{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/37548"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/37548","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Compact and Extended Radio Sources Classification using Deep Convolutional Neural Networks","abstract":"Upcoming surveys with new radio observatories such as the Square Kilometer Array will generate a wealth of imaging data containing large numbers of radio sources. Different classes of radio sources can be used as tracers of the cosmic environment, including the dark matter density field, to address key cosmological questions. Classifying these sources based on morphology is thus an important step toward achieving the science goals of next generation radio surveys. Extended Radio Sources have been traditionally classified as Fanaroff-Riley (FR) I and II, although some exhibit more complex 'bent' morphologies arising from environmental factors or intrinsic properties. In this work we present the FIRST Classifier, an on-line system for automated classification of Compact and Extended radio sources. We developed the FIRST Classifier based on a trained Deep Convolutional Neural Network Model to automate the morphological classification of compact and extended radio sources observed in the FIRST radio survey. Our model was trained independently for 20 times and achieved an average accuracy, precision, recall and F1 of 0.98. The current version of the FIRST classifier is able to identify the morphological class for a single source or for a list of sources as Compact or Extended (FRI, FRII and BENT).","abstract_html":"Upcoming surveys with new radio observatories such as the Square Kilometer Array will generate a wealth of imaging data containing large numbers of radio sources. Different classes of radio sources can be used as tracers of the cosmic environment, including the dark matter density field, to address key cosmological questions. Classifying these sources based on morphology is thus an important step toward achieving the science goals of next generation radio surveys. Extended Radio Sources have been traditionally classified as Fanaroff-Riley (FR) I and II, although some exhibit more complex 'bent' morphologies arising from environmental factors or intrinsic properties. In this work we present the FIRST Classifier, an on-line system for automated classification of Compact and Extended radio sources. We developed the FIRST Classifier based on a trained Deep Convolutional Neural Network Model to automate the morphological classification of compact and extended radio sources observed in the FIRST radio survey. Our model was trained independently for 20 times and achieved an average accuracy, precision, recall and F1 of 0.98. The current version of the FIRST classifier is able to identify the morphological class for a single source or for a list of sources as Compact or Extended (FRI, FRII and BENT).","abstract_has_math":false,"creators":["Alhassan, Wathela"],"institution":"Department of Astronomy","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Taylor, A R","Vaccari, Mattia"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-24T01:34:01Z","subjects":["Astronomy"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/37548","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Taylor, A R","Vaccari, Mattia"]},{"key":"dc:creator","label":"Author","values":["Alhassan, Wathela"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-03-29T11:21:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-03-29T11:21:30Z"]},{"key":"dc:date.issued","label":"Date","values":["2019"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Astronomy"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Astronomy"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/37548"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Upcoming surveys with new radio observatories such as the Square Kilometer Array will generate a wealth of imaging data containing large numbers of radio sources. Different classes of radio sources can be used as tracers of the cosmic environment, including the dark matter density field, to address key cosmological questions. Classifying these sources based on morphology is thus an important step toward achieving the science goals of next generation radio surveys. Extended Radio Sources have been traditionally classified as Fanaroff-Riley (FR) I and II, although some exhibit more complex 'bent' morphologies arising from environmental factors or intrinsic properties. In this work we present the FIRST Classifier, an on-line system for automated classification of Compact and Extended radio sources. We developed the FIRST Classifier based on a trained Deep Convolutional Neural Network Model to automate the morphological classification of compact and extended radio sources observed in the FIRST radio survey. Our model was trained independently for 20 times and achieved an average accuracy, precision, recall and F1 of 0.98. The current version of the FIRST classifier is able to identify the morphological class for a single source or for a list of sources as Compact or Extended (FRI, FRII and BENT)."]},{"key":"dc:title","label":"Title","values":["Compact and Extended Radio Sources Classification using Deep Convolutional Neural Networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Taylor, A R","Vaccari, Mattia"],"dc:creator":["Alhassan, Wathela"],"dc:date.accessioned":["2023-03-29T11:21:30Z"],"dc:date.available":["2023-03-29T11:21:30Z"],"dc:date.issued":["2019"],"dc:description.abstract":["Upcoming surveys with new radio observatories such as the Square Kilometer Array will generate a wealth of imaging data containing large numbers of radio sources. Different classes of radio sources can be used as tracers of the cosmic environment, including the dark matter density field, to address key cosmological questions. Classifying these sources based on morphology is thus an important step toward achieving the science goals of next generation radio surveys. Extended Radio Sources have been traditionally classified as Fanaroff-Riley (FR) I and II, although some exhibit more complex 'bent' morphologies arising from environmental factors or intrinsic properties. In this work we present the FIRST Classifier, an on-line system for automated classification of Compact and Extended radio sources. We developed the FIRST Classifier based on a trained Deep Convolutional Neural Network Model to automate the morphological classification of compact and extended radio sources observed in the FIRST radio survey. Our model was trained independently for 20 times and achieved an average accuracy, precision, recall and F1 of 0.98. The current version of the FIRST classifier is able to identify the morphological class for a single source or for a list of sources as Compact or Extended (FRI, FRII and BENT)."],"dc:identifier.uri":["http://hdl.handle.net/11427/37548"],"dc:publisher.department":["Department of Astronomy"],"dc:subject":["Astronomy"],"dc:title":["Compact and Extended Radio Sources Classification using Deep Convolutional Neural Networks"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters","MSc"]},"updated_at":"2026-07-24T01:34:01Z"}