Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Disentangling multiple gas kinematic drivers in the Perseus galaxy cluster

Galaxy clusters, the Universe’s largest halo structures, are filled with an X-ray-emitting gas with a temperature between 10 million and 100 million degrees. Their evolution is shaped by energetic processes such as feedback from supermassive black holes (SMBHs) and mergers with other cosmic structures1,2,3. The imprints of these processes on gas kinematics remain largely unknown, restricting our understanding of energy conversion within clusters4. High-resolution spectral mapping with the X-Ray Imaging and Spectroscopy Mission (XRISM) observatory5 offers a way forward6,7. Here we present XRISM kinematic measurements of the Perseus cluster, radially covering the extent of its cool core. We find direct evidence for at least two dominant drivers of gas motions operating on distinct physical scales: a small-scale driver in the inner approximately 60 kpc, probably associated with the SMBH feedback; and a large-scale driver in the outer core, powered by mergers. This finding suggests that, during the active phase, SMBH feedback drives gas motions, which, if fully dissipated into heat, could have a substantial role in offsetting radiative cooling losses in the Perseus core. Our study underscores the necessity of kinematic mapping observations of extended sources to robustly characterize the velocity fields and their role in the evolution of massive halos. It further offers a kinematic diagnostic for SMBH feedback models.

This is a preview of subscription content, access via your institution

Access options

Buy this article

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: High-resolution X-ray image and spectrum of the Perseus cluster.
Fig. 2: Radial profile of gas kinematic properties in the Perseus cluster measured with XRISM/Resolve.
Fig. 3: Kinematic maps of the hot gas in the Perseus cluster.
Fig. 4: Estimated turbulent dissipation heating and radiative cooling rates in the Perseus cluster, assuming that the observed velocity broadening is owing to turbulence.

Similar content being viewed by others

Data availability

The observational data analysed during this study are available in the NASA HEASARC repository (https://heasarc.gsfc.nasa.gov/docs/xrism/). XRISM observation IDs 000154000, 000155000, 000156000, 000157000 and 000158000 were used, in addition to Chandra observation IDs 3209, 4289, 4946–4953, 6139, 6145, 6146, 11713–11716, 12025, 12033, 12036 and 12037. The atomic databases used in this study are available online at AtomDB (http://www.atomdb.org/).

Code availability

Publicly released versions of the FLASH code are available via the Flash Center for Computational Science’s website (https://flash.rochester.edu/). Our turbulent simulation set-ups used the FLASH built-in Stir unit. The base simulation code AREPO is publicly available at https://arepo-code.org/ and https://gitlab.mpcdf.mpg.de/vrs/arepo. The specific branch of AREPO used in this study includes a custom AGN implementation developed by Rainer Weinberger, as described in ref. 78. This branch is not publicly available, but access may be granted by the code developer upon reasonable request.

References

  1. Vikhlinin, A. A., Kravtsov, A. V., Markevich, M. L., Sunyaev, R. A. & Churazov, E. M. Clusters of galaxies. Phys. Usp. 57, 317–341 (2014).

    Article  ADS  Google Scholar 

  2. Fabian, A. C. Observational evidence of active galactic nuclei feedback. Annu. Rev. Astron. Astrophys. 50, 455–489 (2012).

    Article  ADS  CAS  Google Scholar 

  3. Kravtsov, A. V. & Borgani, S. Formation of galaxy clusters. Annu. Rev. Astron. Astrophys. 50, 353–409 (2012).

    Article  ADS  Google Scholar 

  4. Simionescu, A. et al. Constraining gas motions in the intra-cluster medium. Space Sci. Rev. 215, 24 (2019).

    Article  ADS  Google Scholar 

  5. Tashiro, M. et al. Status of X-Ray Imaging and Spectroscopy Mission (XRISM). In Proc. SPIE 11444, 1144422 (2020).

  6. Hitomi Collaboration The quiescent intracluster medium in the core of the Perseus cluster. Nature 535, 117–121 (2016).

    Article  ADS  Google Scholar 

  7. Hitomi Collaboration Atmospheric gas dynamics in the Perseus cluster observed with Hitomi. Publ. Astron. Soc. Jpn 70, 9 (2018).

    Article  ADS  Google Scholar 

  8. Forman, W., Kellogg, E., Gursky, H., Tananbaum, H. & Giacconi, R. Observations of the extended X-ray sources in the Perseus and Coma clusters from UHURU. Astrophys. J. 178, 309–316 (1972).

    Article  ADS  Google Scholar 

  9. Conselice, C. J., Gallagher III, J. S. & Wyse, R. F. On the nature of the NGC 1275 system. Astron. J. 122, 2281–2300 (2001).

    Article  ADS  CAS  Google Scholar 

  10. Fabian, A. C. et al. A deep Chandra observation of the Perseus cluster: shocks and ripples. Mon. Not. R. Astron. Soc. 344, L43–L47 (2003).

    Article  ADS  Google Scholar 

  11. Churazov, E., Forman, W., Jones, C. & Böhringer, H. XMM-Newton observations of the Perseus cluster. I. The temperature and surface brightness structure. Astrophys. J. 590, 225–237 (2003).

    Article  ADS  Google Scholar 

  12. Urban, O. et al. Azimuthally resolved X-ray spectroscopy to the edge of the Perseus cluster. Mon. Not. R. Astron. Soc. 437, 3939–3961 (2014).

    Article  ADS  Google Scholar 

  13. van Weeren, R. J. et al. LOFAR high-band antenna observations of the Perseus cluster: the discovery of a giant radio halo. Astron. Astrophys. 692, A12 (2024).

    Article  Google Scholar 

  14. Boehringer, H., Voges, W., Fabian, A. C., Edge, A. C. & Neumann, D. M. A ROSAT HRI study of the interaction of the X-ray emitting gas and radio lobes of NGC 1275. Mon. Not. R. Astron. Soc. 264, L25–L28 (1993).

    Article  ADS  Google Scholar 

  15. Churazov, E., Forman, W., Jones, C. & Böhringer, H. Asymmetric, arc minute scale structures around NGC 1275. Astron. Astrophys. 356, 788–794 (2000).

    ADS  Google Scholar 

  16. Zhuravleva, I. et al. Turbulent heating in galaxy clusters brightest in X-rays. Nature 515, 85–87 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  17. Zhang, C., Churazov, E. & Schekochihin, A. A. Generation of internal waves by buoyant bubbles in galaxy clusters and heating of intracluster medium. Mon. Not. R. Astron. Soc. 478, 4785–4798 (2018).

    Article  ADS  Google Scholar 

  18. Zhang, C. et al. Bubble-driven gas uplift in galaxy clusters and its velocity features. Mon. Not. R. Astron. Soc. 517, 616–631 (2022).

    Article  ADS  CAS  Google Scholar 

  19. Simionescu, A. et al. Large-scale motions in the Perseus galaxy cluster. Astrophys. J. 757, 182 (2012).

    Article  ADS  Google Scholar 

  20. Walker, S. A., ZuHone, J., Fabian, A. & Sanders, J. The split in the ancient cold front in the Perseus cluster. Nat. Astron. 2, 292–296 (2018).

    Article  ADS  Google Scholar 

  21. ZuHone, J. A., Markevitch, M. & Lee, D. Sloshing of the magnetized cool gas in the cores of galaxy clusters. Astrophys. J. 743, 16 (2011).

    Article  ADS  Google Scholar 

  22. Ichinohe, Y., Simionescu, A., Werner, N., Fabian, A. C. & Takahashi, T. Substructures associated with the sloshing cold front in the Perseus cluster. Mon. Not. R. Astron. Soc. 483, 1744–1753 (2019).

    Article  ADS  CAS  Google Scholar 

  23. Bellomi, E. et al. On the origin of the ancient, large-scale cold front in the Perseus cluster of galaxies. Astrophys. J. 974, 234 (2024).

    Article  ADS  Google Scholar 

  24. Lea, S. M. The dynamics of the intergalactic medium in the vicinity of clusters of galaxies. Astrophys. J. 203, 569–580 (1976).

    Article  ADS  Google Scholar 

  25. Cowie, L. L. & Binney, J. Radiative regulation of gas flow within clusters of galaxies: a model for cluster X-ray sources. Astrophys. J. 215, 723–732 (1977).

    Article  ADS  CAS  Google Scholar 

  26. Fabian, A. C. & Nulsen, P. E. J. Subsonic accretion of cooling gas in clusters of galaxies. Mon. Not. R. Astron. Soc. 180, 479–484 (1977).

    Article  ADS  Google Scholar 

  27. Peterson, J. R. & Fabian, A. C. X-ray spectroscopy of cooling clusters. Phys. Rep. 427, 1–39 (2006).

    Article  ADS  Google Scholar 

  28. Fabian, A. C. et al. Hidden cooling flows in clusters of galaxies. Mon. Not. R. Astron. Soc. 515, 3336–3345 (2022).

    Article  ADS  CAS  Google Scholar 

  29. Kolmogorov, A. The local structure of turbulence in incompressible viscous fluid for very large Reynolds’ numbers. Akad. Nauk SSSR Dokl. 30, 301–305 (1941).

    ADS  MathSciNet  Google Scholar 

  30. Ishisaki, Y. et al. Status of Resolve instrument onboard X-Ray Imaging and Spectroscopy Mission (XRISM). Proc. SPIE 12181, 121811S (2022).

  31. Gendron-Marsolais, M. et al. Revealing the velocity structure of the filamentary nebula in NGC 1275 in its entirety. Mon. Not. R. Astron. Soc. 479, L28–L33 (2018).

    Article  ADS  CAS  Google Scholar 

  32. Vigneron, B. et al. High-spectral-resolution observations of the optical filamentary nebula surrounding NGC 1275. Astrophys. J. 962, 96 (2024).

    Article  ADS  Google Scholar 

  33. ZuHone, J. A., Miller, E. D., Simionescu, A. & Bautz, M. W. Simulating Astro-H observations of sloshing gas motions in the cores of galaxy clusters. Astrophys. J. 821, 6 (2016).

    Article  ADS  Google Scholar 

  34. Zhang, C. et al. Mapping the Perseus galaxy cluster with XRISM: gas kinematic features and their implications for turbulence. Astron. Astrophys. https://doi.org/10.1051/0004-6361/202557660 (2025).

  35. Sanders, J. S. et al. Measuring bulk flows of the intracluster medium in the Perseus and Coma galaxy clusters using XMM-Newton. Astron. Astrophys. 633, A42 (2020).

    Article  CAS  Google Scholar 

  36. Zhuravleva, I., Churazov, E., Kravtsov, A. & Sunyaev, R. Constraints on the ICM velocity power spectrum from the X-ray lines width and shift. Mon. Not. R. Astron. Soc. 422, 2712–2724 (2012).

    Article  ADS  Google Scholar 

  37. Miniati, F. The Matryoshka Run. II. Time-dependent turbulence statistics, stochastic particle acceleration, and microphysics impact in a massive galaxy cluster. Astrophys. J. 800, 60 (2015).

    Article  ADS  Google Scholar 

  38. Shi, X., Nagai, D. & Lau, E. T. Multiscale analysis of turbulence evolution in the density-stratified intracluster medium. Mon. Not. R. Astron. Soc. 481, 1075–1082 (2018).

    Article  ADS  CAS  Google Scholar 

  39. Heinrich, A., Chen, Y.-H., Heinz, S., Zhuravleva, I. & Churazov, E. Constraining black hole feedback in galaxy clusters from X-ray power spectra. Mon. Not. R. Astron. Soc. 505, 4646–4654 (2021).

    Article  ADS  CAS  Google Scholar 

  40. Li, Y. et al. Direct detection of black hole-driven turbulence in the centers of galaxy clusters. Astrophys. J. Lett. 889, L1 (2020).

    Article  ADS  CAS  Google Scholar 

  41. Timmerman, R. et al. Measuring cavity powers of active galactic nuclei in clusters using a hybrid X-ray-radio method. A new window on feedback opened by subarcsecond LOFAR-VLBI observations. Astron. Astrophys. 668, A65 (2022).

    Article  CAS  Google Scholar 

  42. Hitomi Collaboration Measurements of resonant scattering in the Perseus cluster core with Hitomi SXS. Publ. Astron. Soc. Jpn 70, 10 (2018).

    Article  ADS  Google Scholar 

  43. Zhuravleva, I. V., Churazov, E. M., Sazonov, S. Y., Sunyaev, R. A. & Dolag, K. Resonant scattering in galaxy clusters for anisotropic gas motions on various spatial scales. Astron. Lett. 37, 141–153 (2011).

    Article  ADS  CAS  Google Scholar 

  44. Kang, W. et al. A deep redshift survey of the Perseus cluster (A426): spatial distribution and kinematics of galaxies. Astrophys. J. Suppl. Ser. 272, 22 (2024).

    Article  ADS  CAS  Google Scholar 

  45. Heinrich, A. et al. Merger-driven multiscale ICM density perturbations: testing cosmological simulations and constraining plasma physics. Mon. Not. R. Astron. Soc. 528, 7274–7299 (2024).

    Article  ADS  CAS  Google Scholar 

  46. Ota, N., Nagai, D. & Lau, E. T. Constraining hydrostatic mass bias of galaxy clusters with high-resolution X-ray spectroscopy. Publ. Astron. Soc. Jpn 70, 51 (2018).

    Article  ADS  CAS  Google Scholar 

  47. Bourne, M. A. & Sijacki, D. AGN jet feedback on a moving mesh: cocoon inflation, gas flows and turbulence. Mon. Not. R. Astron. Soc. 472, 4707–4735 (2017).

    Article  ADS  CAS  Google Scholar 

  48. Ehlert, K., Weinberger, R., Pfrommer, C. & Springel, V. Connecting turbulent velocities and magnetic fields in galaxy cluster simulations with active galactic nuclei jets. Mon. Not. R. Astron. Soc. 503, 1327–1344 (2021).

    Article  ADS  CAS  Google Scholar 

  49. Fielding, D. B. et al. First results from SMAUG: uncovering the origin of the multiphase circumgalactic medium with a comparative analysis of idealized and cosmological simulations. Astrophys. J. 903, 32 (2020).

    Article  ADS  CAS  Google Scholar 

  50. XRISM Collaboration The XRISM first-light observation: velocity structure and thermal properties of the supernova remnant N132D. Publ. Astron. Soc. Jpn 76, 1186–1201 (2024).

    Article  ADS  Google Scholar 

  51. Xrism Collaboration XRISM Spectroscopy of the Fe Kα emission line in the Seyfert active galactic nucleus NGC 4151 reveals the disk, broad-line region, and torus. Astrophys. J. Lett. 973, L25 (2024).

    Article  ADS  Google Scholar 

  52. Porter, F. S. et al. In-flight performance of the XRISM/Resolve detector system. In Proc. SPIE 13093, 130931K (2024).

  53. Dauser, T. et al. SIXTE: a generic X-ray instrument simulation toolkit. Astron. Astrophys. 630, A66 (2019).

    Article  Google Scholar 

  54. Hitomi Collaboration Hitomi observation of radio galaxy NGC 1275: The first X-ray microcalorimeter spectroscopy of Fe-Kα line emission from an active galactic nucleus. Publ. Astron. Soc. Jpn 70, 13 (2018).

    Article  ADS  Google Scholar 

  55. Reynolds, C. S. et al. Probing the circumnuclear environment of NGC 1275 with high-resolution X-ray spectroscopy. Mon. Not. R. Astron. Soc. 507, 5613–5624 (2021).

    Article  ADS  CAS  Google Scholar 

  56. Fukazawa, Y. et al. X-ray and GeV gamma-ray variability of the radio galaxy NGC 1275. Astrophys. J. 855, 93 (2018).

    Article  ADS  Google Scholar 

  57. Vikhlinin, A. et al. Chandra temperature profiles for a sample of nearby relaxed galaxy clusters. Astrophys. J. 628, 655–672 (2005).

    Article  ADS  CAS  Google Scholar 

  58. Arnaud, K. A. XSPEC: the first ten years. In Proc. Astronomical Data Analysis Software and Systems V, Astronomical Society of the Pacific Conference Series Vol. 101 (eds Jacoby, G. H. & Barnes, J.) 17–20 (Astronomical Society of the Pacific, 1996).

  59. Willingale, R., Starling, R. L. C., Beardmore, A. P., Tanvir, N. R. & O’Brien, P. T. Calibration of X-ray absorption in our Galaxy. Mon. Not. R. Astron. Soc. 431, 394–404 (2013).

    Article  ADS  Google Scholar 

  60. Lodders, K., Palme, H. & Gail, H.-P. in Landolt Börnstein—Group VI Astronomy and Astrophysics 4B (Solar System) (ed. Trümper, J. E.) Ch. 4.4 (Springer, 2009).

  61. Gilfanov, M. R., Syunyaev, R. A. & Churazov, E. M. Radial brightness profiles of resonance X-ray lines in galaxy clusters. Sov. Astron. Lett. 13, 3 (1987).

    ADS  Google Scholar 

  62. Kilbourne, C. A. et al. In-flight calibration of Hitomi Soft X-ray Spectrometer. (1) Background. Publ. Astron. Soc. Jpn 70, 18 (2018).

    Article  ADS  Google Scholar 

  63. Tang, X. & Churazov, E. Sound wave generation by a spherically symmetric outburst and AGN feedback in galaxy clusters. Mon. Not. R. Astron. Soc. 468, 3516–3532 (2017).

    Article  ADS  CAS  Google Scholar 

  64. Sutherland, R. S. & Dopita, M. A. Cooling functions for low-density astrophysical plasmas. Astrophys. J. Suppl. Ser. 88, 253 (1993).

    Article  ADS  CAS  Google Scholar 

  65. Sreenivasan, K. R. On the universality of the Kolmogorov constant. Phys. Fluids 7, 2778–2784 (1995).

    Article  ADS  MathSciNet  Google Scholar 

  66. Kaneda, Y., Ishihara, T., Yokokawa, M., Itakura, K. & Uno, A. Energy dissipation rate and energy spectrum in high resolution direct numerical simulations of turbulence in a periodic box. Phys. Fluids 15, L21–L24 (2003).

    Article  ADS  CAS  Google Scholar 

  67. Fryxell, B. et al. FLASH: an adaptive mesh hydrodynamics code for modeling astrophysical thermonuclear flashes. Astrophys. J. Suppl. Ser. 131, 273–334 (2000).

    Article  ADS  CAS  Google Scholar 

  68. Eswaran, V. & Pope, S. B. An examination of forcing in direct numerical simulations of turbulence. Comput. Fluids 16, 257–278 (1988).

    Article  ADS  Google Scholar 

  69. Schmidt, W., Hillebrandt, W. & Niemeyer, J. C. Numerical dissipation and the bottleneck effect in simulations of compressible isotropic turbulence. Comput. Fluids 35, 353–371 (2006).

    Article  CAS  Google Scholar 

  70. Federrath, C., Roman-Duval, J., Klessen, R. S., Schmidt, W. & Mac Low, M. M. Comparing the statistics of interstellar turbulence in simulations and observations: solenoidal versus compressive turbulence forcing. Astron. Astrophys. 512, A81 (2010).

    Article  ADS  Google Scholar 

  71. Porter, D. H., Jones, T. W. & Ryu, D. Vorticity, shocks, and magnetic fields in subsonic, ICM-like turbulence. Astrophys. J. 810, 93 (2015).

    Article  ADS  Google Scholar 

  72. Brethouwer, G., Billant, P., Lindborg, E. & Chomaz, J. M. Scaling analysis and simulation of strongly stratified turbulent flows. J. Fluid Mech. 585, 343 (2007).

    Article  ADS  MathSciNet  Google Scholar 

  73. Shi, X. & Zhang, C. Turbulence decay in the density-stratified intracluster medium. Mon. Not. R. Astron. Soc. 487, 1072–1081 (2019).

    Article  ADS  CAS  Google Scholar 

  74. Mohapatra, R., Federrath, C. & Sharma, P. Turbulent density and pressure fluctuations in the stratified intracluster medium. Mon. Not. R. Astron. Soc. 500, 5072–5087 (2021).

    Article  ADS  Google Scholar 

  75. Bellomi, E. et al. Disentangling AGN Feedback and Sloshing in the Perseus Cluster with XRISM: Insights from Simulations. Preprint at https://arxiv.org/abs/2512.12754 (2026).

  76. Springel, V. E pur si muove: Galilean-invariant cosmological hydrodynamical simulations on a moving mesh. Mon. Not. R. Astron. Soc. 401, 791–851 (2010).

    Article  ADS  Google Scholar 

  77. Weinberger, R., Springel, V. & Pakmor, R. The AREPO public code release. Astrophys. J. Suppl. Ser. 248, 32 (2020).

    Article  ADS  Google Scholar 

  78. Weinberger, R., Ehlert, K., Pfrommer, C., Pakmor, R. & Springel, V. Simulating the interaction of jets with the intracluster medium. Mon. Not. R. Astron. Soc. 470, 4530–4546 (2017).

    Article  ADS  CAS  Google Scholar 

  79. ZuHone, J. A., Markevitch, M., Weinberger, R., Nulsen, P. & Ehlert, K. How merger-driven gas motions in galaxy clusters can turn AGN bubbles into radio relics. Astrophys. J. 914, 73 (2021).

    Article  ADS  CAS  Google Scholar 

  80. Vogelsberger, M. et al. A model for cosmological simulations of galaxy formation physics. Mon. Not. R. Astron. Soc. 436, 3031–3067 (2013).

    Article  ADS  CAS  Google Scholar 

  81. Sanders, J. S. & Fabian, A. C. A deeper X-ray study of the core of the Perseus galaxy cluster: the power of sound waves and the distribution of metals and cosmic rays. Mon. Not. R. Astron. Soc. 381, 1381–1399 (2007).

    Article  ADS  CAS  Google Scholar 

  82. Fabian, A. C. et al. Do sound waves transport the AGN energy in the Perseus cluster? Mon. Not. R. Astron. Soc. 464, L1–L5 (2017).

    Article  ADS  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by JSPS KAKENHI grant numbers JP22H00158, JP22H01268, JP22K03624, JP23H04899, JP21K13963, JP24K00638, JP24K17105, JP21K13958, JP21H01095, JP23K20850, JP24H00253, JP21K03615, JP24K00677, JP20K14491, JP23H00151, JP19K21884, JP20H01947, JP20KK0071, JP23K20239, JP24K00672, JP24K17104, JP24K17093, JP20K04009, JP21H04493, JP20H01946, JP23K13154, JP19K14762, JP20H05857, JP23K03459 and JP25K23398. Additional support came from NASA grant numbers 80NSSC23K0650, 80NSSC20K0733, 80NSSC18K0978, 80NSSC20K0883, 80NSSC20K0737, 80NSSC24K0678, 80NSSC18K1684, 80NNSC22K1922 and 80GSFC21M0002. A.B. was supported by JSPS KAKENHI grant number JP23H01211. E.B. acknowledges support from NASA grants 80NSSC24K1148 and 80NSSC24K1774. L.C. acknowledges support from NASA grant 80NSSC25K7064. C.D. acknowledges support from STFC through grant ST/T000244/1. R.F. was supported by JSPS KAKENHI grant number JP23K03454. L.G. acknowledges financial support from the Canadian Space Agency (grant 18XARMSTMA). Y.M. was supported by JSPS KAKENHI grant number JP23K22548. M.M. acknowledges support from Yamada Science Foundation. P.P. acknowledges support from NASA grants 80NSSC18K0988 and 80NSSC23K1656 and NASA contract NAS8-0360. M.S. acknowledges the support by the RIKEN Pioneering Project Evolution of Matter in the Universe (r-EMU) and Rikkyo University Special Fund for Research (Rikkyo SFR). A.T. and the present research are in part supported by the Kagoshima University postdoctoral research programme (KU-DREAM). Y.T. was supported by the Strategic Research Center of Saitama University. S.Y. acknowledges support by the RIKEN SPDR Program. I.Z., A.H. and C.Z. acknowledge partial support from the Alfred P. Sloan Foundation through the Sloan Research Fellowship. I.Z. performed part of the work at the Kavli Institute for Theoretical Physics (KITP) supported by grant NSF PHY-2309135. C.Z. was supported by the GACR grant 21-13491X. J.H.-L. acknowledges the Canadian Space Agency (CSA) grant 22EXPXRISM. S.U. acknowledges the supports from the National Science and Technology Council of Taiwan (111-2112-M-001-026-MY3) and by Program for Forming Japan’s Peak Research Universities (J-PEAKS), respectively. T.Y. and A.T. acknowledge support by NASA under award number 80GSFC24M0006. Part of this work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. This work was supported by the JSPS Core-to-Core Program, JPJSCCA20220002. The simulations presented in this paper were carried out using the Midway computing cluster provided by the University of Chicago Research Computing Center. The software used in this work was developed in part by the DOE NNSA and DOE Office of Science supported Flash Center for Computational Science at the University of Chicago and the University of Rochester.

Author information

Authors and Affiliations

Consortia

Contributions

C.Z., A. Heinrich and I.Z. performed the data analysis, explored systematic uncertainties, interpreted the results and prepared the paper. C.Z. also developed controlled simulations of stratified turbulence. E. Bellomi developed the tailored AGN feedback and sloshing simulations. As the leader of the Perseus cluster target team in the XRISM Science Team, I.Z. oversaw the work on the project. F.S.P. and M.E.E. calibrated the energy scale and gain and estimated calibration uncertainties. A.O., F.M., S. Ueda, J.M. and Y. Ichinohe contributed to the Resolve data analysis. M. Markevitch, A.O., K.F., S. Kobayashi, K. Matsushita, R.M. and Y. Fukazawa contributed to resolving the AGN contribution in spectral modelling. C.K. assisted with the NXB modelling and contributed to related discussions. T. Yaqoob provided valuable insights into the XRISM mirror uncertainties. B.V. and J.H.-L. provided the SITELLE analysis of the multiphase gas. E. Bellomi, M.E.E., Y. Fujita, J.H.-L., Y. Ichinohe, M. Markevitch, K. Matsushita, B.M., F.M., A.O., N.O., F.S.P., A. Simionescu, P.C.S., N.T., S. Ueda, B.V. and J.Z. reviewed the paper and contributed to discussions. The scientific goals of XRISM were discussed and developed over 7 years by the XRISM Science Team, all members of which are authors of this paper. All the instruments were prepared by the joint efforts of the team.

Corresponding authors

Correspondence to Congyao Zhang, Annie Heinrich, Irina Zhuravleva or Elena Bellomi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 X-ray structures within the Perseus core and default radial binning scheme adopted for the analysis.

(a) Residual X-ray/Chandra image of the Perseus cluster in the 2−8 keV band, with arrows highlighting prominent structures relevant to our analysis. (b) The color pixels and grey circles/annuli show detector and sky regions (No. 1–6 from inner to outer), respectively, with the black dashed circle marking the innermost sky region. White contours illustrate the regions that contribute 90% photons to each pointing based on our SIXTE simulations.

Extended Data Fig. 2 Gas radial velocity profiles for varied binning schemes (a) and spatial-spectral mixing (b).

Formatting is equivalent to Fig. 2. In panel (a), black triangles correspond to the full-FOV profile (broad radial bins), red circles to the nominal profile of Fig. 2, and blue squares to the profile of Fig. 3 (narrow radial bins). In panel (b), the red circles are unchanged from Fig. 2, while blue squares/black triangles show profiles where off-axis ARFs, and therefore SSM, are increased/decreased by 30% (Supplementary Information).

Extended Data Fig. 3 Constraints on AGN parameters: photon index Γ vs. flux between 2−10 keV.

The contours indicate the implied 1, 2, 3σ parameter regimes from the two independent approaches utilizing Chandra spectra for the ICM component. Pink contours are based on the ratio of fluxes between sky regions 1/2 and 1/3 in the 4−6 keV band, which lacks strong emission lines, while blue contours are from the measurements of the equivalent width of Fe He-α line complex (Methods). For modeling the ICM in this work, we fixed the AGN flux at 31 × 10−12 erg s−1 cm−2, shown as the black point with 1σ error bars of the photon index.

Extended Data Fig. 4 XRISM/Resolve spectra from detector regions 1-3 and their best-fit models.

For each region, we show a broad-band spectrum from 3–11 keV and a detailed view of the Fe XXV He-α triplet on the right. Pink, orange, blue, and green curves represent the best-fit models of the NXB, the combined scattered ICM, the ICM, and the AGN components, respectively. The cyan curves on the right show the Fe XXV He-αw line Gaussian component, while the red curves show the total models. Residuals normalized by the statistical errors, i.e., (data-model)/error, are displayed in the lower panels with two different binning schemes: black points correspond to the binning with minimum significance \({\sigma }_{\min }=3\), while purple points show the binning with \({\sigma }_{\min }=20\).

Extended Data Fig. 5 XRISM/Resolve spectra from detector regions 4-6 and their best-fit models.

Notations are equivalent to Extended Data Fig. 4.

Extended Data Fig. 6 Statistical 1σ uncertainties for the velocity maps presented in Fig. 3.

Velocity dispersion errors are shown on the left, while the bulk velocity errors are on the right. All other notations are the same as in Fig. 3.

Extended Data Fig. 7 Effective length of the Perseus cluster.

(a) A sketch of the effective length concept in a stratified cluster atmosphere. The length along the LOS, which corresponds to the region size that contributes most to the measured flux/spectra, increases with the projected distance from the cluster center. (b) A radial profile of the normalized X-ray surface brightness in Perseus, adapted from11, is shown in black. The effective length, defined as the region size where 50% of the flux is collected, is shown with the red curve. The shaded red region indicates the scales associated with 40–60% of the flux contribution. The blue dashed curve represents the X-ray-weighted LOS length scale.

Extended Data Fig. 8 Numerical simulations of stratified turbulence with various driving scales.

(a) Projected velocity dispersion along the LOS for the simulation run with the injection scale inj = 500 kpc, weighted with (left) and without (right) X-ray emissivity, (b) Same as the panel (a) but for the run with inj = 50 kpc. (c) Comparisons between the simulated emissivity-weighted velocity dispersion radial profiles and the XRISM measurement (see also Fig. 2). See Methods for the description of the simulations.

Extended Data Fig. 9 Azimuthally averaged velocity dispersion centered on the cluster core from the tailored sloshing simulations compared with the XRISM observations.

Red crosses show XRISM’s LOS velocity dispersion presented in this work. The shaded regions indicate the azimuthally-averaged X-ray emissivity-weighted velocity dispersion radial profiles from the simulations including only sloshing (blue) and sloshing plus AGN jet feedback (violet). Both simulations share the same initial conditions and are shown at the same epoch. This figure is a simplified adaptation of Fig. 1 from ref. 75.

Extended Data Fig. 10 Modeled radial velocity dispersion profiles produced by propagating sound waves.

(a) Assuming conserved energy flux Fsw ( ≡ αFagn) of the sound waves starting from the cluster radius r0 powered by the central AGN (Fagn = 5 × 1044 erg s−1). (b) Assuming sound waves compensate a fraction (β) of cooling loss everywhere within the radius \({r}_{\max }\). Red points are our measured velocity dispersion, same as in Fig. 2.

Extended Data Table 1 Best-fit parameters of the ICM

Supplementary information

Supplementary Information

This file contains Supplementary Information.

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

The XRISM Collaboration. Disentangling multiple gas kinematic drivers in the Perseus galaxy cluster. Nature (2026). https://doi.org/10.1038/s41586-025-10017-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41586-025-10017-x

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing