Abstract
Gravitational wave (GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries (MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise. Addressing this gap, we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data’s complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation.
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References
J. Aasi, et al. (The LIGO Scientific Collaboration), Class. Quantum Grav. 32, 074001 (2015).
F. Acernese, et al. (Virgo Collaboration), Class. Quantum Grav. 32, 024001 (2015).
T. Akutsu, et al. (KAGRA Collaboration), Nat. Astron. 3, 35 (2019).
B. P. Abbott, et al. (The LIGO Scientific Collaboration), Living Rev. Relativ. 23, 3 (2020).
P. Amaro-Seoane, et al. (LISA Consortium), arXiv: 1702.00786.
J. Baker, et al. (LISA Consortium), arXiv: 1907.06482.
W. R. Hu, and Y. L. Wu, Natl. Sci. Rev. 4, 685 (2017).
Z. Luo, Y. Wang, Y. Wu, W. Hu, and G. Jin, Prog. Theor. Exp. Phys. 2021, 83 (2021).
Y. L. Wu, et al. (The Taiji Scientific Collaboration), Commun. Phys. 4, 34 (2021).
J. Luo, L. S. Chen, H. Z. Duan, Y. G. Gong, S. Hu, J. Ji, Q. Liu, J. Mei, V. Milyukov, M. Sazhin, C. G. Shao, V. T. Toth, H. B. Tu, Y. Wang, Y. Wang, H. C. Yeh, M. S. Zhan, Y. Zhang, V. Zharov, and Z. B. Zhou, Class. Quantum Grav. 33, 035010 (2016).
Z. Luo, M. Zhang, and Y. Wu, Chin. J. Space Sci. 40, 691 (2020).
X. Zhong, W. B. Han, Z. Luo, and Y. Wu, Sci. China-Phys. Mech. Astron. 66, 230411 (2023), arXiv: 2305.04478.
G. L. Li, Y. Tang, and Y. L. Wu, Sci. China-Phys. Mech. Astron. 65, 100412 (2022), arXiv: 2112.14041.
Y. L. Wu, Sci. China-Phys. Mech. Astron. 66, 260411 (2023), arXiv: 2208.03290.
Z. Luo, Z. K. Guo, G. Jin, Y. Wu, and W. Hu, Results Phys. 16, 102918 (2020).
Z. Luo, M. Zhang, and Y. Wu, Chin. J. Space Sci. 42, 536 (2022).
J. B. Bayle, B. Bonga, C. Caprini, D. Doneva, M. Muratore, A. Petiteau, E. Rossi, and L. Shao, Nat. Astron. 6, 1334 (2022).
L. Speri, N. Karnesis, A. I. Renzini, and J. R. Gair, Nat. Astron. 6, 1356 (2022).
R. Umstatter, N. Christensen, M. Hendry, R. Meyer, V. Simha, J. Veitch, S. Vigeland, and G. Woan, Phys. Rev. D 72, 022001 (2005).
N. J. Cornish, and K. Shuman, Phys. Rev. D 101, 124008 (2020).
N. Karnesis, M. L. Katz, N. Korsakova, J. R. Gair, and N. Stergioulas, arXiv: 2303.02164.
C. R. Weaving, L. K. Nuttall, I. W. Harry, S. Wu, and A. Nitz, arXiv: 2306.16429.
S. H. Strub, L. Ferraioli, C. Schmelzbach, S. C. Sthler, and D. Giardini, arXiv: 2307.03763.
N. J. Cornish, and J. Crowder, Phys. Rev. D 72, 043005 (2005).
T. B. Littenberg, N. J. Cornish, K. Lackeos, and T. Robson, Phys. Rev. D 101, 123021 (2020).
T. B. Littenberg, and N. J. Cornish, Phys. Rev. D 107, 063004 (2023).
Q. Baghi (LDC Working Group), The LISA Data challenges, in 56th Rencontres de Moriond on Gravitation (2022), arXiv: 2204.12142.
G. Pratten, A. Klein, C. J. Moore, H. Middleton, N. Steinle, P. Schmidt, and A. Vecchio, Phys. Rev. D 107, 123026 (2023).
W. H. Ruan, H. Wang, C. Liu, and Z. K. Guo, Phys. Lett. B 841, 137904 (2023).
W.-H. Ruan, H. Wang, C. Liu, and Z.-K. Guo, arXiv: 2307.14844.
P. Amaro-Seoane, et al. (LISA Collaboration), Living Rev. Relativ. 26, 2 (2023), arXiv: 2203.06016.
R. Gold, V. Paschalidis, M. Ruiz, S. L. Shapiro, Z. B. Etienne, and H. P. Pfeiffer, Phys. Rev. D 90, 104030 (2014), arXiv: 1410.1543.
M. I. Jordan, and T. M. Mitchell, Science 349, 255 (2015).
E. Cuoco, J. Powell, M. Cavagliá, K. Ackley, M. Bejger, C. Chatterjee, M. Coughlin, S. Coughlin, P. Easter, R. Essick, H. Gabbard, T. Gebhard, S. Ghosh, L. Haegel, A. Iess, D. Keitel, Z. Márka, S. Márka, F. Morawski, T. Nguyen, R. Ormiston, M. Pürrer, M. Razzano, K. Staats, G. Vajente, and D. Williams, Mach. Learn.-Sci. Technol. 2, 011002 (2021).
Y. LeCun, Y. Bengio, and G. Hinton, Nature 521, 436 (2015).
H. Gabbard, C. Messenger, I. S. Heng, F. Tonolini, and R. MurraySmith, Nat. Phys. 18, 112 (2022).
C. Chatterjee, L. Wen, K. Vinsen, M. Kovalam, and A. Datta, Phys. Rev. D 100, 103025 (2019).
S. R. Green, C. Simpson, and J. Gair, Phys. Rev. D 102, 104057 (2020).
S. R. Green, and J. Gair, Mach. Learn.-Sci. Technol. 2, 03LT01 (2021).
A. Delaunoy, A. Wehenkel, T. Hinderer, S. Nissanke, C. Weniger, A. R. Williamson, and G. Louppe, arXiv: 2010.12931.
P. G. Krastev, K. Gill, V. A. Villar, and E. Berger, Phys. Lett. B 815, 136161 (2021).
H. Shen, E. A. Huerta, E. O’Shea, P. Kumar, and Z. Zhao, Mach. Learn.-Sci. Technol. 3, 015007 (2022).
M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno, and B. Scholkopf, Phys. Rev. Lett. 127, 241103 (2021).
M. Dax, S. R. Green, J. Gair, M. Purrer, J. Wildberger, J. H. Macke, A. Buonanno, and B. Scholkopf, Phys. Rev. Lett. 130, 171403 (2023).
A. J. K. Chua, and M. Vallisneri, Phys. Rev. Lett. 124, 041102 (2020).
I. Kobyzev, S. J. D. Prince, and M. A. Brubaker, IEEE Trans. Pattern Anal. Mach. Intell. 43, 3964 (2021).
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, J. Mach. Learn. Res. 22, 1 (2021).
J. Langendorff, A. Kolmus, J. Janquart, and C. Van Den Broeck, Phys. Rev. Lett. 130, 171402 (2023).
D. Ruhe, K. Wong, M. Cranmer, and P. Forré, arXiv: 2211.09008.
M. J. Williams, J. Veitch, and C. Messenger, Phys. Rev. D 103, 103006 (2021).
M. Crisostomi, K. Dey, E. Barausse, and R. Trotta, arXiv: 2305.18528.
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, in Advances in Neural Information Processing Systems, edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, and R. Garnett, volume 32 (Curran Associates, Inc., 2019).
K. He, X. Zhang, S. Ren, and J. Sun, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016).
M. Dax, S. R. Green, J. Gair, M. Deistler, B. Scholkopf, and J. H. Macke, arXiv: 2111.13139.
M. L. Katz, S. Marsat, A. J. K. Chua, S. Babak, and S. L. Larson, Phys. Rev. D 102, 023033 (2020).
M. L. Katz, arXiv: 2111.01064.
L. London, S. Khan, E. Fauchon-Jones, C. Garc’ia, M. Hannam, S. Husa, X. Jim’enez-Forteza, C. Kalaghatgi, F. Ohme, and F. Pannarale, 120, 161102 (2018).
S. Marsat, and J. G. Baker, arXiv: 1806.10734.
S. Marsat, J. G. Baker, and T. D. Canton, Phys. Rev. D 103, 083011 (2021).
M. Katz, mikekatz04/BBHx: First official public release (Zenodo, Frankfurt, 2021).
C. García-Quirós, M. Colleoni, S. Husa, H. Estellés, G. Pratten, A. Ramos-Buades, M. Mateu-Lucena, and R. Jaume, Phys. Rev. D 102, 064002 (2020).
G. Pratten, C. García-Quirós, M. Colleoni, A. Ramos-Buades, H. Estellés, M. Mateu-Lucena, R. Jaume, M. Haney, D. Keitel, J. E. Thompson, and S. Husa, Phys. Rev. D 103, 104056 (2021).
M. Vallisneri, J. Crowder, and M. Tinto, Class. Quantum Grav. 25, 065005 (2008).
G. Wang, W. T. Ni, W. B. Han, S. C. Yang, and X. Y. Zhong, Phys. Rev. D 102, 024089 (2020).
G. Wang, and W. T. Ni, Phys. Scr. 98, 075005 (2023).
N. J. Cornish, and T. B. Littenberg, Phys. Rev. D 76, 083006 (2007).
T. Robson, N. J. Cornish, N. Tamanini, and S. Toonen, Phys. Rev. D 98, 064012 (2018).
M. L. Katz, mikekatz04/GBGPU: First official public release! (Zenodo, Frankfurt, 2022).
G. Wang, Z. Yan, B. Hu, and W. T. Ni, Phys. Rev. D 107, 124022 (2023).
V. Korol, E. M. Rossi, and E. Barausse, Mon. Not. R. Astron. Soc. 483, 5518 (2019).
C. Liu, W. H. Ruan, and Z. K. Guo, Phys. Rev. D 107, 064021 (2023).
X. H. Zhang, S. D. Zhao, S. D. Mohanty, and Y. X. Liu, Phys. Rev. D 106, 102004 (2022).
I. Loshchilov, and F. Hutter, arXiv: 1608.03983.
D. P. Kingma, and J. Ba, arXiv: 1412.6980.
N. J. Cornish, Phys. Rev. D 105, 044007 (2022).
M. Vallisneri, and C. R. Galley, Class. Quantum Grav. 29, 124015 (2012).
J. Veitch, V. Raymond, B. Farr, W. Farr, P. Graff, S. Vitale, B. Aylott, K. Blackburn, N. Christensen, M. Coughlin, W. Del Pozzo, F. Feroz, J. Gair, C. J. Haster, V. Kalogera, T. Littenberg, I. Mandel, R. O’Shaughnessy, M. Pitkin, C. Rodriguez, C. Röver, T. Sidery, R. Smith, M. Van Der Sluys, A. Vecchio, W. Vousden, and L. Wade, Phys. Rev. D 91, 042003 (2015).
J. Skilling, Bayesian Anal. 1, 833 (2006).
J. Veitch, and A. Vecchio, Phys. Rev. D 81, 062003 (2010).
G. Ashton, M. Hübner, P. D. Lasky, C. Talbot, K. Ackley, S. Biscoveanu, Q. Chu, A. Divakarla, P. J. Easter, B. Goncharov, F. H. Vivanco, J. Harms, M. E. Lower, G. D. Meadors, D. Melchor, E. Payne, M. D. Pitkin, J. Powell, N. Sarin, R. J. E. Smith, and E. Thrane, Astrophys. J. Suppl. Ser. 241, 27 (2019).
S. Marsat, J. G. Baker, and T. D. Canton, Phys. Rev. D 103, 083011 (2021).
G. Pratten, P. Schmidt, H. Middleton, and A. Vecchio, arXiv: 2307.13026.
D.-A. Clevert, T. Unterthiner, and S. Hochreiter, arXiv: 1511.07289.
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, anflows: normalizing flows in PyTorch (Zenodo, Frankfurt, 2020).
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This work was supported by the National Key Research and Development Program of China (Grant Nos. 2021YFC2203004, and 2021YFC2201903). He Wang was supported by the National Natural Science Foundation of China (Grant Nos. 12147103, and 12247187), and the Fundamental Research Funds for the Central Universities. Minghui Du and Bo Liang collaborated extensively on the code debugging and results compilation process. Minghui Du took charge of preparing the Taiji datasets and developing the software used for training, testing, and inference. Minghui Du introduced the innovative concept of transforming reference times, enriching the methodological approach. Bo Liang conducted an independent inference study to validate the reproducibility of our model. Bo Liang contributed significantly to code debugging and model fine-tuning, ensuring the robustness of the methodology. He Wang assumed a leadership role, spearheading the overall project and coordinating the manuscript’s writing. He Wang also played a pivotal role in verifying the credibility of the model’s unbiased estimation and validating the multimodality of the posterior distribution. Peng Xu made invaluable contributions by refining the narrative structure of the manuscript and enhancing its logical progression. Peng Xu’s insightful suggestions greatly enriched the overall quality of the paper. Ziren Luo and Yueliang Wu provided foundational contributions that set the tone for the manuscript. Their valuable insights and feedback played a crucial role in shaping the writing and submission process. Yueliang Wu secured the major funding for this research endeavor and conceived the overarching research direction, providing the visionary framework within which the study unfolded. All authors actively participated in the development of ideas, as well as the writing and rigorous reviewing of this manuscript. We are thankful to Zhoujian Cao for many helpful discussions.
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Du, M., Liang, B., Wang, H. et al. Advancing space-based gravitational wave astronomy: Rapid parameter estimation via normalizing flows. Sci. China Phys. Mech. Astron. 67, 230412 (2024). https://doi.org/10.1007/s11433-023-2270-7
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DOI: https://doi.org/10.1007/s11433-023-2270-7