This thesis aim to utilize MC generated sample to find the selection characterize H → W W → lνqq final state. We choose the VH production mode and require the vector boson to decay to leptons. We focus on the high Higgs pT region, in which the two jets from the Higgs boson decay get close to each other and form a large-R (R=1.0) jet. We will utilize the additional leptons and the large-R jet to identify the lνqq final state. With Luminosity 58 fb−1,√s = 13TeV the best significance for WH sample is 0.51, ZH(Z→ee) is 2.70, ZH(Z→ μμ) is 0.28.

Abstract i
發表聲明書 ii
1 Introduction 1
1.1 Higgs boson physics review . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Standard Model and Higgs boson . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Higgs boson production mode . . . . . . . . . . . . . . . . . . . . . . 2
1.1.3 Higgs boson decay channel . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.4 Overview of H → W W → lνqq . . . . . . . . . . . . . . . . . . . . 4
1.2 Motivation and study method . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 The ATLAS Detector and Data Sample 9
2.1 The Large Hadron collider Experiment . . . . . . . . . . . . . . . . . . . . . . 9
2.2 What happened after p-p collision? . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 The Coordinate system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4 The ATLAS Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4.1 Inner Detector: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4.2 Calorimeter: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4.3 Muon Spectrometer: . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4.4 Missing transverse energy: . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5 Trigger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.6 lepton identification and b-tagging jet . . . . . . . . . . . . . . . . . . . . . . 17
2.6.1 lepton identification . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.6.2 b-tagging jet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.7 object reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.7.1 Electron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.7.2 Muon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.7.3 Jet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.7.4 Large-R jets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.7.5 MET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.8 Monte Carlo Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3 Observable and Event Selection 25
3.1 Signal properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2 Signal candidate optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3 Observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.1 Low-level object . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.2 High-level object . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
iii
3.3.3 Observable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4 Event Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4.1 Preselection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4.2 W H signal selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4.3 ZH (Z → ee) signal selection . . . . . . . . . . . . . . . . . . . . . . 36
3.4.4 ZH (Z → μμ) signal selection . . . . . . . . . . . . . . . . . . . . . . 37
4 ML Analysis 41
4.1 ML method Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.1.1 Boost Decision Tree (BDT) . . . . . . . . . . . . . . . . . . . . . . . 41
4.1.2 Deep Neural Network (DNN) . . . . . . . . . . . . . . . . . . . . . . 42
4.1.3 ROC curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2 Training Sample and Model Structure . . . . . . . . . . . . . . . . . . . . . . 44
4.3 Cross Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.4 Model Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.4.1 Input variable Optimization . . . . . . . . . . . . . . . . . . . . . . . 47
4.4.2 Model setting Optimization . . . . . . . . . . . . . . . . . . . . . . . 48
5 Results and Conclusion 53
5.1 Analysis Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2 Cutflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
References 61

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