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Keisuke Fujii¹, Ichihiro Yamada², Masahiro Hasuo¹

¹Department of Mechanical Engineering and Science, Graduate School of Engineering, Kyoto University, Kyoto 615-8540, Japan.

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|February 3, 2017

Summary

This summary is machine-generated.

We developed a novel method to improve calibration parameters in multichannel measurement systems. This technique enhances measurement accuracy by accounting for dependent noise, significantly boosting data reliability.

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Area of Science:

Physics
Plasma Physics
Measurement Science

Background:

Multichannel measurement systems require precise calibration for accurate data acquisition.
Variations in sensitivity and noise amplitude across channels introduce significant uncertainties.
Existing methods often struggle to account for dependent noise in calibration parameters.

Purpose of the Study:

To develop a robust method for inferring calibration parameters in multichannel measurement systems.
To model and incorporate dependent noise and latent function properties into Gaussian process kernels.
To enhance the accuracy and reliability of measurements from complex systems like plasma diagnostics.

Main Methods:

Developed a method to infer calibration parameters (sensitivity, noise amplitude) from experimental data.
Modeled uncertainties in calibration parameters as dependent noise.
Implemented statistical properties of dependent noise and latent functions within a Gaussian process kernel.
Inferred parameters based on the statistical differences between dependent noise and latent functions.

Main Results:

Applied the method to a 141-channel Thomson scattering electron density measurement system for Large Helical Device plasma.
Evaluated correction factors for sensitivity (varying by ~10%) and noise amplitude (~2%) for each channel.
Demonstrated a five-fold increase in measurement accuracy after sensitivity correction.
Showed improved certainty in spatial derivative inference.

Conclusions:

The developed method effectively infers calibration parameters, significantly enhancing measurement accuracy.
Accounting for dependent noise within Gaussian process kernels is crucial for precise calibration.
This approach offers substantial improvements for complex multichannel diagnostic systems in plasma physics and beyond.