KalCal: a novel calibration framework for radio interferometry using the Kalman Filter and Smoother
- Authors: Welman, Brian Allister
- Date: 2024-10-11
- Subjects: Radio interferometers , Calibration , Kalman filtering , Bayesian inference , Signal processing , Radio astronomy , MeerKAT
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/467127 , vital:76818
- Description: Calibration in radio interferometry is essential for correcting measurement errors. Traditional methods employ maximum likelihood techniques and non-linear least squares solvers but face challenges due to the data volumes and increased noise sensitivity of contemporary instruments such as MeerKAT. A common approach for mitigating these issues is using “solution intervals”, which helps manage the data volume and reduces overfitting. However, inappropriate interval sizes can degrade calibration quality, and determining optimal sizes is challenging, often relying on brute-force methods. This study introduces Kalman Filtering and Smoothing in Calibration (KalCal), a new framework for calibration that combines the Kalman Filter, Kalman Smoother, and the energy function: the negative logarithm of the Bayesian evidence. KalCal offers Bayesian-optimal solutions as probability densities and models calibration effects with lower computational requirements than iterative approaches. Unlike traditional methods, which require all the data for a particular solution to be in memory simultaneously, KalCal’s recursive computations only need a single pass through the data with appropriate prior information. The energy function provides the means for KalCal to determine this prior information. Theoretical contributions include additions to complex optimisation literature and the “Kalman-Woodbury Identity” that reformulates the traditional Kalman Filter. A Python implementation of the KalCal framework was benchmarked against solution intervals as implemented in the QuartiCal package. Simulations show KalCal matching solution intervals in high Signal-to-Noise Ratio (SNR) scenarios and surpassing them in low SNR conditions. Moreover, the energy function produced minima that coincide with KalCal’s Mean Square Error (MSE) on the true gain signal. This result is significant as the MSE is unavailable in real applications. Further research is needed to assess the computational feasibility and intricacies of KalCal. , Thesis (MSc) -- Faculty of Science, Physics and Electronics, 2024
- Full Text:
- Date Issued: 2024-10-11
- Authors: Welman, Brian Allister
- Date: 2024-10-11
- Subjects: Radio interferometers , Calibration , Kalman filtering , Bayesian inference , Signal processing , Radio astronomy , MeerKAT
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/467127 , vital:76818
- Description: Calibration in radio interferometry is essential for correcting measurement errors. Traditional methods employ maximum likelihood techniques and non-linear least squares solvers but face challenges due to the data volumes and increased noise sensitivity of contemporary instruments such as MeerKAT. A common approach for mitigating these issues is using “solution intervals”, which helps manage the data volume and reduces overfitting. However, inappropriate interval sizes can degrade calibration quality, and determining optimal sizes is challenging, often relying on brute-force methods. This study introduces Kalman Filtering and Smoothing in Calibration (KalCal), a new framework for calibration that combines the Kalman Filter, Kalman Smoother, and the energy function: the negative logarithm of the Bayesian evidence. KalCal offers Bayesian-optimal solutions as probability densities and models calibration effects with lower computational requirements than iterative approaches. Unlike traditional methods, which require all the data for a particular solution to be in memory simultaneously, KalCal’s recursive computations only need a single pass through the data with appropriate prior information. The energy function provides the means for KalCal to determine this prior information. Theoretical contributions include additions to complex optimisation literature and the “Kalman-Woodbury Identity” that reformulates the traditional Kalman Filter. A Python implementation of the KalCal framework was benchmarked against solution intervals as implemented in the QuartiCal package. Simulations show KalCal matching solution intervals in high Signal-to-Noise Ratio (SNR) scenarios and surpassing them in low SNR conditions. Moreover, the energy function produced minima that coincide with KalCal’s Mean Square Error (MSE) on the true gain signal. This result is significant as the MSE is unavailable in real applications. Further research is needed to assess the computational feasibility and intricacies of KalCal. , Thesis (MSc) -- Faculty of Science, Physics and Electronics, 2024
- Full Text:
- Date Issued: 2024-10-11
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
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