like SVD are essential for maximizing the utility of ECEi data.
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