Data-driven sensitivity inference for Thomson scattering electron density measurement systems

Keisuke Fujii, Ichihiro Yamada, Masahiro Hasuo

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

We developed a method to infer the calibration parameters of multichannel measurement systems, such as channel variations of sensitivity and noise amplitude, from experimental data. We regard such uncertainties of the calibration parameters as dependent noise. The statistical properties of the dependent noise and that of the latent functions were modeled and implemented in the Gaussian process kernel. Based on their statistical difference, both parameters were inferred from the data. We applied this method to the electron density measurement system by Thomson scattering for the Large Helical Device plasma, which is equipped with 141 spatial channels. Based on the 210 sets of experimental data, we evaluated the correction factor of the sensitivity and noise amplitude for each channel. The correction factor varies by ≈10%, and the random noise amplitude is ≈2%, i.e., the measurement accuracy increases by a factor of 5 after this sensitivity correction. The certainty improvement in the spatial derivative inference was demonstrated.

Original languageEnglish
Article number013508
JournalReview of Scientific Instruments
Volume88
Issue number1
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

Funding

The authors thank the development team of pyGPs, the Python implementation of the GP. The authors would also like to thank the LHD Experiment Group for the smooth LHD operation. Part of this work was supported by JSPS KAKENHI Grant Number 26610191 and the National Institute for Fusion Science (Nos. NIFS14KLPH023 and NIFS13KLPF032).

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