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研究生：	葉政威 Yeh, Cheng-Wei
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
指導教授：	郭家銘 Kuo, Chia-Ming
口試委員:
學位類別：	碩士 Master
系所名稱：	理學院 - 物理學系 Department of Physics
論文出版年：	2018
畢業學年度：	106
語文別：	英文
論文頁數：	122
中文關鍵詞：	高能物理、希格斯玻色子
外文關鍵詞：	High energy physics, Higgs boson
相關次數：	點閱：15 下載：0
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　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。

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.

Contents
Introduction 1
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Theoretical overview . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.1 Higgs mechanism . . . . . . . . . . . . . . . . . . . . . . . . 2
2.2 Resonant pair production . . . . . . . . . . . . . . . . . . . 5
2.3 Non-resonance pair production . . . . . . . . . . . . . . . . 8
2.4 Decay channels . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.5 Previous result . . . . . . . . . . . . . . . . . . . . . . . . . 14
ATLAS Run I search . . . . . . . . . . . . . . . . . . . . . . 14
CMS Run I search . . . . . . . . . . . . . . . . . . . . . . . . 15
The LHC Machine and the CMS detector 19
1 The Large Hadron Collider . . . . . . . . . . . . . . . . . . . . . . 19
2 The Compact Muon Solenoid . . . . . . . . . . . . . . . . . . . . . 21
2.1 Magnetic system . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2 Tracking system . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3 Electromagnetic calorimeter (ECAL) . . . . . . . . . . . . . 24
2.4 Hadronic calorimeter (HCAL) . . . . . . . . . . . . . . . . 27
2.5 Muon system . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.6 Trigger and data acquisition system . . . . . . . . . . . . . 29
Multivariate data analysis 33
1 Toolkit for Multivariate Data Analysis (TMVA) . . . . . . . . . . . 33
1.1 Boosted decision trees (BDT) . . . . . . . . . . . . . . . . . 33
1.2 Gradient Boosting and pruning . . . . . . . . . . . . . . . . 34
2 Photon identiﬁcation . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.1 Di-photon vertex identiﬁcation . . . . . . . . . . . . . . . . 37
2.2 Photon identiﬁcation . . . . . . . . . . . . . . . . . . . . . . 38
3 Photon energy regression . . . . . . . . . . . . . . . . . . . . . . . 39
4 B-jet identiﬁcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5 B-jet energy regression . . . . . . . . . . . . . . . . . . . . . . . . . 46
5.1 Training samples . . . . . . . . . . . . . . . . . . . . . . . . 46
5.2 Training method . . . . . . . . . . . . . . . . . . . . . . . . 47
5.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Mass resolution . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.4 The improvement from the three additional input vari-
ables and the training with leading and trailing jets sep-
arately . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.5 Data validation . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.6 Impact on the results . . . . . . . . . . . . . . . . . . . . . . 65
6 HH → b
¯
bγγ categorization MVA . . . . . . . . . . . . . . . . . . . 65
Event selection 69
1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
1.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
1.2 Resonant Monte Carlo signal samples . . . . . . . . . . . . 69
1.3 Non-resonant Monte Carlo signal samples . . . . . . . . . 72
1.4 Background simulation . . . . . . . . . . . . . . . . . . . . 74
2 Physical objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
2.1 The H → γγ candidate . . . . . . . . . . . . . . . . . . . . . 74
Trigger mimic pre-selection . . . . . . . . . . . . . . . . . . 74
H → γγ selection . . . . . . . . . . . . . . . . . . . . . . . . 75
2.2 The H → b
¯
b candidate . . . . . . . . . . . . . . . . . . . . . 76
2.3 The di-Higgs system . . . . . . . . . . . . . . . . . . . . . . 76
2.4 Signal selection efﬁciency . . . . . . . . . . . . . . . . . . . 78
Modeling 79
1 Signal modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
1.1 Correlation Studies . . . . . . . . . . . . . . . . . . . . . . . 80
2 Background modeling . . . . . . . . . . . . . . . . . . . . . . . . . 81
2.1 Bias Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
2.2 Correlation Studies . . . . . . . . . . . . . . . . . . . . . . . 86
2.3 Single Higgs background modeling . . . . . . . . . . . . . 87
3 Systematic uncertainties . . . . . . . . . . . . . . . . . . . . . . . . 89
Results 91
1 Resonance results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
2 Non-resonance results . . . . . . . . . . . . . . . . . . . . . . . . . 91
Conclusion 95
Bibliography 97
                                

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