Discovery and classification of compact radio sources in the MeerKAT Galactic Centre data
- Authors: Rammala, Isabella Dineo
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/432218 , vital:72852
- Description: Access restricted. Expected release date in 2025. , Thesis (PhD) -- Faculty of Science, Physics and Electronics, 2023
- Full Text:
- Date Issued: 2023-10-13
- Authors: Rammala, Isabella Dineo
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Doctoral theses , text
- Identifier: http://hdl.handle.net/10962/432218 , vital:72852
- Description: Access restricted. Expected release date in 2025. , Thesis (PhD) -- Faculty of Science, Physics and Electronics, 2023
- Full Text:
- Date Issued: 2023-10-13
M3: Mining Mini-Halos with MeerKAT
- Authors: Trehaeven, Keegan Somerset
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424754 , vital:72181
- Description: This work aims to showcase the MeerKAT telescope’s capabilities and related calibration and imaging software in studying the emission of radio mini-halos. These diffuse radio synchrotron sources surround a Brightest Cluster Galaxy (BCG) in relatively relaxed clusters out to a few 100 kpc in size. They are difficult to image because of their relatively low surface brightness and small angular size. Hence, they could not be studied in great detail by previous generations of radio telescopes and much about their nature, particularly the exact production mechanism, is not yet fully understood. Thus, for the first time, MeerKAT observed a sample of five galaxy clusters to investigate the central radio mini-halo in each. Studying these sources requires the deepest images generated from the data and the effective subtraction of any projected sources obscuring or contaminating the underlying diffuse emission. Therefore, I describe the data reduction used to create third-generation calibrated, primary beam corrected, point source subtracted Stokes I L-band continuum images of these clusters. For first- and second-generation calibration, I use the CARACal pipeline, which implements software optimised explicitly for MeerKAT data. For third-generation calibration, I use the faceted approach of killMS and DDFacet, and then I perform visibility-plane point source subtraction to disentangle the compact and diffuse emissions. I then measured the size, flux density, in-band spectral properties, and radio power of the central mini-halos. I present the first new mini-halo detection by MeerKAT (MACS J2140.2-2339, Trehaeven et al. accepted), the first spectral index maps of these mini-halos, which show very interesting distributions, and a ∼100 kpc II southern extension to the ACO 3444 mini-halo previously unseen in archival VLA data. Thereafter, I present a multi-wavelength case study for two complementary mini-halos from our sample and show via a radio-to-X-ray spatial correlation test that they might be caused by different particle (re)-acceleration mechanisms. Through these initial science results, I have shown that future observations of radio mini-halos with MeerKAT are an exciting prospect that can lead to a better understanding of the fundamental physics behind these sources. , Thesis (MSc) -- Faculty of Science, Physics and Electronics, 2023
- Full Text:
- Date Issued: 2023-10-13
- Authors: Trehaeven, Keegan Somerset
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424754 , vital:72181
- Description: This work aims to showcase the MeerKAT telescope’s capabilities and related calibration and imaging software in studying the emission of radio mini-halos. These diffuse radio synchrotron sources surround a Brightest Cluster Galaxy (BCG) in relatively relaxed clusters out to a few 100 kpc in size. They are difficult to image because of their relatively low surface brightness and small angular size. Hence, they could not be studied in great detail by previous generations of radio telescopes and much about their nature, particularly the exact production mechanism, is not yet fully understood. Thus, for the first time, MeerKAT observed a sample of five galaxy clusters to investigate the central radio mini-halo in each. Studying these sources requires the deepest images generated from the data and the effective subtraction of any projected sources obscuring or contaminating the underlying diffuse emission. Therefore, I describe the data reduction used to create third-generation calibrated, primary beam corrected, point source subtracted Stokes I L-band continuum images of these clusters. For first- and second-generation calibration, I use the CARACal pipeline, which implements software optimised explicitly for MeerKAT data. For third-generation calibration, I use the faceted approach of killMS and DDFacet, and then I perform visibility-plane point source subtraction to disentangle the compact and diffuse emissions. I then measured the size, flux density, in-band spectral properties, and radio power of the central mini-halos. I present the first new mini-halo detection by MeerKAT (MACS J2140.2-2339, Trehaeven et al. accepted), the first spectral index maps of these mini-halos, which show very interesting distributions, and a ∼100 kpc II southern extension to the ACO 3444 mini-halo previously unseen in archival VLA data. Thereafter, I present a multi-wavelength case study for two complementary mini-halos from our sample and show via a radio-to-X-ray spatial correlation test that they might be caused by different particle (re)-acceleration mechanisms. Through these initial science results, I have shown that future observations of radio mini-halos with MeerKAT are an exciting prospect that can lead to a better understanding of the fundamental physics behind these sources. , Thesis (MSc) -- Faculty of Science, Physics and Electronics, 2023
- Full Text:
- Date Issued: 2023-10-13
Semantic segmentation of astronomical radio images: a computer vision approach
- Authors: Kupa, Ramadimetse Sydil
- Date: 2023-03-29
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/422378 , vital:71937
- Description: The new generation of radio telescopes, such as the MeerKAT, ASKAP (Australian Square Kilometre Array Pathfinder) and the future Square Kilometre Array (SKA), are expected to produce vast amounts of data and images in the petabyte region. Therefore, the amount of incoming data at a specific point in time will overwhelm any current traditional data analysis method being deployed. Deep learning architectures have been applied in many fields, such as, in computer vision, machine vision, natural language processing, social network filtering, speech recognition, machine translation, bioinformatics, medical image analysis, and board game programs. They have produced results which are comparable to human expert performance. Hence, it is appealing to apply it to radio astronomy data. Image segmentation is one such area where deep learning techniques are prominent. The images from the new generation of telescopes have a high density of radio sources, making it difficult to classify the sources in the image. Identifying and segmenting sources from radio images is a pre-processing step that occurs before sources are put into different classes. There is thus a need for automatic segmentation of the sources from the images before they can be classified. This work uses the Unet architecture (originally developed for biomedical image segmentation) to segment radio sources from radio astronomical images with 99.8% accuracy. After segmenting the sources we use OpenCV tools to detect the sources on the mask images, then the detection is translated to the original image where borders are drawn around each detected source. This process automates and simplifies the pre-processing of images for classification tools and any other post-processing tool that requires a specific source as an input. , Thesis (MSc) -- Faculty of Science, Physics and Electronics, 2023
- Full Text:
- Date Issued: 2023-03-29
- Authors: Kupa, Ramadimetse Sydil
- Date: 2023-03-29
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/422378 , vital:71937
- Description: The new generation of radio telescopes, such as the MeerKAT, ASKAP (Australian Square Kilometre Array Pathfinder) and the future Square Kilometre Array (SKA), are expected to produce vast amounts of data and images in the petabyte region. Therefore, the amount of incoming data at a specific point in time will overwhelm any current traditional data analysis method being deployed. Deep learning architectures have been applied in many fields, such as, in computer vision, machine vision, natural language processing, social network filtering, speech recognition, machine translation, bioinformatics, medical image analysis, and board game programs. They have produced results which are comparable to human expert performance. Hence, it is appealing to apply it to radio astronomy data. Image segmentation is one such area where deep learning techniques are prominent. The images from the new generation of telescopes have a high density of radio sources, making it difficult to classify the sources in the image. Identifying and segmenting sources from radio images is a pre-processing step that occurs before sources are put into different classes. There is thus a need for automatic segmentation of the sources from the images before they can be classified. This work uses the Unet architecture (originally developed for biomedical image segmentation) to segment radio sources from radio astronomical images with 99.8% accuracy. After segmenting the sources we use OpenCV tools to detect the sources on the mask images, then the detection is translated to the original image where borders are drawn around each detected source. This process automates and simplifies the pre-processing of images for classification tools and any other post-processing tool that requires a specific source as an input. , Thesis (MSc) -- Faculty of Science, Physics and Electronics, 2023
- Full Text:
- Date Issued: 2023-03-29
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