[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125764-en":3,"doc-seo-125764-105":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},125764,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Interferometric Image Reconstruction using Closure Invariants and Machine Learning","Interferometric closure invariants capture calibration-independent morphological details of astronomical sources, yet a direct mapping from closure invariants to morphologies is rarely established for general cases. This work tests simple machine learning models for morphological classification and parameter estimation using closure invariants derived from a sparsely covered aperture. Logistic regression, multilayer perceptron, and random forest models achieve ≳80% classification accuracy (excluding LR) and improve with increased coverage; parameter estimation via MLP enables image reconstruction, with performance limited by parameter degeneracies, supporting robust morphology constraints under challenging VLBI conditions.","arXiv :2311 .06349v3 [ astro-ph .IM] 25 Aug 2024  \nInterferometric Image Reconstruction using Closure Invariants and Machine Learning  \nNithyanandan Thyagarajan, 1★ Lucas Hoefs, 1,2 and O. Ivy Wong 1,3  \n1 Commonwealth Scientific and Industrial Research Organisation (CSIRO), Space & Astronomy, P. O. Box 1130, Bentley, WA 6102, Australia  \n2 Mechatronics Engineering Department, Curtin University, Bentley, WA 6102, Australia  \n3 International Centre for Radio Astronomy Research, The University of Western Australia, Crawley, WA 6009, Australia  \nAccepted 2024 August 1 . Received 2024 July 19; in original form 2023 November 9  \nABSTRACT  \nInterferometric closure invariants encode calibration-independent details of an object’s morphology. Excepting simple cases, a direct backward transformation from closure invariants to morphologies is not well established. We demonstrate using simple Machine Learning models that closure invariants can aid in morphological classification and parameter estimation. We consider six phenomenologically parametrised morphologies: point-like, uniform circular disc, crescent, dual disc, crescent with elliptical accretion disc, and crescent with double jet lobes. Using logistic regression (LR), multi-layer perceptron (MLP), and random forest models on closure invariants obtained from a sparsely covered aperture, we find that all methods except LR can classify morphologies with ≳80% accuracy, which improves with greater aperture coverage. Separately from the classification problem, given an independently confirmed class, we estimate parameters of uniform circular disc, crescent, and dual disc morphologies using simple MLP models, and parametrically reconstruct images. The estimated parameters and images correspond well withinputs, but the accuracy worsens when degeneracies between parameters are present. This independent approach to interferometric imaging under challenging observing conditions such as that faced by the Event Horizon Telescope and Very Long Baseline Interferometry in general can complement other methods in robustly constraining an object’s morphology.  \nKey words: Algorithms – Machine Learning – methods: data analysis – techniques: image processing – techniques: interferometric  \n1 INTRODUCTION  \nImage synthesis using radio interferometric measurements requires an array of receiver elements sampling spatial correlations of the radiation incident on the aperture to infer the spatial intensity distribution on the sky within the telescope’s field of view (Thompson et al. 2017; Taylor et al. 1999) . As the spatial resolution of the image scales inversely with the largest spacing between the interferometer array elements, obtaining very fine details in the image requires a technique called Very Long Baseline Interferometry (VLBI) which requires array elements widely separated from each other, with typical separations spanning continental or even planet-sized scales. Due to the sparseness of measurements and demanding requirements of accurate signal calibration required in maintaining a high degree of spatial coherence while combining signals from such large separations, VLBI imaging is extremely challenging in general (Thompson et al. 2017; Walker 1999) .  \nThere are certain invariants in interferometry like closure phases (Jennison 1958) and closure amplitudes (Twiss et al. 1960) that are immune to propagation and instrumental effects that are associable with individual array elements, and are thus independent of calibration and errors therein. Being true observables of the observed object’s morphology, they implicitly or explicitly serve as useful an-  \n★ [E-mail: Nithyanandan.Thyagarajan@csiro.au](E-mail: Nithyanandan.Thyagarajan@csiro.au)  \nchors for inferring an object’s morphology. A few examples of classic VLBI successes that used closure quantities include the discovery of the double-lobed structures of Cygnus A (Jennison 1957; Jennison & Latham 1959) and Centaurus A (Twiss et al. 1960),","cbCaivi3q9PpGUaM","https://ap.wps.com/l/cbCaivi3q9PpGUaM","pdf",5474209,1,17,"English","en",105,"# Abstract\n# Introduction\n## Interferometric imaging and VLBI challenges\n## Closure phases and closure amplitudes as invariants\n## Motivation for calibration-robust reconstruction","[{\"question\":\"What are closure invariants, and why are they useful in interferometric imaging?\",\"answer\":\"Closure invariants encode calibration-independent morphological information. Because they are immune to propagation and element-based instrumental effects, they provide observables tied to the object’s structure.\"},{\"question\":\"How do the authors use machine learning in this work?\",\"answer\":\"They apply logistic regression, multilayer perceptrons, and random forests to closure invariants to perform morphological classification. They also use MLP models to estimate parameters for specific morphologies and to reconstruct images.\"},{\"question\":\"What limits the accuracy of parameter estimation and reconstructed images?\",\"answer\":\"Accuracy degrades when degeneracies arise between morphology parameters. The estimated parameters and images match inputs well in non-degenerate cases, but worsen under ambiguous parameter combinations.\"}]","Interferometric Image Reconstruction using Closure Invariants and Machine Learning | PDF",1785901081,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"interferometric-image-reconstruction-using-closure-invariants-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/interferometric-image-reconstruction-using-closure-invariants-and-machine-learning/125764/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What are closure invariants, and why are they useful in interferometric imaging?","Question",{"text":75,"@type":76},"Closure invariants encode calibration-independent morphological information. Because they are immune to propagation and element-based instrumental effects, they provide observables tied to the object’s structure.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the authors use machine learning in this work?",{"text":80,"@type":76},"They apply logistic regression, multilayer perceptrons, and random forests to closure invariants to perform morphological classification. They also use MLP models to estimate parameters for specific morphologies and to reconstruct images.",{"name":82,"@type":73,"acceptedAnswer":83},"What limits the accuracy of parameter estimation and reconstructed images?",{"text":84,"@type":76},"Accuracy degrades when degeneracies arise between morphology parameters. The estimated parameters and images match inputs well in non-degenerate cases, but worsen under ambiguous parameter combinations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]