Bayesian Integrated Data Analysis of Fast-Ion Measurements by Velocity-Space Tomography

M. Salewski*, M. Nocente, A. S. Jacobsen, F. Binda, C. Cazzaniga, J. Eriksson, B. Geiger, G. Gorini, C. Hellesen, V. G. Kiptily, T. Koskela, S. B. Korsholm, T. Kurki-Suonio, F. Leipold, D. Moseev, S. K. Nielsen, J. Rasmussen, P. A. Schneider, S. E. Sharapov, M. StejnerM. Tardocchi, JET Contributors, ASDEX Upgrade Team, EUROfusion MST1 Team

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)


Bayesian integrated data analysis combines measurements from different diagnostics to jointly measure plasma parameters of interest such as temperatures, densities, and drift velocities. Integrated data analysis of fast-ion measurements has long been hampered by the complexity of the strongly non-Maxwellian fast-ion distribution functions. This has recently been overcome by velocity-space tomography. In this method two-dimensional images of the velocity distribution functions consisting of a few hundreds or thousands of pixels are reconstructed using the available fast-ion measurements. Here we present an overview and current status of this emerging technique at the ASDEX Upgrade tokamak and the JET toamak based on fast-ion D-alpha spectroscopy, collective Thomson scattering, gamma-ray and neutron emission spectrometry, and neutral particle analyzers. We discuss Tikhonov regularization within the Bayesian framework. The implementation for different types of diagnostics as well as the uncertainties are discussed, and we highlight the importance of integrated data analysis of all available detectors.

Original languageEnglish
Pages (from-to)23-36
Number of pages14
JournalFusion Science and Technology
Issue number1-2
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed


  • fast ions
  • Tokamaks
  • velocity-space tomography

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