- Physical Review Journals
All JournalsPhysics Magazine

Physical Review D

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Accurate and scalable inference for binary neutron stars in next-generation gravitational-wave detectors

Nir Guttman1,2,*, A. Makai Baker3, Paul D. Lasky1,2, and Eric Thrane1,2

  • *Contact author: nir.guttman@monash.edu
PDF

Phys. Rev. D 114, 063035 – Published 18 September, 2026

DOI: https://doi.org/10.1103/95x8-csn6

Abstract

Next-generation gravitational-wave observatories will observe binary neutron-star mergers with much higher signal-to-noise ratios, over much longer durations and across broader frequency bands than current detectors. These long-duration signals present a major computational challenge for Bayesian parameter estimation. Reduced-order quadrature is a promising approach for accelerating inference, but in this regime its standard construction encounters severe memory and accuracy limitations. We present a practical reduced-order quadrature construction for long binary neutron-star signals with time-dependent detector response and full effects of the observatories’ free-spectral range. Our approach combines improved adaptive frequency sampling, disk-backed streaming, and subbanded reduced-order quadrature construction, enabling efficient and accurate reduced-order models for signals that were previously intractable within the reduced-order quadrature framework. We demonstrate for the first time reduced-order Bayesian inference on an approximately 2 h binary neutron-star signal extending down to 5 Hz and with signal-to-noise ratio 2090. We show the resulting reduced-order quadrature remains sufficiently accurate for practical inference. The full analysis is carried out in about 48 h using 128 CPU cores. We also find that, when time-dependent detector-response effects are included, a single Cosmic Explorer detector can localize such a signal to a 90% credible sky area of approximately 41  deg2, with important implications for multimessenger astronomy and cosmology. These results demonstrate that reduced-order methods can make next-generation binary neutron-star inference computationally feasible.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (59)

  1. D. Reitze et al., Bull. AAS 51 (2019).
  2. M. Maggiore, C. V. D. Broeck, N. Bartolo, E. Belgacem, D. Bertacca, M. A. Bizouard, M. Branchesi, S. Clesse, S. Foffa, J. García-Bellido, S. Grimm, J. Harms, T. Hinderer, S. Matarrese, C. Palomba, M. Peloso, A. Ricciardone, and M. Sakellariadou, J. Cosmol. Astropart. Phys. 03 (2020) 050.
  3. T. L. S. Collaboration, Classical Quantum Gravity 32, 074001 (2015).
  4. F. Acernese et al., Classical Quantum Gravity 32, 024001 (2014).
  5. T. Akutsu et al., Prog. Theor. Exp. Phys. 2021, 05A101 (2021).
  6. J. Veitch et al., Phys. Rev. D 91, 042003 (2015).
  7. Q. Hu and J. Veitch, Phys. Rev. D 112, 084039 (2025).
  8. S. E. Field, C. R. Galley, J. S. Hesthaven, J. Kaye, and M. Tiglio, Phys. Rev. X 4, 031006 (2014).
  9. B. Zackay, L. Dai, and T. Venumadhav, arXiv:1806.08792.
  10. M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno, and B. Schölkopf, Phys. Rev. Lett. 127, 241103 (2021).
  11. M. Dax, S. R. Green, J. Gair, N. Gupte, M. Pürrer, V. Raymond, J. Wildberger, J. H. Macke, A. Buonanno, and B. Schölkopf, Nature (London) 639, 49 (2025).
  12. Q. Hu, J. Irwin, Q. Sun, C. Messenger, L. Suleiman, I. S. Heng, and J. Veitch, Astrophys. J. Lett. 987, L17 (2025).
  13. S. Morisaki, Phys. Rev. D 104, 044062 (2021).
  14. N. J. Cornish, Phys. Rev. D 104, 104054 (2021).
  15. N. Sharma, A. Vijaykumar, and P. Kumar, Phys. Rev. D 114, 044007 (2026).
  16. K. Krishna, A. Vijaykumar, A. Ganguly, C. Talbot, S. Biscoveanu, R. N. George, N. Williams, and A. Zimmerman, arXiv:2312.06009.
  17. K. W. K. Wong, M. Isi, and T. D. P. Edwards, Astrophys. J. 958, 129 (2023).
  18. T. Wouters, P. T. H. Pang, T. Dietrich, and C. Van Den Broeck, Phys. Rev. D 110, 083033 (2024).
  19. P. Baral, S. Morisaki, I. M. n. Hernandez, and J. Creighton, Phys. Rev. D 108, 043010 (2023).
  20. P. Baral, S. Morisaki, I. Gupta, and J. Creighton, Classical Quantum Gravity 42, 215007 (2025).
  21. P. Canizares, S. E. Field, J. Gair, V. Raymond, R. Smith, and M. Tiglio, Phys. Rev. Lett. 114, 071104 (2015).
  22. S. Morisaki, R. Smith, L. Tsukada, S. Sachdev, S. Stevenson, C. Talbot, and A. Zimmerman, Phys. Rev. D 108, 123040 (2023).
  23. R. Smith, S. E. Field, K. Blackburn, C.-J. Haster, M. Pürrer, V. Raymond, and P. Schmidt, Phys. Rev. D 94, 044031 (2016).
  24. R. Smith, S. Borhanian, B. Sathyaprakash, F. Hernandez Vivanco, S. E. Field, P. Lasky, I. Mandel, S. Morisaki, D. Ottaway, B. J. J. Slagmolen, E. Thrane, D. Töyrä, and S. Vitale, Phys. Rev. Lett. 127, 081102 (2021).
  25. A. M. Baker, P. D. Lasky, E. Thrane, and J. Golomb, Phys. Rev. D 112, 102004 (2025).
  26. A. Virtuoso and E. Milotti, Phys. Rev. D 111, 064058 (2025).
  27. M. Rakhmanov, J. D. Romano, and J. T. Whelan, Classical Quantum Gravity 25, 184017 (2008).
  28. R. Essick, S. Vitale, and M. Evans, Phys. Rev. D 96, 084004 (2017).
  29. S. E. Field, C. R. Galley, F. Herrmann, J. S. Hesthaven, E. Ochsner, and M. Tiglio, Phys. Rev. Lett. 106, 221102 (2011).
  30. M. Tiglio and A. Villanueva, Living Rev. Relativity 25, 2 (2022).
  31. E. Thrane and C. Talbot, Pub. Astron. Soc. Aust. 36, e010 (2019).
  32. A. M. Baker, gwrombus: Gravitational-wave reduced-order modeling and quadrature tools (2025), gitHub repository, https://github.com/amakaib/gwrombus.
  33. ADACS Australia, Rombus: Reduced-order modeling and quadrature framework (2023), gitHub repository, https://github.com/ADACS-Australia/Rombus.
  34. G. Ashton et al., Astrophys. J. Suppl. Ser. 241, 27 (2019).
  35. I. M. Romero-Shaw et al., Mon. Not. R. Astron. Soc. 499, 3295 (2020).
  36. L. Lindblom, B. J. Owen, and D. A. Brown, Phys. Rev. D 78, 124020 (2008).
  37. C. Messick et al., Phys. Rev. D 95, 042001 (2017).
  38. B. P. Abbott et al., Classical Quantum Gravity 34, 044001 (2017).
  39. T. Dietrich, A. Samajdar, S. Khan, N. K. Johnson-McDaniel, R. Dudi, and W. Tichy, Phys. Rev. D 100, 044003 (2019).
  40. M. Hannam, P. Schmidt, A. Bohé, L. Haegel, S. Husa, F. Ohme, G. Pratten, and M. Pürrer, Phys. Rev. Lett. 113, 151101 (2014).
  41. B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), Phys. Rev. Lett. 119, 161101 (2017).
  42. S. De, D. Finstad, J. M. Lattimer, D. A. Brown, E. Berger, and C. M. Biwer, Phys. Rev. Lett. 121, 091102 (2018).
  43. C. Pankow, K. Chatziioannou, E. A. Chase, T. B. Littenberg, M. Evans, J. McIver, N. J. Cornish, C.-J. Haster, J. Kanner, V. Raymond, S. Vitale, and A. Zimmerman, Phys. Rev. D 98, 084016 (2018).
  44. R. Abbott et al. (LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration), Phys. Rev. X 13, 041039 (2023).
  45. J. Skilling, in Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, edited by R. Fischer, R. Preuss, and U. V. Toussaint, American Institute of Physics Conference Series (2004), Vol. 735, pp. 395–405, 10.1063/1.1835238.
  46. J. Skilling, Bayesian Anal. 1, 833 (2006).
  47. J. S. Speagle, Mon. Not. R. Astron. Soc. 493, 3132 (2020).
  48. R. J. E. Smith, G. Ashton, A. Vajpeyi, and C. Talbot, Mon. Not. R. Astron. Soc. 498, 4492 (2020).
  49. E. Payne, C. Talbot, and E. Thrane, Phys. Rev. D 100, 123017 (2019).
  50. B. D. Lackey, eosinference: Use neutron star observations to measure the equation of state of nuclear matter, GitHub repository (2017), https://github.com/benjaminlackey/eosinference.
  51. B. D. Lackey and L. Wade, Phys. Rev. D 91, 043002 (2015).
  52. W. Zhao and L. Wen, Phys. Rev. D 97, 064031 (2018).
  53. M. Nicholl and I. Andreoni, Phil. Trans. R. Soc. A 383, 20240126 (2025).
  54. B. P. Abbott et al., Nature (London) 551, 85 (2017).
  55. J. R. Gair et al., Astron. J. 166, 22 (2023).
  56. M. Kramer et al., Phys. Rev. X 11, 041050 (2021).
  57. K. Walker, R. Smith, E. Thrane, and D. J. Reardon, Phys. Rev. D 110, 043013 (2024).
  58. A. J. Dittmann, M. C. Miller, F. K. Lamb, I. M. Holt, C. Chirenti, M. T. Wolff, S. Bogdanov, S. Guillot, W. C. G. Ho, S. M. Morsink, Z. Arzoumanian, and K. C. Gendreau, Astrophys. J. 974, 295 (2024).
  59. C. Cutler and E. E. Flanagan, Phys. Rev. D 49, 2658 (1994).

Outline

Information

Phys. Rev. D 114, 063035– Published 18 September, 2026
  • Received 12 June 2026
  • Accepted 21 August 2026
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation