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Non-Thermal Pressure due to Gas Motions in the Intracluster Medium: Confronting XRISM/Resolve with TNG-Cluster Simulations

T0 review · 3 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Projection and azimuthal sampling cannot explain the extreme quiescence of gas motions in Abell 2029, which falls below nearly every simulated cool-core cluster.

desk verdict Careful forward-modeling study: projection and azimuthal sampling cannot explain Abell 2029's extremely low non-thermal pressure, though the percentile claim would be stronger with propagated errors and formation-history conditioning. read the letter →

arxiv 2608.04757 v1 pith:COPGBA5M submitted 2026-08-05 astro-ph.CO astro-ph.GAastro-ph.HE

Erwin T. Lau , Naomi Ota , Daisuke Nagai This is my paper · ORCID
classification astro-ph.COastro-ph.GAastro-ph.HE
keywords intraclustermediumnon-thermalpressureX-rayspectroscopyXRISM/Resolvegalaxyclustersimulationsturbulencecool-coreclustersAbell2029
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Galaxy clusters hold hot gas whose random motions add a non-thermal pressure that supports the gas and affects estimates of cluster mass. New XRISM/Resolve measurements find that the cluster Abell 2029 has much weaker gas motions than simulations predict. This paper uses the TNG-Cluster simulation suite, a set of 352 high-resolution zoom-in cosmological cluster runs, to ask whether the gap could be an observational artifact: looking along one line of sight, or sampling only some directions, can make gas motions look smaller than they really are. The paper finds that projection and azimuthal sampling do lower the inferred non-thermal pressure fraction and that this bias grows with radius, but they cannot bring a typical simulated cool-core cluster down to Abell 2029's level. Under both the turbulence-only and the turbulence-plus-bulk definitions of non-thermal pressure, Abell 2029's measured points sit below roughly the 0th to 6th percentile of the simulated cool-core cluster distribution at every radius, which the authors read as evidence for rare dynamical conditions or missing physics in current simulations.

What carries the argument

The key machinery is a forward-modeling pipeline that converts each simulated cluster into a mock XRISM/Resolve observation: emission-weighted photon generation, the instrument response matrix and ancillary response, then single-temperature spectral fits of spectra extracted in eight azimuthal arms with four radial pointings along each of three orthogonal projections. This pipeline puts the simulated and observed quantities on exactly the same footing, allowing the recovered velocity dispersion, bulk velocity, and non-thermal pressure fraction to be compared with both projected and intrinsic three-dimensional values. The companion analytic piece is a two-scale fitting function for the non-thermal pressure fraction, f_nth(r) = f_0 - A_nth[1 - exp(-(r/(a R_500c))^$\alpha$)] + (f_infinity - f_0 + A_nth)[1 - exp(-(r/(b R_200m))^$\beta$)], whose inner exponential captures the cool-core suppression and whose outer exponential captures the rise toward the accretion shock, with R_500c and R_200m as the two characteristic overdensity radii.

What would settle it

A targeted test would be a larger XRISM/Resolve survey of relaxed cool-core clusters: if a substantial fraction of such systems show non-thermal pressure fractions as low as Abell 2029's, then the simulated population is not representative and the tension weakens; if such systems remain rare, the projection explanation is excluded and the tension stands.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that A2029's extremely low non-thermal pressure fraction is not a projection illusion. The authors forward-model full XRISM/Resolve observations from simulated clusters, tracing intrinsic three-dimensional gas motions through emission weighting, instrumental response, projection, and eight-direction azimuthal sampling, and compare the recovered line-of-sight velocity dispersion, bulk velocity, and non-thermal pressure fraction with both projected and intrinsic three-dimensional values. The mock-recovered dispersion and non-thermal pressure fraction underestimate the intrinsic three-dimensional values by roughly 10 percent for cool-core clusters and up to 25 percent for weak and non-cool-core clusters at about 0.2 R_500c, and the deficit grows outward, partially canceling the intrinsic outward rise. Yet A2029 remains below essentially the entire simulated cool-core distribution: its three measured non-thermal pressure fractions fall at the 1.9th, 0.0th, and 6.1st percentiles under the turbulence-only definition, and at the 0.9th, 0.0th, and 2.8th percentiles once coherent bulk motions are included. Because A2029's observed bulk-to-turbulent velocity ratio is consistent with the simulations, the disagreement is not about how the gas-motion budget is partitioned but about the unusually low absolute amplitude of all gas motions in that cluster.

Load-bearing premise

The argument assumes that the simulated cool-core clusters span the same range of gas-motion amplitudes as real clusters; if the galaxy-formation feedback model in the simulations systematically over-produces gas motions, the percentile comparison makes Abell 2029 look rarer than it is.

Editorial extensions

If this is right

  • A flat or declining observed non-thermal pressure fraction profile does not imply a flat or declining intrinsic three-dimensional profile, because the projection-and-sampling deficit grows with radius and partially cancels the intrinsic outward rise.
  • Including coherent bulk motions in the non-thermal pressure budget widens rather than closes the gap between A2029 and the simulations, since simulated clusters carry substantial line-of-sight bulk flows while A2029 has low amplitude in both turbulence and bulk motion.
  • The non-thermal pressure fraction anti-correlates with formation redshift with Spearman coefficients near -0.5 at several radii, so early-forming, relaxed clusters are expected to be quieter; A2029's formation history, with a major merger roughly four gigayears in the past, puts it at the extreme of that trend.
  • Turbulent dissipation alone provides only about ten percent of the required core heating in cool-core clusters, so the simulations imply that an additional heat source, plausibly AGN feedback, is necessary to offset radiative cooling in the cores.
  • The two-scale fitting function reproduces the V-shaped core profile and the outer rise of the non-thermal pressure fraction across cool-core, weak-cool-core, and non-cool-core classes, giving a calibrated benchmark for comparing other simulations and future X-ray measurements.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Going beyond the paper, if A2029 is genuinely a rare dynamical state, similarly quiet relaxed cool-core clusters should accumulate as the XRISM sample grows; the paper notes that Abell 1795 already shows comparably low values, and a handful more such systems would shift the interpretation from rare outliers toward a systematic simulation bias.
  • A testable extension the paper leaves for future work is to reconstruct A2029's assembly history from its merger record and compare its formation redshift against the calibrated formation-redshift versus non-thermal-pressure relation; a match would strengthen the early-forming-system explanation, while a mismatch would point more strongly to missing core physics.
  • The percentile comparison rests on one specific galaxy-formation subgrid model, so rerunning the same mock pipeline with alternative AGN feedback prescriptions would reveal whether A2029's sub-6th-percentile placement is robust or an artifact of that single feedback implementation.
  • The appendix shows that mass-weighted and emission-weighted estimates of the bulk-to-turbulent velocity ratio differ substantially in cluster cores, which suggests that X-ray line measurements may systematically under-weight cold, low-emissivity gas; future multi-line diagnostics with different emissivity weightings could directly probe this bias.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. The paper uses the TNG-Cluster cosmological MHD simulation suite to characterize the radial profile of the non-thermal pressure fraction f_nth in the ICM, stratified by cool-core state and formation history, and provides a two-scale phenomenological fitting function. It forward-models mock XRISM/Resolve observations of all 352 clusters with pyXSIM/SOXS, extracting spectra in eight azimuthal sectors along three orthogonal projections, and quantifies the mapping between mock-recovered, projected, and intrinsic three-dimensional quantities. Applied to Abell 2029, the mock comparison shows that A2029's three measured f_nth values fall at the 0th-6th percentile of the simulated cool-core pointing distribution under the turbulence-only definition and below the 3rd percentile when bulk motions are included, leading the authors to conclude that projection and azimuthal sampling cannot account for A2029's quiescence and that rare dynamical states or missing physics are required.

Significance. If the central claim survives scrutiny, the paper offers one of the most direct quantifications to date of the apparent tension between XRISM measurements of ICM gas motions and cosmological simulations, and its forward-modeling pipeline is a reusable template for interpreting microcalorimeter observations. The explicit within-simulation accounting of biases among mock, projected, and intrinsic 3D quantities is careful, and the analysis is reproducible because it uses public TNG-Cluster data and public mock-observation codes. The two-scale fitting function with tabulated parameters and the reported f_nth-formation-redshift anti-correlation are useful products for the community, and the headline percentile claim is falsifiable. However, as detailed below, the percentile analysis needs to be conditioned on A2029's assembly state and mass, and it needs to propagate the observational uncertainties, before the 'missing physics' interpretation is fully supported.

major comments (3)
  1. [3.2.2 and 3.1.4, Figures 8 and 11] The percentile ranks quoted in Section 3.2.2 (0.0th-6.1st percentile in the turbulence-only case, 0.0th-2.8th with bulk motions included) are computed against the pooled cool-core mock-pointing distribution without conditioning on formation history or mass. This is in tension with Section 3.1.4, which reports a Spearman anti-correlation of approximately -0.5 between 1+z_form and f_nth, notes that A2029's last major merger occurred roughly 4 Gyr ago, and states that A2029's low f_nth is therefore 'an expected consequence of its formation history.' Because A2029 is an early-forming system, the appropriate null distribution is the conditional f_nth distribution of simulated cool-core clusters at comparably high z_form (and comparable mass), not the full population distribution. Given the scatter visible in Figure 8 and the moderate strength of the correlation, conditioning could shift the percentile ranks substantially. Please recompute the percentile ranks for the high-z_form subsample of the CC population (e.g., the top quartile in z_form or z_form > 1.5) and report where A2029's three measured points fall in the 1+z_form versus f_nth planes of Figure 8; this directly tests whether the 'extreme outlier' claim is robust or is an artifact of population averaging.
  2. [3.2.2, Figure 11] The comparison treats the three XRISM measurements of A2029 (f_nth = 0.021, 0.003, 0.0067) as exact point values; no measurement uncertainties are quoted in the text or figure caption, and the percentile ranks are computed without an error budget. The claim that the intermediate point sits at the 0.0th percentile is a statement about the mock distribution relative to a point estimate: an upward uncertainty of even a factor of 2-3 on f_nth = 0.003 would place it within the simulated CC scatter, and the conclusion depends on all three radii simultaneously. The percentile ranks should be recomputed by convolving the A2029 posterior distributions (including systematic uncertainties in the fitted temperature and velocity dispersion) with the simulated distribution, or at minimum the sensitivity of the quoted percentiles to the measurement uncertainties should be stated.
  3. [2.1 and 3.2.2] The manuscript does not report A2029's mass (e.g., M_500) or compare it with the mass distribution of the 85 simulated cool-core halos, so the reader cannot assess whether A2029 sits in a well-sampled region of the CC population. Given that the high-mass, high-z_form corner of TNG-Cluster is sparsely populated and that gas motion amplitudes are mass- and assembly-dependent, the population-level percentile claim requires a statement of the sample's coverage of the (M_500, z_form) region occupied by A2029. Please report A2029's mass, the mass range of the simulated CC sample, and ideally repeat the percentile analysis on mass-matched or z_form-matched subsamples.
minor comments (7)
  1. [3.1 and 3.2.1, Eqs. (10) and (25)] The factor-of-3 convention discussion following Eq. (10) is hard to reconcile with Eq. (25), which is described as 'following the convention in XRISM Collaboration et al. (2025b)' but takes the same functional form f_nth = sigma^2/(sigma^2 + c_s^2/gamma) as the Section 3.1 definition in the isotropic limit. Please state explicitly which convention the A2029 values quoted in Section 3.2.2 are in, and confirm that the Figure 11 comparison applies the same convention to both mock and observed points.
  2. [3.2.2] Reporting the intermediate A2029 point as the '0.0th percentile' overstates the precision of a finite sample; the rank is better expressed as 'below all N sampled pointings,' with N stated. The azimuthal arms and projections of the same cluster are strongly correlated, so the effective number of independent systems entering each radial bin is much smaller than the nominal ~2000 pointings; a cluster-level percentile (e.g., using each CC cluster's median over projections and sectors) would be a useful complement.
  3. [3.1.4] Typo: 'intepreting' should read 'interpreting.'
  4. [Title page affiliation] The affiliation for D. Nagai contains a typo: 'Y ale University.'
  5. [Figure 2 caption] The caption states that the maps span '3x3 Mpc'; please write '3 Mpc x 3 Mpc' for clarity.
  6. [Appendix, Figure 12] The notation for the streaming ratio is inconsistent: the text uses |v_bulk|/sigma_1D while the figure caption uses |v_r|/sigma_1D; please unify.
  7. [4.3] The phrase 'the Line Emission Mapper or its re-incarnation' is informal for a journal article; please refer to the mission by its current name and status.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the A2029 percentile comparison is an external benchmark test, not a reconstruction from the paper's own fitted quantities.

full rationale

The central claim—that projection and azimuthal sampling cannot explain A2029's low non-thermal pressure fractions—rests on forward-modeled mock XRISM observations of the external TNG-Cluster suite compared with external XRISM measurements. The mock-to-projected and mock-to-3D ratios are internal calibrations that quantify, rather than presuppose, the projection bias. The percentile statements use the full mock distribution, not the fitted two-scale function, so no fitted parameter is renamed as a prediction. Self-citations (e.g., Lau et al. 2009 for the velocity-dispersion definition of non-thermal pressure) supply standard definitions and context but are not load-bearing: the A2029 comparison uses the XRISM Collaboration et al. (2025b) convention and would stand unchanged if those definitions were replaced by any other consistent mapping. Section 4.2's concession that the IllustrisTNG model is one subgrid realization is a representativeness caveat about the simulation ensemble, not a circular step. No equation reduces to its own input, and no uniqueness claim is imported from the authors' prior work.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The two-scale fitting function contributes seven descriptive parameters fitted to median simulated profiles (Table 1), plus an order-unity heating efficiency eta=1 in Eq. 20. These parameters do not enter the A2029 percentile comparison, which uses the raw mock distributions. No new physical entities are introduced. The main hidden inputs are the IllustrisTNG subgrid model and the assumptions underlying the mock spectra.

free parameters (8)
  • f0 (central f_nth) = 0.103 (CC), 0.054 (WCC), 0.051 (NCC), 0.067 (All)
    Central value in the two-scale fitting function (Eq. 24), fitted to the median simulated f_nth profile of each cool-core subsample; descriptive, not used in the A2029 percentile analysis.
  • f_inf (asymptotic f_nth) = 0.48 (CC), 0.49 (WCC), 0.53 (NCC), 0.50 (All)
    Asymptotic outer value in Eq. 24, fitted to simulated medians.
  • A_nth (core drop amplitude) = 0.033 (CC), -0.076 (WCC), -0.13 (NCC), -0.017 (All)
    Amplitude controlling the inner profile shape in Eq. 24; fitted.
  • a (inner scale) = 0.041 (CC), 0.37 (WCC), 0.29 (NCC), 0.16 (All)
    Inner dimensionless scale in Eq. 24; fitted.
  • b (outer scale) = 0.70 (CC), 0.74 (WCC), 0.78 (NCC), 0.75 (All)
    Outer dimensionless scale in Eq. 24; fitted.
  • alpha (inner steepness) = 3.71 (CC), 0.72 (WCC), 0.74 (NCC), 2.51 (All)
    Inner transition sharpness in Eq. 24; fitted.
  • beta (outer steepness) = 1.89 (CC), 2.20 (WCC), 2.39 (NCC), 2.27 (All)
    Outer transition sharpness in Eq. 24; fitted.
  • eta (turbulent heating efficiency) = 1
    Order-of-unity efficiency in Eq. 20 for the Kolmogorov turbulent heating rate; set by hand, not fitted.
assumptions (5)
  • domain assumption IllustrisTNG subgrid model, including the kinetic/thermal AGN feedback prescription, produces a realistic ICM velocity field.
    Invoked throughout Section 2.1 and used to generate all simulated profiles and mock observations; the authors flag in Section 4.2 that this is one realization of subgrid physics.
  • domain assumption X-ray emissivity follows collisional ionization equilibrium with APEC/PYATOMDB, and only thermal line broadening is modeled.
    Sections 2.4 and 2.6; this underlies both the emission weighting and the mock spectra; realistic non-thermal broadening and multi-temperature structure could alter the fitted dispersion.
  • domain assumption The single-temperature bapec fit to the mock spectrum recovers the quantity XRISM measures.
    Section 3.2.1; the authors note multi-temperature, multi-velocity structure along the line of sight can bias the single-component dispersion, citing Truong et al. 2024.
  • domain assumption Non-thermal pressure can be estimated from the 1D velocity dispersion as P_turb = rho sigma_1D^2 (isotropic equivalence).
    Eq. 10 in Section 2.3, following Lau et al. 2009; anisotropy is measured separately via beta, so the isotropic conversion is an approximation.
  • domain assumption Turbulent heating follows a Kolmogorov cascade with injection scale r and efficiency eta=1.
    Eq. 20 in Section 3.1.2, following Zhuravleva et al. 2014; the heating/cooling balance result depends on this choice.

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Cite this review

Pith. "Pith review of Non-Thermal Pressure due to Gas Motions in the Intracluster Medium: Confronting XRISM/Resolve with TNG-Cluster Simulations." pith.science (2026). https://pith.science/paper/COPGBA5M

@misc{pith2026260804757,
  author       = {Pith},
  title        = {Pith review of: Non-Thermal Pressure due to Gas Motions in the Intracluster Medium: Confronting XRISM/Resolve with TNG-Cluster Simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/COPGBA5M}},
  note         = {Machine review of arXiv:2608.04757}
}
read the original abstract

Intracluster medium (ICM) gas motions probe cluster assembly, feedback, and non-thermal pressure support, but recent XRISM observations reveal velocity dispersions and non-thermal pressure fractions systematically lower than simulations predict, with extreme systems such as Abell 2029 falling below nearly all simulated clusters. Using the TNG-Cluster simulations, we show that the non-thermal pressure fraction depends sensitively on cool-core state and formation history, and provide a two-scale fitting function capturing both the inner cool-core suppression and outer rise of the radial profile. By forward-modeling mock XRISM observations and comparing them with both projected and intrinsic three-dimensional quantities, we find that azimuthal variations and projection effects contribute to the deficit in the observed velocity dispersion and non-thermal pressure fraction. This bias increases with radius and partially offsets the intrinsic outward rise in the true three-dimensional non-thermal pressure fraction. However, these effects cannot explain the extremely low values of the non-thermal pressure fraction observed in Abell 2029, which fall below approximately the 0th - 6th percentiles of the simulated cool-core cluster distribution at every measured radius under both the turbulence-only and turbulence-plus-bulk definitions. The remaining tension points to rare dynamical conditions or missing physics affecting the amplitude of gas motions in current ICM models.

Figures

Figures reproduced from arXiv: 2608.04757 by the authors.

Figure 1
Figure 1. Distribution of cool-core (CC), weak cool-core (WCC), and non￾cool-core (NCC) clusters in the TNG-Cluster simulation sample, as a func￾tion of log of the central cooling time tcool measured within 0.015R500c of each halo. cesses. In CC systems, the central ICM is strongly influenced by radiative cooling and feedback from the central supermassive black hole (SMBH), whereas in NCC systems and at large radii, gas dynam… view at source ↗
Figure 2
Figure 2. X-ray emission-weighted projected maps of a representative cool-core cluster (HaloID 20) projected along the x-axis. From left to right, the panels show the X-ray emission-weighted temperature Tsp, line-of-sight bulk velocity vLOS, and line-of-sight velocity dispersion σLOS. The maps span 3 × 3 Mpc centered on the cluster, with color bars indicating the ranges of temperature, LOS velocity, and LOS velocity dispersio… view at source ↗
Figure 3
Figure 3. Mock XRISM/Resolve map with 1Ms exposure of the projection along the x-axis for a halo in TNG-Cluster with HaloID 20, a CC cluster in the TNG-Cluster simulation (the same projected cluster as in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Left panel: Median profiles of the non-thermal pressure fraction as a function of scaled radius r/R200m in bins of cool-core state: strong cool-core (CC), weak cool-core (WCC), and non-cool-core (NCC), with central cooling times tcool/Gyr ≤ 1, tcool/Gyr ∈ [1.0,7.7), an…
Figure 5
Figure 5. Figure 5: Left panel: volumetric turbulent heating rate Qheat (solid) and radiative cooling rate Qcool = ne nH Λ(T ,Z) (dashed) as a function of scaled radius for the clusters binned by their cool-core states (CC: blue; WCC: green; NCC: orange). Middle panel: Ratio Qheat/Qcool .…
Figure 7
Figure 7. Figure 7: Non-thermal pressure fraction profile for every TNG-Cluster halo, as a function of scaled radius r/R500c, color-coded by formation red￾shift zform, defined as the redshift at which the cluster attained half of its present-day mass. 3.1.4 Dependence on Cluster Formation…
Figure 8
Figure 8. Figure 8: The non-thermal pressure fraction measured at three different radii: 0.1R500c (left panel), 0.5R500c (middle panel), and 1.0R500c (right panel), plotted as a function of halo formation redshift 1 + zform. The color indicates the central cooling time tcool . The plots s…
Figure 9
Figure 9. Figure 9: Profiles of velocity dispersion σ (top left), bulk velocity Vbulk (top right), the turbulence-only non-thermal pressure fraction fnth (bottom left), and the bulk-to-turbulent velocity ratio |Vbulk|/σ (bottom right), for the 8 az￾imuthal directions of a cool-core cluste…
Figure 10
Figure 10. Figure 10: Ratio of the mock-recovered to a reference value, Xmock/Xref , as a function of projected radius r/R500c, for the line-of-sight velocity dispersion σ (top left), bulk velocity Vbulk (top right), the bulk-to-turbulent velocity ratio Rv ≡ |Vbulk|/σ (bottom left), and th…
Figure 11
Figure 11. Figure 11: Left panel: Non-thermal pressure fraction profiles obtained from analyzing mock XRISM maps for the CC (blue), WCC (green), and NCC (orange) clusters. The solid lines are the median over all three orthogonal projections of all clusters within the bin, and the shaded re…
Figure 12
Figure 12. Figure 12: Comparison of mass-weighted (solid lines) and emission-weighted (dashed lines) kinematic profiles for the 352 TNG-Cluster halos at z = 0, stratified by cool-core class: cool-core (CC, blue), weak cool-core (WCC, green), and non-cool-core (NCC, orange). Shaded bands en…

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Works this paper leans on

56 extracted references · 42 canonical work pages

  1. [1]

    W., Brunetti, G., Brüggen, M., & Eckert, D

    Angelinelli, M., Vazza, F., Giocoli, C., Ettori, S., Jones, T. W., Brunetti, G., Brüggen, M., & Eckert, D. 2020, MNRAS, 495, 1, 864

    work page 2020

  2. [2]

    Aung, H., Nagai, D., & Lau, E. T. 2021, MNRAS, 508, 2, 2071

    work page 2021

  3. [3]

    Ayromlou, M. et al. 2024, A&A, 690, A20

    work page 2024

  4. [4]

    Bellomi, E. et al. 2025, arXiv e-prints, arXiv:2512.12754

    arXiv 2025

  5. [5]

    Biffi, V . et al. 2016, ApJ, 827, 112

    work page 2016

  6. [6]

    Bourne, M. A. & Sijacki, D. 2017, MNRAS, 472, 4, 4707

    work page 2017

  7. [7]

    2005, MNRAS, 364, 753

    Dolag, K., Vazza, F., Brunetti, G., & Tormen, G. 2005, MNRAS, 364, 753

    2005

  8. [8]

    Fabian, A. C. 2012, ARA&A, 50, 455

    2012

Show all 56 references
  1. [9]

    2025, PASJ, 77, S270

    Fujita, Y ., Fukushima, K., Sato, K., Fukazawa, Y ., & Kondo, M. 2025, PASJ, 77, S270

    2025

  2. [10]

    2023, MNRAS, 518, 3, 4238 Hitomi Collaboration et al

    Santos, A., & Knebe, A. 2023, MNRAS, 518, 3, 4238 Hitomi Collaboration et al. 2016, Nature, 535, 7610, 117 Hitomi Collaboration et al. 2018, PASJ, 70, 2, 11

    2023

  3. [11]

    T., Gaspari, M., Nagai, D., & Coppi, P

    Lau, E. T., Gaspari, M., Nagai, D., & Coppi, P. 2017, ApJ, 849, 1, 54

    2017

  4. [12]

    T., Kravtsov, A

    Lau, E. T., Kravtsov, A. V ., & Nagai, D. 2009, ApJ, 705, 1129

    2009

  5. [13]

    T., Nagai, D., Avestruz, C., Nelson, K., & Vikhlinin, A

    Lau, E. T., Nagai, D., Avestruz, C., Nelson, K., & Vikhlinin, A. 2015, ApJ, 806, 1, 68

    2015

  6. [14]

    T., Nagai, D., & Nelson, K

    Lau, E. T., Nagai, D., & Nelson, K. 2013, ApJ, 777, 151

    2013

  7. [15]

    2024, A&A, 687, A129

    Lehle, K., Nelson, D., Pillepich, A., Truong, N., & Rohr, E. 2024, A&A, 687, A129

    2024

  8. [16]

    Li, Y . et al. 2020, ApJL, 889, 1, L1

    2020

  9. [17]

    McDonald, M. et al. 2013, ApJ, 774, 1, 23

    2013

  10. [18]

    R., Fabian, A

    McNamara, B. R., Fabian, A. C., Russell, H. R., Nulsen, P. E. J., Simionescu, A., Majumder, A., Miller, E. D., & Sarkar, A. 2026, arXiv e-prints, arXiv:2604.19607

    2026 arXiv

  11. [19]

    McNamara, B. R. & Nulsen, P. E. J. 2007, ARA&A, 45, 117

    2007

  12. [20]

    T., Avestruz, C., Nelson, K., & Rudd, D

    Nagai, D., Lau, E. T., Avestruz, C., Nelson, K., & Rudd, D. H. 2013, ApJ, 777, 137

    2013

  13. [21]

    Nagai, D., Vikhlinin, A., & Kravtsov, A. V . 2007, ApJ, 655, 98

    2007

  14. [22]

    2024, A&A, 686, A157

    Truong, N. 2024, A&A, 686, A157

    2024

  15. [23]

    H., Shaw, L., & Nagai, D

    Nelson, K., Rudd, D. H., Shaw, L., & Nagai, D. 2012, ApJ, 751, 121

    2012

  16. [24]

    Nguyen-Dang, N. T. 2026, PASJ, 78, 2, 719

    2026

  17. [25]

    2006, ApJ, 640, 2, 673

    Ota, N., Kitayama, T., Masai, K., & Mitsuda, K. 2006, ApJ, 640, 2, 673

    2006

  18. [26]

    Ota, N., Nagai, D., & Lau, E. T. 2018, PASJ, 70, 3, 51

    2018

  19. [27]

    2025, MNRAS, 544, 4, 4188

    Prunier, M., Ubertosi, F., Hlavacek-Larrondo, J., & Pillepich, A. 2025, MNRAS, 544, 4, 4188

    2025

  20. [28]

    2005, ApJL, 618, 1, L1

    Diaferio, A., & Murante, G. 2005, ApJL, 618, 1, L1

    2005

  21. [29]

    2004, MNRAS, 351, 237

    Rasia, E., Tormen, G., & Moscardini, L. 2004, MNRAS, 351, 237

    2004

  22. [30]

    Rasia, E. et al. 2006, MNRAS, 369, 2013

    2006

  23. [31]

    2026, arXiv e-prints, arXiv:2606.05355

    Nelson, D. 2026, arXiv e-prints, arXiv:2606.05355

    2026 arXiv

  24. [32]

    Sarkar, A. et al. 2026, arXiv e-prints, arXiv:2606.08097

    2026 arXiv

  25. [33]

    Schaye, J. et al. 2023, MNRAS, 526, 4, 4978

    2023

  26. [34]

    D., Nagai, D., Bhattacharya, S., & Lau, E

    Shaw, L. D., Nagai, D., Bhattacharya, S., & Lau, E. T. 2010, ApJ, 725, 1452

    2010

  27. [35]

    & Komatsu, E

    Shi, X. & Komatsu, E. 2014, MNRAS, 442, 521

    2014

  28. [36]

    2015, MNRAS, 448, 1020

    Shi, X., Komatsu, E., Nelson, K., & Nagai, D. 2015, MNRAS, 448, 1020

    2015

  29. [37]

    2020, MNRAS, 495, 1, 784

    Shi, X., Nagai, D., Aung, H., & Wetzel, A. 2020, MNRAS, 495, 1, 784

    2020

  30. [38]

    Shi, X., Nagai, D., & Lau, E. T. 2018, MNRAS, 481, 1, 1075

    2018

  31. [39]

    & Zhang, C

    Shi, X. & Zhang, C. 2019, MNRAS, 487, 1, 1072

    2019

  32. [40]

    Simionescu, A. et al. 2019, Space Sci. Rev., 215, 2, 24

    2019

  33. [41]

    2010, MNRAS, 401, 2, 791

    Springel, V . 2010, MNRAS, 401, 2, 791

    2010

  34. [42]

    2024, A&A, 686, A200

    Truong, N., Pillepich, A., Nelson, D., Zhuravleva, I., Lee, W., Ayromlou, M., & Lehle, K. 2024, A&A, 686, A200

    2024

  35. [43]

    W., Eckert, D., Brüggen, M., Brunetti, G., & Gheller, C

    Vazza, F., Angelinelli, M., Jones, T. W., Eckert, D., Brüggen, M., Brunetti, G., & Gheller, C. 2018, MNRAS, 481, 1, L120

    2018

  36. [44]

    & Brunetti, G

    Vazza, F. & Brunetti, G. 2026, A&A, 705, A129

    2026

  37. [45]

    2009, A&A, 504, 33 Publications of the Astronomical Society of Japan(2026), Vol

    Vazza, F., Brunetti, G., Kritsuk, A., Wagner, R., Gheller, C., & Norman, M. 2009, A&A, 504, 33 Publications of the Astronomical Society of Japan(2026), Vol. 00, No. 015

    2026

  38. [46]

    2017, MNRAS, 464, 1, 210

    Ryu, D. 2017, MNRAS, 464, 1, 210

    2017

  39. [47]

    2012, A&A, 544, A103

    Vazza, F., Roediger, E., & Brüggen, M. 2012, A&A, 544, A103

    2012

  40. [48]

    B., Blanton, E

    Watson, C. B., Blanton, E. L., Randall, S. W., Clarke, T. E., & ZuHone, J. A. 2026, ApJ, 996, 1, 106 XRISM Collaboration et al. 2025a, ApJL, 993, 1, L11 XRISM Collaboration et al. 2025b, PASJ, 77, S242 XRISM Collaboration et al. 2025c, ApJL, 985, 1, L20 XRISM Collaboration et ...

    2026

  41. [49]

    Yang, H.-Y . K. & Reynolds, C. S. 2016, ApJ, 829, 90

    2016

  42. [50]

    2026, arXiv e-prints, arXiv:2601.05803

    Zhuravleva, I. 2026, arXiv e-prints, arXiv:2601.05803

    2026

  43. [51]

    Zhang, C., Churazov, E., & Schekochihin, A. A. 2018, MNRAS, 478, 4, 4785

    2018

  44. [52]

    2025, ApJ, 985, 2, 204

    Zhang, Y ., Shi, X., & Nagai, D. 2025, ApJ, 985, 2, 204

    2025

  45. [53]

    Zhuravleva, I. et al. 2014, Nature, 515, 85

    2014

  46. [54]

    ZuHone, J. A. & Hallman, E. J. 2016, pyXSIM: Synthetic X-ray observations generator, Astrophysics Source Code Library, record ascl:1608.002

    2016

  47. [55]

    2023, SOXS: Simulated Observations of X-ray

    Santos, F., & Bourdin, H. 2023, SOXS: Simulated Observations of X-ray

    2023

  48. [56]

    Sources, Astrophysics Source Code Library, record ascl:2301.024 Appendix. Comparison between mass-weighted and emission-weighted profiles Figure 12 compares the mass-weighted (solid lines) and emission- weighted (dashed lines) profiles off nth,|v bulk|/σ1D, andβas a function o...

    2026

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