[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127968-en":3,"doc-seo-127968-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127968,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Measurement of the cc¯ Charge Asymmetry with the LHCb Detector and Quantum Machine Learning Methods - Final Dissertation","LHCb reconstructs and identifies hadronic jets in the forward region of proton-proton collisions with high precision thanks to its tracking and calorimeter systems. The detector has shown effective separation of b-jets and b-bar-jets using quantum machine learning (QML). This thesis develops a QML-based algorithm to distinguish c-jets from c-bar-jets, enabling a first measurement of the cc¯ charge asymmetry, an electroweak observable sensitive to new physics. The study also estimates expected LHCb sensitivity by comparing classical and quantum approaches and links potential results to intrinsic charm in the proton.","UNIVERSIT `A DEGLI STUDI DI PADOVA Dipartimento di Fisica e Astronomia “Galileo Galilei”  \nMaster Degree in Physics  \nFinal Dissertation  \nMeasurement of the cc¯ charge asymmetry with the LHCb detector and Quantum Machine Learning  \nmethods  \nThesis supervisor  \nProf./Dr. Donatella Lucchesi Thesis co-supervisor  \nProf./Dr. Lorenzo Sestini  \nCandidate  \nJory Hagen  \nAcademic Year 2024/2025  \nAbstract  \nThanks to the excellent tracking and calorimeter systems, LHCb can precisely reconstruct and identify hadronic jets in the forward region of proton-proton collisions. Moreover, LHCb has demonstrated that it can precisely separate b-jets (jets generated from b quarks) from b-bar-jets (generated from anti-b quarks) by the means of Quantum Machine Learning (QML) algorithms. In this thesis an algorithm, based on QML methods, for separating c-jets and c-bar-jets will be developed. This task is fundamental for measuring the so-called c c-bar charge asymmetry, an observable sensitive to new physics contribution in the Electroweak sector, that has never been measured before. Its determination could be also used to determine the intrinsic charm component of the proton. In this work the performance of the “classical” and quantum algorithms for c-jets identification will be compared, and the expected LHCb sensitivity on the measurement of the cc-bar charge asymmetry with the new algorithms will be determined.  \nContents  \n1 Introduction 4  \n2 Theoretical Framework: 6  \n2.1 The Standard Model of Particle Physics .......................... 6  \n2.2 Charge Asymmetry ...................................... 9  \n2.3 Cross Sections ......................................... 10  \n2.4 Cross Section and Asymmetry Predictions ......................... 14  \n2.5 Intrinsic Charm of the Proton ................................ 15  \n2.6 New Physics Processes .................................... 17  \n2.6.1 U(1)D Gauge Group Proposal for LEP AbFB Anomaly .............. 17  \n3 The LHCb Detector 21  \n3.1 Tracking ............................................ 22  \n3.1.1 Vertex Locator (VELO) Pre-Upgrade ....................... 23  \n3.1.2 Vertex Locator (Current) .............................. 23  \n3.1.3 Upstream Tracker (UT): Formerly (TT) ...................... 24  \n3.1.4 T1-T3 Trackers (Pre-Upgrade) .......................... 25  \n3.1.5 Scintillating Fiber Tracker (SciFi) ......................... 25  \n3.2 Particle Identification .................................... 26  \n3.2.1 RICH I & II ..................................... 26  \n3.2.2 RICH I ........................................ 26  \n3.2.3 RICH II ........................................ 27  \n3.2.4 Calorimeters ..................................... 27  \n3.2.5 Muon System ..................................... 29  \n3.3 Trigger ............................................. 29  \n3.3.1 Original ........................................ 29  \n3.3.2 Upgraded ....................................... 31  \n4 Jet Reconstruction 33  \n4.1 Jet Formation ......................................... 33  \n4.2 Track and Calorimeter Cluster Selection .......................... 34  \n4.3 Anti-kT Jet Reconstruction Algorithm ........................... 35  \n5 Heavy Flavour Dijet Selection at LHCb 38  \n6 Quantum Machine Learning 41  \n6.1 Quantum Computing ..................................... 41  \n6.1.1 Qubits ......................................... 41  \n6.1.2 Quantum Gates .................................... 43  \nPauli Gates ...................................... 44  \nHadamard ....................................... 45  \nCNOT ......................................... 45  \n6.1.3 Quantum Algorithms ................................ 46  \n6.1.4 Constructing a Hamiltonian ............................. 46  \n6.2 Machine Learning ....................................... 47  \n6.2.1 Classical ........................................ 48  \nBoosted Decision Trees ............................... 48  \nDeep Neural Networks ............................","cbCaicnMhrYXhTTg","https://ap.wps.com/l/cbCaicnMhrYXhTTg","pdf",7777351,3,1,96,"English","en",105,"# Introduction\n# Theoretical Framework\n## Standard Model of Particle Physics\n## Charge Asymmetry\n## Cross Sections\n## Intrinsic Charm of the Proton\n## New Physics Processes\n# The LHCb Detector\n## Tracking\n## Particle Identification\n## Trigger\n# Jet Reconstruction\n# Heavy Flavour Dijet Selection at LHCb\n# Quantum Machine Learning\n## Quantum Computing\n## Machine Learning\n# Procedure For Measuring the cc¯ Charge Asymmetry\n## Preparation of Dijet Dataset and Optimization of Classification Algorithm\n## Determination of Tagging Power and Optimal Data Cuts\n## Measurement of Charge Asymmetry\n# Results\n## Feature Importance\n## BDT Optimization Results\n## DNN Optimization Results\n## QML Optimization Results","[{\"question\":\"What is the goal of this thesis regarding jets and charge asymmetry?\",\"answer\":\"To develop a QML-based algorithm that separates c-jets from c-bar-jets, enabling measurement of the cc¯ charge asymmetry in the electroweak sector.\"},{\"question\":\"Why is the cc¯ charge asymmetry important in this work?\",\"answer\":\"It is an observable sensitive to potential new physics contributions and has not been measured before; its determination may also help infer intrinsic charm in the proton.\"},{\"question\":\"How does the thesis evaluate classical versus quantum methods?\",\"answer\":\"It compares performance of classical algorithms and quantum algorithms for c-jet identification, then determines the expected LHCb sensitivity for the cc¯ charge asymmetry using the new QML algorithms.\"}]","Measurement of the cc¯ Charge Asymmetry with the LHCb Detector and Quantum Machine Learning Methods - Final Dissertation | PDF",1785943463,242,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"measurement-of-the-cc-charge-asymmetry-with-the-lhcb-detector-and-quantum-machine-learning-methods-final-dissertation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/measurement-of-the-cc-charge-asymmetry-with-the-lhcb-detector-and-quantum-machine-learning-methods-final-dissertation/127968/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",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 this thesis regarding jets and charge asymmetry?","Question",{"text":76,"@type":77},"To develop a QML-based algorithm that separates c-jets from c-bar-jets, enabling measurement of the cc¯ charge asymmetry in the electroweak sector.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why is the cc¯ charge asymmetry important in this work?",{"text":81,"@type":77},"It is an observable sensitive to potential new physics contributions and has not been measured before; its determination may also help infer intrinsic charm in the proton.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the thesis evaluate classical versus quantum methods?",{"text":85,"@type":77},"It compares performance of classical algorithms and quantum algorithms for c-jet identification, then determines the expected LHCb sensitivity for the cc¯ charge asymmetry using the new QML algorithms.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]