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A younger Universe implied by satellite pair correlations from SDSS observations of massive galaxy groups

Nature Astronomy volume 8, pages 538–544 (2024)Cite this article

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Abstract

Many of the satellites of galactic-mass systems such as the Miky Way, Andromeda and Centaurus A show evidence of coherent motions to a larger extent than most of the systems predicted by the standard cosmological model. It is an open question if correlations in satellite orbits are present in systems of different masses. Here we report an analysis of the kinematics of satellite galaxies around massive galaxy groups. Unlike what is seen in Milky Way analogues, we find an excess of diametrically opposed pairs of satellites that have line-of-sight velocity offsets from the central galaxy of the same sign. This corresponds to a 6.0σ (P value = 9.9 × 10−10) detection of non-random satellite motions. Such excess is predicted by up-to-date cosmological simulations but the magnitude of the effect is considerably lower than in observations. The observational data is discrepant at the 4.1σ and 3.6σ level with the expectations of the Millennium and the Illustris TNG300 cosmological simulations, potentially indicating that massive galaxy groups assembled later in the real Universe. The detection of velocity correlations of satellite galaxies and tension with theoretical predictions is robust against changes in sample selection. Using the largest sample to date, our findings demonstrate that the motions of satellite galaxies represent a challenge to the current cosmological model.

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Fig. 1: Velocity correlation of the two most massive satellite galaxies in SDSS.
Fig. 2: Contrasting satellite velocities in observations and cosmological simulations.

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Data availability

All data used in this work is publicly available. The SDSS data are available at the Sloan Digital Sky Survey (https://www.sdss.org/). The data for TNG300 and TNG100 can be accessed at https://www.tng-project.org. The data for Millennium can be accessed at http://gavo.mpa-garching.mpg.de/Millennium/. The other data that support the results of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

We use standard data reduction tools in Python environments.

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Acknowledgements

We thank Prof. Dr. Simon White for his constructive comments. This work is supported by the National SKA Program of China (Nos. 2022SKA0110201 and 2022SKA0110200), the National Natural Science Foundation of China (NSFC; 12033008, 12273053, 12125302, 11988101 and 12022307), the K.C. Wong Education Foundation, CAS Project for Young Scientists in Basic Research Grant (No. YSBR-062) and science research grants from the China Manned Space Project with No. CMS-CSST-2021-A03 and No. CMS-CSST-2021-B03. M.C. acknowledges the support from the EU Horizon 2020 Research and Innovation Programme under a Marie Skłodowska-Curie grant agreement 794474 (DancingGalaxies). W.W. acknowledges the support from the Yangyang Development Fund. Q. Guo thanks European Union’s HORIZON-MSCA-2021-SE-01 Research and Innovation programme under the Marie Sklodowska-Curie grant agreement number 101086388.

Author information

Authors and Affiliations

  1. Key Laboratory for Computational Astrophysics, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, China

    Qing Gu, Qi Guo, Shi Shao, Wenxiang Pei, Liang Gao & Jie Wang

  2. Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University, Beijing, China

    Qing Gu, Qi Guo, Wenxiang Pei, Liang Gao & Jie Wang

  3. School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing, China

    Qing Gu, Qi Guo, Wenxiang Pei, Liang Gao & Jie Wang

  4. Leiden Observatory, Leiden University, Leiden, the Netherlands

    Marius Cautun

  5. Department of Astronomy, Shanghai Jiao Tong University, Shanghai, China

    Wenting Wang

  6. Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai, China

    Wenting Wang

  7. School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China

    Liang Gao

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  1. Qing Gu
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Contributions

Q. Guo led and played a part in all aspects of the analysis. Q. Gu compiled the data and carried out most of the data reduction and analysis, and wrote the paper. All authors contributed to the analysis and the writing of the paper.

Corresponding authors

Correspondence to Qi Guo or Shi Shao.

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Extended data

Extended Data Fig. 1 Galaxy selections.

Redshift versus stellar mass of the two most massive satellites in SDSS. The black curve shows the 90% completeness stellar mass value as a function of redshift. The dashed vertical line denotes z = 0.065, which is the redshift cut of primaries and satellites, and the dashed horizontal line corresponds to galaxies more massive than 1010.08M⊙, which is the lower mass threshold we use to select satellite galaxies.

Extended Data Fig. 2 The excess of satellite pairs with correlated velocity in simple models.

a) Central galaxies move with respect to the host dark matter halo with a velocity dispersion σc = fσσs, where fσ is a fractional value between 0 and 1. b) A fraction fc of centrals that are actually misidentified satellite galaxies. c) The fraction of correlated pairs considering both the effects of velocity dispersion of centrals relative to their halos and the misidentification of centrals. The black solid line illustrates the corresponding region reported in the literature.

Extended Data Fig. 3 The velocity correlation of neighbour galaxy pairs within a more extended region in SDSS.

The comparison of correlated fraction between satellite pairs located from 0.1 Mpc to 1 Mpc in projection (black solid curve with dots) and neighbours located from 5 Mpc to 6 Mpc in projection (cyan solid curve with squares). The horizontal dashed line represents the random distribution. Shade regions represent the standard deviations of corresponding bootstrap samples.

Extended Data Fig. 4 Redshift distributions.

Comparison of the redshift distributions of the primary galaxies in SDSS (black solid), MS (blue dashed) and TNG300 (orange red dotted).

Extended Data Fig. 5 Comparison between observation and simulations.

a) The angular distributions in SDSS and simulations. b) The distributions of relative line-of-sight velocity difference in observation and simulations. c) The distributions of the difference in stellar mass between satellite and primary in SDSS, MS and TNG300. d) The distributions of the difference in absolute magnitudes between satellite and primary in SDSS, MS and TNG300. e) The distributions of stellar mass of primary galaxies. f) The distributions of stellar mass of satellite galaxies. Overall, we observe a good agreement between simulations and observations.

Extended Data Fig. 6 The velocity correlation of satellites for samples with stricter isolation criteria.

a) and b) Same as Fig. 2 in the main paper but for systems whose primaries are the brightest galaxies within a larger volume, rp < 2 Mpc and ∣Δν∣ < 2000 km s−1. It results in a ~ 21% reduction in sample size. In SDSS, the excess of satellite pairs with correlated velocities is similar to our main results. The significance of this excess is lower due to the smaller sample size. c) and d) Fraction of correlated pairs and the significance of the excess for systems which choose a larger minimum magnitude difference between primary and satellite galaxies within 0.5 Mpc projected distance, specifically 1.5 magnitudes. The results are compared to those obtained with a minimum magnitude difference of 1 magnitude, which is used in the main analysis. e) and f) Similar to c) and d) but for a minimum difference of 2 magnitude. The solid curves with solid markers and dashed curves with empty markers are for 1 and 1.5 (or 2) magnitude differences, respectively. Results from SDSS, MS and TNG300 are shown in black, blue and orange red curves, respectively. It demonstrates the robustness of our findings, even when implementing more stringent isolation criteria for the selection of primaries and satellites. The shaded regions in the left three panels correspond to the standard deviations of bootstrap samples.

Extended Data Fig. 7 The velocity correlation of satellites at different redshifts in simulations.

a) Cumulative fraction of correlated galaxy pairs as a function of tolerance angle in the MS. The blue solid line with diamonds represents the MS result at z = 0, taken from Fig. 2a in the main text. The orange dashed line shows the cumulative fraction of correlated pairs at z = 1. b) Cumulative fraction of correlated galaxy pairs as a function of tolerance angle in the TNG300. The orange red solid line with squares duplicates the TNG300 result at z = 0, from Fig. 2a in the main text. The cyan dashed line illustrates the cumulative fraction of correlated pairs at z = 1. The shadow regions indicate the corresponding standard deviations of bootstrap samples.

Source data

Source Data Fig. 1

Statistical Source Data.

Source Data Fig. 2

Statistical Source Data.

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Gu, Q., Guo, Q., Cautun, M. et al. A younger Universe implied by satellite pair correlations from SDSS observations of massive galaxy groups. Nat Astron 8, 538–544 (2024). https://doi.org/10.1038/s41550-023-02192-6

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