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A statistical approach to automated detection of multi-component radio sources
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 ide...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2021
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| _version_ | 1867613280664551424 |
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| access_status_str | Open Access |
| author | Smith, Jeremy Stewart |
| author2 | Taylor, Russell |
| author_browse | Smith, Jeremy Stewart Taylor, Russell |
| author_facet | Taylor, Russell Smith, Jeremy Stewart |
| author_sort | Smith, Jeremy Stewart |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/32986 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:37.862Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/32986 A statistical approach to automated detection of multi-component radio sources Smith, Jeremy Stewart Taylor, Russell data science 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. 2021-02-24T19:18:22Z 2021-02-24T19:18:22Z 2020 2021-02-24T19:17:49Z Master Thesis Masters MSc http://hdl.handle.net/11427/32986 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | data science Smith, Jeremy Stewart A statistical approach to automated detection of multi-component radio sources |
| thesis_degree_str | Master's |
| title | A statistical approach to automated detection of multi-component radio sources |
| title_full | A statistical approach to automated detection of multi-component radio sources |
| title_fullStr | A statistical approach to automated detection of multi-component radio sources |
| title_full_unstemmed | A statistical approach to automated detection of multi-component radio sources |
| title_short | A statistical approach to automated detection of multi-component radio sources |
| title_sort | statistical approach to automated detection of multi component radio sources |
| topic | data science |
| url | http://hdl.handle.net/11427/32986 |
| work_keys_str_mv | AT smithjeremystewart astatisticalapproachtoautomateddetectionofmulticomponentradiosources AT smithjeremystewart statisticalapproachtoautomateddetectionofmulticomponentradiosources |