Using Machine Learning to Estimate the Photometric Redshift of Galaxies
| dc.contributor.author | Salim, Shayaan | |
| dc.contributor.co-supervisor | Bau, Hairong | |
| dc.contributor.supervisor | Komin, Nukri | |
| dc.date.accessioned | 2024-10-18T16:49:02Z | |
| dc.date.available | 2024-10-18T16:49:02Z | |
| dc.date.issued | 2023-08 | |
| dc.description | A research report submitted in partial fulfillment of the requirements for the degree of Master of Science (Artificial Intelligence), to the Faculty of Science, the School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023. | |
| dc.description.abstract | Machine learning has emerged as a crucial tool in the field of cosmology and astrophysics, leading to extensive research in this area. This research study aims to utilize machine learning models to estimate the redshift of galaxies, with a primary focus on utilizing photometric data to obtain accurate results. Five machine learning algorithms, including XGBoost, Random Forests, K-nearest neighbors, Artificial Neural Networks, and Polynomial Regression, are employed to estimate the redshifts, trained on photometric data derived from the Sloan Digital Sky Survey (SDSS) Data Release 17 database. Furthermore, various input parameters from the SDSS database are explored to achieve the most accurate redshift values. The research incorporates a comparative analysis, utilizing different evaluation metrics and statistical tests to determine the best-performing algorithm. The results indicate that the XGBoost algorithm achieves the highest accuracy, with an R2 value of 0.94, a Root Mean Square Error (RMSE) of 0.03, and a Mean Absolute Average Percentage (MAPE) of 12.04% when trained on the optimal feature subset. In comparison, the base model achieved an R2 of 0.84, a RMSE of 0.05, and a MAPE of 20.89%. The study contributes to the existing literature by utilizing photometric data during model training and comparing different high-performing algorithms from the literature. | |
| dc.description.submitter | MM2024 | |
| dc.faculty | Faculty of Science | |
| dc.identifier | 0000-0001-5733-9134 | |
| dc.identifier.citation | Salim, Shayaan. (2023). Using Machine Learning to Estimate the Photometric Redshift of Galaxies. [Master's dissertation, University of the Witwatersrand, Johannesburg]. https://hdl.handle.net/10539/41702 | |
| dc.identifier.uri | https://hdl.handle.net/10539/41702 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | ©2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Computer Science and Applied Mathematics | |
| dc.subject | Machine Learning | |
| dc.subject | Genetic Algorithms | |
| dc.subject | Photometric Redshift Estimation | |
| dc.subject | Astrophysics | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-9: Industry, innovation and infrastructure | |
| dc.title | Using Machine Learning to Estimate the Photometric Redshift of Galaxies | |
| dc.type | Dissertation |