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 language | English |
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Pages (from-to) | 445-457 |
Number of pages | 13 |
Journal | Journal of Fusion Energy |
Volume | 38 |
Issue number | 3-4 |
DOIs | |
Publication status | Published - 1 Aug 2019 |
Keywords
- Bayesian inference
- Data analysis
- Gaussian process
- Nuclear fusion
- Plasma physics
- Soft X-ray
- Tokamak
- Tomography