Mohale, Koketso2024-11-052024-11-052023https://hdl.handle.net/10566/17331This thesis presents work motivated by the belief that the next generation of discoveries in the field of astronomy will be made by the marriage of advanced data analysis algorithms in the form of unsupervised learning techniques, and the unprecedented volumes and complexities of data from the next generation of surveys. For several years, computers have been governed by Moore’s law, which posited that computing power would double every two years. The consequence was that computing has also become increasingly cost-effective, which has been a driving force in the ability to generate and analyse large volumes of datasets. These include machine learning advances like the use of deep learning and scalable techniques such as self-supervised learning which have been revolutionising areas of research, for example, natural language processing and computer vision. Similarly, astronomy is also met with a rapid growth in the availability of large datasets. Morden sky observing instruments such as the radio telescope MeerKAT and the optical telescope Blanco (which was used for the Dark Energy Survey) are already producing data volumes at unprecedented scales. The next generation of instruments like the Square Kilometre Array (SKA) and the Vera C. Rubin Observatory are expected to produce orders of magnitude more astronomical data at higher resolution and sensitivity. Ongoing efforts in the form of surveys and data analysis techniques in astronomy are motivated in part by outstanding questions in galaxy evolution and cosmology as well as the potential to discover new unknown phenomena.enMachine LearningMoore’s lawMeerKATSquare Kilometre Array (SKA)AstronomyThesis