A Systematic Visualisation Framework for Radio-Imaging Pipelines
- Authors: Andati, Lexy Acherwa Livoyi
- Date: 2021-04
- Subjects: Radio interferometers , Radio astronomy -- Data processing , Radio astronomy -- Data processing -- Software , Jupyter
- Language: English
- Type: thesis , text , Masters , MSc
- Identifier: http://hdl.handle.net/10962/177338 , vital:42812
- Description: Pipelines for calibration and imaging of radio interferometric data produce many intermediate images and other data products (gain tables, etc.) These often contain valuable information about the quality of the data and the calibration, and can provide the user with valuable insights, if only visualised in the right way. However, the deluge of data that we’re experiencing with modern instruments means that most of these products are never looked at, and only the final images and data products are examined. Furthermore, the variety of imaging algorithms currently available, and the range of their options, means that very different results can be produced from the same set of original data. Proper understanding of this requires a systematic comparison that can be carried out both by individual users locally, and by the community globally. We address both problems by developing a systematic visualisation framework based around Jupyter notebooks, enriched with interactive plots based on the Bokeh and Datashader visualisation libraries. , Thesis (MSc) -- Faculty of Science, Department of Physics and Electronics, 2021
- Full Text:
- Date Issued: 2021-04
- Authors: Andati, Lexy Acherwa Livoyi
- Date: 2021-04
- Subjects: Radio interferometers , Radio astronomy -- Data processing , Radio astronomy -- Data processing -- Software , Jupyter
- Language: English
- Type: thesis , text , Masters , MSc
- Identifier: http://hdl.handle.net/10962/177338 , vital:42812
- Description: Pipelines for calibration and imaging of radio interferometric data produce many intermediate images and other data products (gain tables, etc.) These often contain valuable information about the quality of the data and the calibration, and can provide the user with valuable insights, if only visualised in the right way. However, the deluge of data that we’re experiencing with modern instruments means that most of these products are never looked at, and only the final images and data products are examined. Furthermore, the variety of imaging algorithms currently available, and the range of their options, means that very different results can be produced from the same set of original data. Proper understanding of this requires a systematic comparison that can be carried out both by individual users locally, and by the community globally. We address both problems by developing a systematic visualisation framework based around Jupyter notebooks, enriched with interactive plots based on the Bokeh and Datashader visualisation libraries. , Thesis (MSc) -- Faculty of Science, Department of Physics and Electronics, 2021
- Full Text:
- Date Issued: 2021-04
Automation of source-artefact classification
- Sebokolodi, Makhuduga Lerato Lydia
- Authors: Sebokolodi, Makhuduga Lerato Lydia
- Date: 2017
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/4920 , vital:20743
- Description: The high sensitivities of modern radio telescopes will enable the detection of very faint astrophysical sources in the distant Universe. However, these high sensitivities also imply that calibration artefacts, which were below the noise for less sensitive instruments, will emerge above the noise and may limit the dynamic range capabilities of these instruments. Detecting faint emission will require detection thresholds close to the noise and this may cause some of the artefacts to be incorrectly detected as real emission. The current approach is to manually remove the artefacts, or set high detection thresholds in order to avoid them. The former will not be possible given the large quantities of data that these instruments will produce, and the latter results in very shallow and incomplete catalogues. This work uses the negative detection method developed by Serra et al. (2012) to distinguish artefacts from astrophysical emission in radio images. We also present a technique that automates the identification of sources subject to severe direction-dependent (DD) effects and thus allows them to be flagged for DD calibration. The negative detection approach is shown to provide high reliability and high completeness catalogues for simulated data, as well as a JVLA observation of the 3C147 field (Mitra et al., 2015). We also show that our technique correctly identifies sources that require DD calibration for datasets from the KAT-7, LOFAR, JVLA and GMRT instruments.
- Full Text:
- Date Issued: 2017
- Authors: Sebokolodi, Makhuduga Lerato Lydia
- Date: 2017
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/4920 , vital:20743
- Description: The high sensitivities of modern radio telescopes will enable the detection of very faint astrophysical sources in the distant Universe. However, these high sensitivities also imply that calibration artefacts, which were below the noise for less sensitive instruments, will emerge above the noise and may limit the dynamic range capabilities of these instruments. Detecting faint emission will require detection thresholds close to the noise and this may cause some of the artefacts to be incorrectly detected as real emission. The current approach is to manually remove the artefacts, or set high detection thresholds in order to avoid them. The former will not be possible given the large quantities of data that these instruments will produce, and the latter results in very shallow and incomplete catalogues. This work uses the negative detection method developed by Serra et al. (2012) to distinguish artefacts from astrophysical emission in radio images. We also present a technique that automates the identification of sources subject to severe direction-dependent (DD) effects and thus allows them to be flagged for DD calibration. The negative detection approach is shown to provide high reliability and high completeness catalogues for simulated data, as well as a JVLA observation of the 3C147 field (Mitra et al., 2015). We also show that our technique correctly identifies sources that require DD calibration for datasets from the KAT-7, LOFAR, JVLA and GMRT instruments.
- Full Text:
- Date Issued: 2017
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