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
PyMORESANE: A Pythonic and CUDA-accelerated implementation of the MORESANE deconvolution algorithm
- Authors: Kenyon, Jonathan
- Date: 2015
- Subjects: Radio astronomy , Imaging systems in astronomy , MOdel REconstruction by Synthesis-ANalysis Estimators (MORESANE)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5563 , http://hdl.handle.net/10962/d1020098
- Description: The inadequacies of the current generation of deconvolution algorithms are rapidly becoming apparent as new, more sensitive radio interferometers are constructed. In light of these inadequacies, there is renewed interest in the field of deconvolution. Many new algorithms are being developed using the mathematical framework of compressed sensing. One such technique, MORESANE, has recently been shown to be a powerful tool for the recovery of faint difuse emission from synthetic and simulated data. However, the original implementation is not well-suited to large problem sizes due to its computational complexity. Additionally, its use of proprietary software prevents it from being freely distributed and used. This has motivated the development of a freely available Python implementation, PyMORESANE. This thesis describes the implementation of PyMORESANE as well as its subsequent augmentation with MPU and GPGPU code. These additions accelerate the algorithm and thus make it competitive with its legacy counterparts. The acceleration of the algorithm is verified by means of benchmarking tests for varying image size and complexity. Additionally, PyMORESANE is shown to work not only on synthetic data, but on real observational data. This verification means that the MORESANE algorithm, and consequently the PyMORESANE implementation, can be added to the current arsenal of deconvolution tools.
- Full Text:
- Date Issued: 2015
- Authors: Kenyon, Jonathan
- Date: 2015
- Subjects: Radio astronomy , Imaging systems in astronomy , MOdel REconstruction by Synthesis-ANalysis Estimators (MORESANE)
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5563 , http://hdl.handle.net/10962/d1020098
- Description: The inadequacies of the current generation of deconvolution algorithms are rapidly becoming apparent as new, more sensitive radio interferometers are constructed. In light of these inadequacies, there is renewed interest in the field of deconvolution. Many new algorithms are being developed using the mathematical framework of compressed sensing. One such technique, MORESANE, has recently been shown to be a powerful tool for the recovery of faint difuse emission from synthetic and simulated data. However, the original implementation is not well-suited to large problem sizes due to its computational complexity. Additionally, its use of proprietary software prevents it from being freely distributed and used. This has motivated the development of a freely available Python implementation, PyMORESANE. This thesis describes the implementation of PyMORESANE as well as its subsequent augmentation with MPU and GPGPU code. These additions accelerate the algorithm and thus make it competitive with its legacy counterparts. The acceleration of the algorithm is verified by means of benchmarking tests for varying image size and complexity. Additionally, PyMORESANE is shown to work not only on synthetic data, but on real observational data. This verification means that the MORESANE algorithm, and consequently the PyMORESANE implementation, can be added to the current arsenal of deconvolution tools.
- Full Text:
- Date Issued: 2015
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