[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118761-en":3,"doc-seo-118761-105":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},118761,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","MiraBest - A Dataset of Morphologically Classified Radio Galaxies for Machine Learning","Current and next-generation observatories generate massive datasets that drive demand for automated machine learning in astronomy. Yet standardized datasets to benchmark and compare different algorithms remain limited. MiraBest addresses this gap by providing a public batched dataset of 1256 radio-loud AGN drawn from NVSS and FIRST, filtered to 0.03 \u003C z \u003C 0.1 and manually labelled using the Fanaroff–Riley morphology scheme. The work details dataset construction, sample selection, structure, and compares it with prior datasets, then extends it via cross-matching to 2100 sources for broader machine-learning use.","arXiv :2305 . 11108v1 [ astro-ph .IM] 18 May 2023  \nMiraBest: A Dataset of Morphologically Classified Radio Galaxies for Machine Learning  \nFiona A. M. Porter, 1 ★ and Anna M. M. Scaife 1,2  \n1 Jodrell Bank Centre for Astrophysics, Department of Physics & Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL UK  \n2 The Alan Turing Institute, Euston Road, London NW1 2DB, UK  \nAccepted XXX. Received YYY; in original form ZZZ  \nABSTRACT  \nThe volume of data from current and future observatories has motivated the increased development and application of automated machine learning methodologies for astronomy. However, less attention has been given to the production of standardised datasets for assessing the performance of different machine learning algorithms within astronomy and astrophysics. Here we describe in detail the MiraBest dataset, a publicly available batched dataset of 1256 radio-loud AGN from NVSS and FIRST,filtered to 0.03 \u003C 􀁉 \u003C 0. 1, manually labelled by Miraghaei and Best (2017) according to the Fanaroff-Riley morphological classification, created for machine learning applications and compatible for use with standard deep learning libraries. We outline the principles underlying the construction of the dataset, the sample selection and pre-processing methodology, dataset structure and composition, as well as a comparison of MiraBest to other datasets used in the literature. Existing applications that utilise the MiraBest dataset are reviewed, and an extended dataset of 2100 sources is created by cross-matching MiraBest with other catalogues of radio-loud AGN that have been used more widely in the literature for machine learning applications.  \nKey words: astronomical data bases – methods: data analysis – radio continuum: galaxies  \n1 INTRODUCTION  \nIn radio astronomy, morphological classification using convolutional neural networks (CNNs) and deep learning is becoming increasingly common for object classification, in particular with respect to the classification of radio galaxies (see e.g. Aniyan & Thorat 2017; Alger et al. 2018; Wu et al. 2018; Lukic et al. 2018; Lukic et al. 2019; Tanget al. 2019; Wang et al. 2021; Ntwaetsile & Geach 2021; Bowles et al. 2021; Sadeghi et al. 2021; Scaife & Porter 2021; Becker et al. 2021; Mohan et al. 2022; Slijepcevic et al. 2022, etc.) . Many of these works have focused on the morphological classification of radio galaxies following the Fanaroff-Riley classification scheme (FR; Fanaroff & Riley 1974), used to group radio-loud active galactic nuclei (AGN) by examining the locations of their regions of greatest luminosity relative to overall source extent. The initial scheme posited that there were two major populations of such sources-those which were corebrightened, with their peak luminosity concentrated at a radius of less than half than the overall angular size of the source from its centre (FR Type I), and those which were edge-brightened, with their peak luminosity concentrated at a radius of more than half the angular size of the source (FR Type II), and that there was a division in luminosity between the two populations at approximately 1025 Watts Hz−1 sr−1 , with edge-brightened sources having a higher intrinsic luminosity than core-brightened sources. As described, this taxonomy requires that an AGN is associated with well-resolved extended emission external to the AGN core in order to be classifed as either FRI or FRII.  \n★ E-mail: fi[ona.porter-2@manchester.ac.uk](ona.porter-2@manchester.ac.uk) (FP)  \nWhile the Fanaroff-Riley scheme was initially viewed as having a very straightforward luminosity boundary between morphological classes (Fanaroff & Riley 1974), further study has shown that this isnot the case, see (e.g.) Hardcastle & Croston (2020) for a review. In recent studies, sources have been detected which have raised questions about the use of this boundary; for example, Mingo et al. (2019) found that around 20% ofFRII galaxies in their sample had radio luminosity ","cbCaidSy0zwAkc46","https://ap.wps.com/l/cbCaidSy0zwAkc46","pdf",470574,1,14,"English","en",105,"# Abstract\n# Introduction\n## Fanaroff–Riley morphological classification and CNN-based approaches\n## Motivation for standardized datasets\n## Radio surveys enabling larger samples\n# Dataset description and comparison","[{\"question\":\"What problem does the MiraBest dataset address?\",\"answer\":\"It provides a standardized, publicly available dataset to assess and compare the performance of different machine learning algorithms for radio-galaxy morphology classification.\"},{\"question\":\"What sources and filtering were used to build MiraBest?\",\"answer\":\"MiraBest uses 1256 radio-loud AGN from NVSS and FIRST, then filters the sample to 0.03 \\u003c z \\u003c 0.1.\"},{\"question\":\"How was the dataset labelled and how is it extended?\",\"answer\":\"Labels follow the Fanaroff–Riley morphological classification scheme and were manually assigned. An extended dataset of 2100 sources is created by cross-matching MiraBest with other radio-loud AGN catalogues used in the literature.\"}]","MiraBest - A Dataset of Morphologically Classified Radio Galaxies for Machine Learning | PDF",1785720096,35,{"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},"mirabest-a-dataset-of-morphologically-classified-radio-galaxies-for-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/mirabest-a-dataset-of-morphologically-classified-radio-galaxies-for-machine-learning/118761/",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","2026-08-03",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 problem does the MiraBest dataset address?","Question",{"text":75,"@type":76},"It provides a standardized, publicly available dataset to assess and compare the performance of different machine learning algorithms for radio-galaxy morphology classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What sources and filtering were used to build MiraBest?",{"text":80,"@type":76},"MiraBest uses 1256 radio-loud AGN from NVSS and FIRST, then filters the sample to 0.03 \u003C z \u003C 0.1.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the dataset labelled and how is it extended?",{"text":84,"@type":76},"Labels follow the Fanaroff–Riley morphological classification scheme and were manually assigned. An extended dataset of 2100 sources is created by cross-matching MiraBest with other radio-loud AGN catalogues used in the literature.","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"]