此篇論文利用由 ATLAS 偵測器在 Run-2 期間所搜集的 13 TeV 質子對撞數據 (139 fb−1)，進行機器學習方法應用於大強子對撞實驗之希格斯玻色子量測的實驗分析。本研究提出引入深層神經網路(DNN)方法篩選膠子融合雙噴流事 件，並將其輸出分佈與當前實驗選用的雙輕子橫質量做比較。統計擬合結果顯示深層網路輸出分佈作為判別變量可以提升 36% 的實驗靈敏度。

The study presents the application of machine learning methods in the combined measurement of gluon-gluon fusion and vector boson fusion Higgs decaying to a pair of W bosons, with 139 fb−1 of proton-proton collision at a center-of-mass energy of 13 TeV collected by the ATLAS detector at the Large Hadron Collider (LHC). The deep neural network (DNN) algorithm is trained and tested to enhance the sensitivity for ggF 2-jet events; the DNN shape output is used as a discriminant variable to the fit as a replacement for the default di-lepton transverse mass. The profile likelihood fit result shows an improvement of 36% in sensitivity using the DNN-trained output.

Contents iii
List of Tables iv
List of Figures vii

1 Introduction 1
1.1 The Discovery of the Higgs Boson................... 1
1.2 The Higgs Boson and the Standard Model...................3
1.3 Overview of the H → WW∗ → lνlν Analysis................... 5
1.4 The Application of Deep Neural Networks in the ggF 2-jet Analysis 7

2 The ATLAS experiment at the LHC 9
2.1 The Large Hadron Collider....................... 9
2.2 The ATLAS Detector.......................... 10
2.2.1 The Coordinate System .................... 10
2.2.2 The Components of the ATLAS Detector................... 11
2.3 Trigger and Data Acquisition System................. 13
2.4 Object Reconstruction, Identification, and Selection ..... 14

3 H → W W ∗ Analysis Strategy 17
3.1 Signal and Background Processes ................... 17
3.1.1 ggF and VBF Production Channels................... 17
3.1.2 The Properties of H → WW∗ decay ............. 18
3.1.3 Backgrounds .......................... 19
3.2 Data and Monte Carlo Samples .................... 19
3.2.1 Data Samples.......................... 19
3.2.2 Monte Carlo Samples...................... 19
3.3 Composite Observables......................... 21
3.3.1 Background Rejection ..................... 22
3.3.2 Topological Variables...................... 24
3.3.3 VBFObservable(orthogonal cuts)................... 24
3.4 Event Selection ............................. 26
3.4.1 Preselection ........................... 26
3.4.2 Signal Region Selection .................... 27
3.4.3 Background Estimation and Control Region Selection ....... 33

4 DNN in the H → WW∗ Analysis 37
4.1 Machine Learning in High Energy Physics ................... 37
4.2 Introduction to Deep Neural Networks ................ 38
4.2.1 Artificial Neuron and Neural Network ................... 39
4.2.2 DNN Training and Hyper-parameters ................... 41
4.3 DNN shape as final discriminant for ggF 2-jet category ......... 42
4.4 Significance Calculation ........................ 43
4.4.1 Binning optimization and the Flat-signal rebinning ......... 43
4.5 Training Samples and Architectures .................. 44

5 Result and Conclusion 52
5.1 DNN performance ........................... 52
5.2 Fit result ................................ 53
5.3 Conclusion................................ 54
A DNN Validation in Control Regions 57

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