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
Link between ghost artefacts, source suppression and incomplete calibration sky models
- Authors: Nunhokee, Chuneeta Devi
- Date: 2015
- Subjects: Interferometry , Calibration
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5556 , http://hdl.handle.net/10962/d1017900
- Description: Calibration is a fundamental step towards producing radio interferometric images. However, naive calibration produces calibration artefacts, in the guise of spurious emission, buried in the thermal noise. This work investigates these calibration artefacts, henceforth referred to as “ghosts”. A 21 cm observation with the Westerbork Synthesis Radio Telescope yielded similar ghost sources, and it was anticipated that they were due to calibrating with incomplete sky models. An analytical ghost distribution of a two-source scenario is derived to substantiate this theory and to seek answers to the related bewildering features (regular ghost pattern, points spread function-like sidelobes, independent of model flux). The theoretically predicted ghost distribution qualitatively matches with the observational ones and shows high dependence on the array geometry. The theory draws the conclusion that both the ghost phenomenon and suppression of the unmodelled flux have the same root cause. In addition, the suppression of the unmodelled flux is studied as functions of unmodelled flux, differential gain solution interval and the number of sources subjected to direction-dependent gains. These studies summarise that the suppression rate is constant irrespective of the degree of incompleteness of the calibration sky model. In the presence of a direction-dependent effect, the suppression drastically increases; however, this increase can be compensated for by using longer solution intervals.
- Full Text:
- Date Issued: 2015
- Authors: Nunhokee, Chuneeta Devi
- Date: 2015
- Subjects: Interferometry , Calibration
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5556 , http://hdl.handle.net/10962/d1017900
- Description: Calibration is a fundamental step towards producing radio interferometric images. However, naive calibration produces calibration artefacts, in the guise of spurious emission, buried in the thermal noise. This work investigates these calibration artefacts, henceforth referred to as “ghosts”. A 21 cm observation with the Westerbork Synthesis Radio Telescope yielded similar ghost sources, and it was anticipated that they were due to calibrating with incomplete sky models. An analytical ghost distribution of a two-source scenario is derived to substantiate this theory and to seek answers to the related bewildering features (regular ghost pattern, points spread function-like sidelobes, independent of model flux). The theoretically predicted ghost distribution qualitatively matches with the observational ones and shows high dependence on the array geometry. The theory draws the conclusion that both the ghost phenomenon and suppression of the unmodelled flux have the same root cause. In addition, the suppression of the unmodelled flux is studied as functions of unmodelled flux, differential gain solution interval and the number of sources subjected to direction-dependent gains. These studies summarise that the suppression rate is constant irrespective of the degree of incompleteness of the calibration sky model. In the presence of a direction-dependent effect, the suppression drastically increases; however, this increase can be compensated for by using longer solution intervals.
- Full Text:
- Date Issued: 2015
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