Enabling scientific discovery in astronomical data with machine learning
| dc.contributor.author | Etsebeth, Verlon | |
| dc.date.accessioned | 2025-12-05T09:18:50Z | |
| dc.date.available | 2025-12-05T09:18:50Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | The history of astronomy is defined by observational milestones that have profoundly transformed the understanding of the universe. Galileo’s pioneering use of the telescope allowed for detailed observations of celestial objects, such as the moons of Jupiter, providing empirical support for the then controversial heliocentric model (Galilei, 1610). Later advancements, including the development of space-based observatories like the Hubble Space Telescope (HST, Neal, 1990) and Karl Jansky’s groundbreaking discovery of radio waves from the Milky Way (Jansky, 1933), laid the foundation for modern astronomy. Astronomy has always been a data-driven science, but recent technological advances have brought about unprecedented volumes of data. Large-scale surveys and modern telescopes, such as the Sloan Digital Sky Survey (SDSS, York et al., 2000) and the Dark Energy Camera Legacy Survey (DECaLS, Dey et al., 2019) already produced terabytes of data. Future facilities will produce an even greater flow of data. The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST, Ivezi´c et al., 2019) is projected to capture approximately 20 TB per night. Similarly, the Square Kilometre Array (SKA, Dewdney et al., 2009) is expected to generate up to 700 PB of data annually. In addition, Euclid (Scaramella et al., 2022) has begun delivering data and is expected to contribute significantly to the wealth of astronomical data and aims to map the geometry of the dark Universe with unprecedented accuracy (Tutusaus et al., 2023). | |
| dc.identifier.citation | N/A | |
| dc.identifier.uri | https://hdl.handle.net/10566/21521 | |
| dc.language.iso | en | |
| dc.publisher | University of the Western Cape | |
| dc.relation.ispartofseries | N/A | |
| dc.subject | Radio waves | |
| dc.subject | Microwaves | |
| dc.subject | Infrared radiation | |
| dc.subject | Visible light | |
| dc.subject | Ultraviolet (UV) radiation | |
| dc.title | Enabling scientific discovery in astronomical data with machine learning | |
| dc.type | Thesis |