Raman calibration of the LHD YAG Thomson scattering for electron-density measurements

Yamada, I.; Narihara, K.; Hayashi, Hiroshi

doi:10.1063/1.1538362

Cited by 18 publications

(14 citation statements)

References 10 publications

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“…Given its high spatial and temporal resolution, the TS system has been installed in almost all major magnetic plasma confinement devices [12,[15][16][17][18].…”

Section: Application To Lhd-ts System Datamentioning

confidence: 99%

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

Fujii

Yamada

Hasuo

2017

Review of Scientific Instruments

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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.

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“…Given its high spatial and temporal resolution, the TS system has been installed in almost all major magnetic plasma confinement devices [12,[15][16][17][18].…”

Section: Application To Lhd-ts System Datamentioning

confidence: 99%

“…Using these statistical models, we show that the inference of these noises is straightforward in the GPR framework. We also applied this technique to the TS system for n e spatial distribution measurement in the Large Helical Device (LHD-TS system) [12,13], an example of which is shown in Fig. 1(a).…”

Section: Introductionmentioning

confidence: 99%

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

Fujii

Yamada

Hasuo

2017

Review of Scientific Instruments

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“…In our TS system, the amount of stray light tends to be larger than the conventional TS system due to the multiple reflection of the laser light. In Raman scattering measurement, an interference filter, which has high blocking power at the wavelength of stray light, can be used because the wavelength of the scattering light is shifted from that of laser light [10]. Figure 8 shows the cross sections of Raman scattering light from nitrogen and oxygen and the transparent characteristics of four interference filters.…”

Section: Calibration By Raman Scatteringmentioning

confidence: 99%

Development of 2-D Thomson Scattering Measurement Using Multiple Reflection and the Time-of-Flight of Laser Light

Sumikawa

Yamashita

Kawamori

et al. 2007

Plasma and Fusion Research

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A new two-dimensional Thomson scattering measurement (2-D TS) system has been developed using multiple reflections and time-of-flight (TOF) of laser light. Its new ideas of our 2-D TS system are (1) to reflect YAG laser light for multiple times to cover the whole r − z plane of the ST (Spherical Tokamak) plasma, and (2) to reduce the number of polychromators and detectors using the time delay of the scattered light along the laser beam. We measured for the first time, Rayleigh scattering light signals with 50 ns time difference from two measurement points using a single detector, demonstrating the basic principle of the 2-D Thomson scattering system.

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“…As an example, in Figs. 1 and 2, we show typical spatial distributions of electron temperature (T e ) and density (N e ) obtained by Thomson scattering in the Large Helical Device (LHD) project [5,6]. The first difference from the textbook example is that the noise distribution in the data is unknown.…”

Section: Introductionmentioning

confidence: 99%

Robust Regression for Automatic Fusion Plasma Analysis Based on Generative Modeling

Fujii

Suzuki

Hasuo

2019

IEEE Trans. Plasma Sci.

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The first step to realize automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are obtained routinely. However, it has been difficult to construct algorithms that fit all the data without over-and under-fitting. In this paper, we show that this difficulty originates from the lack of knowledge of the probability distribution that the measurement data follow. We demonstrate the use of a machine learning technique to estimate the data distribution and to construct an optimal generative model. We show that the fitting algorithm based on the generative modeling outperforms classical heuristic methods in terms of the stability as well as the accuracy.keywords: automatic analysis, Beyesian method, neural network, variational inference

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Raman calibration of the LHD YAG Thomson scattering for electron-density measurements

Cited by 18 publications

References 10 publications

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

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

Development of 2-D Thomson Scattering Measurement Using Multiple Reflection and the Time-of-Flight of Laser Light

Robust Regression for Automatic Fusion Plasma Analysis Based on Generative Modeling

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