[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118521-en":3,"doc-seo-118521-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118521,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Using Machine Learning to Estimate the Photometric Redshift of Galaxies","Machine learning has become a key method in cosmology and astrophysics for inferring galaxy properties from observations. This study estimates galaxy redshift using photometric data, applying five algorithms—XGBoost, Random Forests, K-nearest neighbors, Artificial Neural Networks, and Polynomial Regression—trained on photometric inputs from the Sloan Digital Sky Survey (SDSS) Data Release 17. The work evaluates multiple feature subsets and parameters from SDSS, then compares models through evaluation metrics and statistical tests. Results show XGBoost achieves the best accuracy, with R2 = 0.94, RMSE = 0.03, and MAPE = 12.04% on the optimal subset, outperforming a base model (R2 = 0.84, RMSE = 0.05, MAPE = 20.89%).","Using Machine Learning to Estimate the Photometric Redshift of Galaxies  \nShayaan Salim  \nSupervisor(s):  \nNukri Komin, Hairong Bau  \nA research report submitted in partial fulfillment of the requirements for the degree of Master of Science in Artificial Intelligence  \nin the  \nSchool of Computer Science and Applied Mathematics University of the Witwatersrand, Johannesburg  \n1 August 2023  \ni  \nDeclaration  \nI, Shayaan Salim, declare that this research report is my own, unaided work. It is being submitted for the degree of Master of Science in Artificial Intelligence at the University of the Witwatersrand, Johannesburg. It has not been submitted for any degree or examination at any other university.  \nShayaan Salim 1 August 2023  \nii  \nAbstract  \nMachine 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.  \niii  \nAcknowledgements  \nThe author gratefully acknowledges the efforts of Dr. Helen Robertson, that provided detailed guidance through the creation of lecture material and consultations. The author would also like to express gratitude to Prof. Nukri Komin and Dr. Hairong Bau for supervising the project and Prof. Terence Van Zyl for assisting with the template used for this report.  \niv  \nContents  \nDeclaration i  \nAbstract ii  \nAcknowledgements iii  \nList of Figures vi  \nList of Tables vii  \n1 Introduction 1  \n1.1 Literature review .............................. 3  \n1.2 Problem Statement ............................. 6  \n1.3 Research Aims and Objectives ....................... 7  \n1.3.1 Research Aims ........................... 7  \n1.3.2 Objectives .............................. 7  \n1.4 Limitations .................................. 8  \n1.5 Overview ................................... 8  \n2 Research Methodology 10  \n2.1 Available Methodologies .......................... 10  \n2.2 Adopted Approach ............................. 12  \n2.2.1 Machine Learning Models ..................... 12  \nRandom Forests ........................... 12  \nXGBoost ............................... 14  \nANN ................................. 16  \nKNN ................................. 18  \nPolynomial Regression ....................... 20  \nGenetic Algorithms ......................... 22  \nv  \n2.2.2 Data Collection and Data Pre-Processing ............ 23  \n2.2.3 Evaluation Metric .......................... 27  \n2.2.4 Summary of Packages Used .................... 29  \n2.2.5 Ethical Considerations ....................... 29  \n2.2.6 Overview of Implementation Process .............. 30  \n3 Results and Analysis 31  \n3.1 Results and Analysis ............................ 31  \n3.1.1 Data Analysis ............................ 31  \n3.1.2 Hyperparameter Tuning .","cbCailnU6aRMLzZz","https://ap.wps.com/l/cbCailnU6aRMLzZz","pdf",1392949,1,71,"English","en",105,"# Introduction\n## Literature review\n## Problem Statement\n## Research Aims and Objectives\n## Limitations\n## Overview\n# Research Methodology\n## Available Methodologies\n## Adopted Approach\n### Machine Learning Models\n### Data Collection and Data Pre-Processing\n### Evaluation Metric\n### Summary of Packages Used\n### Ethical Considerations\n### Overview of Implementation Process\n# Results and Analysis\n## Results and Analysis\n## Summary\n# Conclusion\n# Appendix","[{\"question\":\"What is the goal of this research study?\",\"answer\":\"The study aims to estimate the redshift of galaxies using machine learning, emphasizing photometric data to produce accurate predictions.\"},{\"question\":\"Which machine learning algorithms are used for redshift estimation?\",\"answer\":\"The research employs XGBoost, Random Forests, K-nearest neighbors, Artificial Neural Networks, and Polynomial Regression, with additional genetic algorithms for optimization.\"},{\"question\":\"How does the best model perform compared with the base model?\",\"answer\":\"When trained on the optimal feature subset, XGBoost reaches R2 = 0.94, RMSE = 0.03, and MAPE = 12.04%, compared with the base model’s R2 = 0.84, RMSE = 0.05, and MAPE = 20.89%.\"}]","Using Machine Learning to Estimate the Photometric Redshift of Galaxies | 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is the goal of this research study?","Question",{"text":76,"@type":77},"The study aims to estimate the redshift of galaxies using machine learning, emphasizing photometric data to produce accurate predictions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are used for redshift estimation?",{"text":81,"@type":77},"The research employs XGBoost, Random Forests, K-nearest neighbors, Artificial Neural Networks, and Polynomial Regression, with additional genetic algorithms for optimization.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the best model perform compared with the base model?",{"text":85,"@type":77},"When trained on the optimal feature subset, XGBoost reaches R2 = 0.94, RMSE = 0.03, and MAPE = 12.04%, compared with the base model’s R2 = 0.84, RMSE = 0.05, and MAPE = 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