Contents
1 The Standard Model of particle physics 23
1.1 The elementary particles . . . . 23
1.2 The fundamental interactions . . . . 24
1.3 Gauge symmetries: a brief introduction . . . . 27
1.4 The Drell-Yan process . . . . 31
1.5 The photon induced process . . . . 32
1.6 The effective field theory . . . . 34
1.7 Summary . . . . 34
2 The beyond Standard Model of particle physics 37 2.1 Motivation for new physics . . . . 37
2.2 New heavy particles decaying into a lepton pair . . . . 40
2.3 New physics in top quark production . . . . 43
2.4 Summary . . . . 45
3 The CMS experiment at LHC 47 3.1 The Large Hadron Collider (LHC) . . . . 47
3.1.1 Proton proton collision . . . . 48
3.1.2 Pile up . . . . 48
3.1.3 Luminosity . . . . 50
3.2 The Compact Muon Solenoid (CMS) . . . . 50
3.2.1 Coordinate conventions . . . . 52
3.2.2 Tracking system . . . . 53
3.2.3 Electromagnetic calorimeter . . . . 55
3.2.4 Hadronic calorimeter . . . . 59
3.2.5 Magnet . . . . 60
3.2.6 Muon system . . . . 60
3.2.7 Trigger . . . . 61
3.3 Summary . . . . 62
4 Object reconstruction 63 4.1 Electrons and Photons . . . . 63
4.1.1 Electrons . . . . 67
4.1.2 Photons . . . . 68
4.2 Muons . . . . 69
4.3 Jets and Bjets . . . . 70
4.3.1 b-jets . . . . 71
4.4 Missing transverse energy . . . . 71
4.5 Particle-flow algorithm . . . . 71
4.6 Summary . . . . 72
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5 Searching for High Mass Resonances in Dielectron Final State 73
5.1 Data and MC samples . . . . 73
5.2 Trigger . . . . 76
5.2.1 Method for Measuring Trigger Efficiencies in Data . . . . 76
5.2.2 Primary Signal Trigger: L1 Efficiency . . . . 77
5.2.3 Primary Signal Trigger: HLT Efficiency . . . . 78
5.2.4 Other Trigger Efficiencies . . . . 78
5.3 Object and Event Selection . . . . 81
5.4 Mass Resolution and Scale . . . . 83
5.5 HEEP ID Efficiency and Scale Factor . . . . 90
5.5.1 Tag and probe method . . . . 90
5.5.2 HEEP ID efficiencies and scale factors . . . 102
5.6 Standard Model Backgrounds . . . 110
5.6.1 SM Drell-Yan background . . . 110
5.6.2 t¯t and t¯t -like backgrounds . . . 115
5.6.3 Jet background . . . 117
5.7 Invariant Mass Spectra . . . 122
5.7.1 Complementary plot . . . 128
5.8 Statistical Interpretation . . . 131
5.8.1 Upper limits . . . 135
5.9 Summary . . . 138
6 Search for New Physics via Top Quark Production in Dilepton Final State 139 6.1 Data-sets and MC Samples . . . 140
6.1.1 Data samples . . . 140
6.1.2 MC samples . . . 140
6.2 Triggers . . . 141
6.3 Object Identification . . . 142
6.3.1 Lepton selection . . . 142
6.3.2 Jet selection . . . 143
6.3.3 Missing Transverse Energy . . . 144
6.3.4 Scale factors . . . 144
6.3.5 Top p
Treweighting . . . 144
6.4 Event Selection . . . 145
6.4.1 Event selection (step 1) . . . 145
6.4.2 Event selection (step 2) . . . 145
6.5 Background Predictions . . . 147
6.5.1 Prompt Background . . . 147
6.5.2 Fake Background . . . 148
6.6 Data/MC Comparison . . . 149
6.7 Signal Extraction Using Neural Networks Tools . . . 157
6.7.1 Data/MC comparison for MVA input variables . . . 158
6.8 Systematic Uncertainties . . . 169
6.9 Results . . . 178
6.9.1 Limit setting procedure . . . 178
6.9.2 Exclusion limits on C
Geffective coupling . . . 179
6.9.3 Exclusion limits on C
tG, C
(3)φqand C
tWeffective couplings . . . 181
6.9.4 Exclusion limits on C
uGand C
cGeffective couplings . . . 184
6.10 Summary . . . 187
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