研究 Dalitz Higgs 的 Muon 效率用於 Run II 和多變量電子用於 CMS 的 Run III

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沙塔(Saba Taj) 查詢紙本館藏

畢業系所

物理學系

論文名稱

研究 Dalitz Higgs 的 Muon 效率用於 Run II 和多變量電子用於 CMS 的 Run III
(Study of Muon Efficiency for Dalitz Higgs for Run II and Multivariate Electron ID for Run III at CMS)

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★ TCAD simulation of silicon detector	★ Assembly and Beam Test Analysis of sPHENIX INTT Detector

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摘要(中)

本篇論文主要呈現利用標記和探測 (T&P) 方法測量渺子(μ)的效率。利用 Z → μμ 和 J/Ψ → μμ 事件，分別對渺子對 pT 和 η 的效率進行測量。
此測量使用了 2016、2017 和 2018 年的 UL 數據。此分析使用座落在大型強子對撞機(LHC)的緊湊渺子線圈(CMS)在 Run -2 期間於對撞能量為 13 兆電子伏特 (TeV)時所收集的質子與質子對撞數據，對應總光度 137.1/fb 之事件。電子識別在 CMS 實驗尋找新物理學和精密測量中扮演重要角色。與傳統的基於切割的識別(cut-base idification)相比，多變量技術(multive technique)顯示出顯著的背景抑制能力。
自大強子對撞機 Run-1 以來，CMS 實驗使用 BDT 識別電子，且該策略有穩定的進步，讓真正電子與背景電子分離有更好的表現。背景電子可以是非瞬發電子(prompt electron, 即噴射流中產生的電子或來自光子轉換的電子或與任何真正電子完全無關的重建電子。
本篇論文並不考慮來自濤輕子衰變的電子。本篇論文使用 XG-Boost 作為多變量工具對大強子對撞機 Run-3 進行電子多變量的訓練鑑定。

摘要(英)

The thesis reports on muon efficiencies being measured with the Tag and Probe (T&P) method performed on Z → μμ and J/Ψ → μμ events in bins of pT and η. Measurements are extracted using 2016, 2017 and 2018 UL data. The analysis is performed on data collected by the CMS experiment at the LHC from proton-proton collisions with a center- of-mass energy of 13 TeV during the full Run-2 period, corresponding to an integrated luminosity of 137.1 fb−1.
Electron identification has been playing a crucial role in the search for new physics and also precision measurements at CMS. The multivariate technique has shown significant background rejection power, compared to the conventional cut-based identification. The CMS experiment identifies electrons with BDTs since the LHC Run 1, and this strategy was steadily improved. True prompt electrons are separated from the background, which can be either non-prompt electrons (i.e., electrons in jets or from converted photons) or reconstructed electrons entirely unrelated to any real electron. Electrons from tau leptons decays are not considered. In this thesis, the training of the electron multivariate identification for the LHC Run3 results using XG-Boost as a multivariate tool have been done.

關鍵字(中)

★ 介子 (μ) 測量效率
★ 電子鑑定

關鍵字(英)

★ Single muon efficiency
★ Electron Identification

論文目次

Contents
1 StandardModel1
1.1 TheStandardModel.............................. 1
1.2 ElementaryParticles.............................. 2
1.2.1 IntermediateVectorBoson....................... 2
1.2.2 Fermions................................. 3
1.3 InteractingFieldsinStandardModel..................... 4
1.4 HiggsBoson................................... 6
1.4.1 Higgsbosonproduction........................ 6
1.4.2 HiggsBosonDecay........................... 7
1.5 DalitzHiggs................................... 10
2 Experimentalsetup12
2.1 TheLHC.................................... 12
2.1.1 Operation................................ 13
2.2 Luminosity................................... 14
2.3 TheCMSDetector............................... 15
2.3.1 PurposeofCMSDetector....................... 16
2.3.2 TheCoordinateSystem........................ 16
2.3.3 FeaturesofCMSDetector....................... 17
2.4 SectionsofCMSDetector........................... 17
2.4.1 TrackingSystem............................ 18
2.5 TheCalorimeter................................. 19
2.5.1 ElectromagneticCalorimeter...................... 19
2.5.2 HadronicCalorimeter(HCAL)..................... 21
2.6 TheMagnet................................... 22
2.7 TheMuonDetectors.............................. 23
2.8 TriggerandDataAcquisitionSystem(TriDAS)............... 24
3 AnalysisStrategy26
3.1 Introduction................................... 26
3.2 AnalysisStrategy................................ 28
3.2.1 BackgroundComposition........................ 28
3.2.2 StatisticalMethods........................... 29
3.3 SimulatedSamples............................... 31
3.3.1 DataSamples.............................. 32
3.4 Triggers..................................... 33
3.4.1 IsoMu27(SingleMuon)Trigger.................... 34
3.5 ObjectandEventSelection.......................... 36
3.5.1 Muons.................................. 36
3.6 MuonEfficiencyMeasurements........................ 38
3.6.1 ReconstructionandIdentification................... 38
3.6.2 Impactparameterrequirements.................... 38
3.6.3 Isolationrequirements......................... 41
3.6.4 Trackingefficiency........................... 41
3.6.5 OverallResults............................. 41
4 MultivariateElectronID 44
4.1 MachineLearning................................ 44
4.1.1 TrainingaModelinMachineLearning................ 44
4.1.2 Thebias-variancetradeoff....................... 46
4.2 BoostedDecisionTrees:............................ 48
4.2.1 GradientBoostingfordecisiontrees.................. 50
4.3 MultivariateelectronIdentificationforLHCRun3.............. 51
4.4 GeneralTrainingStrategyandrequirements................. 52
4.4.1 Inputvariables............................. 53
4.4.2 XGBoostHyperparameter....................... 56
4.4.3 Rankingofvariables.......................... 60
4.4.4 XGBoostclassifier........................... 61
4.4.5 Efficiencies................................ 61
4.4.6 ReceiverOperatingCharacteristicscurve............... 61
4.5 Conventionalmethod.............................. 65
5 SummaryandConclusion67

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指導教授

郭家銘(Kuo, Chia Ming)

審核日期

2023-2-1

推文