[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118571-en":3,"doc-seo-118571-105":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},118571,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Precision Machine Learning for the LHC Simulation Chain - Dissertation","The LHC simulation chain forms a proven and indispensable toolkit for precision measurements at the general-purpose detectors at the LHC. While individual components may not all originate from first-principle physics, the overall chain has shown strong accuracy and reliability over the past decade. Growing data statistics and rising experimental demands motivate continuous refinement. This work investigates how state-of-the-art generative machine learning can improve both forward simulation speed and unfolding of detector effects, demonstrating high-precision, high-accuracy solutions that enhance scalability and robustness for future LHC analyses.","Precision Machine Learning for the LHC Simulation Chain  \nDissertation  \nSofia Palacios Schweitzer  \nDissertation  \nsubmitted to the  \nCombined Faculty of Mathematics, Engineering and Natural Sciences of Heidelberg University, Germany  \nfor the degree of  \nDoctor of Natural Sciences  \nPut forward by  \nSofia Palacios Schweitzer  \nborn in Berlin, Germany  \nOral examination: 02.07.2025  \nPrecision Machine Learning for the LHC Simulation Chain  \nReferees: Dr. Anja Butter  \nProf. Dr. Björn Malte Schäfer  \nAbstract  \nThe simulation chain of LHC physics is a well-established and indispensable toolkit for conducting precision measurements at general-purpose detectors at the LHC. While not all components are derived from first-principle physics, the simulation chain as a whole has demonstrated remarkable accuracy and reliability over the past decade. To keep pace with increasing experimental demands and growing data statistics, the simulation chain undergoes continuous refinement. In recent years, the rise of machine learning has opened new avenues for further advancing the simulation and analysis pipeline of high energy physics. In this work, we explore the integration of state-of-the-art generative machine learning algorithms into different stages of the LHC simulation chain. First, we test their ability to enhance forward simulations by improving the generation speed, particularly in computationally intensive steps. Second, we apply generative machine learning models to the inverse problem of unfolding detector effects, offering an alternative to traditional techniques. We show that both tasks can be solved using machine learning with high precision and accuracy, demonstrating the potential of these approaches to significantly improve the scalability and robustness of future LHC analyses.  \nZusammenfassung  \nDie LHC-Simulationskette ist ein etabliertes und unverzichtbares Werkzeug für präzise Messungen an den Allzweckdetektoren des LHCs. Obwohl nicht alle Bestandteile auf fundamentaler Physik basieren, hat sich die Kette als Ganzes in den letzten zehn Jahren als äußerst genau und zuverlässig erwiesen. Um den steigenden experimentellen Anforderungen und der wachsenden Datenmenge gerecht zu werden, wird die Simulationskette kontinuierlich weiterentwickelt. In den letzten Jahren haben Fortschritte im Bereich des maschinellen Lernens neue Möglichkeiten eröffnet, um die Simulations-und Analyseinfrastruktur der Hochenergiephysik weiter zu verbessern. In dieser Arbeit untersuchen wir den Einsatz moderner Algorithmen des generativen, maschinellen Lernens in verschiedenen Phasen der LHC-Simulationskette. Zunächst analysieren wir deren Potenzial zur Verbesserung der Vorwärtssimulation, insbesondere in rechenintensiven Schritten. Anschließend wenden wir generative Modelle des maschinellen Lernens auf das inverse Problem der Korrektur von Detektoreffekten an, als Alternative zu klassischen Verfahren. Wir zeigen, dass beide Aufgaben durch maschinelles Lernen mit hoher Präzision und Genauigkeit gelöst werden können und diese Ansätze damit ein grosses Potenzial zur Verbesserung zukünftiger LHC-Analysen versprechen.  \nContents  \nPreface iii  \n1 Introduction 1  \n2 High energy physics 3  \n2. 1 Standard Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n2.2 Collider Physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6  \n2.2. 1 Parametrization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6  \n2.2.2 Hard Scattering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7  \n2.2.3 Parton Shower . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8  \n2.2.4 Hadronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9  \n2.2.5 Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10  \n2.2.6 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11  \n2.2.7 Statistical testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12  \n2.3 M","cbCaiaeoB116LRDS","https://ap.wps.com/l/cbCaiaeoB116LRDS","pdf",4909938,1,134,"English","en",105,"# Contents\n## 1 Introduction\n## 2 High energy physics\n## 3 Machine Learning\n## 4 Fast Event Generation\n## 5 Generative Unfolding","[{\"question\":\"What is the main focus of the dissertation on LHC simulation?\",\"answer\":\"The work studies integrating state-of-the-art generative machine learning into different stages of the LHC simulation chain to improve both simulation and analysis.\"},{\"question\":\"How is generative machine learning used to improve forward simulations?\",\"answer\":\"It is tested for its ability to enhance forward simulation, especially by improving generation speed in computationally intensive steps.\"},{\"question\":\"How does the dissertation address unfolding detector effects?\",\"answer\":\"Generative machine learning models are applied to the inverse problem of unfolding detector effects, providing an alternative approach to traditional unfolding techniques.\"}]","Precision Machine Learning for the LHC Simulation Chain - Dissertation | PDF",1785684307,338,{"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},"precision-machine-learning-for-the-lhc-simulation-chain-dissertation","",{"@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/precision-machine-learning-for-the-lhc-simulation-chain-dissertation/118571/",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","2026-08-02",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 main focus of the dissertation on LHC simulation?","Question",{"text":76,"@type":77},"The work studies integrating state-of-the-art generative machine learning into different stages of the LHC simulation chain to improve both simulation and analysis.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is generative machine learning used to improve forward simulations?",{"text":81,"@type":77},"It is tested for its ability to enhance forward simulation, especially by improving generation speed in computationally intensive steps.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the dissertation address unfolding detector effects?",{"text":85,"@type":77},"Generative machine learning models are applied to the inverse problem of unfolding detector effects, providing an alternative approach to traditional unfolding techniques.","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"]