Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks.

Journal

The Review of scientific instruments
ISSN: 1089-7623
Titre abrégé: Rev Sci Instrum
Pays: United States
ID NLM: 0405571

Informations de publication

Date de publication:
01 May 2021
Historique:
entrez: 10 7 2021
pubmed: 11 7 2021
medline: 11 7 2021
Statut: ppublish

Résumé

The relationship between simulated ion cyclotron emission (ICE) signals s and the corresponding 1D velocity distribution function fv

Identifiants

DOI: 10.1063/5.0041456 PMID: 34243325
pubmed: 34243325
doi: 10.1063/5.0041456
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

053528

Auteurs

B S Schmidt (BS)

Department of Physics, Technical University of Denmark, Kgs. Lyngby, Denmark.
ORCID: 0000000153029489

M Salewski (M)

Department of Physics, Technical University of Denmark, Kgs. Lyngby, Denmark.
ORCID: 000000023699679X

B Reman (B)

Laboratoire Plasma et Conversion d'Energie, Université Toulouse, Toulouse, France.
ORCID: 0000000335079444

R O Dendy (RO)

Centre for Fusion, Space and Astrophysics, University of Warwick, Coventry, United Kingdom.

D Moseev (D)

Max-Planck-Institut für Plasmaphysik, Greifswald, Garching, Germany.
ORCID: 0000000179558565

R Ochoukov (R)

Max-Planck-Institut für Plasmaphysik, Greifswald, Garching, Germany.
ORCID: 000000025936113X

A Fasoli (A)

École Polytechnique Fédérale de Lausanne, Swiss Plasma Center, Lausanne, Switzerland.

M Baquero-Ruiz (M)

École Polytechnique Fédérale de Lausanne, Swiss Plasma Center, Lausanne, Switzerland.

H Järleblad (H)

Department of Physics, Technical University of Denmark, Kgs. Lyngby, Denmark.
ORCID: 000000031126686X

Classifications MeSH