Fast analysis of collective Thomson scattering spectra on Wendelstein 7-X

Two methods for fast analysis of Collective Thomson Scattering (CTS) spectra are presented: Function Parametrization (FP) and feedforward Artificial Neural Networks (ANNs). At this time, a CTS diagnostic is being commissioned at the Wendelstein 7-X (W7-X) stellarator, with ion temperature measuremen...

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Bibliographic Details
Published in:Review of scientific instruments 2018-08, Vol.89 (8), p.083507-083507
Main Authors: van den Berg, J., Abramovic, I., Lopes Cardozo, N. J., Moseev, D.
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer simulation
Data analysis
Diagnostic systems
Ion temperature
Mapping
Neural networks
Parameterization
Parameters
Random noise
Scientific apparatus & instruments
Spectra
Thomson scattering
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Summary:Two methods for fast analysis of Collective Thomson Scattering (CTS) spectra are presented: Function Parametrization (FP) and feedforward Artificial Neural Networks (ANNs). At this time, a CTS diagnostic is being commissioned at the Wendelstein 7-X (W7-X) stellarator, with ion temperature measurements in the plasma core as its primary goal. A mapping was made from a database of simulated CTS spectra to the corresponding ion and electron temperatures (Ti and Te). The mean absolute mapping errors are 4.2% and 9.9% relative to the corresponding Ti, for the ANN and FP, respectively, for spectra with Gaussian noise equivalent to 10% of the average of the spectral maxima in the database at 650 sampling points per GHz and within a limited parameter space. Although FP provides some insight into the information contents of the CTS spectra, ANNs provide a higher accuracy and noise robustness, are easier to implement, and are more adaptable to a larger parameter space. These properties make ANN mappings a promising all-round method for fast CTS data analysis. Addition of impurity concentrations to the current parameter space will enable fast bulk ion temperature measurements in the plasma core region of W7-X.
ISSN:0034-6748
1089-7623
DOI:10.1063/1.5035416