Measuring the RFI environment of the South African SKA site
- Authors: Manners, Paul John
- Date: 2007
- Subjects: Radio telescopes , Radio telescopes -- South Africa , Radio astronomy , Radio astronomy -- South Africa , Square Kilometer Array (Spacecraft) , Radio -- Interference -- Measurement
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5474 , http://hdl.handle.net/10962/d1005259 , Radio telescopes , Radio telescopes -- South Africa , Radio astronomy , Radio astronomy -- South Africa , Square Kilometer Array (Spacecraft) , Radio -- Interference -- Measurement
- Description: The Square Kilometre Array (SKA) Project is an international effort to build the world’s largest radio telescope. It will be 100 times more sensitive than any other radio telescope currently in existence and will consist of thousands of dishes placed at baselines up to 3000 km. In addition to its increased sensitivity it will operate over a very wide frequency range (current specification is 100 MHz - 22 GHz) and will use frequency bands not primarily allocated to radio astronomy. Because of this the telescope needs to be located at a site with low levels of radio frequency interference (RFI). This implies a site that is remote and away from human activity. In bidding to host the SKA, South Africa was required to conduct an RFI survey at its proposed site for a period of 12 months. Apart from this core site, where more than half the SKA dishes may potentially be deployed, the measurement of remote sites in Southern Africa was also required. To conduct measurements at these sites, three mobile measurement systems were designed and built by the South African SKA Project. The design considerations, implementation and RFI measurements recorded during this campaign will be the focus for this dissertation.
- Full Text:
- Date Issued: 2007
- Authors: Manners, Paul John
- Date: 2007
- Subjects: Radio telescopes , Radio telescopes -- South Africa , Radio astronomy , Radio astronomy -- South Africa , Square Kilometer Array (Spacecraft) , Radio -- Interference -- Measurement
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5474 , http://hdl.handle.net/10962/d1005259 , Radio telescopes , Radio telescopes -- South Africa , Radio astronomy , Radio astronomy -- South Africa , Square Kilometer Array (Spacecraft) , Radio -- Interference -- Measurement
- Description: The Square Kilometre Array (SKA) Project is an international effort to build the world’s largest radio telescope. It will be 100 times more sensitive than any other radio telescope currently in existence and will consist of thousands of dishes placed at baselines up to 3000 km. In addition to its increased sensitivity it will operate over a very wide frequency range (current specification is 100 MHz - 22 GHz) and will use frequency bands not primarily allocated to radio astronomy. Because of this the telescope needs to be located at a site with low levels of radio frequency interference (RFI). This implies a site that is remote and away from human activity. In bidding to host the SKA, South Africa was required to conduct an RFI survey at its proposed site for a period of 12 months. Apart from this core site, where more than half the SKA dishes may potentially be deployed, the measurement of remote sites in Southern Africa was also required. To conduct measurements at these sites, three mobile measurement systems were designed and built by the South African SKA Project. The design considerations, implementation and RFI measurements recorded during this campaign will be the focus for this dissertation.
- Full Text:
- Date Issued: 2007
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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