[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123272-en":3,"doc-seo-123272-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123272,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning enhanced tomographic reconstruction for multispectral imaging on TCV","A multispectral camera system infers a two-dimensional map of tokamak plasma parameters from spectral emissions. Measured camera signals are line-integrated along the toroidal direction, while inference requires poloidal emissivity on the poloidal plane. Tomographic reconstruction can provide emissivity, but classical methods are too slow for closed-loop, real-time control. The work introduces two machine-learning approaches to reconstruct poloidal emissivities directly from line-integrated measurements, achieving higher accuracy on synthetic data and sufficient speed for real-time control when properly implemented.","Machine learning enhanced tomographic reconstruction for multispectral imaging on TCV  \nCitation for published version (APA):  \nTCV team, van Leeuwen, L. , Schoukens, M. , Citrin, J. , van Berkel, M. , Duval, B. , & Perek, A. (2025) . Machine learning enhanced tomographic reconstruction for multispectral imaging on TCV. Plasma Physics and Controlled Fusion, 67(2), Article 025024. [https://doi.org/10.1088/1361-6587/ada856](https://doi.org/10.1088/1361-6587/ada856)  \nDocument license:  \nCC BY  \nDOI:  \n10.1088/1361-6587/ada856  \nDocument status and date:  \nPublished: 01/02/2025  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 03. Aug. 2026  \nPlasma Physics and Controlled Fusion  \nPAPER • OPEN ACCESS  \nMachine learning enhanced tomographic  \nreconstruction for multispectral imaging on TCV  \nTo cite this article: Loek van Leeuwen et al 2025 Plasma Phys. Control. Fusion 67 025024  \nView the article online for updates and enhancements.  \nYou may also like  \n-Cross-scale turbulence in space plasmas: old concepts, recent findings, and future challenges  \nTommaso Alberti, Simone Benella, Mirko Stumpo et al.  \n-Two-dimensional inference of divertor plasma characteristics: advancements to a multi-instrument Bayesian analysis system  \nD Greenhouse, C Bowman, B Lipschultz et al.  \n-The joint recognition of multi-MHD instabilities on HL-2A  \nXiaobo Zhu, Zongyu Yang, Fan Xia et al.  \nThis content was downloaded from IP address [131.155.24.16](131.155.24.16) on 11/02/2025 at 08:09  \nPlasma Phys. Control. Fusion 67 (2025) 025024 (14pp) [https://doi.org/10.1088/1361-6587/ada856](https://doi.org/10.1088/1361-6587/ada856)  \nMachine learning enhanced tomographic reconstruction for multispectral imaging on TCV  \nLoek van Leeuwen 1,2 􀁂, Maarten Schoukens1 􀁂, Jonathan Citrin2 􀁂 ,  \nMatthijs van Berkel2 􀁂, Basil Duval3 􀁂, Artur Perek3, ∗ 􀁂 and the TCV Team4  \n1 Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands  \n2 Dutch Institute for Fundamental Energy Research (DIFFER), De Zaale 20, 5612 AJ Eindhoven, The Netherlands  \n3 Ecole Polytechnique Fédérale de Lausanne (EPFL), Swiss Plasma Center (SPC), CH-1015 Lausanne, Switzerlands  \n[E-mail: artur.perek@epfl.ch](E-mail: art","cbCaihubVgNI0kqh","https://ap.wps.com/l/cbCaihubVgNI0kqh","pdf",2457689,1,16,"English","en",105,"# Abstract\n## Introduction\n## Problem context and motivation\n## Proposed machine learning approaches\n## Results and real-time applicability\n## Keywords","[{\"question\":\"What imaging and inference problem does the paper address?\",\"answer\":\"It uses a multispectral camera to infer a 2D tokamak plasma-parameter map from spectral emissions, but the measurements are line-integrated and must be converted to poloidal emissivity for inference.\"},{\"question\":\"Why are classical tomographic reconstruction methods insufficient?\",\"answer\":\"Classical techniques are reported to be too slow to use the reconstructed emissivities for real-time control.\"},{\"question\":\"What does the paper contribute to overcome the speed limitation?\",\"answer\":\"It presents two machine-learning approaches that accelerate reconstruction of poloidal emissivities from line-integrated camera data, improving accuracy on synthetic data and enabling real-time control with the right implementation.\"}]","Machine learning enhanced tomographic reconstruction for multispectral imaging on TCV | 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imaging and inference problem does the paper address?","Question",{"text":75,"@type":76},"It uses a multispectral camera to infer a 2D tokamak plasma-parameter map from spectral emissions, but the measurements are line-integrated and must be converted to poloidal emissivity for inference.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are classical tomographic reconstruction methods insufficient?",{"text":80,"@type":76},"Classical techniques are reported to be too slow to use the reconstructed emissivities for real-time control.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the paper contribute to overcome the speed limitation?",{"text":84,"@type":76},"It presents two machine-learning approaches that accelerate reconstruction of poloidal emissivities from line-integrated camera data, improving accuracy on synthetic data and enabling real-time control with the right 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