Publication:
Exploring The Synergy Of Template And Machine Learning Methods To Improve Photometric Redshifts

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dc.contributor.authorKhalfan, Alshuaili Ishaq Yahya
dc.date.accessioned2026-02-26T07:01:43Z
dc.date.available2026-02-26T07:01:43Z
dc.date.issued2024-10
dc.description.abstractThis 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. ( Password P-SD0060/21(R )
dc.identifier.urihttps://erepo.usm.my/handle/123456789/23688
dc.language.isoen
dc.subjectPhotometry
dc.titleExploring The Synergy Of Template And Machine Learning Methods To Improve Photometric Redshifts
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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