[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123109-en":3,"doc-seo-123109-105":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},123109,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Classification of Extragalactic X-Ray Sources Through the Use of a Machine Learning Pipeline","This thesis presents a machine-learning pipeline for classifying extragalactic X-ray sources, using a structured workflow from data preparation to model verification. The work reviews key X-ray discovery background and emission mechanisms, then details machine-learning methods, model-improvement practices, and evaluation via confusion matrices. It describes dataset construction and transformations, including modifications to a galactic training dataset and application of SMOTE for oversampling, followed by feature selection and hyperparameter tuning to validate performance.","CLASSIFICATION OF EXTRAGALACTIC X-RAY  \nSOURCES THROUGH THE USE OF A  \nMACHINE LEARNING PIPELINE  \nby  \nNicholas Moore, BSc  \nA thesis submitted to the Graduate Council of Texas State University in partial fulfillment of the requirements for the degree of Master of Science  \nwith a Major in Physics  \nAugust 2024  \nCommittee Members:  \nBlagoy Rangelov, Chair  \nChristopher Bruner  \nAditya Togi  \nCOPYRIGHT  \nby Nicholas Moore  \n2024  \nACKNOWLEDGMENTS  \nI would like to express my gratitude to my advisor, Dr. Blagoy Rangelov, for his assistance and guidance during my time as a Masters student. In addition, I would like to thank the rest of my thesis committee, Dr. Christopher Bruner and Dr. Aditya Togi, for their time and comments.  \nTABLE OF CONTENTS  \nPage  \nLIST OF TABLES ...........................................................................................................................v  \n[LIST OF FIGURES ....................................................................................................................... vi](LIST OF FIGURES ....................................................................................................................... vi)  \n[LIST OF ABBREVIATIONS...............................](LIST OF ABBREVIATIONS...............................)........................................................................ vii  \nABSTRACT................................................................................................................................. viii  \nCHAPTER  \n1. INTRODUCTION ........................................................................................................... 1  \n1.1 The discovery of X-rays..................................................................................... 1  \n1.2 Properties of X-rays ........................................................................................... 1  \n1.3 Early X-ray Discoveries.....................................................................................2  \n1.4 X-ray Emission Processes ..................................................................................4  \n1.5 Sources of X-rays...............................................................................................8  \n2. METHODS IN MACHINE LEARNING ......................................................................13  \n2.1 Types of Machine Learning Methods ..............................................................13  \n2.2 Machine Learning Process and Steps ..............................................................16  \n2.3 Improving the Model ....................................................................................... 19  \n2.4 The Confusion Matrix ......................................................................................21  \n2.5 The Random Forest Classifier .........................................................................22  \n3. THE MODEL.................................................................................................................27  \n3. 1 Existing Training Dataset ................................................................................28  \n3.2 Modifying the Galactic Training Dataset for Extragalactic Applications .......29  \n3.3 Magnitude Transformation ..............................................................................30  \n3.4 The SMOTE Package and Oversampling ........................................................31  \n3.5 Feature Selection..............................................................................................32  \n3.6 Tuning the Hyperparameters............................................................................36  \n3.7 Verifying the Model.........................................................................................37  \n3.8 Conclusions ......................................................................................................38  \n4. CONCLUSION AND FUTURE WORK ...........................................","cbCaicuUxQCfXbVy","https://ap.wps.com/l/cbCaicuUxQCfXbVy","pdf",942710,1,52,"English","en",105,"# Chapter 1. Introduction\n## The discovery of X-rays\n## Properties of X-rays\n## Early X-ray Discoveries\n## X-ray Emission Processes\n## Sources of X-rays\n# Chapter 2. Methods in Machine Learning\n## Types of Machine Learning Methods\n## Machine Learning Process and Steps\n## Improving the Model\n## The Confusion Matrix\n## The Random Forest Classifier\n# Chapter 3. The Model\n## Existing Training Dataset\n## Modifying the Galactic Training Dataset for Extragalactic Applications\n## Magnitude Transformation\n## The SMOTE Package and Oversampling\n## Feature Selection\n## Tuning the Hyperparameters\n## Verifying the Model\n## Conclusions\n# Chapter 4. Conclusion and Future Work","[{\"question\":\"What is the goal of the machine learning pipeline in this thesis?\",\"answer\":\"To classify extragalactic X-ray sources by training a model on prepared and transformed datasets, then validating its performance using standard evaluation tools.\"},{\"question\":\"Which machine learning evaluation method is used to judge model results?\",\"answer\":\"The confusion matrix is used to evaluate the trained model’s predictions on the validation set.\"},{\"question\":\"How is class imbalance addressed during training?\",\"answer\":\"The thesis uses the SMOTE package with oversampling to help balance classes in the training data.\"}]","Classification of Extragalactic X-Ray Sources Through the Use of a Machine Learning Pipeline | PDF",1785814682,131,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"classification-of-extragalactic-x-ray-sources-through-the-use-of-a-machine-learning-pipeline","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/classification-of-extragalactic-x-ray-sources-through-the-use-of-a-machine-learning-pipeline/123109/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the goal of the machine learning pipeline in this thesis?","Question",{"text":76,"@type":77},"To classify extragalactic X-ray sources by training a model on prepared and transformed datasets, then validating its performance using standard evaluation tools.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning evaluation method is used to judge model results?",{"text":81,"@type":77},"The confusion matrix is used to evaluate the trained model’s predictions on the validation set.",{"name":83,"@type":74,"acceptedAnswer":84},"How is class imbalance addressed during training?",{"text":85,"@type":77},"The thesis uses the SMOTE package with oversampling to help balance classes in the training data.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]