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Advancing space-based gravitational wave astronomy: Rapid parameter estimation via normalizing flows

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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

  1. J. Aasi, et al. (The LIGO Scientific Collaboration), Class. Quantum Grav. 32, 074001 (2015).

    Article  ADS  Google Scholar 

  2. F. Acernese, et al. (Virgo Collaboration), Class. Quantum Grav. 32, 024001 (2015).

    Article  ADS  Google Scholar 

  3. T. Akutsu, et al. (KAGRA Collaboration), Nat. Astron. 3, 35 (2019).

    Article  ADS  Google Scholar 

  4. B. P. Abbott, et al. (The LIGO Scientific Collaboration), Living Rev. Relativ. 23, 3 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. P. Amaro-Seoane, et al. (LISA Consortium), arXiv: 1702.00786.

  6. J. Baker, et al. (LISA Consortium), arXiv: 1907.06482.

  7. W. R. Hu, and Y. L. Wu, Natl. Sci. Rev. 4, 685 (2017).

    Article  CAS  Google Scholar 

  8. Z. Luo, Y. Wang, Y. Wu, W. Hu, and G. Jin, Prog. Theor. Exp. Phys. 2021, 83 (2021).

    Article  Google Scholar 

  9. Y. L. Wu, et al. (The Taiji Scientific Collaboration), Commun. Phys. 4, 34 (2021).

    Article  Google Scholar 

  10. 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).

    Article  ADS  Google Scholar 

  11. Z. Luo, M. Zhang, and Y. Wu, Chin. J. Space Sci. 40, 691 (2020).

    Article  ADS  Google Scholar 

  12. X. Zhong, W. B. Han, Z. Luo, and Y. Wu, Sci. China-Phys. Mech. Astron. 66, 230411 (2023), arXiv: 2305.04478.

    Article  ADS  Google Scholar 

  13. G. L. Li, Y. Tang, and Y. L. Wu, Sci. China-Phys. Mech. Astron. 65, 100412 (2022), arXiv: 2112.14041.

    Article  ADS  Google Scholar 

  14. Y. L. Wu, Sci. China-Phys. Mech. Astron. 66, 260411 (2023), arXiv: 2208.03290.

    Article  ADS  Google Scholar 

  15. Z. Luo, Z. K. Guo, G. Jin, Y. Wu, and W. Hu, Results Phys. 16, 102918 (2020).

    Article  Google Scholar 

  16. Z. Luo, M. Zhang, and Y. Wu, Chin. J. Space Sci. 42, 536 (2022).

    Article  ADS  Google Scholar 

  17. J. B. Bayle, B. Bonga, C. Caprini, D. Doneva, M. Muratore, A. Petiteau, E. Rossi, and L. Shao, Nat. Astron. 6, 1334 (2022).

    Article  ADS  Google Scholar 

  18. L. Speri, N. Karnesis, A. I. Renzini, and J. R. Gair, Nat. Astron. 6, 1356 (2022).

    Article  ADS  Google Scholar 

  19. R. Umstatter, N. Christensen, M. Hendry, R. Meyer, V. Simha, J. Veitch, S. Vigeland, and G. Woan, Phys. Rev. D 72, 022001 (2005).

    Article  ADS  Google Scholar 

  20. N. J. Cornish, and K. Shuman, Phys. Rev. D 101, 124008 (2020).

    Article  ADS  MathSciNet  CAS  Google Scholar 

  21. N. Karnesis, M. L. Katz, N. Korsakova, J. R. Gair, and N. Stergioulas, arXiv: 2303.02164.

  22. C. R. Weaving, L. K. Nuttall, I. W. Harry, S. Wu, and A. Nitz, arXiv: 2306.16429.

  23. S. H. Strub, L. Ferraioli, C. Schmelzbach, S. C. Sthler, and D. Giardini, arXiv: 2307.03763.

  24. N. J. Cornish, and J. Crowder, Phys. Rev. D 72, 043005 (2005).

    Article  ADS  Google Scholar 

  25. T. B. Littenberg, N. J. Cornish, K. Lackeos, and T. Robson, Phys. Rev. D 101, 123021 (2020).

    Article  ADS  MathSciNet  CAS  Google Scholar 

  26. T. B. Littenberg, and N. J. Cornish, Phys. Rev. D 107, 063004 (2023).

    Article  ADS  CAS  Google Scholar 

  27. Q. Baghi (LDC Working Group), The LISA Data challenges, in 56th Rencontres de Moriond on Gravitation (2022), arXiv: 2204.12142.

  28. G. Pratten, A. Klein, C. J. Moore, H. Middleton, N. Steinle, P. Schmidt, and A. Vecchio, Phys. Rev. D 107, 123026 (2023).

    Article  ADS  CAS  Google Scholar 

  29. W. H. Ruan, H. Wang, C. Liu, and Z. K. Guo, Phys. Lett. B 841, 137904 (2023).

    Article  CAS  Google Scholar 

  30. W.-H. Ruan, H. Wang, C. Liu, and Z.-K. Guo, arXiv: 2307.14844.

  31. P. Amaro-Seoane, et al. (LISA Collaboration), Living Rev. Relativ. 26, 2 (2023), arXiv: 2203.06016.

    Article  ADS  Google Scholar 

  32. 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.

    Article  ADS  Google Scholar 

  33. M. I. Jordan, and T. M. Mitchell, Science 349, 255 (2015).

    Article  ADS  MathSciNet  CAS  PubMed  Google Scholar 

  34. 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).

    Article  ADS  Google Scholar 

  35. Y. LeCun, Y. Bengio, and G. Hinton, Nature 521, 436 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  36. H. Gabbard, C. Messenger, I. S. Heng, F. Tonolini, and R. MurraySmith, Nat. Phys. 18, 112 (2022).

    Article  CAS  Google Scholar 

  37. C. Chatterjee, L. Wen, K. Vinsen, M. Kovalam, and A. Datta, Phys. Rev. D 100, 103025 (2019).

    Article  ADS  CAS  Google Scholar 

  38. S. R. Green, C. Simpson, and J. Gair, Phys. Rev. D 102, 104057 (2020).

    Article  ADS  MathSciNet  CAS  Google Scholar 

  39. S. R. Green, and J. Gair, Mach. Learn.-Sci. Technol. 2, 03LT01 (2021).

    Article  Google Scholar 

  40. A. Delaunoy, A. Wehenkel, T. Hinderer, S. Nissanke, C. Weniger, A. R. Williamson, and G. Louppe, arXiv: 2010.12931.

  41. P. G. Krastev, K. Gill, V. A. Villar, and E. Berger, Phys. Lett. B 815, 136161 (2021).

    Article  CAS  Google Scholar 

  42. H. Shen, E. A. Huerta, E. O’Shea, P. Kumar, and Z. Zhao, Mach. Learn.-Sci. Technol. 3, 015007 (2022).

    Article  ADS  Google Scholar 

  43. M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno, and B. Scholkopf, Phys. Rev. Lett. 127, 241103 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  44. 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).

    Article  ADS  CAS  PubMed  Google Scholar 

  45. A. J. K. Chua, and M. Vallisneri, Phys. Rev. Lett. 124, 041102 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  46. I. Kobyzev, S. J. D. Prince, and M. A. Brubaker, IEEE Trans. Pattern Anal. Mach. Intell. 43, 3964 (2021).

    Article  PubMed  Google Scholar 

  47. G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, J. Mach. Learn. Res. 22, 1 (2021).

    Google Scholar 

  48. J. Langendorff, A. Kolmus, J. Janquart, and C. Van Den Broeck, Phys. Rev. Lett. 130, 171402 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  49. D. Ruhe, K. Wong, M. Cranmer, and P. Forré, arXiv: 2211.09008.

  50. M. J. Williams, J. Veitch, and C. Messenger, Phys. Rev. D 103, 103006 (2021).

    Article  ADS  CAS  Google Scholar 

  51. M. Crisostomi, K. Dey, E. Barausse, and R. Trotta, arXiv: 2305.18528.

  52. 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).

  53. K. He, X. Zhang, S. Ren, and J. Sun, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016).

  54. M. Dax, S. R. Green, J. Gair, M. Deistler, B. Scholkopf, and J. H. Macke, arXiv: 2111.13139.

  55. M. L. Katz, S. Marsat, A. J. K. Chua, S. Babak, and S. L. Larson, Phys. Rev. D 102, 023033 (2020).

    Article  ADS  CAS  Google Scholar 

  56. M. L. Katz, arXiv: 2111.01064.

  57. 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).

    CAS  Google Scholar 

  58. S. Marsat, and J. G. Baker, arXiv: 1806.10734.

  59. S. Marsat, J. G. Baker, and T. D. Canton, Phys. Rev. D 103, 083011 (2021).

    Article  ADS  CAS  Google Scholar 

  60. M. Katz, mikekatz04/BBHx: First official public release (Zenodo, Frankfurt, 2021).

    Google Scholar 

  61. 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).

    Article  ADS  MathSciNet  Google Scholar 

  62. 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).

    Article  ADS  CAS  Google Scholar 

  63. M. Vallisneri, J. Crowder, and M. Tinto, Class. Quantum Grav. 25, 065005 (2008).

    Article  ADS  Google Scholar 

  64. G. Wang, W. T. Ni, W. B. Han, S. C. Yang, and X. Y. Zhong, Phys. Rev. D 102, 024089 (2020).

    Article  ADS  CAS  Google Scholar 

  65. G. Wang, and W. T. Ni, Phys. Scr. 98, 075005 (2023).

    Article  ADS  Google Scholar 

  66. N. J. Cornish, and T. B. Littenberg, Phys. Rev. D 76, 083006 (2007).

    Article  ADS  Google Scholar 

  67. T. Robson, N. J. Cornish, N. Tamanini, and S. Toonen, Phys. Rev. D 98, 064012 (2018).

    Article  ADS  CAS  Google Scholar 

  68. M. L. Katz, mikekatz04/GBGPU: First official public release! (Zenodo, Frankfurt, 2022).

    Google Scholar 

  69. G. Wang, Z. Yan, B. Hu, and W. T. Ni, Phys. Rev. D 107, 124022 (2023).

    Article  ADS  CAS  Google Scholar 

  70. V. Korol, E. M. Rossi, and E. Barausse, Mon. Not. R. Astron. Soc. 483, 5518 (2019).

    Article  ADS  CAS  Google Scholar 

  71. C. Liu, W. H. Ruan, and Z. K. Guo, Phys. Rev. D 107, 064021 (2023).

    Article  ADS  CAS  Google Scholar 

  72. X. H. Zhang, S. D. Zhao, S. D. Mohanty, and Y. X. Liu, Phys. Rev. D 106, 102004 (2022).

    Article  ADS  CAS  Google Scholar 

  73. I. Loshchilov, and F. Hutter, arXiv: 1608.03983.

  74. D. P. Kingma, and J. Ba, arXiv: 1412.6980.

  75. N. J. Cornish, Phys. Rev. D 105, 044007 (2022).

    Article  ADS  CAS  Google Scholar 

  76. M. Vallisneri, and C. R. Galley, Class. Quantum Grav. 29, 124015 (2012).

    Article  ADS  Google Scholar 

  77. 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).

    Article  ADS  Google Scholar 

  78. J. Skilling, Bayesian Anal. 1, 833 (2006).

    Article  MathSciNet  Google Scholar 

  79. J. Veitch, and A. Vecchio, Phys. Rev. D 81, 062003 (2010).

    Article  ADS  Google Scholar 

  80. 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).

    Article  ADS  CAS  Google Scholar 

  81. S. Marsat, J. G. Baker, and T. D. Canton, Phys. Rev. D 103, 083011 (2021).

    Article  ADS  CAS  Google Scholar 

  82. G. Pratten, P. Schmidt, H. Middleton, and A. Vecchio, arXiv: 2307.13026.

  83. D.-A. Clevert, T. Unterthiner, and S. Hochreiter, arXiv: 1511.07289.

  84. C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, anflows: normalizing flows in PyTorch (Zenodo, Frankfurt, 2020).

    Google Scholar 

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Correspondence to He Wang or Yueliang Wu.

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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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