[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125978-en":3,"doc-seo-125978-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125978,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning-based anomaly detection for radio telescopes - Thesis","Machine learning-based anomaly detection for radio telescopes develops data-driven methods to identify irregular signals in radio astronomy measurements. The research focuses on model learning from radio astronomy spectrograms, evaluation on simulated and LOFAR data, and the use of representations to support reliable detection. It further introduces a nearest-neighbour approach in latent space and applies related techniques to radio-frequency interference detection. Experiments analyze performance, efficiency, ablation effects, and practical suitability for real-time scientific data processing.","UvA-DARE (Digital Academic Repository)  \nMachine learning-based anomaly detection for radio telescopes  \nMesarcik, M. B.  \nPublication date  \n2024  \nDocument Version  \nFinal published version  \nLink to publication  \nCitation for published version (APA):  \nMesarcik, M. B. (2024) . Machine learning-based anomaly detection for radio telescopes.[Thesis, fully internal, Universiteit van Amsterdam] .  \nGeneral rights  \nIt 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), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:13 Jan 2025  \nMachine learning-based anomaly detection for radio telescopes  \nMichael Mesarcik  \nMACHINE LEARNING-BASED ANOMALY DETECTION FOR RADIO TELESCOPES  \nmichael mesarcik  \nThis work was carried out in the ASCI graduate school.  \nASCI dissertation series number: 450  \nThis work is part of the “Perspectief” research programme “Efficient Deep Learning”(EDL, [https://efficientdeeplearning.nl](https://efficientdeeplearning.nl)), which is financed by the Dutch Research Council (NWO) domain Applied and Engineering Sciences (TTW) . The research makes use of radio astronomy data from the LOFAR telescope, which is operated by ASTRON (Netherlands Institute for Radio Astronomy), an institute belonging to the Netherlands Foundation for Scientific Research (NWO-I)  \nCopyright © 2024 Michael Mesarcik.  \nCover Design by: Tammy Joubert  \nThesis template: classicthesis by André Miede and Ivo Pletikosi´c. Printed and bound by Ipskamp printing  \nISBN: 978-94-6473-446-1  \nMachine learning-based anomaly detection for radio telescopes  \nACADEMISCH PROEFSCHRIFT  \nter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus [prof. dr. ir. P.P.C.C. Verbeek](prof. dr. ir. P.P.C.C. Verbeek)  \nten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op woensdag 24 april 2024, te 16.00 uur  \ndoor Michael Benno Mesarcik geboren te ZAF  \nPromotiecommissie  \nPromotores:  \nCopromotores:  \nOverige leden:  \nprof. dr. R.V. van Nieuwpoort [prof. dr. ir. C.T.A.M. de Laat](prof. dr. ir. C.T.A.M. de Laat)  \n[dr. ir. A.J. Boonstra](dr. ir. A.J. Boonstra)[ ](dr. ir. A.J. Boonstra)[dr. E.B. Ranguelova](dr. E.B. Ranguelova)  \n[dr. P. Grosso](dr. P. Grosso)  \nprof. dr. P.T. Groth [prof. dr. ir. H. Corporaal](prof. dr. ir. H. Corporaal)[ ](prof. dr. ir. H. Corporaal)[prof. dr. A.M.M. Scaife](prof. dr. A.M.M. Scaife)[dr. B.A. Rowlinson](dr. B.A. Rowlinson)[ ](dr. B.A. Rowlinson)[prof. dr. ir. S.J. Wijnholds](prof. dr. ir. S.J. Wijnholds)  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam  \nASTRON  \nNetherlands eScience Center  \nUniversiteit van Amsterdam  \nUniversiteit van Amsterdam Eindhoven University of Technology University of Manchester Universiteit van Amsterdam ASTRON  \nFaculteit der Natuurwetenschappen, Wiskunde en Informatica  \nTo vertebrae C4, C5 and C7, your support is appreciated.  \nCONTENTS  \n1 introduction 1  \n1.1 System health management in radio telescopes 3  \n1.2 Machine learning-based anomaly detection 5  ","cbCairaZc7KpS5Zo","https://ap.wps.com/l/cbCairaZc7KpS5Zo","pdf",45757489,4,1,155,"English","en",105,"# Contents\n## 1 introduction\n## 2 background\n## 3 learning representations of radio astronomy spectrograms\n## 4 nearest neighbour-based anomaly detection\n## 5 radio frequency interference detection","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis addresses anomaly detection in radio telescope data, including identifying irregular signals and radio-frequency interference patterns in observational streams.\"},{\"question\":\"Which machine learning approach is used for learning data representations?\",\"answer\":\"It uses a variational autoencoder (VAE) to learn representations from radio astronomy spectrograms and evaluates both quantitative and qualitative behavior on simulated and LOFAR data.\"},{\"question\":\"How is anomaly detection performed in the nearest-neighbour section?\",\"answer\":\"It presents NLN (Nearest Latent Neighbours), including problem formulation, an NLN algorithm, experiments, and analysis of time/memory efficiency and ablation results.\"}]","Machine learning-based anomaly detection for radio telescopes - Thesis | PDF",1785902357,391,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-based-anomaly-detection-for-radio-telescopes-thesis-125978","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/machine-learning-based-anomaly-detection-for-radio-telescopes-thesis-125978/125978/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-18","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address?","Question",{"text":76,"@type":77},"The thesis addresses anomaly detection in radio telescope data, including identifying irregular signals and radio-frequency interference patterns in observational streams.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning approach is used for learning data representations?",{"text":81,"@type":77},"It uses a variational autoencoder (VAE) to learn representations from radio astronomy spectrograms and evaluates both quantitative and qualitative behavior on simulated and LOFAR data.",{"name":83,"@type":74,"acceptedAnswer":84},"How is anomaly detection performed in the nearest-neighbour section?",{"text":85,"@type":77},"It presents NLN (Nearest Latent Neighbours), including problem formulation, an NLN algorithm, experiments, and analysis of time/memory efficiency and ablation results.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]