ATLAS探测器上双玻色子WZ产生截面的精确测量[S]

1、ATLAS探测器上双玻色子WZ产生截面的精确测量S    英文题名 Measurement of Diboson WZ Production Cross Section with the ATLAS Detector  专业 粒子物理与原子核物理  关键词 LHC; ATLAS; 标准模型; 双玻色子; WZ; 产生截面; 英文关键词 LHC; ATLAS; Standard Model; Diboson; WZ; Cross Section; 中文摘要 本论文通过Monte-Carlo模拟数据测量双玻色子W±Z的产生截面,

2、来检验粒子物理中 的标准模型。标准模型预言W+Z的产生截面是29.4 pb,W?Z的产生截面是18.4 pb。欧洲核子中 心(CERN)所建造的大型强子对撞机(LHC)提供了中心能量为14TeV的质子-质子对撞束流,运行在 LHC上的ATLAS探测器被用来探测W和Z粒子。双玻色子WZ的研究利用了ATLAS CSC模拟数据,其中 包括了触发信息、探测器刻度和位置修正。双玻色子WZ的测量是通过测量W和Z衰变产生的三轻 子末态(eee, ee,e,和)进行的,其中W衰变到一个带电轻子和中微子,Z衰 变到一对电荷符号相反的轻子对。有关WZ事例的判选、探测效率和本底压制都将详细讲述。经 过分析,对于积分

3、亮度1 fb?1的数据,我们可以观测到53个W±Z信号事例和8个本底事例,信噪比 达到了6.7。双玻色子W±Z在ppWZ + X( S = 14 TeV)时的测量截面为:?Br =53. 43?+160.5.0fb(包含了事例判选效率),这和标准模型的理论预言是相符合的。最后介绍了数据 分析过程中使用的一种新的统计学方法:Boosted Deci. 英文摘要 This dissertation describes a test of the Standard Model (SM) of particle physics by measuring the probabilit

4、y, or cross section, of simultaneously producing a W boson and a Z boson from Monte-Carlo simulated data of proton-proton collisions. The SM predicts the total W+Z production cross section to be 29.4 pb and the W?Z production cross section to be 18.4 pb. The 14 TeV center-of-mass energy proton-proto

5、n collisions are provided by the CERN (European Organization for Nuclear Research) LHC (Large Hadron Co. 摘要 5-6 ABSTRACT 6 Chapter 1 Introduction 11-18 Chapter 2 Standard Model and WZ Physics 18-26     2.1 The Standard Model and Particle Physics 18-19     2.2 Electrowea

6、k Interactions 19-22     2.3 WZ Production in the Standard Model 22-26         2.3.1 WZ Production Mechanisms 22-23         2.3.2 Standard Model Predictions for WZ Production 23-24      

7、0;  2.3.3 Experimental Signature of WZ Production 24-26 Chapter 3 LHC and ATLAS 26-52     3.1 CERN 26-27     3.2 The LHC project 27-34         3.2.1 The design concept of the LHC 29-31         3.

8、2.2 Performance of the LHC 31-34     3.3 LHC experiments 34-36         3.3.1 ATLAS 34         3.3.2 CMS 34         3.3.3 LHCb and B physics 34-35      

9、0;  3.3.4 ALICE and quark-gluon plasma 35-36     3.4 The ATLAS experiment 36-52         3.4.1 The ATLAS detector 36-38         3.4.2 Magnet System 38-40         3.4.3 Inner De

10、tector 40-43         3.4.4 Calorimetry 43-45         3.4.5 Muon system 45-49         3.4.6 Trigger and data-acquisition system 49-52 Chapter 4 Data Set 52-61     4.1 Introduction 5

11、2     4.2 Cross-Sections of Physical Processes 52-55     4.3 Monte Carlo Simulation for ATLAS 55-56     4.4 The Monte Carlo Event Generators for WZ analysis 56-58     4.5 Experimental signals and background 58-61 Chapter 5 Data Analysis 61-

12、81     5.1 Physics objects reconstruction and lepton ID efficiencies 61-64         5.1.1 Electron identification and selection efficiency 61-62         5.1.2 Muon reconstruction and identification efficiency 62-63 &

13、#160;       5.1.3 Jets 63-64         5.1.4 Missing transverse energy 64     5.2 Pre-selection of the W±Z events 64-68     5.3 Final selection with tightened straight cuts 68-71     5.4

14、Final selection using Boosted Decision Tree technique 71-76     5.5 Results 76-81         5.5.1 W±Z detection sensitivity for 1fb?1 integrated luminosity 76-77         5.5.2 Cross-section measurement uncertaint

15、y studies 77-79         5.5.3 BDT training stability test 79-81 Chapter 6 Advanced Data Analysis Method 81-92     6.1 Introduction 81     6.2 Boosted Decision Trees (BDT) 81-90         6.2.1 Booking o

16、ptions 82-84         6.2.2 Description and implementation 84-89         6.2.3 Variable ranking 89-90         6.2.4 Performance 90     6.3 Use BDT method in TMVA package 90-92 Concl

17、usion 92-93 Appendices 93-119 Reference 119-124 Acknowledgments 124-126 Publications 126         3.4.3 Inner Detector 40-43         3.4.4 Calorimetry 43-45         3.4.5 Muon system 45-49  &

18、#160;      3.4.6 Trigger and data-acquisition system 49-52 Chapter 4 Data Set 52-61     4.1 Introduction 52     4.2 Cross-Sections of Physical Processes 52-55     4.3 Monte Carlo Simulation for ATLAS 55-56     4.4 T

19、he Monte Carlo Event Generators for WZ analysis 56-58     4.5 Experimental signals and background 58-61 Chapter 5 Data Analysis 61-81     5.1 Physics objects reconstruction and lepton ID efficiencies 61-64         5.1.1 Electron identi

20、fication and selection efficiency 61-62         5.1.2 Muon reconstruction and identification efficiency 62-63         5.1.3 Jets 63-64         5.1.4 Missing transverse energy 64    

21、; 5.2 Pre-selection of the W±Z events 64-68     5.3 Final selection with tightened straight cuts 68-71     5.4 Final selection using Boosted Decision Tree technique 71-76     5.5 Results 76-81         5.5.1 W±Z

22、 detection sensitivity for 1fb?1 integrated luminosity 76-77         5.5.2 Cross-section measurement uncertainty studies 77-79         5.5.3 BDT training stability test 79-81 Chapter 6 Advanced Data Analysis Method 81-92  

23、60;  6.1 Introduction 81     6.2 Boosted Decision Trees (BDT) 81-90         6.2.1 Booking options 82-84         6.2.2 Description and implementation 84-89         6.2.3 Variab

24、le ranking 89-90         6.2.4 Performance 90     6.3 Use BDT method in TMVA package 90-92 Conclusion 92-93 Appendices 93-119 Reference 119-124 Acknowledgments 124-126 Publications 126         3.4.3 Inner Detector 4

25、0-43         3.4.4 Calorimetry 43-45         3.4.5 Muon system 45-49         3.4.6 Trigger and data-acquisition system 49-52 Chapter 4 Data Set 52-61     4.1 Introduction 52  

26、   4.2 Cross-Sections of Physical Processes 52-55     4.3 Monte Carlo Simulation for ATLAS 55-56     4.4 The Monte Carlo Event Generators for WZ analysis 56-58     4.5 Experimental signals and background 58-61 Chapter 5 Data Analysis 61-81  

27、;   5.1 Physics objects reconstruction and lepton ID efficiencies 61-64         5.1.1 Electron identification and selection efficiency 61-62         5.1.2 Muon reconstruction and identification efficiency 62-63  

28、60;      5.1.3 Jets 63-64         5.1.4 Missing transverse energy 64     5.2 Pre-selection of the W±Z events 64-68     5.3 Final selection with tightened straight cuts 68-71     5.4 Final selection using Boosted Decision Tree techni