[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121863-en":3,"doc-seo-121863-105":30,"detail-sidebar-cat-0-en-105":94},{"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":4,"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},121863,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning for radio galaxy morphology analysis","This doctoral thesis, authored by Rafaël Inayat Jacobus Mostert, explores the application of machine learning techniques to the analysis of radio galaxy morphology. The research focuses on developing and implementing computational models to classify and understand the diverse shapes and structures of radio galaxies, a crucial aspect of extragalactic astronomy and cosmology. The study delves into various machine learning algorithms and their effectiveness in processing large datasets of radio telescope observations. By leveraging these advanced analytical tools, the thesis aims to contribute to a deeper understanding of galaxy evolution, the processes driving the formation of large-scale structures in the universe, and the physical mechanisms responsible for the observed morphologies. The work is situated within the broader field of astrophysical research, utilizing cutting-edge computational methods to address complex scientific questions in the study of cosmic objects and their development over time. This research is part of a larger academic endeavor conducted at the University of Leiden, contributing to the growing body of knowledge at the intersection of astrophysics and artificial intelligence.","Machine learning for radio galaxy morphology analysis  \nMostert, R.I.J.  \nCitation  \nMostert, R. I. J. (2024, January 25). Machine learning for radio galaxy morphology analysis. Retrieved from [https://hdl.handle.net/1887/3715061](https://hdl.handle.net/1887/3715061)  \nVersion: Publisher's Version  \nLicense:  Licence agreement concerning inclusion of doctoral thesis in the  \nInstitutional Repository of the University of Leiden  \nDownloaded  \n[https://hdl.handle.net/1887/3715061](https://hdl.handle.net/1887/3715061)  \nfrom:  \nNote: To cite this publication please use the final published version (if applicable) .  \nMachine learning for radio galaxy morphology analysis  \nRafaël Inayat Jacobus Mostert","cbCaieF6rFaoEic4","https://ap.wps.com/l/cbCaieF6rFaoEic4","pdf",9189399,1,2,"English","en",105,"# Machine learning for radio galaxy morphology analysis","[{\"question\":\"What is the primary focus of this doctoral thesis?\",\"answer\":\"The primary focus of this doctoral thesis is the application of machine learning techniques to analyze the morphology of radio galaxies.\"},{\"question\":\"Who is the author of this thesis?\",\"answer\":\"The author of this 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