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Cosmological constraints from non-Gaussian and nonlinear galaxy clustering using the SimBIG inference framework

Nature Astronomy volume 8, pages 1457–1467 (2024)Cite this article

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Abstract

The standard ΛCDM cosmological model predicts the presence of cold dark matter, with the current accelerated expansion of the Universe driven by dark energy. This model has recently come under scrutiny because of tensions in measurements of the expansion and growth histories of the Universe, parameterized using H0 and S8. The three-dimensional clustering of galaxies encodes key cosmological information that addresses these tensions. Here we present a set of cosmological constraints using simulation-based inference that exploits additional non-Gaussian information on nonlinear scales from galaxy clustering, inaccessible with current analyses. We analyse a subset of the Baryon Oscillation Spectroscopic Survey (BOSS) galaxy survey using SimBIG, a new framework for cosmological inference that leverages high-fidelity simulations and deep generative models. We use two clustering statistics beyond the standard power spectrum: the bispectrum and a summary of the galaxy field based on a convolutional neural network. We constrain H0 and S8 1.5 and 1.9 times more tightly than power spectrum analyses. With this increased precision, our constraints are competitive with those of other cosmological probes, even with only 10% of the full BOSS volume. Future work extending SimBIG to upcoming spectroscopic galaxy surveys (DESI, PFS, Euclid) will produce improved cosmological constraints that will develop understanding of cosmic tensions.

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Fig. 1: Cosmological parameter constraints from SimBIG.
Fig. 2: S8 and H0 constraints from SimBIG.
Fig. 3: SimBIGS8 and H0 constraints compared with the literature.
Fig. 4: SimBIG with relaxed robustness.
Fig. 5: SimBIG S8 and H0 forecasts.

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Data availability

The observational data from SDSS-III BOSS used in this work are publicly available at https://data.sdss.org/sas/. The Quijote N-simulations in this work are also publicly available at https://quijote-simulations.readthedocs.io/.

Code availability

All of the code used in this paper is publicly available at https://changhoonhahn.github.io/simbig/ and https://doi.org/10.5281/zenodo.11640606.

References

  1. Page, L. et al. First-year Wilkinson Microwave Anisotropy Probe (WMAP) observations: beam profiles and window functions. Astrophys. J. Suppl. Ser. 148, 39 (2003).

    ADS  Google Scholar 

  2. Bennett, C. L. et al. Nine-year Wilkinson Microwave Anisotropy Probe (WMAP) observations: final maps and results. Astrophys. J. Suppl. Ser. 208, 20 (2013).

    ADS  Google Scholar 

  3. Planck Collaboration Planck 2018 results. VI. Cosmological parameters. Astron. Astrophys. 641, A6 (2020).

    Google Scholar 

  4. Aiola, S. et al. The Atacama Cosmology Telescope: DR4 maps and cosmological parameters. J. Cosmol. Astropart. Phys. 2020, 047 (2020).

  5. Bernardeau, F., Colombi, S., Gaztanaga, E. & Scoccimarro, R. Large-scale structure of the Universe and cosmological perturbation theory. Phys. Rep. 367, 1–248 (2002).

    ADS  MathSciNet  Google Scholar 

  6. Alam, S. et al. The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological analysis of the DR12 galaxy sample. Mon. Not. R. Astron. Soc. 470, 2617–2652 (2017).

    ADS  Google Scholar 

  7. Perlmutter, S. et al. Measurements of ω and λ from 42 high-redshift supernovae. Astrophys. J. 517, 565–586 (1999).

    ADS  Google Scholar 

  8. Riess, A. G. et al. Observational evidence from supernovae for an accelerating universe and a cosmological constant. Astron. J. 116, 1009–1038 (1998).

    ADS  Google Scholar 

  9. Scolnic, D. M. et al. The complete light-curve sample of spectroscopically confirmed SNe Ia from Pan-STARRS1 and cosmological constraints from the combined Pantheon sample. Astrophys. J. 859, 101 (2018).

    ADS  Google Scholar 

  10. Brout, D. et al. The Pantheon+ analysis: cosmological constraints. Astrophys. J. 938, 110 (2022).

    ADS  Google Scholar 

  11. Schramm, D. N. & Turner, M. S. Big-bang nucleosynthesis enters the precision era. Rev. Mod. Phys. 70, 303 (1998).

    ADS  Google Scholar 

  12. Steigman, G. Primordial nucleosynthesis in the precision cosmology era. Annu. Rev. Nucl. Part. Sci. 57, 463–491 (2007).

    ADS  Google Scholar 

  13. Iocco, F., Mangano, G., Miele, G., Pisanti, O. & Serpico, P. D. Primordial nucleosynthesis: from precision cosmology to fundamental physics. Phys. Rep. 472, 1–76 (2009).

    ADS  Google Scholar 

  14. Cyburt, R. H., Fields, B. D., Olive, K. A. & Yeh, T.-H. Big bang nucleosynthesis: present status. Rev. Mod. Phys. 88, 015004 (2016).

    ADS  Google Scholar 

  15. Abdalla, E. et al. Cosmology intertwined: a review of the particle physics, astrophysics, and cosmology associated with the cosmological tensions and anomalies. J. High Energy Astrophys. 34, 49–211 (2022).

    ADS  Google Scholar 

  16. Freedman, W. L. Measurements of the Hubble constant: tensions in perspective. Astrophys. J. 919, 16 (2021).

    ADS  Google Scholar 

  17. Kamionkowski, M. & Riess, A. G. The Hubble tension and early dark energy. Annu. Rev. Nucl. Part. Sci. 73, 153–180 (2023).

    ADS  Google Scholar 

  18. Riess, A. G. et al. A comprehensive measurement of the local value of the Hubble constant with 1 km s−1 Mpc−1 uncertainty from the Hubble Space Telescope and the SH0ES Team. Astrophys. J. Lett. 934, L7 (2022).

    ADS  Google Scholar 

  19. Troxel, M. A. et al. Dark Energy Survey Year 1 results: cosmological constraints from cosmic shear. Phys. Rev. D 98, 043528 (2018).

    ADS  Google Scholar 

  20. Asgari, M. et al. KiDS-1000 cosmology: cosmic shear constraints and comparison between two point statistics. Astron. Astrophys. 645, A104 (2021).

    Google Scholar 

  21. Amon, A. et al. Dark Energy Survey Year 3 results: cosmology from cosmic shear and robustness to data calibration. Phys. Rev. D 105, 023514 (2022).

    ADS  Google Scholar 

  22. Dalal, R. et al. Hyper Suprime-Cam Year 3 Results: cosmology from cosmic shear power spectra. Phys. Rev. D 108, 123519 (2023).

    ADS  Google Scholar 

  23. Madhavacheril, M. S. et al. The Atacama Cosmology Telescope: DR6 gravitational lensing map and cosmological parameters. Astrophys. J. 962, 113 (2024).

    ADS  Google Scholar 

  24. Meerburg, P. D. Alleviating the tension at low ℓ through axion monodromy. Phys. Rev. D 90, 063529 (2014).

    ADS  Google Scholar 

  25. Chudaykin, A., Gorbunov, D. & Tkachev, I. Dark matter component decaying after recombination: sensitivity to baryon acoustic oscillation and redshift space distortion probes. Phys. Rev. D 97, 083508 (2018).

    ADS  Google Scholar 

  26. Di Valentino, E., Melchiorri, A., Mena, O. & Vagnozzi, S. Nonminimal dark sector physics and cosmological tensions. Phys. Rev. D 101, 063502 (2020).

    ADS  Google Scholar 

  27. Abellán, G. F., Murgia, R., Poulin, V. & Lavalle, J. Implications of the S8 tension for decaying dark matter with warm decay products. Phys. Rev. D 105, 063525 (2022).

    ADS  Google Scholar 

  28. Eisenstein, D. J., Hu, W. & Tegmark, M. Cosmic complementarity: H0 and Ωm from combining cosmic microwave background experiments and redshift surveys. Astrophys. J. Lett. 504, L57 (1998).

    ADS  Google Scholar 

  29. Eisenstein, D. J. et al. Detection of the baryon acoustic peak in the large-scale correlation function of SDSS luminous red galaxies. Astrophys. J. 633, 560 (2005).

    ADS  Google Scholar 

  30. Cole, S. et al. The 2dF Galaxy Redshift Survey: power-spectrum analysis of the final data set and cosmological implications. Mon. Not. R. Astron. Soc. 362, 505 (2005).

    ADS  Google Scholar 

  31. Ivanov, M. M. and Philcox, O. H. E. Measuring H0 with spectroscopic surveys. Preprint at https://doi.org/10.48550/arXiv.2305.07977 (2023).

  32. DESI Collaboration et al. The DESI experiment part I: science, targeting, and survey design. Preprint at https://arxiv.org/abs/1611.00036 (2016).

  33. DESI Collaboration et al. The DESI experiment part II: instrument design. Preprint at https://arxiv.org/abs/1611.00037 (2016).

  34. Abareshi, B. et al. Overview of the instrumentation for the Dark Energy Spectroscopic Instrument. Astron. J. 164, 207 (2022).

    ADS  Google Scholar 

  35. Takada, M. et al. Extragalactic science, cosmology, and Galactic archaeology with the Subaru Prime Focus Spectrograph. Publ. Astron. Soc. Jpn 66, R1 (2014).

    ADS  Google Scholar 

  36. Laureijs, R. et al. Euclid Definition Study Report. Preprint at https://arxiv.org/abs/1110.3193 (2011).

  37. Spergel, D. et al. Wide-Field InfrarRed Survey Telescope–Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report. Preprint at https://arxiv.org/abs/1503.03757 (2015).

  38. Beutler, F. et al. The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: anisotropic galaxy clustering in Fourier space. Mon. Not. R. Astron. Soc. 466, 2242 (2017).

  39. d’Amico, G. et al. The cosmological analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure. J. Cosmol. Astropart. Phys. 2020, 005 (2020).

    Google Scholar 

  40. Ivanov, M. M., Simonović, M. & Zaldarriaga, M. Cosmological parameters from the BOSS galaxy power spectrum. J. Cosmol. Astropart. Phys. 2020, 042 (2020).

    Google Scholar 

  41. Chen, S.-F., Vlah, Z. & White, M. A new analysis of galaxy 2-point functions in the BOSS survey, including full-shape information and post-reconstruction BAO. J. Cosmol. Astropart. Phys. 2022, 008 (2022).

    Google Scholar 

  42. Kobayashi, Y., Nishimichi, T., Takada, M. & Miyatake, H. Full-shape cosmology analysis of the SDSS-III BOSS galaxy power spectrum using an emulator-based halo model: a 5% determination of σ8. Phys. Rev. D 105, 083517 (2022).

    ADS  Google Scholar 

  43. Scoccimarro, R., Feldman, H. A., Fry, J. N. & Frieman, J. A. The bispectrum of IRAS redshift catalogs. Astrophys. J. 546, 652 (2001).

    ADS  Google Scholar 

  44. Verde, L. et al. The 2dF Galaxy Redshift Survey: the bias of galaxies and the density of the Universe. Mon. Not. R. Astron. Soc. 335, 432 (2002).

    ADS  Google Scholar 

  45. Gil-Marín, H. et al. The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: RSD measurement from the power spectrum and bispectrum of the DR12 BOSS galaxies. Mon. Not. R. Astron. Soc. 465, 1757 (2017).

  46. D’Amico, G., Donath, Y., Lewandowski, M., Senatore, L. & Zhang, P. The BOSS bispectrum analysis at one loop from the Effective Field Theory of Large-Scale Structure. J. Cosmol. Astropart. Phys. 2024, 40 (2024).

    MathSciNet  Google Scholar 

  47. Philcox, O. H. E. & Ivanov, M. M. BOSS DR12 full-shape cosmology: Λ CDM constraints from the large-scale galaxy power spectrum and bispectrum monopole. Phys. Rev. D 105, 043517 (2022).

    ADS  MathSciNet  Google Scholar 

  48. Ivanov, M. M. et al. Cosmology with the galaxy bispectrum multipoles: optimal estimation and application to BOSS data. Phys. Rev. D 107, 083515 (2023).

    ADS  Google Scholar 

  49. Hahn, C., Villaescusa-Navarro, F., Castorina, E. & Scoccimarro, R. Constraining Mν with the bispectrum. Part I. Breaking parameter degeneracies. J. Cosmol. Astropart. Phys. 2020, 040 (2020).

    Google Scholar 

  50. Hahn, C. & Villaescusa-Navarro, F. Constraining Mν with the bispectrum. Part II. The information content of the galaxy bispectrum monopole. J. Cosmol. Astropart. Phys. 2021, 029 (2021).

  51. Massara, E., Villaescusa-Navarro, F., Ho, S., Dalal, N. & Spergel, D. N. Using the marked power spectrum to detect the signature of neutrinos in large-scale structure. Phys. Rev. Lett. 126, 011301 (2021).

    ADS  Google Scholar 

  52. Wang, Y. et al. Extracting high-order cosmological information in galaxy surveys with power spectra. Nat. Commun. 7, 130 (2024).

    Google Scholar 

  53. Hou, J., Moradinezhad Dizgah, A., Hahn, C. & Massara, E. Cosmological information in skew spectra of biased tracers in redshift space. J. Cosmol. Astropart. Phys. 2023, 045 (2023).

    MathSciNet  Google Scholar 

  54. Eickenberg, M. et al. Wavelet moments for cosmological parameter estimation. Preprint at https://arxiv.org/abs/2204.07646 (2022).

  55. Zhai, Z. et al. The Aemulus Project. III. Emulation of the galaxy correlation function. Astrophys. J. 874, 95 (2019).

    ADS  Google Scholar 

  56. Storey-Fisher, K. et al. The Aemulus Project VI: emulation of beyond-standard galaxy clustering statistics to improve cosmological constraints. Astrophys. J. 961, 208 (2024).

    ADS  Google Scholar 

  57. Yuan, S., Garrison, L. H., Eisenstein, D. J. & Wechsler, R. H. Stringent σ8 constraints from small-scale galaxy clustering using a hybrid MCMC + emulator framework. Mon. Not. R. Astron. Soc. 515, 871 (2022).

  58. Zhai, Z. The Aemulus Project V: cosmological constraint from small-scale clustering of BOSS galaxies. Astrophys. J. 948, 99 (2023).

    ADS  Google Scholar 

  59. Paillas, E. et al. Cosmological constraints from density-split clustering in the BOSS CMASS galaxy sample. Mon. Not. R. Astron. Soc. 531, 898 (2024).

    ADS  Google Scholar 

  60. Valogiannis, G., Yuan, S. & Dvorkin, C. Precise cosmological constraints from BOSS galaxy clustering with a simulation-based emulator of the wavelet scattering transform. Phys. Rev. D 109, 10 (2024).

    MathSciNet  Google Scholar 

  61. Allen, S. W., Evrard, A. E. & Mantz, A. B. Cosmological parameters from observations of galaxy clusters. Annu. Rev. Astron. Astrophys. 49, 409 (2011).

    ADS  Google Scholar 

  62. Kravtsov, A. V. & Borgani, S. Formation of galaxy clusters. Annu. Rev. Astron. Astrophys. 50, 353 (2012).

    ADS  Google Scholar 

  63. Dutcher, D. et al. Measurements of the E-mode polarization and temperature–E-mode correlation of the CMB from SPT-3G 2018 data. Phys. Rev. D 104, 022003 (2021).

    ADS  Google Scholar 

  64. Lesci, G. F. et al. AMICO galaxy clusters in KiDS-DR3: constraints on cosmological parameters and on the normalisation of the mass–richness relation from clustering. Astron. Astrophys. 665, A100 (2022).

    Google Scholar 

  65. Banerjee, A. & Abel, T. Nearest neighbour distributions: new statistical measures for cosmological clustering. Mon. Not. R. Astron. Soc. 500, 5479 (2021).

    ADS  Google Scholar 

  66. Valogiannis, G. & Dvorkin, C. Towards an optimal estimation of cosmological parameters with the wavelet scattering transform. Phys. Rev. D 105, 103534 (2022).

    ADS  MathSciNet  Google Scholar 

  67. Ross, A. J. et al. The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: analysis of potential systematics. Mon. Not. R. Astron. Soc. 424, 564 (2012).

    ADS  Google Scholar 

  68. Ross, A. J. et al. The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: observational systematics and baryon acoustic oscillations in the correlation function. Mon. Not. R. Astron. Soc. 464, 1168 (2017).

  69. Guo, H., Zehavi, I. & Zheng, Z. A new method to correct for fiber collisions in galaxy two-point statistics. Astrophys. J. 756, 127 (2012).

    ADS  Google Scholar 

  70. Hahn, C., Scoccimarro, R., Blanton, M. R., Tinker, J. L. & Rodríguez-Torres, S. A. The effect of fiber collisions on the galaxy power spectrum multipoles. Mon. Not. R. Astron. Soc. 467, 1940 (2017).

  71. Smith, A. et al. Correcting for fibre assignment incompleteness in the DESI Bright Galaxy Survey. Mon. Not. R. Astron. Soc. 484, 1285 (2019).

    ADS  Google Scholar 

  72. Hahn, C. et al. SimBIG: a forward modeling approach to analyzing galaxy clustering. Proc. Natl Acad. Sci. USA 120, 42 (2023).

    Google Scholar 

  73. Hahn, C. et al. SimBIG: mock challenge for a forward modeling approach to galaxy clustering. J. Cosmol. Astropart. Phys. 2023, 010 (2023).

    Google Scholar 

  74. Cranmer, K., Brehmer, J. & Louppe, G. The frontier of simulation-based inference. Proc. Natl Acad. Sci. USA 117, 30055 (2020).

    ADS  MathSciNet  Google Scholar 

  75. Eisenstein, D. J. et al. SDSS-III: massive spectroscopic surveys of the distant Universe, the Milky Way, and extra-solar planetary systems. Astron. J. 142, 72 (2011).

    ADS  Google Scholar 

  76. Dawson, K. S. et al. The Baryon Oscillation Spectroscopic Survey of SDSS-III. Astron. J. 145, 10 (2013).

    ADS  Google Scholar 

  77. Villaescusa-Navarro, F. et al. The Quijote simulations. Astrophys. J. Suppl. Ser. 250, 2 (2020).

    ADS  Google Scholar 

  78. Behroozi, P. S., Wechsler, R. H. & Wu, H.-Y. The rockstar phase-space temporal halo finder and the velocity offsets of cluster cores. Astrophys. J. 762, 109 (2013).

    ADS  Google Scholar 

  79. Berlind, A. A. & Weinberg, D. H. The halo occupation distribution: toward an empirical determination of the relation between galaxies and mass. Astrophys. J. 575, 587 (2002).

    ADS  Google Scholar 

  80. Zheng, Z., Coil, A. L. & Zehavi, I. Galaxy evolution from halo occupation distribution modeling of DEEP2 and SDSS galaxy clustering. Astrophys. J. 667, 760 (2007).

    ADS  Google Scholar 

  81. Zentner, A. R. et al. Constraints on assembly bias from galaxy clustering. Mon. Not. R. Astron. Soc. 485, 1196 (2019).

    ADS  Google Scholar 

  82. Hadzhiyska, B. et al. Galaxy assembly bias and large-scale distribution: a comparison between IllustrisTNG and a semi-analytic model. Mon. Not. R. Astron. Soc. 508, 698 (2021).

  83. Tabak, E. G. & Vanden-Eijnden, E. Density estimation by dual ascent of the log-likelihood. Commun. Math. Sci. 8, 217 (2010).

    MathSciNet  Google Scholar 

  84. Tabak, E. G. & Turner, C. V. A family of nonparametric density estimation algorithms. Commun. Pure Appl. Math. 66, 145 (2013).

    MathSciNet  Google Scholar 

  85. Papamakarios, G., Pavlakou, T. & Murray, I. Masked autoregressive flow for density estimation. Preprint at https://arxiv.org/abs/1705.07057 (2017).

  86. Durkan, C., Bekasov, A., Murray, I. & Papamakarios, G. Neural spline flows. Preprint at https://doi.org/10.48550/arXiv.1906.04032 (2019).

  87. Greenberg, D. S., Nonnenmacher, M. & Macke, J. H. Automatic posterior transformation for likelihood-free inference. Preprint at https://arxiv.org/abs/1905.07488 (2019).

  88. Tejero-Cantero, A. et al. sbi: a toolkit for simulation-based inference. J. Open Source Softw. 5, 2505 (2020).

    ADS  Google Scholar 

  89. Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna: a next-generation hyperparameter optimization framework. Preprint at https://doi.org/10.48550/arXiv.1907.10902 (2019).

  90. Modi, C. et al. Sensitivity analysis of simulation-based inference for galaxy clustering. Preprint at https://doi.org/10.48550/arXiv.2309.15071 (2023).

  91. Fry, J. N. Gravity, bias, and the galaxy three-point correlation function. Phys. Rev. Lett. 73, 215 (1994).

    ADS  Google Scholar 

  92. Matarrese, S., Verde, L. & Heavens, A. F. Large-scale bias in the Universe: bispectrum method. Mon. Not. R. Astron. Soc. 290, 651 (1997).

  93. Scoccimarro, R. The bispectrum: from theory to observations. Astrophys. J. 544, 597 (2000).

    ADS  Google Scholar 

  94. Talts, S., Betancourt, M., Simpson, D., Vehtari, A. & Gelman A. Validating Bayesian inference algorithms with simulation-based calibration. Preprint at https://arxiv.org/abs/1804.06788 (2020).

  95. Lemos, P., Coogan, A., Hezaveh, Y. & Perreault-Levasseur, L. Sampling-based accuracy testing of posterior estimators for general inference. In Proc. Machine Learning Research Vol. 202 (eds Krause, A. et al.) 19256–19273 (PMLR, 2023).

  96. Aver, E., Olive, K. A. & Skillman, E. D. The effects of He I λ10830 on helium abundance determinations. J. Cosmol. Astropart. Phys. 2015, 011 (2015).

    Google Scholar 

  97. Cooke, R. J., Pettini, M. & Steidel, C. C. One percent determination of the primordial deuterium abundance. Astrophys. J. 855, 102 (2018).

    ADS  Google Scholar 

  98. Schöneberg, N., Lesgourgues, J. & Hooper, D. C. The BAO+BBN take on the Hubble tension. J. Cosmol. Astropart. Phys. 2019, 029 (2019).

    Google Scholar 

  99. Aiola, S. et al. The Atacama Cosmology Telescope: DR4 maps and cosmological parameters. J. Cosmol. Astropart. Phys. 2020, 047 (2020).

    Google Scholar 

  100. Secco, L. F. et al. Dark Energy Survey Year 3 results: cosmology from cosmic shear and robustness to modeling uncertainty. Phys. Rev. D 105, 023515 (2022).

    ADS  Google Scholar 

  101. Sugiyama, S. et al. Hyper Suprime-Cam Year 3 Results: cosmology from galaxy clustering and weak lensing with HSC and SDSS using the minimal bias model. Phys. Rev. D 108, 123521 (2023).

    ADS  Google Scholar 

  102. Bocquet, S. et al. SPT clusters with DES and HST weak lensing. II. Cosmological constraints from the abundance of massive halos. Preprint at https://arxiv.org/abs/2401.02075 (2024).

  103. Krolewski, A., Ferraro, S. & White, M. Cosmological constraints from unWISE and Planck CMB lensing tomography. J. Cosmol. Astropart. Phys. 2021, 028 (2021).

    Google Scholar 

  104. White, M. et al. Cosmological constraints from the tomographic cross-correlation of DESI luminous red galaxies and Planck CMB lensing. J. Cosmol. Astropart. Phys. 2022, 007 (2022).

    Google Scholar 

  105. Philcox, O. H. E. & Ivanov, M. M. The BOSS DR12 full-shape cosmology: Λ CDM constraints from the large-scale galaxy power spectrum and bispectrum monopole. Phys. Rev. D 104, 043517 (2022).

    ADS  MathSciNet  Google Scholar 

  106. Hinshaw, G. et al. Nine-year Wilkinson Microwave Anisotropy Probe (WMAP) observations: cosmological parameter results. Astrophys. J. Suppl. Ser. 208, 19 (2013).

    ADS  Google Scholar 

  107. Denzel, P., Coles, J. P., Saha, P. & Williams, L. L. R. The Hubble constant from eight time-delay galaxy lenses. Mon. Not. R. Astron. Soc. 501, 784 (2021).

    ADS  Google Scholar 

  108. Kourkchi, E. et al. Cosmicflows-4: the calibration of optical and infrared Tully–Fisher relations. Astrophys. J. 896, 3 (2020).

    ADS  Google Scholar 

  109. Palmese, A. et al. A statistical standard siren measurement of the Hubble constant from the LIGO/Virgo gravitational wave compact object merger GW190814 and Dark Energy Survey galaxies. Astrophys. J. Lett. 900, L33 (2020).

    ADS  Google Scholar 

  110. Regaldo-Saint Blancard, B. et al. Galaxy clustering analysis with SimBIG and the wavelet scattering transform. Phys. Rev. D 109, 083535 (2024).

    ADS  Google Scholar 

  111. Huang, D., Bharti, A., Souza, A., Acerbi, L. & Kaski, S. Learning robust statistics for simulation-based inference under model misspecification. Preprint at https://doi.org/10.48550/arXiv.2305.15871 (2023).

  112. Chaussidon, E. et al. Target selection and validation of DESI quasars. Astrophys. J. 944, 107 (2023).

    ADS  Google Scholar 

  113. Hahn, C. et al. The DESI Bright Galaxy Survey: final target selection, design, and validation. Astron. J. 165, 253 (2023).

    ADS  Google Scholar 

  114. Raichoor, A. et al. Target selection and validation of DESI emission line galaxies. Astron. J. 165, 126 (2023).

    ADS  Google Scholar 

  115. Zhou, R. et al. Target selection and validation of DESI luminous red galaxies. Astron. J. 165, 58 (2023).

    ADS  Google Scholar 

  116. Euclid Collab. Euclid preparation. I. The Euclid Wide Survey. Astron. Astrophys. 662, A112 (2022).

    Google Scholar 

  117. Scoccimarro, R. Fast estimators for redshift-space clustering. Phys. Rev. D 92, 083532 (2015).

    ADS  Google Scholar 

  118. Feldman, H. A., Kaiser, N. & Peacock, J. A. Power spectrum analysis of three-dimensional redshift surveys. Astrophys. J. 426, 23 (1994).

    ADS  Google Scholar 

  119. Hahn, C., Eickenberg, M., Ho, S. & SimBIG. Cosmological constraints from the nonlinear galaxy bispectrum. Phys. Rev. D 109, 083534 (2024).

    ADS  Google Scholar 

  120. Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P. & Wilson, A. G. A simple baseline for bayesian uncertainty in deep learning. Adv. Neural Inf. Process. Syst. 32 (2019).

  121. Wilson, A. G. & Izmailov, P. Bayesian deep learning and a probabilistic perspective of generalization. Adv. Neural Inf. Process. Syst. 33, 4697 (2020).

    Google Scholar 

  122. Lemos, P. et al. Robust simulation-based inference in cosmology with bayesian neural networks. Mach. Learning Sci. Technol. 4, 01LT01 (2023).

    Google Scholar 

  123. Lemos, P. et al. Field-level simulation-based inference of galaxy clustering with convolutional neural networks. Phys. Rev. D 109, 083536 (2024).

    ADS  Google Scholar 

  124. Alves de Oliveira, R., Li, Y., Villaescusa-Navarro, F., Ho, S. & Spergel, D. N. Fast and accurate non-linear predictions of universes with deep learning. Preprint at https://arxiv.org/abs/2012.00240 (2020).

  125. Li, Y. et al. AI-assisted superresolution cosmological simulations. Proc. Natl Acad. Sci. USA 118, e2022038118 (2021).

    Google Scholar 

  126. Ni, Y. et al. AI-assisted superresolution cosmological simulations-II. Halo substructures, velocities, and higher order statistics. Mon. Not. R. Astron. Soc. 507, 1021 (2021).

    ADS  Google Scholar 

  127. Schaurecker, D., Li, Y., Tinker, J., Ho, S. & Refregier, A., Super-resolving dark matter halos using generative deep learning. Preprint at https://arxiv.org/abs/2111.06393 (2021).

  128. Jamieson, D. et al. Field level neural network emulator for cosmological N-body simulations. Astrophys. J. 952, 145 (2023).

    ADS  Google Scholar 

  129. Zhang, X. et al. AI-assisted super-resolution cosmological simulations III: time evolution. Mon. Not. R. Astron. Soc. 528, 281–293 (2024).

    ADS  Google Scholar 

  130. Feng, Y., Chu, M.-Y., Seljak, U. & McDonald, P. FASTPM: a new scheme for fast simulations of dark matter and haloes. Mon. Not. R. Astron. Soc. 463, 2273 (2016).

  131. Dai, B., Feng, Y., Seljak, U. & Singh, S. High mass and halo resolution from fast low resolution simulations. J. Cosmol. Astropart. Phys. 2020, 002 (2020).

    Google Scholar 

  132. Modi, C., Lanusse, F. & Seljak, U. FlowPM: distributed TensorFlow implementation of the FastPM cosmological N-body solver. Astron. Comput. 37, 100505 (2021).

    ADS  Google Scholar 

  133. Li, Y. et al. Differentiable cosmological simulation with the adjoint method. Astrophys. J. Suppl. Ser. 270, 36 (2024).

    ADS  Google Scholar 

  134. Modi, C., Chen, S.-F. & White, M. Simulations and symmetries. Mon. Not. R. Astron. Soc. 492, 5754 (2020).

    ADS  Google Scholar 

  135. Hadzhiyska, B., García-García, C., Alonso, D., Nicola, A. & Slosar, A. Hefty enhancement of cosmological constraints from the DES Y1 data using a hybrid effective field theory approach to galaxy bias. J. Cosmol. Astropart. Phys. 2021, 020 (2021).

    Google Scholar 

  136. Kokron, N., DeRose, J., Chen, S.-F., White, M. & Wechsler, R. H. The cosmology dependence of galaxy clustering and lensing from a hybrid N-body-perturbation theory model. Mon. Not. R. Astron. Soc. 505, 1422 (2021).

    ADS  Google Scholar 

  137. DeRose, J., Chen, S.-F., Kokron, N. & White, M. Precision redshift-space galaxy power spectra using Zel’dovich control variates. J. Cosmol. Astropart. Phys. 2023, 008 (2023).

    MathSciNet  Google Scholar 

  138. Modi, C. & Philcox, O. H. E. Hybrid SBI or how I learned to stop worrying and learn the likelihood. Preprint at https://doi.org/10.48550/arXiv.2309.10270 (2023).

Download references

Acknowledgements

We thank P. Melchior, U. Seljak, B. D. Wandelt and the members of the Simons Collaboration on Learning the Universe (https://www.learning-the-universe.org/) for valuable discussions. We also thank M. M. Ivanov and Y. Kobayashi for providing us with the posteriors used for comparison. This work was supported by the AI Accelerator programme of the Schmidt Futures Foundation No 922 (C.H.), the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement No 101025187 (J.H.) and the Tomalla Foundation for Research in Gravity (A.M.D.). The work reported on in this paper was substantially performed using the Princeton Research Computing resources at Princeton University, which is a consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology’s Research Computing.

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Authors and Affiliations

  1. Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA

    ChangHoon Hahn, Liam Parker & David Spergel

  2. Department of Physics, Université de Montréal, Montréal, Quebec, Canada

    Pablo Lemos

  3. Mila—Quebec Artificial Intelligence Institute, Montréal, Quebec, Canada

    Pablo Lemos

  4. Center for Computational Mathematics, Flatiron Institute, New York, NY, USA

    Pablo Lemos, Bruno Régaldo-Saint Blancard, Michael Eickenberg & Chirag Modi

  5. Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA

    Shirley Ho, Chirag Modi & David Spergel

  6. Department of Astronomy, University of Florida, Gainesville, FL, USA

    Jiamin Hou

  7. Max-Planck-Institut für Extraterrestrische Physik, Garching bei München, Germany

    Jiamin Hou

  8. Waterloo Centre for Astrophysics, University of Waterloo, Waterloo, Ontario, Canada

    Elena Massara

  9. Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada

    Elena Massara

  10. Département de Physique Théorique, Université de Genève, Geneva, Switzerland

    Azadeh Moradinezhad Dizgah

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Contributions

C.H., P.L., L.P., B.R.-S.B., M.E., S.H., J.H., E.M., C.M., A.M.D. and D.S. designed the research. C.H., P.L., L.P. and B.R.-S.B. performed the data analysis. C.H. wrote the draft. All co-authors contributed to the improvement of the analysis and the manuscript.

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Correspondence to ChangHoon Hahn.

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Hahn, C., Lemos, P., Parker, L. et al. Cosmological constraints from non-Gaussian and nonlinear galaxy clustering using the SimBIG inference framework. Nat Astron 8, 1457–1467 (2024). https://doi.org/10.1038/s41550-024-02344-2

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