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
A pilot wide-field VLBI survey of the GOODS-North field
- Authors: Akoto-Danso, Alexander
- Date: 2019
- Subjects: Radio astronomy , Very long baseline interferometry , Radio interometers , Imaging systems in astronomy , Hubble Space Telescope (Spacecraft) -- Observations
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/72296 , vital:30027
- Description: Very Long Baseline Interferometry (VLBI) has significant advantages in disentangling active galactic nuclei (AGN) from star formation, particularly at intermediate to high-redshift due to its high angular resolution and insensitivity to dust. Surveys using VLBI arrays are only just becoming practical over wide areas with numerous developments and innovations (such as multi-phase centre techniques) in observation and data analysis techniques. However, fully automated pipelines for VLBI data analysis are based on old software packages and are unable to incorporate new calibration and imaging algorithms. In this work, the researcher developed a pipeline for VLBI data analysis which integrates a recent wide-field imaging algorithm, RFI excision, and a purpose-built source finding algorithm specifically developed for the 64kx64k wide-field VLBI images. The researcher used this novel pipeline to process 6% (~ 9 arcmin2 of the total 160 arcmin2) of the data from the CANDELS GOODS- North extragalactic field at 1.6 GHz. The milli-arcsec scale images have an average rms of a ~ 10 uJy/beam. Forty four (44) candidate sources were detected, most of which are at sub-mJy flux densities, having brightness temperatures and luminosities of >5x105 K and >6x1021 W Hz-1 respectively. This work demonstrates that automated post-processing pipelines for wide-field, uniform sensitivity VLBI surveys are feasible and indeed made more efficient with new software, wide-field imaging algorithms and more purpose-built source- finders. This broadens the discovery space for future wide-field surveys with upcoming arrays such as the African VLBI Network (AVN), MeerKAT and the Square Kilometre Array (SKA).
- Full Text:
- Date Issued: 2019
- Authors: Akoto-Danso, Alexander
- Date: 2019
- Subjects: Radio astronomy , Very long baseline interferometry , Radio interometers , Imaging systems in astronomy , Hubble Space Telescope (Spacecraft) -- Observations
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/72296 , vital:30027
- Description: Very Long Baseline Interferometry (VLBI) has significant advantages in disentangling active galactic nuclei (AGN) from star formation, particularly at intermediate to high-redshift due to its high angular resolution and insensitivity to dust. Surveys using VLBI arrays are only just becoming practical over wide areas with numerous developments and innovations (such as multi-phase centre techniques) in observation and data analysis techniques. However, fully automated pipelines for VLBI data analysis are based on old software packages and are unable to incorporate new calibration and imaging algorithms. In this work, the researcher developed a pipeline for VLBI data analysis which integrates a recent wide-field imaging algorithm, RFI excision, and a purpose-built source finding algorithm specifically developed for the 64kx64k wide-field VLBI images. The researcher used this novel pipeline to process 6% (~ 9 arcmin2 of the total 160 arcmin2) of the data from the CANDELS GOODS- North extragalactic field at 1.6 GHz. The milli-arcsec scale images have an average rms of a ~ 10 uJy/beam. Forty four (44) candidate sources were detected, most of which are at sub-mJy flux densities, having brightness temperatures and luminosities of >5x105 K and >6x1021 W Hz-1 respectively. This work demonstrates that automated post-processing pipelines for wide-field, uniform sensitivity VLBI surveys are feasible and indeed made more efficient with new software, wide-field imaging algorithms and more purpose-built source- finders. This broadens the discovery space for future wide-field surveys with upcoming arrays such as the African VLBI Network (AVN), MeerKAT and the Square Kilometre Array (SKA).
- Full Text:
- Date Issued: 2019
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