[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118601-en":3,"doc-seo-118601-105":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},118601,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Radiometer calibration using machine learning","Radiometer calibration focuses on correcting receiver-induced effects that distort electromagnetic radiation measurements in radio astronomy. The work addresses impedance-mismatch reflections and frequency-dependent non-linear gain variations, where traditional comparison-based methods such as Dicke switching may struggle. A machine-learning calibration framework is introduced and tested to model complex receiver behavior using known signal sources. The approach targets precision required for radiometric experiments aiming to detect the faint sky-averaged 21-cm line from atomic hydrogen at high redshifts, a central challenge in observational cosmology.","[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 reference source to mitigate errors by comparison. Recent advances in Machine Learning (ML) offer promising alternatives. Neural networks, which are trained using known signal sources, provide a powerful means to model and calibrate complex systems where traditional analytical approaches struggle. These methods are especially relevant for detecting the faint sky-averaged 21-cm signal from atomic hydrogen at high redshifts. This is one of the main challenges in observational Cosmology today. Here, for the first time, we introduce and test a machine learning-based calibration framework capable of achieving the precision required for radiometric experiments aiming to detect the 21-cm line.  \nRadiometers have been integral to the field of radio astronomy since its inception. They measure the intensity of incoming electromagnetic radiation within a specific frequency band, producing a proportional electrical signal at their output. Typically, a radiometer consists ofa radio antenna that captures the radiant energy and an amplifying device such as a Low Noise Amplifier (LNA) which, in combination with several other components described in Sect. Radiometer calibration, is commonly referred to as the “receiver”. The receiver introduces non-linear gain variations and frequency-dependent responses that complicate signal recovery. The incident electromagnetic radiation induces a voltage across the antenna, which can be interpreted as Johnson-Nyquist  \n1Astrophysics Group, Cavendish Laboratory, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, UK. 2Kavli Institute for Cosmology in Cambridge, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK. 3Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK. 4National Astronomical Observatory, Chinese Academy of Science, Beijing 100101, China. 5INAF-Istituto di Radio Astronomia, Via Gobetti 101, 40129 Bologna, Italy. 6Laboratoire AstroParticule et Cosmologie, Université Paris-Cité, 10 Rue Alice Domon et Léonie Duquet, 75013 Paris, France. 7Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch 7602, South Africa. 8Antenna Group, Université catholique de Louvain, 1348 Louvain-laNeuve, Belgium. 9Physics Department, University of Oxford, Parks Road, Oxford OX1 3PU, UK. 10Department of Theoretical Physics, Tata Institute of Fundamental Research, Homi Bhabha Road, M","cbCaibZK5x1oayef","https://ap.wps.com/l/cbCaibZK5x1oayef","pdf",3585512,1,15,"English","en",105,"# Introduction\n# Radiometers and receiver effects\n# Traditional calibration approaches\n# Machine-learning calibration framework\n## Neural network modeling with known signal sources\n# Application to high-redshift 21-cm detection","[{\"question\":\"Why is radiometer calibration necessary in radio astronomy?\",\"answer\":\"Receiver components introduce instrumental effects that can distort recovered signals, requiring calibration to remove or correct those errors.\"},{\"question\":\"What role does impedance mismatch play in calibration?\",\"answer\":\"Impedance mismatch between antenna and receiver can cause unwanted reflections, generating distortions and making receiver response harder to characterize without calibration.\"},{\"question\":\"How does the proposed machine-learning framework support 21-cm signal detection?\",\"answer\":\"It uses neural networks trained with known signal sources to achieve the precision needed to calibrate radiometric experiments targeting the faint sky-averaged 21-cm line at high redshifts.\"}]","Radiometer calibration using machine learning | 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