Search for the production of two Higgs bosons in the final state with two photons and two b quarks in proton-proton collision at √s = 13 TeV

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姓名

葉政威(Yeh, Cheng-Wei) 查詢紙本館藏

畢業系所

物理學系

論文名稱

(Search for the production of two Higgs bosons in the final state with two photons and two b quarks in proton-proton collision at √s = 13 TeV)

相關論文

★ 利用CMS探測器量測7TeV下的Zγ產生截面	★ 以CMS 偵測器在質心質量為8TeV使用雙渺子和三秒子頻道尋找雙電荷希格斯玻色子
★ 在質子對撞能量8TeV下尋找具有雙電子雙渺子末態的激發態輕子	★ Measurement of Zγ production in 5 fb-1 of pp collisions at √s = 7 TeV with the CMS detector
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★ 火花偵測器的製成	★ Search for Exotic Decay of A Higgs Boson into A Dark Photon and a Standard Model Photon in pp Collisions at √s = 13 TeV
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★ nono	★ TCAD simulation of silicon detector
★ Assembly and Beam Test Analysis of sPHENIX INTT Detector	★ 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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本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
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摘要(中)

本篇論文旨在尋找於質子–質子對撞下產生的雙希格斯粒子，分別衰變至一對光子及一對底夸克。
本分析使用了於2016年由大強子對撞機(LHC)產生的質子–質子對撞，總能量為√s=13 TeV，並由緊湊緲子線圈(CMS)所記錄，總亮度達到35.9/fb。
此研究基於標準模型以及超越標準模型的理論，同時尋找非共振衰變的雙希格斯粒子以及由新粒子衰變的雙希格斯粒子。
非共振衰變可用於驗證希格斯機制以及探索其他可能的希格斯粒子與其他粒子的交互作用。
多維度模型預測了兩種與重力相關的新粒子，且這些新粒子可衰變到雙希格斯粒子。
本研究使用機器學習來輔助重建來自底夸克的強子噴流能量，使預測的生產截面的信心水準上限下降百分之十，達到更佳的結果。
本研究沒有觀察到顯著的訊號事件，並提供了實驗上對理論參數以及新粒子重量的限制區間。

摘要(英)

The search is presented for the production of a pair of Higgs bosons in the final state with two photons and two b quarks by full 2016 data, which corresponds to an integrated luminosity of 35.9/fb recorded by the CMS detector.
Both resonant and non-resonant processes are investigated for the Standard Model (SM) and the Beyond the Standard Model (BSM) theories.
The non-resonant production helps us to understand the Higgs field structure in the SM and other possible effects from BSMs.
The resonant production is predicted by many BSMs. In this thesis, the hypothesis with the spin-0 and spin-2 new heavy particles which can decay into two Higgs bosons is searched and compared with the prediction from the warped extra dimension theory.
The b-jet energy regression specifically developed for this analysis are employed to improve the sensitivity about 10%.
The observed results agree with the standard model prediction, and the limits on the exclusion of the BSM productions are also set.

關鍵字(中)

★ 高能物理
★ 希格斯玻色子

關鍵字(英)

★ High energy physics
★ Higgs boson

論文目次

Contents
1 Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Theoretical overview . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Higgs mechanism . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.2 Resonant pair production . . . . . . . . . . . . . . . . . . . 5
1.2.3 Non-resonance pair production . . . . . . . . . . . . . . . . 8
1.2.4 Decay channels . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.2.5 Previous result . . . . . . . . . . . . . . . . . . . . . . . . . 14
ATLAS Run I search . . . . . . . . . . . . . . . . . . . . . . 14
CMS Run I search . . . . . . . . . . . . . . . . . . . . . . . . 15
2 The LHC Machine and the CMS detector 19
2.1 The Large Hadron Collider . . . . . . . . . . . . . . . . . . . . . . 19
2.2 The Compact Muon Solenoid . . . . . . . . . . . . . . . . . . . . . 21
2.2.1 Magnetic system . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.2 Tracking system . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.3 Electromagnetic calorimeter (ECAL) . . . . . . . . . . . . . 24
2.2.4 Hadronic calorimeter (HCAL) . . . . . . . . . . . . . . . . 27
2.2.5 Muon system . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.2.6 Trigger and data acquisition system . . . . . . . . . . . . . 29
3 Multivariate data analysis 33
3.1 Toolkit for Multivariate Data Analysis (TMVA) . . . . . . . . . . . 33
3.1.1 Boosted decision trees (BDT) . . . . . . . . . . . . . . . . . 33
3.1.2 Gradient Boosting and pruning . . . . . . . . . . . . . . . . 34
3.2 Photon identi?cation . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2.1 Di-photon vertex identi?cation . . . . . . . . . . . . . . . . 37
3.2.2 Photon identi?cation . . . . . . . . . . . . . . . . . . . . . . 38
3.3 Photon energy regression . . . . . . . . . . . . . . . . . . . . . . . 39
3.4 B-jet identi?cation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.5 B-jet energy regression . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.5.1 Training samples . . . . . . . . . . . . . . . . . . . . . . . . 46
3.5.2 Training method . . . . . . . . . . . . . . . . . . . . . . . . 47
3.5.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Mass resolution . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.5.4 The improvement from the three additional input vari-
ables and the training with leading and trailing jets sep-
arately . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.5.5 Data validation . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.5.6 Impact on the results . . . . . . . . . . . . . . . . . . . . . . 65
3.6 HH → b
¯
bγγ categorization MVA . . . . . . . . . . . . . . . . . . . 65
4 Event selection 69
4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.1.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.1.2 Resonant Monte Carlo signal samples . . . . . . . . . . . . 69
4.1.3 Non-resonant Monte Carlo signal samples . . . . . . . . . 72
4.1.4 Background simulation . . . . . . . . . . . . . . . . . . . . 74
4.2 Physical objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.2.1 The H → γγ candidate . . . . . . . . . . . . . . . . . . . . . 74
Trigger mimic pre-selection . . . . . . . . . . . . . . . . . . 74
H → γγ selection . . . . . . . . . . . . . . . . . . . . . . . . 75
4.2.2 The H → b
¯
b candidate . . . . . . . . . . . . . . . . . . . . . 76
4.2.3 The di-Higgs system . . . . . . . . . . . . . . . . . . . . . . 76
4.2.4 Signal selection ef?ciency . . . . . . . . . . . . . . . . . . . 78
5 Modeling 79
5.1 Signal modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.1.1 Correlation Studies . . . . . . . . . . . . . . . . . . . . . . . 80
5.2 Background modeling . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.2.1 Bias Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.2.2 Correlation Studies . . . . . . . . . . . . . . . . . . . . . . . 86
5.2.3 Single Higgs background modeling . . . . . . . . . . . . . 87
5.3 Systematic uncertainties . . . . . . . . . . . . . . . . . . . . . . . . 89
6 Results 91
6.1 Resonance results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.2 Non-resonance results . . . . . . . . . . . . . . . . . . . . . . . . . 91
7 Conclusion 95
Bibliography 97

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指導教授

郭家銘(Kuo, Chia-Ming)

審核日期

2018-7-18

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