Applied machine learning for radio galaxy classification and anomalous source detection
| dc.contributor.advisor | Grobler, T. L. | en_ZA |
| dc.contributor.advisor | Kleynhans, W. | en_ZA |
| dc.contributor.author | Brand, Kevin | En_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Science. Dept. of Computer Science. | en_ZA |
| dc.date.accessioned | 2025-01-28T10:38:25Z | |
| dc.date.available | 2025-01-28T10:38:25Z | |
| dc.date.issued | 2024-12 | |
| dc.description | Thesis (MSc)--Stellenbosch University, 2024. | en_ZA |
| dc.description.abstract | The classification of radio sources and the identification of anomalous sources play a vital role in the development of the understanding regarding various cosmological processes, such as the formation and evolution of galaxies and how they interact with their local environments. As the new generation of radio telescopes — such as the square kilometre array (SKA) — come online, a massive influx is expected with respect to the number of observations of radio sources that will be generated. This increase makes the manual evaluation and classification of radio sources by experts infeasible. Approaches have been considered that enable the general public to assist with these classifications. However, it is not clear whether these approaches will be able to keep up with the growing rates at which radio telescopes produce observations. Thus, a growing body of literature is investigating whether these tasks can be automated by applying machine learning models instead. In this thesis we extended the work conducted in the literature by further investigating the automation of morphological classification and anomalous source detection. We investigated two adaptations when applying convolutional neural networks (CNNs) to morphological classification, as these models have been shown to be particularly useful in this regard. We investigated the impact of standardising source orientation prior to CNN training and found that it leads to improvements in classification performance. However — apart from faster training times — it provided no benefits when compared to rotational augmentation, with rotational augmentation leading to better classification results. | en_ZA |
| dc.description.version | Masters | en_ZA |
| dc.format.extent | 247 pages : ill. | |
| dc.identifier.uri | https://scholar.sun.ac.za/handle/10019.1/131591 | |
| dc.language.iso | en | en_ZA |
| dc.publisher | Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.title | Applied machine learning for radio galaxy classification and anomalous source detection | en_ZA |
| dc.type | Thesis | en_ZA |