Bayesian Data Analysis for Gaussian Process Tomography

T. Wang, D. Mazon, J. Svensson, A. Liu, C. Zhou, L. Xu, L. Hu, Y. Duan, G. Verdoolaege

Research output: Contribution to journalArticlepeer-review

Abstract

Bayesian inference is used in many scientific areas as a conceptually well-founded data analysis framework. In this paper, we give a brief introduction to Bayesian probability theory and its application to the tomography problem in fusion research by means of a Gaussian process prior. This Gaussian process tomography (GPT) method is used for reconstruction of the local soft X-ray (SXR) emissivity in WEST and EAST based on line-integrated data. By modeling the SXR emissivity field in a poloidal cross-section as a Gaussian process, Bayesian SXR tomography can be carried out in a robust and extremely fast way. Owing to the short execution time of the algorithm, GPT is an important candidate for providing real-time feedback information on impurity transport and for fast MHD control. In addition, the Bayesian formulism allows for uncertainty analysis of the inferred emissivity.

Original languageEnglish
Pages (from-to)445-457
Number of pages13
JournalJournal of Fusion Energy
Volume38
Issue number3-4
DOIs
Publication statusPublished - 1 Aug 2019

Keywords

  • Bayesian inference
  • Data analysis
  • Gaussian process
  • Nuclear fusion
  • Plasma physics
  • Soft X-ray
  • Tokamak
  • Tomography

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