Exploring the Morphologies of High-Redshift Galaxies with Deep Learning
This thesis explores different machine learning techniques for the study of galaxy morphology, morphological classification, and the morphological evolution of galaxies. We utilise data from all of the CANDELS fields imaged with HST, as well as data from the CEERS program imaged with JWST. In Cha...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2024
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| Online Access: | https://eprints.nottingham.ac.uk/79446/ |