[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121261-en":3,"doc-seo-121261-105":30,"detail-sidebar-cat-0-en-105":91},{"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},121261,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Likelihood free inference with advanced machine learning techniques - Diplomarbeit","Hypothetical particles outside the Standard Model can manifest as small deviations in the data tails collected with the Compact Muon Solenoid experiment at the Large Hadron Collider. These deviations are modeled by effective operators of mass dimension higher than four, with the Standard Model treated as a low-energy approximation. The thesis develops simulation-based inference to learn optimal test statistics for four-fermion operator insertions in four-top and tt/tb channels, using polynomial structures in Wilson coefficients and nuisance-free unbinned likelihood-ratio limits up to quadratic order. Deep neural networks combined with LSTM layers extract information from variable-length jet systems as well as scalar observables, validated through training workflows and projected limits on Wilson coefficients for multiple scenarios.","Diplomarbeit  \nLikelihood free inference with advanced machine learning techniques  \nzur Erlangung des akademischen Grades  \nDiplom-Ingenieurin  \nim Rahmen des Studiums  \nTechnische Physik  \neingereicht von  \nLena Wild, BSc MA MA  \nMatrikelnummer: 11824551  \nausgeführt am Institut für Hochenergiephysik der Österreichischen Akademie der Wissenschaften  \nBetreuung  \nBetreuer: Privatdoz. DI Dr. Robert Schöfbeck  \nWien, 12.06.2023  \nUnterschrift Verfasserin Unterschrift Betreuer  \nAbstract  \nAlthough many theories locate hypothetical phenomena beyond the Standard Model at energy scales far above the current experimental reach, tiny deviations from the Standard Model prediction are expected to show in the tails of data collected with the Compact Muon Solenoid experiment atthe Large Hadron Collider at CERN. These deviations can be described through the insertion of effective operators of mass dimension higher than four, treating the Standard Model as a low-energy approximation of the hypothetical high energy theory.  \nIn this thesis, we adopt novel machine learning techniques based on simulation (simulation-based inference) to teach the machine the optimal test statistic according to the Neyman-Pearson lemma for four-fermion operator insertions in four-top production and production of two top and two bottom quarks. The centerpiece of this approach consists in exploiting the polynomial structure of the effective field theory prediction as a function of the coefficients of the operators, i.e. , the Wilson coefficients. Hence, learning only a small number of coefficient functions allows to parametrize an optimal classifier in the full parameter space. With the learned coefficient functions at hand, we then set nuisance-free limits in an unbinned likelihood ratio test up to quadratic order in the polynomial expansion. In this way, we investigate the neural network’s performance in learning the yield-and shape-related modifications, thus probing new forces between four heavy quarks.  \nOn the machine learning side, we combine Deep Neural Networks with Long Short Time Memory layers to extract information not only from scaler observables, but also from the variable length jet system in analogy to speech recognition. By also probing this Multivariate Analysis setup in the simpler, more robust setting of multi-classification, we test, optimize and cross-validate the configuration of the network.  \nIn instantiating a complete workflow of sample generation, training with simulation-based inference, and the limit setting procedure, we obtain projected limits on the Wilson coefficients of four-fermion operators in tt and tb with and without t background. Thus, we demonstrate the potential of these novel neural network architectures and machine learning techniques for future analyses.  \nContents  \n1 Introduction 1  \n2 CERN, LHC and CMS 2  \n2.1 CERN-The European Council for Nuclear Research .................. 2  \n2.2 LHC-The Large Hadron Collider ............................ 2  \n2.3 CMS-The Compact Muon Solenoid ........................... 5  \n2.3.1 CMS detection systems .............................. 6  \n2.3.2 Trigger and Data Acquisition at CMS ...................... 8  \n3 The Standard Model and Standard Model Effective Field Theory 9  \n3.1 The Standard Model and beyond ............................. 9  \n3.2 Standard Model Effective Field Theory .......................... 10  \n3.2.1 The SMEFT Lagrangian at mass dimension 6 .................. 10  \n3.3 The physics cases: tt and tb ............................. 10  \n3.4 Multi-classification in tt and backgrounds ....................... 11  \n3.5 SMEFT in tt / tb ................................... 12  \n4 Learning the SMEFT with Simulation-based Inference 15  \n4.1 Simulation based inference ................................. 15  \n4.1.1 The optimal test statistic ............................. 15  \n4.1.2 Learning EFT effects from simulation ...................... 16  \n4.1.3 Learning the polynomial dependence ..................","cbCaiuKG6Fnn0slg","https://ap.wps.com/l/cbCaiuKG6Fnn0slg","pdf",5399567,1,80,"English","en",105,"# Introduction\n# CERN, LHC and CMS\n## CERN-The European Council for Nuclear Research\n## LHC-The Large Hadron Collider\n## CMS-The Compact Muon Solenoid\n# The Standard Model and Standard Model Effective Field Theory\n## The Standard Model and beyond\n## Standard Model Effective Field Theory\n## Multi-classification in tt and backgrounds\n# Learning the SMEFT with Simulation-based Inference\n## Simulation based inference\n## Limit setting in 1D and 2D\n# Multivariate Analysis\n## Basic Priniciples of MVA\n## The MVA architecture\n# Data Generation and Training\n## The input data\n## Hyperparameter optimization\n# Results\n## Training for signal only\n## Training with signal and background\n## Summary of the limit setting\n# Conclusion and Outlook","[{\"question\":\"What is the main goal of this thesis?\",\"answer\":\"To learn optimal test statistics for SMEFT effects using likelihood-free, simulation-based machine learning, and to derive projected limits on Wilson coefficients for four-fermion operators in tt and tb-related production channels.\"},{\"question\":\"How does the approach use effective field theory structure?\",\"answer\":\"It exploits the polynomial dependence of EFT predictions on Wilson coefficients, enabling learning of a small set of coefficient functions and constructing an optimal classifier across the full parameter space.\"},{\"question\":\"What machine learning architecture is used and what data does it extract from?\",\"answer\":\"The work combines Deep Neural Networks with Long Short Term Memory layers to learn from both scalar observables and variable-length jet system inputs, validated via training, cross-validation, and hyperparameter optimization.\"}]","Likelihood free inference with advanced machine learning techniques - Diplomarbeit | PDF",1785734742,202,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"likelihood-free-inference-with-advanced-machine-learning-techniques-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@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/likelihood-free-inference-with-advanced-machine-learning-techniques-thesis/121261/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this thesis?","Question",{"text":75,"@type":76},"To learn optimal test statistics for SMEFT effects using likelihood-free, simulation-based machine learning, and to derive projected limits on Wilson coefficients for four-fermion operators in tt and tb-related production channels.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the approach use effective field theory structure?",{"text":80,"@type":76},"It exploits the polynomial dependence of EFT predictions on Wilson coefficients, enabling learning of a small set of coefficient functions and constructing an optimal classifier across the full parameter space.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning architecture is used and what data does it extract from?",{"text":84,"@type":76},"The work combines Deep Neural Networks with Long Short Term Memory layers to learn from both scalar observables and variable-length jet system inputs, validated via training, cross-validation, and hyperparameter optimization.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":21,"slug":99},"Literature","literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]