Dedicated photon identification for H ⭢ Zγ with the CMS detector at √ s = 13 TeV

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沈聿陞(Yu-Sheng Shen) 查詢紙本館藏

畢業系所

物理學系

論文名稱

(Dedicated photon identification for H ⭢ Zγ with the CMS detector at √ s = 13 TeV)

相關論文

★ 利用CMS探測器量測7TeV下的Zγ產生截面	★ 以CMS 偵測器在質心質量為8TeV使用雙渺子和三秒子頻道尋找雙電荷希格斯玻色子
★ 在質子對撞能量8TeV下尋找具有雙電子雙渺子末態的激發態輕子	★ Measurement of Zγ production in 5 fb-1 of pp collisions at √s = 7 TeV with the CMS detector
★ Search for a Higgs boson decaying into γ∗γ → eeγ in pp collisions at √s = 8 TeV with the CMS detector	★ Measurement of Z boson production in the electron decay channel in p+Pb collisions at √sNN = 5.02 TeV with the CMS detector
★ 火花偵測器的製成	★ 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
★ Search for Exotic Decay of A Higgs Boson into A Dark Photon and a Standard Model Photon in pp Collisions at √s = 13 TeV	★ Search for a Higgs boson decay into γ*γ→μμγ in pp collisions at √s = 13 TeV
★ Search for the rare decays of Z and Higgs bosons to J/ψ plus photon at √s = 13 TeV	★ Measurement of Zγ production cross section in pp collisions at sqrt(s) = 13 TeV with the CMS detector
★ Search for H→Zγ→bbγ produced in association with a Z boson in proton-proton collisions at √s = 13 TeV with the CMS detector at the LHC	★ nono
★ TCAD simulation of silicon detector	★ Assembly and Beam Test Analysis of sPHENIX INTT Detector

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至系統瀏覽論文 (2026-9-26以後開放)

摘要(中)

評估希格斯玻色子罕見衰變的測量有助於理解粒子物理標準模型
（SM）以及希格斯玻色子衰變至正負電子對和光子的貢獻。尋找希格斯
玻色子衰變至Z玻色子和光子具有相對乾淨的終態，但在很大程度上受到
不可避免的背景p-p→Zγ和可減少的背景Z玻色子及噴流過程的影響。為
增強LHC Run-2數據集的分析，已實施了多種新的分析技術。光子識別在
多變量分析中起著關鍵作用，可減少Z+Jets的貢獻。本研究考慮了來自
H→Zγ的光子和來自Z+Jets的光子的特定變數，使用LHC Run-2 UltraLegacy（UL）模擬數據進行了機器學習。學習結果與CMS-EGM團隊為
Ultra-Legacy的標准光子識別模型進行了比較。預期的中位數顯著性為
1.15σ，表明改進了8.4％。我們的目標是將這一新發展應用於Run-2 UltraLegacy和Run-3分析。

摘要(英)

The measurement of the rare decay of the Higgs boson would help understand the
Standard Model(SM) of particle physics and the NLO contribution of H→ll γ final
state. The search for H→Zγ has a relatively clean final state but is significantly contributed by irreducible background SM Zγ and reducible background Z+Jets processes.
To enhance the analysis of the LHC Run-2 dataset, several new analysis techniques
have been implemented. Photon identification plays a crucial role in reducing the
contribution of Z+jets in multivariate analysis. This study considers specific shower
shape and isolation variables of prompt photons from H→Zγ and jet-fake photons
from Z+Jets using LHC Run-2 Ultra-Legacy(UL) simulations. The training results are
compared with standard photon identification trained by CMS-EGM groups for ULtraLegacy. The expected significance is 1.15 σ, indicating an improvement of 8.4%. We aim
to implement this new development for Run-2 Ultra-Legacy and Run-3 analysis.

關鍵字(中)

★ 希格斯玻色子
★ 光子
★ 機器學習

關鍵字(英)

★ Higgs boson
★ photon
★ XGBoost

論文目次

List of Figures xv
List of Tables xxi
1 Introduction 1
1.1 Standard Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Higgs mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Higgs productions and decays . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 The rare Higgs decay H → Zγ → llγ . . . . . . . . . . . . . . . . . . . . . 8
1.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4.2 The features of the decay . . . . . . . . . . . . . . . . . . . . . . . 10
1.4.3 Background composition . . . . . . . . . . . . . . . . . . . . . . . 10
1.4.4 Previous results from CMS and ATLAS . . . . . . . . . . . . . . . 10
2 Experimental apparatus 13
2.1 Large Hadron Collider . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Compact Muon Solenoid . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3 Dedicated Photon MVA 19
3.1 Dedicated MVA training set-up . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.1 Training samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Signal samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Background samples . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.2 Preselection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.3 Event weighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.1.4 Training variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.1.5 Hyperparameter sets . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Training results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2.1 Comparison between dedicated photon ID and EGM ID . . . . . 27
3.2.2 Photon ID efficiency comparisons in pT and ηsc region . . . . . . . 31
3.3 Neural Network algorithm trained for dedicated ID . . . . . . . . . . . . 34
4 Validation of shower shape and isolation corrections 37
4.1 Vaildation with Z to µµγ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2 Mismatch of ESEoverRawE correction . . . . . . . . . . . . . . . . . . . . 49
4.3 The Validation of photon ID . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5 Analysis Strategy 53
5.1 MC samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2 Trigger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.3 Object reconstruction and identification . . . . . . . . . . . . . . . . . . . 54
5.3.1 Muons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.3.2 Electrons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.3.3 Photons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.4 Event Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.5 Final state radiation photon recovery . . . . . . . . . . . . . . . . . . . . . 57
5.6 kinematic refit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.7 Event categorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.8 Background and Signal modeling . . . . . . . . . . . . . . . . . . . . . . . 60
5.8.1 Non-resonant background modeling . . . . . . . . . . . . . . . . . 61
F-test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.8.2 Signal modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6 Result and Conclusion 71
6.1 Asymptotic limit and significance . . . . . . . . . . . . . . . . . . . . . . . 71
6.1.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
A Input variables of dedicated MVA ID 75
A.1 ECAL barrel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
A.2 ECAL endcap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
B Dedicated phton ID training output 81
B.1 Learning curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Bibliography 83

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指導教授

郭家銘(Chia-Ming Kuo)

審核日期

2023-10-5

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