Fast-ion phase-space tomography with wave–particle interactions in the ion cyclotron frequency range as prior

Abstract The fast-ion distribution function in fusion plasmas can be inferred by inverting Doppler-shifted measurements from fast-ion diagnostics. The full fast-ion distribution function can be parametrised by three constants of motion with the addition of a binary index. However, with a limited number of measurements, cogent prior information must be added to regularise the inverse problem, enabling the reconstruction of the distribution function. In this paper, we demonstrate how to incorporate wave–particle interactions in the ion cyclotron range of frequencies (ICRFs) as prior information with the future ITER tokamak as a test case. We find that the addition of ICRF physics as prior information improves the reconstruction of a test ICRF-heated fast-ion distribution function in ITER using synthetic data based on the planned collective Thomson scattering sightlines and the planned gamma-ray spectroscopy sightlines. The addition of such prior information is beneficial in the case of a limited phase-space coverage of fast-ion diagnostics.

Authors

Institutions

Publication Details

Journal
Nuclear Fusion
Published
2025-03-24
DOI
https://doi.org/10.1088/1741-4326/adc400
Citations
7
Primary Topic
Magnetic confinement fusion research
Type
article
Field-Weighted Citation Impact
13.09

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fast-ion phase-space tomography with wave–particle interactions in the ion cyclotron frequency range as prior

L.-G. Eriksson, M. Nocente, Y. Kazakov, Mads Rud Larsen et al.
7 citations
Nuclear Fusion
Magnetic confinement fusion research
13.09
article

Fast-ion phase-space tomography with wave–particle interactions in the ion cyclotron frequency range as prior

L.-G. Eriksson, M. Nocente, Y. Kazakov, Mads Rud Larsen, M. Salewski, D. Moseev, Yiqiu Dong, J. Rasmussen, Bernard Reman, Per Christian Hansen, J. Eriksson, S. B. Korsholm, H. Järleblad, A. Snicker, Otso Hyvärinen, A. Valentini
article en
7 citations

Abstract

Abstract The fast-ion distribution function in fusion plasmas can be inferred by inverting Doppler-shifted measurements from fast-ion diagnostics. The full fast-ion distribution function can be parametrised by three constants of motion with the addition of a binary index. However, with a limited number of measurements, cogent prior information must be added to regularise the inverse problem, enabling the reconstruction of the distribution function. In this paper, we demonstrate how to incorporate wave–particle interactions in the ion cyclotron range of frequencies (ICRFs) as prior information with the future ITER tokamak as a test case. We find that the addition of ICRF physics as prior information improves the reconstruction of a test ICRF-heated fast-ion distribution function in ITER using synthetic data based on the planned collective Thomson scattering sightlines and the planned gamma-ray spectroscopy sightlines. The addition of such prior information is beneficial in the case of a limited phase-space coverage of fast-ion diagnostics.

Nuclear FusionVol. 65(5)
Uppsala University (SE), Statistics Finland (FI), University of Helsinki (FI), Max Planck Institute for Plasma Physics - Greifswald (DE), University of Milano-Bicocca (IT), Chalmers University of Technology (SE), VTT Technical Research Centre of Finland (FI), Technical University of Denmark (DK)
Villum Fonden, EUROfusion, European Commission, Euratom Research and Training Programme
Openalex Percentile: Top 2%
Magnetic confinement fusion research
13.09
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.