Data-Driven Sensitivity Inference for Thomson Scattering Electron Density Measurement Systems

TL;DR

This paper presents a data-driven method using Gaussian processes to infer calibration parameters for Thomson scattering electron density measurements, significantly improving measurement accuracy and certainty.

Contribution

The novel approach models calibration uncertainties as dependent noise and infers parameters directly from experimental data, enhancing measurement precision.

Findings

Sensitivity correction varies by approximately 10%.

Measurement accuracy improves by a factor of 5.

Certainty in spatial derivative inference is increased.

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 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 $\approx$ 10\%, and the random noise amplitude is $\approx$ 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.