{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/32986"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/32986","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"A statistical approach to automated detection of multi-component radio sources","abstract":"Advances in radio astronomy are allowing for deeper and wider areas of the sky to be observed than ever before. Source counts of future radio surveys are expected to number in the tens of millions. Source finding techniques are used to identify sources in a radio image, however, these techniques identify single distinct sources and are challenged to identify multi-component sources, that is to say, where two or more distinct sources belong to the same underlying physical phenomenon, such as a radio galaxy. Identification of such phenomena is an important step in generating catalogues from surveys on which much of the radio astronomy science is based. Historically, identifying multi-component sources was conducted by visual inspection, however, the size of future surveys makes manual identification prohibitive. An algorithm to automate this process using statistical techniques is proposed. The algorithm is demonstrated on two radio images. The output of the algorithm is a catalogue where nearest neighbour source pairs are assigned a probability score of being a component of the same physical object. By applying several selection criteria, pairs of sources which are likely to be multi-component sources can be determined. Radio image cutouts are then generated from this selection and may be used as input into radio source classification techniques. Successful identification of multi-component sources using this method is demonstrated.","abstract_html":"Advances in radio astronomy are allowing for deeper and wider areas of the sky to be observed than ever before. Source counts of future radio surveys are expected to number in the tens of millions. Source finding techniques are used to identify sources in a radio image, however, these techniques identify single distinct sources and are challenged to identify multi-component sources, that is to say, where two or more distinct sources belong to the same underlying physical phenomenon, such as a radio galaxy. Identification of such phenomena is an important step in generating catalogues from surveys on which much of the radio astronomy science is based. Historically, identifying multi-component sources was conducted by visual inspection, however, the size of future surveys makes manual identification prohibitive. An algorithm to automate this process using statistical techniques is proposed. The algorithm is demonstrated on two radio images. The output of the algorithm is a catalogue where nearest neighbour source pairs are assigned a probability score of being a component of the same physical object. By applying several selection criteria, pairs of sources which are likely to be multi-component sources can be determined. Radio image cutouts are then generated from this selection and may be used as input into radio source classification techniques. Successful identification of multi-component sources using this method is demonstrated.","abstract_has_math":false,"creators":["Smith, Jeremy Stewart"],"institution":"Department of Statistical Sciences","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Taylor, Russell"],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:23:14Z","subjects":["data science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/32986","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Taylor, Russell"]},{"key":"dc:creator","label":"Author","values":["Smith, Jeremy Stewart"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-02-24T19:18:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-02-24T19:18:22Z"]},{"key":"dc:date.issued","label":"Date","values":["2020"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Statistical Sciences"]},{"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":["data science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/32986"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Advances in radio astronomy are allowing for deeper and wider areas of the sky to be observed than ever before. Source counts of future radio surveys are expected to number in the tens of millions. Source finding techniques are used to identify sources in a radio image, however, these techniques identify single distinct sources and are challenged to identify multi-component sources, that is to say, where two or more distinct sources belong to the same underlying physical phenomenon, such as a radio galaxy. Identification of such phenomena is an important step in generating catalogues from surveys on which much of the radio astronomy science is based. Historically, identifying multi-component sources was conducted by visual inspection, however, the size of future surveys makes manual identification prohibitive. An algorithm to automate this process using statistical techniques is proposed. The algorithm is demonstrated on two radio images. The output of the algorithm is a catalogue where nearest neighbour source pairs are assigned a probability score of being a component of the same physical object. By applying several selection criteria, pairs of sources which are likely to be multi-component sources can be determined. Radio image cutouts are then generated from this selection and may be used as input into radio source classification techniques. Successful identification of multi-component sources using this method is demonstrated."]},{"key":"dc:title","label":"Title","values":["A statistical approach to automated detection of multi-component radio sources"]}]}],"canonical_facts":{"dc:contributor.advisor":["Taylor, Russell"],"dc:creator":["Smith, Jeremy Stewart"],"dc:date.accessioned":["2021-02-24T19:18:22Z"],"dc:date.available":["2021-02-24T19:18:22Z"],"dc:date.issued":["2020"],"dc:description.abstract":["Advances in radio astronomy are allowing for deeper and wider areas of the sky to be observed than ever before. Source counts of future radio surveys are expected to number in the tens of millions. Source finding techniques are used to identify sources in a radio image, however, these techniques identify single distinct sources and are challenged to identify multi-component sources, that is to say, where two or more distinct sources belong to the same underlying physical phenomenon, such as a radio galaxy. Identification of such phenomena is an important step in generating catalogues from surveys on which much of the radio astronomy science is based. Historically, identifying multi-component sources was conducted by visual inspection, however, the size of future surveys makes manual identification prohibitive. An algorithm to automate this process using statistical techniques is proposed. The algorithm is demonstrated on two radio images. The output of the algorithm is a catalogue where nearest neighbour source pairs are assigned a probability score of being a component of the same physical object. By applying several selection criteria, pairs of sources which are likely to be multi-component sources can be determined. Radio image cutouts are then generated from this selection and may be used as input into radio source classification techniques. Successful identification of multi-component sources using this method is demonstrated."],"dc:identifier.uri":["http://hdl.handle.net/11427/32986"],"dc:publisher.department":["Department of Statistical Sciences"],"dc:subject":["data science"],"dc:title":["A statistical approach to automated detection of multi-component radio sources"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters","MSc"]},"updated_at":"2026-07-22T22:23:14Z"}