Khalfan, Alshuaili Ishaq Yahya (2024) Exploring The Synergy Of Template And Machine Learning Methods To Improve Photometric Redshifts. PhD thesis, Universiti Sains Malaysia.
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Abstract
This thesis explores the use of both template-based and machine learning methods to improve the accuracy of galaxy photometric redshift estimation. The first method involves using template fitting to model the spectral energy distribution of a galaxy and estimate its redshift. The second method uses machine learning algorithms to learn the relationship between a galaxy’s photometric properties and its redshift, based on a training set of spectroscopic redshift measurements. This thesis also aims to investigates the potential synergy between these two methods by combining them in various ways and comparing the results to those obtained using each method individually.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QC Physics > QC1-999 Physics |
| Divisions: | Pusat Pengajian Sains Fizik (School of Physics) > Thesis |
| Depositing User: | Mr Aizat Asmawi Abdul Rahim |
| Date Deposited: | 26 Feb 2026 07:10 |
| Last Modified: | 26 Feb 2026 07:10 |
| URI: | http://eprints.usm.my/id/eprint/63672 |
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