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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74849完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳凱風(Kai-Feng Chen) | |
| dc.contributor.author | Yun-Jing Huang | en |
| dc.contributor.author | 黃筠淨 | zh_TW |
| dc.date.accessioned | 2021-06-17T09:08:47Z | - |
| dc.date.available | 2021-02-20 | |
| dc.date.copyright | 2021-02-20 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-02-05 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74849 | - |
| dc.description.abstract | 隨著重力波探測器精密度的提升，未來觀測到重力波事件的頻率將逐年增 加。由於參數估計相當耗時，不足以應付如此大量的事件數，參數估計的加速方 法正在被研發中。本研究的第一部份致力於描述 GPE+，一個利用圖形運算器平行 加速之重力波參數估計程式。GPE+ 運用了兩種加速方式，重力波模型與概似函數 之計算、以及巢式抽樣之平行化。其中，模型與概似函數之加速已被利用在 GPE 程式，並在一個圖形處理器上達到了比 LALInference 在一個中央處理器上快一 百倍的速度。本研究開發了新的演算法以平行化 GPE 內的巢式抽樣法，由此設計 出一個新的重力波參數估計程式:GPE+。GPE+ 表現出比 GPE 快二至四倍的速度， 並且輸出與 GPE 一致的參數估計結果，代表著 GPE+ 能達到比 LALInference 快 二百至四百倍的速度。GPE+ 的高速平行運算將有利於模擬未來重力波探測器之貢 獻，以及電磁波對硬體之觀測。 本研究第二部分運用了 GPE+ 以模擬大量重力波數據來預測神岡重力波探測 器(KAGRA)於不同靈敏度下(KAGRA+:180 百萬秒差距;hyper KAGRA:500 百萬秒差距)在全球重力波網路的貢獻。本研究結果發現 KAGRA 最大的貢獻在 於增加事件定位的準確度;其中，運用 KAGRA+ 能將精準度提升二倍，而 hyper KAGRA 則能將精準度提升四倍。至於距離以及傾角，KAGRA 則稍有貢獻，但 質量以及自旋卻僅有微小貢獻。事件定位的提升將有利於電磁波對應體的觀測， 而距離量測的準確度提升則能增強重力波作為標準警笛之能力。此結果顯示將 KAGRA 加入全球重力波網路能提升未來哈伯常數之觀測。 | zh_TW |
| dc.description.abstract | With more sensitive gravitational-wave detectors under construction, the detection rate of gravitational waves from compact binary coalescence sources will continue to increase in the near future. This era of multi-detection gravitational wave astronomy presents a challenge for gravitational wave parameter estimation, a time-consuming process even in the single-event case. Thus, an acceleration method for parameter estimation is in demand. The first half of this thesis presents GPE+, a GPU-accelerated parameter estimation program for gravitational waves. The GPU parallelization methods implemented in GPE+ are twofold: (1) the waveform and likelihood calculations, (2) and the nested sampling algorithm. The waveform and likelihood accelerations have been employed in the code GPE, which demonstrated a ∼100 times speedup on one GPU compared with LALInference on one CPU. In this thesis, we parallelized the nested sampling algorithm in GPE by parallelizing the prior sampling portion, and designed a new program: GPE+. GPE+ demonstrates a 2-4 times speedup and produces consistent results compared to its predecessor, GPE, which makes GPE+ 200-400 times faster than LALInference. GPE+ offers the opportunity to perform large simulations to estimate observing scenarios for detector upgrades, and generate sky localization confidence areas in a short amount of time for electromagnetic follow-up of gravitational wave events. The second half of this thesis uses GPE+ to run thousands of simulations with future sensitivities of a gravitational-wave detector network. The simulations emphasize the effects of adding the KAGRA detector in the global network at different sensitivities, the KAGRA+ detector (180 Mpc) and the hyper KAGRA detector (500 Mpc). The results show that including the KAGRA detectors will have the most improvement in sky localization, with KAGRA+ providing a factor of two improvements and hyper KAGRA providing a factor of four improvements. Distance and inclination angle measurements show modest improvements, whereas the mass and spin measurements only exhibit minimal improvements. The sky localization improvement implies that adding KAGRA to the global detector network can enhance the electromagnetic counterpart identification, whereas the distance improvement can better the standard siren method of gravitational waves. Both improvements indicate that adding KAGRA can lead to better measurements of the Hubble constant. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T09:08:47Z (GMT). No. of bitstreams: 1 U0001-0102202109070900.pdf: 3246890 bytes, checksum: de26ea7a728a28098256d1f2d0b6fda5 (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 xxi Chapter 1 Introduction 1 Chapter 2 Gravitational waves in general relativity 3 2.1 General relativity 3 2.2 Gravitational waves 4 2.3 Effects of gravitational waves on test masses 6 2.4 Generation of gravitational waves 9 2.5 Gravitational-wave sources 9 2.5.1 Stochastic background 10 2.5.2 Bursts 10 2.5.3 Continuous waves 10 2.5.4 Compact binary coalescences 11 Chapter 3 Compact binary coalescences (CBC) 13 3.1 Evolution of binary mergers 13 3.2 Waveform models 15 3.2.1 Parameters 15 3.2.2 TaylorF2 16 3.2.3 IMRPhenomPv2 17 Chapter 4 Detectors 19 4.1 The Michelson interferometer 19 4.1.1 Fabry-Perot cavities 21 4.1.2 Power recycling 21 4.1.3 Signal recycling 22 4.2 Antenna patterns 23 4.3 Ground-based interferometers 26 4.3.1 LIGO 26 4.3.2 Virgo 27 4.3.3 KAGRA 27 4.3.4 LIGO-India 28 4.3.5 Australia detector 28 Chapter 5 Gravitational wave data analysis 29 5.1 Noise power spectrum 29 5.2 Matched filtering 30 5.3 Parameter estimation 32 5.3.1 Bayesian inference 33 5.3.2 Model selection 34 5.3.3 Data model 34 5.3.4 Likelihood function 35 5.4 Markov-chain Monte Carlo 36 5.5 Nested sampling 37 5.5.1 Prior mass 40 5.5.2 Sampling a new point 41 5.5.3 Posterior samples 41 5.5.4 Nested sampling algorithm 42 Chapter 6 GPU-parallelized nested sampling (GPE+) 43 6.1 GPU 43 6.2 GPE 44 6.3 GPE speedup (GPE+) 44 6.3.1 Drawing a new sample from the prior 45 6.3.2 Parallel algorithm design 46 6.3.2.1 Reduce number of idle threads 46 6.3.2.2 Cached array 47 6.3.3 Performance test 49 6.3.4 Error analysis 52 6.3.4.1 Possible sources of error 54 6.3.5 Bottlenecks and comparison with other work 55 Chapter 7 Observing Scenarios of KAGRA 57 7.1 Network configurations 57 7.2 Binary black hole mergers 60 7.2.1 Simulated binary black hole population 61 7.2.2 Parameter estimation setup 62 7.2.3 Results 64 7.2.3.1 Sky localization 64 7.2.3.2 Luminosity distance 66 7.2.3.3 Inclination 68 7.2.3.4 Mass 70 7.2.3.5 Spin 75 7.3 Binary neutron star mergers 77 7.3.1 Simulated binary neutron star population 77 7.3.2 Parameter estimation setup 78 7.3.3 Results 80 7.3.3.1 Sky localization 80 7.3.3.2 Luminosity distance 82 7.3.3.3 Inclination 84 7.3.3.4 Mass 85 7.3.3.5 Spin 90 Chapter 8 Conclusions and future work 95 8.1 Summary of results 95 8.1.1 GPE+ 95 8.1.2 Observing scenarios of KAGRA 96 8.2 Future work 97 8.2.1 GPE+ 97 8.2.2 Observing scenarios of KAGRA 98 References 101 | |
| dc.language.iso | en | |
| dc.subject | 緻密雙星耦合 | zh_TW |
| dc.subject | 重力波 | zh_TW |
| dc.subject | 神岡重力波探測器 | zh_TW |
| dc.subject | 圖形處理器 | zh_TW |
| dc.subject | 巢式抽樣 | zh_TW |
| dc.subject | Gravitational Waves | en |
| dc.subject | KAGRA | en |
| dc.subject | Compact Binary Coalescences | en |
| dc.subject | Nested Sampling | en |
| dc.subject | GPU | en |
| dc.title | 以平行加速之巢式抽樣模擬神岡重力波探測器在未來重力波網路之貢獻 | zh_TW |
| dc.title | Observing scenarios of KAGRA in future gravitational-wave networks using GPU-parallelized nested sampling | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 109-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.author-orcid | 0000-0002-2952-8429 | |
| dc.contributor.coadvisor | 灰野禎一(Sadakazu Haino) | |
| dc.contributor.oralexamcommittee | 劉國欽(Guo-Chin Liu) | |
| dc.subject.keyword | 重力波,神岡重力波探測器,緻密雙星耦合,巢式抽樣,圖形處理器, | zh_TW |
| dc.subject.keyword | Gravitational Waves,KAGRA,Compact Binary Coalescences,Nested Sampling,GPU, | en |
| dc.relation.page | 107 | |
| dc.identifier.doi | 10.6342/NTU202100310 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2021-02-08 | |
| 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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