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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張寶棣(Pao-Ti Chang) | |
| dc.contributor.author | Han-Sheng Wang | en |
| dc.contributor.author | 王涵聖 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:01:21Z | - |
| dc.date.available | 2021-11-04 | |
| dc.date.available | 2022-11-23T09:01:21Z | - |
| dc.date.copyright | 2021-11-04 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-26 | |
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Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, in Proceedings of the 32nd International Conference on Machine Learning, edited by F. Bach and D. Blei, volume 37 of Proceedings of Machine Learning Research, pages 448–456, Lille, France, 2015, PMLR. [45] S. Santurkar, D. Tsipras, A. Ilyas, and A. Mądry, How does batch normalization help optimization?, in Proceedings of the 32nd international conference on neural information processing systems, pages 2488–2498, 2018. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79472 | - |
| dc.description.abstract | 本篇論文假設標準模型對目標衰變的分支比預測正確，利用蒙地卡羅模擬探討了在 Belle II 實驗中尋找 B^+ → K^+νν 和 B^0 → K_S^0νν 衰變的可行性。為了辨別訊號事件，我們完整重建了 Υ(4S) → BB 事件中一顆衰變為強子的 B 介子，並限制除一 K 介子外，事件的剩餘部分中不得存在其他粒子。我們透過對抗式神經網路分類器來提升訊號背景比，並使用擬合方法來測量分支比。接著，我們利用艾西莫夫資料集和剖面概似比方法，以估算預期之訊號顯著性和分支比上限。 最後，本篇論文報告了在不同 Belle II 資料量下，所預期得到 90% 信心水準下之分支比上限與顯著性，以及在各 q^2 區間中， 90% 信心水準下之部分分支比上限。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:01:21Z (GMT). No. of bitstreams: 1 U0001-1010202113374800.pdf: 10299796 bytes, checksum: 436da2adffd49755f9c3188994777df9 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i Acknowledgements iii 摘要 v Abstract vii Contents ix List of Figures xv List of Tables xix 1 Introduction 1 1.1 The Standard Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 B-Factory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Belle II Experiment 7 2.1 SuperKEKB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Belle II Detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Vertex Detectors (VXD) . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.2 Central Drift Chamber (CDC) . . . . . . . . . . . . . . . . . . . . 11 2.2.3 Particle Identification Detectors (TOP ARICH) . . . . . . . . . 11 2.2.4 Electromagnetic Calorimeter (ECL) . . . . . . . . . . . . . . . . . 13 2.2.5 Klong and Muon Detector (KLM) . . . . . . . . . . . . . . . . . 14 2.3 Object Reconstruction in Belle II Software . . . . . . . . . . . . . . . . 14 2.3.1 Track reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.2 ECL cluster reconstruction . . . . . . . . . . . . . . . . . . . . . 15 2.3.3 Charged particle identification . . . . . . . . . . . . . . . . . . . . 16 3 Data Samples 17 3.1 Monte-Carlo Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.1.1 Signal samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.2 Background samples . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2 Real Data Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4 Analysis Strategy 21 4.1 Hadronic Tagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.2 Reconstruction of ϒ(4S) Candidates . . . . . . . . . . . . . . . . . . . . 22 4.3 Neural Networking Training . . . . . . . . . . . . . . . . . . . . . . . . 22 4.4 q2 Estimation and Event Reweighting . . . . . . . . . . . . . . . . . . . 23 4.5 Measurement of Branching Fractions . . . . . . . . . . . . . . . . . . . 25 5 Event Selection 27 5.1 Particle Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5.1.1 Track and ECL cluster selection . . . . . . . . . . . . . . . . . . . 27 5.1.2 Particle candidate selection . . . . . . . . . . . . . . . . . . . . . 28 5.2 FEI Skim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5.3 Preselection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5.3.1 Tag-side selection . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5.3.2 Extra-particle-related selection . . . . . . . . . . . . . . . . . . . 31 5.4 Continuum Suppression . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.5 Best Candidate Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.6 Efficiency Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 6 Neural Network Classifiers 37 6.1 Adversarial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 37 6.2 Continuum Suppression . . . . . . . . . . . . . . . . . . . . . . . . . . 39 6.3 B¯B Suppression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 7 Control Channel Study 45 7.1 Reconstruction and FEI Skim . . . . . . . . . . . . . . . . . . . . . . . 45 7.2 Basic Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 7.2.1 Daughter Particle Selection . . . . . . . . . . . . . . . . . . . . . 46 7.2.2 Signal-side Selection . . . . . . . . . . . . . . . . . . . . . . . . . 47 7.2.3 Best Candidate Selection . . . . . . . . . . . . . . . . . . . . . . 48 7.3 Data-MC Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 7.3.1 Modifications of Variables . . . . . . . . . . . . . . . . . . . . . . 48 7.3.2 Histogram Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 7.4 Calibration of Event Selection Efficiency . . . . . . . . . . . . . . . . . 49 8 Signal Extraction 55 8.1 Event Reweighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 8.1.1 Signal normalization . . . . . . . . . . . . . . . . . . . . . . . . . 55 8.1.2 FEI skim efficiencies . . . . . . . . . . . . . . . . . . . . . . . . 56 8.1.3 K-ID efficiencies . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 8.1.4 Event selection efficiencies . . . . . . . . . . . . . . . . . . . . . 57 8.2 PDF modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 8.2.1 Determination of histogram-like PDFs . . . . . . . . . . . . . . . 58 8.2.2 Parameters of PDFs . . . . . . . . . . . . . . . . . . . . . . . . . 63 8.3 Calculation of Expected Significances . . . . . . . . . . . . . . . . . . . 64 8.4 Calculation of Expected Upper Limit . . . . . . . . . . . . . . . . . . . 65 9 Systematic Uncertainties 67 9.1 Tracking Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 9.2 K-ID Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 9.2.1 Uncertainties for total branching fraction measurement . . . . . . . 68 9.2.2 Uncertainties for partial branching fraction measurement . . . . . . 69 9.3 K_S0 Reconstruction Efficiency . . . . . . . . . . . . . . . . . . . . . . . 69 9.4 FEI Skim Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 9.5 Event Selection Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . 70 9.6 Partial Branching Fractions . . . . . . . . . . . . . . . . . . . . . . . . . 72 9.7 Statistical Uncertainty of MC Samples . . . . . . . . . . . . . . . . . . . 72 9.8 Summary Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 10 Results and Conclusion 77 A FEI Skim Definition 81 B Input Variables for Neural Networks 83 B.1 Continuum Suppression . . . . . . . . . . . . . . . . . . . . . . . . . . 83 B.2 BB suppression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 C Details of Neural Networks 87 C.1 Neural Network Structure . . . . . . . . . . . . . . . . . . . . . . . . . 87 C.1.1 Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 C.1.2 Adversary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 C.2 Training Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 D Data-MC Comparison with Control Channel 91 Bibliography 95 | |
| dc.language.iso | en | |
| dc.title | 在 Belle II 實驗中尋找 B→Kνν 衰變之可行性分析 | zh_TW |
| dc.title | Feasibility Study of Searching B→Kνν Decay at Belle II Experiment | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.advisor-orcid | 張寶棣(0000-0003-4064-388X) | |
| dc.contributor.oralexamcommittee | 張敏娟(Hsin-Tsai Liu),王名儒(Chih-Yang Tseng),徐靜戈 | |
| dc.subject.keyword | SuperKEKB,Belle II 實驗,B介子,稀有B衰變,強子標籤法, | zh_TW |
| dc.subject.keyword | SuperKEKB,Belle II Experiment,B meson,rare B decay,hadronic tag, | en |
| dc.relation.page | 99 | |
| dc.identifier.doi | 10.6342/NTU202103640 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-27 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 物理學研究所 | zh_TW |
| 顯示於系所單位： | 物理學系 | |
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