Source Detection with Interferometric Datasets
The detection of sources in interferometric radio data typically relies on extracting information from images, formed by Fourier transform of the underlying visibility dataset, and CLEANed of contaminating sidelobes through iterative deconvolution. Variable and transient radio sources span a large r...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Cambridge University Press
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/43774 |
| _version_ | 1848756804410933248 |
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| author | Trott, Cathryn Wayth, Randall Macquart, Jean-Pierre Tingay, Steven |
| author2 | R E M Griffiths |
| author_facet | R E M Griffiths Trott, Cathryn Wayth, Randall Macquart, Jean-Pierre Tingay, Steven |
| author_sort | Trott, Cathryn |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The detection of sources in interferometric radio data typically relies on extracting information from images, formed by Fourier transform of the underlying visibility dataset, and CLEANed of contaminating sidelobes through iterative deconvolution. Variable and transient radio sources span a large range of variability timescales, and their study has the potential to enhance our knowledge of the dynamic universe. Their detection and classification involve large data rates and non-stationary PSFs, commensal observing programs and ambitious science goals, and will demand a paradigm shift in the deployment of next-generation instruments. Optimal source detection and classification in real time requires efficient and automated algorithms. On short time-scales variability can be probed with an optimal matched filter detector applied directly to the visibility dataset. This paper shows the design of such a detector, and some preliminary detection performance results. |
| first_indexed | 2025-11-14T09:18:01Z |
| format | Conference Paper |
| id | curtin-20.500.11937-43774 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:18:01Z |
| publishDate | 2012 |
| publisher | Cambridge University Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-437742023-02-07T08:01:21Z Source Detection with Interferometric Datasets Trott, Cathryn Wayth, Randall Macquart, Jean-Pierre Tingay, Steven R E M Griffiths R J Hanisch R Seaman methods interferometric radio continuum techniques general statistical The detection of sources in interferometric radio data typically relies on extracting information from images, formed by Fourier transform of the underlying visibility dataset, and CLEANed of contaminating sidelobes through iterative deconvolution. Variable and transient radio sources span a large range of variability timescales, and their study has the potential to enhance our knowledge of the dynamic universe. Their detection and classification involve large data rates and non-stationary PSFs, commensal observing programs and ambitious science goals, and will demand a paradigm shift in the deployment of next-generation instruments. Optimal source detection and classification in real time requires efficient and automated algorithms. On short time-scales variability can be probed with an optimal matched filter detector applied directly to the visibility dataset. This paper shows the design of such a detector, and some preliminary detection performance results. 2012 Conference Paper http://hdl.handle.net/20.500.11937/43774 10.1017/S1743921312001263 Cambridge University Press unknown |
| spellingShingle | methods interferometric radio continuum techniques general statistical Trott, Cathryn Wayth, Randall Macquart, Jean-Pierre Tingay, Steven Source Detection with Interferometric Datasets |
| title | Source Detection with Interferometric Datasets |
| title_full | Source Detection with Interferometric Datasets |
| title_fullStr | Source Detection with Interferometric Datasets |
| title_full_unstemmed | Source Detection with Interferometric Datasets |
| title_short | Source Detection with Interferometric Datasets |
| title_sort | source detection with interferometric datasets |
| topic | methods interferometric radio continuum techniques general statistical |
| url | http://hdl.handle.net/20.500.11937/43774 |