Support vector machine-based feature extractor for L/H transitions in JET.

Area of Science:

• Plasma physics
• Fusion energy research
• Machine learning applications

Background:

• Tokamak plasmas exhibit confinement transitions crucial for fusion energy.
• Characterizing these transitions requires analyzing numerous diagnostic signals.
• Identifying the most relevant signals is essential for efficient plasma control and understanding.

Purpose of the Study:

• To apply Support Vector Machines (SVM) for identifying critical parameters in tokamak plasma confinement transitions.
• To develop a method for reducing a large set of signals to a minimal, informative subset.
• To optimize the selection of diagnostic waveforms for characterizing plasma behavior.

Main Methods:

• Utilized Support Vector Machines (SVM), a machine learning algorithm, for feature selection.
• Implemented a signal discarding process, iteratively removing less relevant waveforms from an initial set of 27.
• Applied the method to a large database of 749 Joint European Torus (JET) discharges.
• Validated the results using an independent dataset of 150 JET discharges.

Main Results:

• Successfully identified a reduced set of relevant waveforms that effectively characterize the confinement transition.
• Demonstrated that SVM can efficiently determine the most informative quantities from complex plasma data.
• Achieved a balance between data reduction and information preservation for tokamak plasma analysis.

Conclusions:

• Support Vector Machines (SVM) provide a powerful tool for analyzing complex fusion plasma data.
• The developed method enables efficient identification of key signals for understanding tokamak confinement regimes.
• This approach enhances the ability to control and optimize fusion plasma performance.