[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118473-en":3,"doc-seo-118473-105":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},118473,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Radiometer calibration using machine learning","Radiometers are essential instruments in radio astronomy, converting electromagnetic radiation into electrical signals through the antenna and Low Noise Amplifier (LNA) receiver chain. Calibration corrects many instrumental effects, but impedance mismatches between antenna and receiver can create signal reflections and distortions. Conventional approaches like Dicke switching rely on comparing antenna measurements against a well-characterised reference source. Machine learning, trained on known signal sources, offers an alternative to model and calibrate complex, traditionally hard-to-handle system responses. The work introduces and tests a machine learning-based calibration framework targeting the precision needed to detect the faint sky-averaged 21-cm hydrogen signal at high redshifts.","University of Groningen  \nRadiometer calibration using machine learning  \nLeeney, S. A. K. ; Bevins, H. T.J. ; Acedo, [E. de](E. de) Lera; Handley, W. J. ; Kirkham, C. ; Patel, R. S. ; Zhu, J. ; Molnar, D. ; Cumner, J. ; Anstey, D.  \nPublished in: Scientific Reports  \nDOI:  \n10.1038/s41598-025-16732-9  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2025  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nLeeney, S. A. K. , Bevins, H. T. J. , Acedo, E. D. L. , Handley, W. J. , Kirkham, C. , Patel, R. S. , Zhu, J. , Molnar, D. , Cumner, J. , Anstey, D. , Artuc, K. , Bernardi, G. , Bucher, M. , Carey, S. , Cavillot, J. , Chiello, R. , Croukamp, W. , de Villiers, D. I. L. , Ely, J. A. , ... Spinelli, M. (2025) . Radiometer calibration using machine learning. Scientific Reports, 15(1), Article 34335. [https://doi.org/10.1038/s41598-025-16732-9](https://doi.org/10.1038/s41598-025-16732-9)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 01-08-2026  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nRadiometer calibration using machine learning  \nS. A. K. Leeney1,2,16􀀍, H. T. J. Bevins1,2,16, [E. de](E. de) Lera Acedo1,2,16, W. J. Handley2,3,16, C. Kirkham1,2,16, R. S. Patel1,2,16, J. Zhu1,2,4,16,19, D. Molnar1,2,16, J. Cumner1,2,16,  \nD. Anstey1,2,16, K. Artuc1,2,16, G. Bernardi5,16, M. Bucher6,7,16, S. Carey1,16, J. Cavillot8,16, R. Chiello9,16, W. Croukamp7,16, D. I. L. de Villiers7,16, J. A. Ely1,16, A. Fialkov2,3,16,  \nT. Gessey-Jones1,2,16, G. Kulkarni10,16, A. Magro11,16, P. D. Meerburg12,16, S. Mittal1,2,16,  \nJ. H. N. Pattison1,16, S. Pegwal7,13,16, C. M. Pieterse7,16, J. R. Pritchard14,16, E. Puchwein15,16,  \nN. Razavi-Ghods1,16, I. L. V. Roque1,16, A. Saxena12,16, K. H. Scheutwinkel1,2,16, P. Scott1,16,  \nE. Shen1,2,16, P. H. Sims1,2,16 & M. Spinelli16,17,18  \nRadiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of electromagnetic radiation, converting this radiation into electrical signals. A radiometer’s primary components are an antenna and a Low Noise Amplifier (LNA), which is the core of the “receiver” chain. Instrumental effects introduced by the receiver are typically corrected or removed during calibration. However, impedance mismatches between the antenna and receiver can introduce unwanted signal reflections and distortions. Traditional calibration methods, such as Dicke switching, alternate the receiver input between the antenna and a wellcharacterised referen","cbCaih6AyrlTHajQ","https://ap.wps.com/l/cbCaih6AyrlTHajQ","pdf",3597612,1,16,"English","en",105,"# Radiometer calibration using machine learning\n## Motivation and background\n## Machine learning calibration framework\n## Precision goals for detecting the 21-cm line","[{\"question\":\"Why is radiometer calibration important in radio astronomy?\",\"answer\":\"Radiometers convert electromagnetic radiation into electrical signals, and instrumental effects from the receiver chain must be corrected during calibration to recover the true signal.\"},{\"question\":\"What limitation of traditional calibration methods is highlighted?\",\"answer\":\"Impedance mismatches between the antenna and receiver can introduce unwanted reflections and distortions, which traditional calibration approaches mitigate mainly through comparison with reference sources.\"},{\"question\":\"What does the machine learning-based framework enable?\",\"answer\":\"The study introduces and tests a machine learning calibration framework designed to reach the precision required for radiometric experiments aiming to detect the faint sky-averaged 21-cm hydrogen signal at high redshifts.\"}]","Radiometer calibration using machine learning | 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is radiometer calibration important in radio astronomy?","Question",{"text":75,"@type":76},"Radiometers convert electromagnetic radiation into electrical signals, and instrumental effects from the receiver chain must be corrected during calibration to recover the true signal.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitation of traditional calibration methods is highlighted?",{"text":80,"@type":76},"Impedance mismatches between the antenna and receiver can introduce unwanted reflections and distortions, which traditional calibration approaches mitigate mainly through comparison with reference sources.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the machine learning-based framework enable?",{"text":84,"@type":76},"The study introduces and tests a machine learning calibration framework designed to reach the precision required for radiometric experiments aiming to detect the faint sky-averaged 21-cm hydrogen signal at high 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