請用此 Handle URI 來引用此文件:
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 | |
dc.identifier.citation | 1. Aasi, J. et al. Advanced LIGO. Classical and Quantum Gravity 32, 074001 (2015).
2. Abbott, B. P. et al. Observation of Gravitational Waves from a Binary Black Hole Merger. Phys. Rev. Lett. 116, 061102 (2016). 3. Abbott, B. P. et al. Tests of General Relativity with GW150914. Phys. Rev. Lett. 116, 221101 (2016). 4. Abbott, B. P. et al. The Rate of Binary Black Hole Mergers Inferred from Advanced LIGO Observations Surrounding GW150914. The Astrophysical Journal 833, L1 (2016). 5. Abbott, B. P. et al. Astrophysical Implications of the Binary Black-hole Merger GW150914. The Astrophysical Journal 818, L22 (2016). 6. Acernese, F. et al. Advanced Virgo: a second-generation interferometric gravitational wave detector. Classical and Quantum Gravity 32, 024001 (2015). 7. Abbott, B. P. et al. GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral. Phys. Rev. Lett. 119, 161101 (2017). 8. Abbott, B. P. et al. Gravitational Waves and Gamma-Rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A. The Astrophysical Journal 848, L13 (2017). 9. Abbott, B. P. et al. Multi-messenger Observations of a Binary Neutron Star Merger. The Astrophysical Journal 848, L12 (2017). 10. Abbott, B. P. et al. A gravitational-wave standard siren measurement of the Hubble constant. Nature 551, 85–88 (2017). 11. Aso, Y. et al. Interferometer design of the KAGRA gravitational wave detector. Phys. Rev. D 88, 043007 (2013). 12. Michimura, Y. et al. Prospects for improving the sensitivity of the cryogenic gravitational wave detector KAGRA. Phys. Rev. D 102, 022008 (2020). 13. Abbott, B. P. et al. Binary Black Hole Mergers in the First Advanced LIGO Observ- ing Run. Phys. Rev. X 6, 041015 (2016). 14. Abbott, B. P. et al. GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs. Phys. Rev. X 9, 031040 (2019). 15. Abbott, R. et al. GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run. arXiv e-prints, arXiv:2010.14527 (2020). 16. Haino, S. GPU-accelerated CBC Parameter Estimation KAGRA Report No. JGW- G1807674 (2018). 17. Haino, S. GW Parameter Estimations and Simulations KAGRA Report No. JGW- G1808212 (2018). 18. Abbott, B. P. et al. Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA. Living Reviews in Relativity 23, 3 (2020). 19. Einstein, A. Die Feldgleichungen der Gravitation. Sitzungsberichte der Königlich Preußischen Akademie der Wissenschaften Berlin, 844–847 (1915). 20. Einstein, A. Näherungsweise Integration der Feldgleichungen der Gravitation. Sitzungs- berichte der Königlich Preußischen Akademie der Wissenschaften Berlin, 688–696 (1916). 21. Schutz, B. A First Course in General Relativity 2nd ed. (Cambridge University Press, 2009). 22. Carroll, S. M. Lecture Notes on General Relativity. arXiv e-prints, gr–qc/9712019 (1997). 23. Misner, C. W., Thorne, K. S. Wheeler, J. A. Gravitation (W. H. Freeman, San Francisco, 1973). 24. Maggiore, M. Gravitational Waves. Vol. 1: Theory and Experiments (Oxford University Press, 2007). 25. Creighton, J. D. E. Anderson, W. G. Gravitational-wave physics and astronomy: An introduction to theory, experiment and data analysis (Wiley-VCH Verlag GmbH, 2011). 26. Sathyaprakash, B. S. Schutz, B. F. Physics, Astrophysics and Cosmology with Gravitational Waves. Living Rev. Rel. 12, 2 (2009). 27. Allen, B. Romano, J. D. Detecting a stochastic background of gravitational radiation: Signal processing strategies and sensitivities. Phys. Rev. D 59, 102001 (1999). 28. Abbott, B. P. et al. All-sky search for short gravitational-wave bursts in the first Advanced LIGO run. Phys. Rev. D 95, 042003 (2017). 29. LALSuite https://lscsoft.docs.ligo.org/lalsuite/. 30. Veitch, J. et al. Parameter estimation for compact binaries with ground-based gravitational- wave observations using the LALInference software library. Phys. Rev. D 91, 042003 (2015). 31. Blanchet, L. Gravitational Radiation from Post-Newtonian Sources and Inspiralling Compact Binaries. Living Reviews in Relativity 17, 2 (2014). 32. Buonanno, A. et al. Comparison of post-Newtonian templates for compact binary inspiral signals in gravitational-wave detectors. Phys. Rev. D 80, 084043 (2009). 33. Ajith, P. et al. Template bank for gravitational waveforms from coalescing binary black holes: Nonspinning binaries. Phys. Rev. D 77, 104017 (2008). 34. Hannam, M. et al. Simple Model of Complete Precessing Black-Hole-Binary Gravitational Waveforms. Phys. Rev. Lett. 113, 151101 (2014). 35. Abbott, B. P. et al. LIGO: the Laser Interferometer Gravitational-Wave Observatory. Reports on Progress in Physics 72, 076901 (2009). 36. Miller, J. et al. Prospects for doubling the range of Advanced LIGO. Phys. Rev. D 91, 062005 (2015). 37. Barsotti, L. et al. The A+ design curve LIGO Report No. LIGO-T1800042 (2018). 38. Degallaix, J. Virgo Collaboration. Advanced Virgo + preliminary studies Report No. VIR-0300A-18 (2018). 39. Somiya, K. Detector configuration of KAGRA–the Japanese cryogenic gravitational-wave detector. Classical and Quantum Gravity 29, 124007 (2012). 40. Haino, S. hyper-KAGRA KAGRA Report No. JGW-G1910533 (2019). 41. Iyer, B. et al. LIGO-India Technical Report No. M1100296-v2 (2011). 42. Bailes, M. et al. Ground-Based Gravitational-Wave Astronomy in Australia: 2019 White Paper. arXiv e-prints, arXiv:1912.06305 (2019). 43. Allen, B. et al. FINDCHIRP: An algorithm for detection of gravitational waves from inspiraling compact binaries. Phys. Rev. D 85, 122006 (2012). 44. Cannon, K. et al. Singular value decomposition applied to compact binary coalescence gravitational-wave signals. Phys. Rev. D 82, 044025 (2010). 45. Abbott, B. P. et al. GW150914: First results from the search for binary black hole coalescence with Advanced LIGO. Phys. Rev. D 93, 122003 (2016). 46. Veitch, J. Vecchio, A. Bayesian coherent analysis of in-spiral gravitational wave signals with a detector network. Phys. Rev. D 81, 062003 (2010). 47. Sivia, D. S. Skilling, J. Data Analysis: A Bayesian Tutorial 2nd ed. (Oxford University Press, 2006). 48. Thrane, E. Talbot, C. An introduction to Bayesian inference in gravitational-wave astronomy: Parameter estimation, model selection, and hierarchical models. Publications of the Astronomical Society of Australia 36, e010 (2019). 49. Abbott, B. P. et al. A guide to LIGO–Virgo detector noise and extraction of transient gravitational-wave signals. Classical and Quantum Gravity 37, 055002 (2020). 50. Gelman, A. et al. Bayesian Data Analysis 3rd ed. (Chapman and Hall/CRC, 2013). 51. Metropolis, N. et al. Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 21, 1087–1092 (1953). 52. Hastings, W. K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97–109 (1970). 53. Skilling, J. Nested Sampling. AIP Conference Proceedings 735, 395–405 (2004). 54. Skilling, J. Nested sampling for general Bayesian computation. Bayesian Anal. 1, 833–859 (2006). 55. Feroz, F., Hobson, M. P. Bridges, M. MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics. Monthly Notices of the Royal Astronomical Society 398, 1601–1614 (2009). 56. Smith, R. J. E. et al. Massively parallel Bayesian inference for transient gravitational-wave astronomy. Monthly Notices of the Royal Astronomical Society 498, 4492– 4502 (2020). 57. Ashton, G. et al. Bilby: A User-friendly Bayesian Inference Library for Gravitational-wave Astronomy. The Astrophysical Journal Supplement Series 241, 27 (2019). 58. Vitale, S. Evans, M. Parameter estimation for binary black holes with networks of third-generation gravitational-wave detectors. Phys. Rev. D 95, 064052 (2017). 59. Chen, H.-Y. et al. Distance measures in gravitational-wave astrophysics and cosmology. Classical and Quantum Gravity 38, 055010 (2021). 60. Miller, J. Hanford Observatory, L. On the inspiral range LIGO Tech. Note No. LIGO-T1500491-v2 (LIGO Laboratory, 2016). 61. Haino, S. Future Plans of Ground Based GW projects KAGRA Report No. JGW- G1910901 (2019). 62. Pankow, C. et al. Localization of Compact Binary Sources with Second-generation Gravitational-wave Interferometer Networks. The Astrophysical Journal 902, 71 (2020). 63. Arun, K. G. et al. Higher-order spin effects in the amplitude and phase of gravitational waveforms emitted by inspiraling compact binaries: Ready-to-use gravitational waveforms. Phys. Rev. D 79, 104023 (2009). 64. Veitch, J. et al. Estimating parameters of coalescing compact binaries with proposed advanced detector networks. Phys. Rev. D 85, 104045 (2012). 65. Vitale, S. et al. Parameter estimation for heavy binary-black holes with networks of second-generation gravitational-wave detectors. Phys. Rev. D 95, 064053 (2017). 66. Aasi, J. et al. Parameter estimation for compact binary coalescence signals with the first generation gravitational-wave detector network. Phys. Rev. D 88, 062001 (2013). 67. Cutler, C. Flanagan, É. E. Gravitational waves from merging compact binaries: How accurately can one extract the binary’s parameters from the inspiral waveform? Phys. Rev. D 49, 2658–2697 (1994). 68. Nissanke, S. et al. Exploring Short Gamma-ray Bursts as Gravitational-wave Standard Sirens. The Astrophysical Journal 725, 496–514 (2010). 69. Abbott, B. P. et al. Properties of the Binary Black Hole Merger GW150914. Phys. Rev. Lett. 116, 241102 (2016). 70. Abbott, B. P. et al. Localization and Broadband Follow-up of the Gravitational-wave Transient GW150914. The Astrophysical Journal 826, L13 (2016). 71. Sanders, J. Kandrot, E. CUDA by Example: An Introduction to General-Purpose GPU Programming 1st ed. (Addison-Wesley Professional, 2010). 72. Henderson, R. W. Goggans, P. M. Parallelized nested sampling. AIP Conference Proceedings 1636, 100–105 (2014). 73. Buchner, J. et al. X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue. Astronomy Astrophysics 564, A125 (2014). 74. Trotta, R. et al. Constraints on Cosmic-ray Propagation Models from A Global Bayesian Analysis. The Astrophysical Journal 729, 106 (2011). 75. Mukherjee, P., Parkinson, D. Liddle, A. R. A Nested Sampling Algorithm for Cosmological Model Selection. The Astrophysical Journal 638, L51–L54 (2006). 76. Easther, R. Peiris, H. V. Bayesian analysis of inflation. II. Model selection and constraints on reheating. Phys. Rev. D 85, 103533 (2012). 77. Planck Collaboration et al. Planck 2018 results. X. Constraints on inflation. Astronomy Astrophysics 641, A10 (2020). 78. Buchmueller, O. et al. The CMSSM and NUHM1 after LHC Run 1. European Physical Journal C 74, 2922 (2014). 79. Martinez, G. D. et al. Comparison of statistical sampling methods with ScannerBit, the GAMBIT scanning module. European Physical Journal C 77, 761 (2017). 80. Baldock, R. J. N. et al. Determining pressure-temperature phase diagrams of materials. Phys. Rev. B 93, 174108 (2016). 81. Russel, P. M. et al. Model Selection and Parameter Inference in Phylogenetics Using Nested Sampling. Systematic Biology 68, 219–233 (2018). 82. Johnson, R., Kirk, P. Stumpf, M. P. H. SYSBIONS: nested sampling for systems biology. Bioinformatics 31, 604–605 (2014). | |
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.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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
U0001-0102202109070900.pdf 目前未授權公開取用 | 3.17 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。