Third generation calibrations for Meerkat Observation of Saraswati Supercluster
- Authors: Kincaid, Robert Daniel
- Date: 2022-10-14
- Subjects: Square Kilometre Array (Project) , Superclusters , Saraswati Supercluster , Radio astronomy , MeerKAT , Calibration
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/362916 , vital:65374
- Description: The international collaboration of the Square Kilometre Array (SKA), which is one of the largest and most challenging science projects of the 21st century, will bring a revolution in radio astronomy in terms of sensitivity and resolution. The recent launch of several new radio instruments, combined with the subsequent developments in calibration and imaging techniques, has dramatically advanced this field over the past few years, thus enhancing our knowledge of the radio universe. Various SKA pathfinders around the world have been developed (and more are planned for construction) that have laid down a firm foundation for the SKA in terms of science while additionally giving insight into the technological requirements required for the projected data outputs to become manageable. South Africa has recently built the new MeerKAT telescope, which is a SKA precursor forming an integral part of SKA-mid component. The MeerKAT instrument has unprecedented sensitivity that can cater for the required science goals of the current and future SKA era. It is noticeable from MeerKAT and other precursors that the data produced by these instruments are significantly challenging to calibrate and image. Calibration-related artefacts intrinsic to bright sources are of major concern since, they limit the Dynamic Range (DR) and image fidelity of the resulting images and cause flux suppression of extended sources. Diffuse radio sources from galaxy clusters in the form of halos, relics and most recently bridges on the Mpc scale, because of their diffuse nature combined with wide field of view (FoV) observations, make them particularly good candidates for testing the different approaches of calibration. , Thesis (MSc) -- Faculty of Science, Physics and Electronics, 2022
- Full Text:
- Date Issued: 2022-10-14
- Authors: Kincaid, Robert Daniel
- Date: 2022-10-14
- Subjects: Square Kilometre Array (Project) , Superclusters , Saraswati Supercluster , Radio astronomy , MeerKAT , Calibration
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/362916 , vital:65374
- Description: The international collaboration of the Square Kilometre Array (SKA), which is one of the largest and most challenging science projects of the 21st century, will bring a revolution in radio astronomy in terms of sensitivity and resolution. The recent launch of several new radio instruments, combined with the subsequent developments in calibration and imaging techniques, has dramatically advanced this field over the past few years, thus enhancing our knowledge of the radio universe. Various SKA pathfinders around the world have been developed (and more are planned for construction) that have laid down a firm foundation for the SKA in terms of science while additionally giving insight into the technological requirements required for the projected data outputs to become manageable. South Africa has recently built the new MeerKAT telescope, which is a SKA precursor forming an integral part of SKA-mid component. The MeerKAT instrument has unprecedented sensitivity that can cater for the required science goals of the current and future SKA era. It is noticeable from MeerKAT and other precursors that the data produced by these instruments are significantly challenging to calibrate and image. Calibration-related artefacts intrinsic to bright sources are of major concern since, they limit the Dynamic Range (DR) and image fidelity of the resulting images and cause flux suppression of extended sources. Diffuse radio sources from galaxy clusters in the form of halos, relics and most recently bridges on the Mpc scale, because of their diffuse nature combined with wide field of view (FoV) observations, make them particularly good candidates for testing the different approaches of calibration. , Thesis (MSc) -- Faculty of Science, Physics and Electronics, 2022
- Full Text:
- Date Issued: 2022-10-14
Machine learning methods for calibrating radio interferometric data
- Authors: Zitha, Simphiwe Nhlanhla
- Date: 2019
- Subjects: Calibration , Radio astronomy -- Data processing , Radio astronomy -- South Africa , Karoo Array Telescope (South Africa) , Radio telescopes -- South Africa , Common Astronomy Software Application (Computer software)
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/97096 , vital:31398
- Description: The applications of machine learning have created an opportunity to deal with complex problems currently encountered in radio astronomy data processing. Calibration is one of the most important data processing steps required to produce high dynamic range images. This process involves the determination of calibration parameters, both instrumental and astronomical, to correct the collected data. Typically, astronomers use a package such as Common Astronomy Software Applications (CASA) to compute the gain solutions based on regular observations of a known calibrator source. In this work we present applications of machine learning to first generation calibration (1GC), using the KAT-7 telescope environmental and pointing sensor data recorded during observations. Applying machine learning to 1GC, as opposed to calculating the gain solutions in CASA, has shown evidence of reducing computation, as well as accurately predict the 1GC gain solutions representing the behaviour of the antenna during an observation. These methods are computationally less expensive, however they have not fully learned to generalise in predicting accurate 1GC solutions by looking at environmental and pointing sensors. We call this multi-output regression model ZCal, which is based on random forest, decision trees, extremely randomized trees and K-nearest neighbor algorithms. The prediction error obtained during the testing of our model on testing data is ≈ 0.01 < rmse < 0.09 for gain amplitude per antenna, and 0.2 rad < rmse <0.5 rad for gain phase. This shows that the instrumental parameters used to train our model more strongly correlate with gain amplitude effects than phase.
- Full Text:
- Date Issued: 2019
- Authors: Zitha, Simphiwe Nhlanhla
- Date: 2019
- Subjects: Calibration , Radio astronomy -- Data processing , Radio astronomy -- South Africa , Karoo Array Telescope (South Africa) , Radio telescopes -- South Africa , Common Astronomy Software Application (Computer software)
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/97096 , vital:31398
- Description: The applications of machine learning have created an opportunity to deal with complex problems currently encountered in radio astronomy data processing. Calibration is one of the most important data processing steps required to produce high dynamic range images. This process involves the determination of calibration parameters, both instrumental and astronomical, to correct the collected data. Typically, astronomers use a package such as Common Astronomy Software Applications (CASA) to compute the gain solutions based on regular observations of a known calibrator source. In this work we present applications of machine learning to first generation calibration (1GC), using the KAT-7 telescope environmental and pointing sensor data recorded during observations. Applying machine learning to 1GC, as opposed to calculating the gain solutions in CASA, has shown evidence of reducing computation, as well as accurately predict the 1GC gain solutions representing the behaviour of the antenna during an observation. These methods are computationally less expensive, however they have not fully learned to generalise in predicting accurate 1GC solutions by looking at environmental and pointing sensors. We call this multi-output regression model ZCal, which is based on random forest, decision trees, extremely randomized trees and K-nearest neighbor algorithms. The prediction error obtained during the testing of our model on testing data is ≈ 0.01 < rmse < 0.09 for gain amplitude per antenna, and 0.2 rad < rmse <0.5 rad for gain phase. This shows that the instrumental parameters used to train our model more strongly correlate with gain amplitude effects than phase.
- Full Text:
- Date Issued: 2019
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