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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳俊輝(Jiun-Huei Proty Wu) | |
| dc.contributor.author | Yu-Chiung Lin | en |
| dc.contributor.author | 林祐群 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:28:35Z | - |
| dc.date.available | 2021-07-20 | |
| dc.date.available | 2022-11-23T09:28:35Z | - |
| dc.date.copyright | 2021-07-20 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-13 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80145 | - |
| dc.description.abstract | 我們提出了一個新的貝氏卷積-長短期記憶神經網路(CLDNN)模型，藉由結合卷積神經網路(CNN)與長短期記憶神經網路(LSTM)，可以偵測到雙星系統從繞行階段到合併所發出的重力波訊號。我們的模型成功偵測到LIGO LivingstonO2觀測資料中包含的7個黑洞雙星合併事件，同時模型輸出的標記涵蓋了完整長度的重力波訊號。利用貝氏神經網路的不確定性估計，我們新定義了一個‘注意’狀態，用以注意那些可能被傳統神經網路視為雜訊的未知訊號。被標記為‘注意’的資料片段可以因此被更仔細地分析。我們使用了40960個樣本來訓練模型，並使用數筆具有512個長度為8秒的實際觀測雜訊片段，且注入信噪比ρopt從0到18的模擬重力波訊號的資料集做效能測試。我們的模型能識別90%以上訊噪比大於7的事件(訊噪比8.5以上可以達到100%)，且對於訊噪比大於8的事件我們的模型可以成功標記95%以上含有訊號的片段。而在使用未最佳化的程式碼與中階GPU的個人電腦上，我們的模型可在合併事件發生後約20秒即可標記出相對應的訊號區間。若使用更大的資料集與高速電腦，我們的模型可具有即時探測，甚至是預警合併事件的潛力。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:28:35Z (GMT). No. of bitstreams: 1 U0001-3006202120565900.pdf: 10850905 bytes, checksum: cbf6ced91a76d53d9b18b44a799ec079 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員審定書 i Acknowledgements ii 摘要 iii Abstract iv Contents v List of Figures vii Chapter 1 Introduction 1 1.1 Gravitational Wave Observations . . . . . . . . . . . . . . . . . . . 3 1.2 Matched-filter Searches for Gravitational Waves from Compact Binary Coalescence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 The Need for Real-time Detection and The Challenge of Matchedfilter searches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 2 Deep Neural Network 9 2.1 Multilayer Perceptron . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . 10 2.3 Recurrent Neural Network . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 DNNs in GW Detection and Their Challenges . . . . . . . . . . . . . 13 Chapter 3 Bayesian Neural Network for Gravitational Wave Detection 15 3.1 Bayesian Neural Network . . . . . . . . . . . . . . . . . . . . . . . 15 3.2 Architecture of Model . . . . . . . . . . . . . . . . . . . . . . . . . 19 Chapter 4 Data Preparation 22 4.1 Real Noise Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2 Simulated Templates of GW Signals . . . . . . . . . . . . . . . . . . 24 4.3 Data Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Chapter 5 Model Training 31 5.1 Training Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.2 Flagging Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Chapter 6 Results 38 6.1 Quantitative Test for Performance . . . . . . . . . . . . . . . . . . . 38 6.2 Performance against Real Events . . . . . . . . . . . . . . . . . . . . 41 Chapter 7 Discussion 46 7.1 Weighing KL Divergence . . . . . . . . . . . . . . . . . . . . . . . 46 7.2 The Prior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 7.3 Size of Monte-Carlo Sampling . . . . . . . . . . . . . . . . . . . . . 50 7.4 Model Calibration and Reliability . . . . . . . . . . . . . . . . . . . 52 7.5 Extensibility for Multi-Detector Network . . . . . . . . . . . . . . . 53 7.6 Multi-Class Detection . . . . . . . . . . . . . . . . . . . . . . . . . 55 7.7 Stride Size of Sliding Window . . . . . . . . . . . . . . . . . . . . . 55 7.8 Forecasting GW Events . . . . . . . . . . . . . . . . . . . . . . . . 57 Chapter 8 Conclusion 59 References 61 | |
| dc.language.iso | en | |
| dc.subject | 重力波 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 貝氏神經網路 | zh_TW |
| dc.subject | Bayesian neural network | en |
| dc.subject | deep learning | en |
| dc.subject | gravitational waves | en |
| dc.title | 使用貝氏神經網路偵測重力波 | zh_TW |
| dc.title | Detecting Gravitational Waves Using Bayesian Neural Network | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.author-orcid | 0000-0003-4939-1404 | |
| dc.contributor.oralexamcommittee | 江國興(Hsin-Tsai Liu),薛熙于(Chih-Yang Tseng),胡德邦,黃崇源 | |
| dc.subject.keyword | 重力波,深度學習,貝氏神經網路, | zh_TW |
| dc.subject.keyword | gravitational waves,deep learning,Bayesian neural network, | en |
| dc.relation.page | 79 | |
| dc.identifier.doi | 10.6342/NTU202101220 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-07-14 | |
| 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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