Pith. sign in

REVIEW 2 major objections 6 minor 1 cited by

The full iron budget in simulated galaxy clusters: The chemistry between gas and stars

T0 review · 2 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The observed cluster iron conundrum is argued to be mostly a stellar-mass bookkeeping gap: simulated clusters with five times more stellar mass at fixed halo mass naturally show an iron share close to unity, while observations, missing…

desk verdict A careful simulation-based accounting of the iron budget in 448 clusters: the iron-share gap to observations is mostly a stellar-mass mismatch, but the gap may be partly definitional. read the letter →

arxiv 2505.07936 v1 pith:6DWOVFT7 submitted 2025-05-12 astro-ph.CO astro-ph.GA

Veronica Biffi , Elena Rasia , Stefano Borgani , Simona Ghizzardi , Umberto Maio , Klaus Dolag , Fabio Gastaldello , Luca Tornatore This is my paper · ORCID
classification astro-ph.COastro-ph.GA
keywords galaxyclustersintraclustermediumironconundrumsharestellarmassfractionchemicalenrichmenthydrodynamicalsimulationsX-rayobservations
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

The paper asks whether the stars found inside massive galaxy clusters can account for the iron observed in the hot gas that fills them—the so-called iron conundrum posed by X-ray observations. Using 448 simulated clusters with masses above $10^{14}$ solar masses at $z = 0.07$, it finds that the iron share between gas and stars is close to one: the two components hold comparable amounts of iron, with only a shallow rise toward higher cluster mass. When the same systems are compared with observational estimates, the simulated iron share is five to eight times smaller at the high-mass end, and the paper traces nearly all of that difference to the stellar side: simulated massive clusters contain roughly five times more stellar mass, and therefore stellar iron, at fixed halo mass, while the iron content of the hot gas agrees with observations. The conclusion is that, within the model, the stellar content is enough to enrich the ICM, and the observed conundrum would largely dissolve once the stellar-mass discrepancy between simulations and observations is resolved.

What carries the argument

The carrying object is the iron share, $\Upsilon_{\mathrm{Fe}}(<R) = M_{\mathrm{Fe,ICM}}(<R)/M_{\mathrm{Fe,*}}(<R)$, the ratio of iron mass in the hot intra-cluster medium to iron mass locked in stars within radius $R$. The paper measures it directly from simulation particles and also through the observational proxy that assumes all stars carry a solar iron abundance, $M_{\mathrm{Fe,*}} \approx Z_{\mathrm{Fe},\odot} M_*$. Around this ratio, the argument splits into two scaling relations: the gas-side relation between ICM iron mass and gas mass, which agrees with observations, and the stellar-side relation between stellar mass fraction and total halo mass, which does not; the second one carries the discrepancy. A third element is the aperture test on the brightest cluster galaxy, which constructs an observational-like stellar mass excluding diffuse intra-cluster light and shows how sensitive the iron-share gap is to the definition of the stellar census.

What would settle it

A deep surface-brightness census of massive clusters—stacking deep optical and near-infrared images to recover intra-cluster light and satellites down to faint limits—that measured stellar mass fractions near 3% within $R_{500}$ at $M_{500}\sim 5\times 10^{14}\,M_\odot$, with near-solar stellar iron abundances, would falsify the paper's attribution: the iron-share gap would shrink or vanish even if the simulations were unchanged. A census confirming the low observed fractions near 1% would instead shift the burden onto star-formation and feedback modelling.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central result is that the observed iron conundrum is reproduced in simulations only as a stellar-mass problem. In 448 simulated clusters with $M_{500} > 10^{14}\,M_\odot$ at $z=0.07$, the ratio $\Upsilon_{\mathrm{Fe}} = M_{\mathrm{Fe,ICM}}/M_{\mathrm{Fe,*}}$—the iron share—averages close to one and rises only mildly with total mass, whereas observational estimates for massive clusters sit almost an order of magnitude higher at fixed mass. The gas is not the culprit: simulated iron mass in the hot intra-cluster medium as a function of gas mass matches the comparison dataset, and simulated iron abundance profiles are broadly consistent with observed ones. The driver is the stellar budget: simulated massive clusters have stellar mass fractions near 3% within $R_{500}$, a factor of roughly 2–5 higher than observed values, which propagates directly into larger stellar iron masses and a smaller iron share. Aperture tests sharpen the point: counting only satellites plus the brightest cluster galaxy inside 50 kpc cuts the simulated stellar mass by a factor of 1.5–3 and shrinks the iron-share gap by about a factor of two, implicating the treatment of diffuse intra-cluster light as a central part of the discrepancy.

Load-bearing premise

The argument assumes that the stellar mass counted within the same cluster region in the simulations and in the observations covers the same population—especially the faint diffuse light between galaxies and unresolved dwarf galaxies; if observers systematically miss part of that light, the apparent overproduction of stellar mass in simulations would be an artifact of the comparison.

Editorial extensions

If this is right

  • If the simulations are representative, the iron conundrum is not a missing-iron problem: the stars inside a cluster hold enough iron to have enriched the hot gas, with the iron share close to one within the virial region.
  • The stellar-to-halo mass relation becomes the primary control knob: simulations that overproduce stellar mass in massive haloes will inherit an over-large stellar iron mass and an under-large iron share.
  • Any revision of star formation or feedback in simulations must suppress the stellar budget without suppressing metal production, since the simulated ICM iron content currently matches observations.
  • A more complete observational census of faint intra-cluster light and low-mass satellites directly sets the size of the conundrum; recovering a factor of 2–5 in stellar mass would bring observed and simulated iron shares into agreement.
  • The simulated iron share is nearly flat in mass, so observationally inferred steep increases of the iron share with mass should be interpreted as evidence about the stellar census rather than about exotic enrichment sources.

Reading between the lines

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

  • A natural next step is to repeat the same gas-versus-star decomposition for elements with different nucleosynthetic origins, such as oxygen; if the stellar-mass gap behaves the same for oxygen, it is a budget effect, whereas a different pattern would point to supernova yield assumptions.
  • The comparison implicitly assumes one solar iron abundance for all cluster stars; direct spectroscopic measurements of stellar iron abundance in brightest cluster galaxies and satellites would convert the iron-share test from a mass comparison into a chemical-evolution test.
  • Because the gas-to-total mass offset contributes only roughly a factor of 1.5 to the discrepancy, calibrating simulated gas fractions to observed relations would be a smaller and cheaper correction than reworking the star formation model.
  • The paper's aperture test could be run directly on mock images of the simulated clusters with the same photometric pipeline used on real data, giving an end-to-end check of whether the residual gap is physical or a measurement-systematics artifact.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 6 minor

Summary. The paper analyzes the iron budget in gas and stars of 448 galaxy clusters with Mtot,500 > 1e14 Msun drawn from the Magneticum Box2/hr cosmological hydrodynamic simulation at z = 0.07. The authors compute the iron share (ICM iron mass / stellar iron mass) within R500 and R200, compare it with observational estimates from Renzini & Andreon (2014) and the X-COP sample of Ghizzardi et al. (2021), and decompose the discrepancy into gas and stellar contributions. The main findings are that simulated clusters have iron share close to unity with a shallow mass trend, about 5–8 times lower than observed at the massive end; ICM iron masses and abundance profiles agree reasonably with observations at fixed gas mass; and simulated stellar masses at fixed halo mass are about a factor of 5 larger than the X-COP-based estimates. This stellar mass difference is identified as the dominant driver of the iron-share gap. The paper also tests the effect of hydrostatic mass bias, the observational-like stellar iron estimate based on a solar abundance assumption, and the impact of restricting the BCG to fixed apertures, finding that the last of these reduces the discrepancy by a factor of 1.5–3.

Significance. If the result holds, it would reframe the classical iron conundrum as primarily a stellar-mass budget mismatch rather than a shortage of ICM iron sources. The paper is valuable because it directly measures iron masses in both components in a large simulation sample, compares with a carefully selected observational dataset (X-COP), and explicitly investigates systematics such as hydrostatic mass bias and BCG aperture effects. The main strengths are the direct calculation of iron masses from particle data, the large sample, and the transparent treatment of known issues. However, the central quantitative conclusion is conditional on the comparability of the simulated and observed stellar mass definitions; the paper acknowledges but does not fully quantify the possible bias from missing diffuse ICL in observations, which limits the strength of the causal attribution to stellar overproduction. The confirmation that stars are responsible for ICM enrichment is, as the authors note, an internal consistency property of the simulation, not an independent test.

major comments (2)
  1. [Sec. 4 and Sec. 3.2.3 / Appendix D] The claim that the iron-share discrepancy is dominated by a factor-of-five stellar mass difference at fixed halo mass is based on comparing the total simulated stellar mass within R500, including the diffuse main-halo component, with observational estimates that may not include all ICL and unresolved satellites. The paper's own tests show that restricting the simulated BCG to a 50 kpc aperture reduces the simulated stellar mass by a factor of 1.5–3 (Sec. 3.2.3) and shrinks the iron-share gap by about a factor of 2 (Appendix D). Given the Brough et al. (2024) result that observational methods systematically underestimate BCG+ICL fractions by a factor of about 3 in simulations, the quantitative attribution of the gap to a genuine overproduction of stars in simulations is not established. The authors should either apply a mock-observation-based correction to the Ghizzardi et al. (2021) stellar masses and recompute the iron share, or explicitly reframe the conclusion in terms of a mismatch between the measured stellar budgets rather than a physical stellar mass excess.
  2. [Sec. 4] The decomposition of the iron-share discrepancy into a factor of ~5 from stellar mass and a factor of ~1.5 from gas mass is not robust to the stellar mass definition. If the stellar mass factor is reduced to ~2 by adopting the 50 kpc BCG aperture, as shown in Appendix D, the gas and stellar contributions become comparable, so identifying the stellar component as 'the dominant contribution' is not a robust statement. The conclusion should be qualified to explicitly reflect the dependence of this decomposition on the assumed stellar-mass aperture and on the unknown ICL bias in the observational data.
minor comments (6)
  1. [Table B.1] The BCES-bisect errors on the slope alpha (0.98, 0.92, 0.79, 0.79) appear to be typos and are inconsistent with the quoted slopes and with the discussion in Appendix B; these values should be corrected.
  2. [Appendix E] The Sartoris et al. (2020) stellar fraction is quoted as '~15%' in Appendix E, whereas Sec. 3.2.2 correctly quotes 'f* = 1.5% +/- 0.4%'; one of these is a typographical error.
  3. [Appendix C] The text refers to the 'Person correlation coefficient'; this should be 'Pearson correlation coefficient'.
  4. [Fig. 7 caption] The caption says 'within R500c'; this should likely be 'within R500'.
  5. [Sec. 3.1] The statement that an uncertainty in the adopted solar iron abundance 'would thus directly translate into the same uncertainty on the iron share' is not generally correct: for a self-consistent observational estimate using Eq. (3), the solar reference cancels between the ICM iron mass (derived from measured abundances) and the stellar iron mass (derived from Eq. 2). The statement only applies to the simulation-based obs-like estimate in which the numerator is independent of the solar reference.
  6. [Fig. 6 and Sec. 3.2.2] The comparison of stellar fractions would benefit from a small table summarizing the IMF, aperture, and ICL treatment for each observational dataset in Fig. 6, since these definitions vary widely and directly affect the size of the claimed discrepancy.

Circularity Check

1 steps flagged · score 2.0 of 10

Central iron-share comparison is anchored to external X-COP data and is not circular; one side conclusion (stars cause ICM enrichment) is a self-definitional consistency statement, explicitly acknowledged by the authors.

  1. self definitional [Section 5 (Summary and Conclusions); see also Section 2 (Simulations) for the stellar-only enrichment model]
    "Consistently with the modelling, this study confirms that the stellar content within simulated present-day massive systems is responsible for the ICM metal enrichment."

    In Magneticum, Fe is produced only by stellar populations (SNIa, SNcc, and AGB stars) through the implemented enrichment model, with no alternative non-stellar iron channel. Therefore the statement that the stellar content caused the ICM iron enrichment is guaranteed by construction of the simulation, not derived from independent evidence. The authors themselves flag this in Section 5: 'On the one hand, this may sound an obvious outcome of the modelling within simulations.' This is a consistency statement and is not load-bearing for the main comparison: the iron-share gap is decomposed by comparing simulated ICM iron masses and stellar masses against external X-COP, Renzini & Andreon, and Loewenstein data, so the central result is not forced by this step.

full rationale

No significant circularity in the paper's central derivation. The iron share is computed directly from simulated ICM and stellar iron masses and compared to external observational estimates (X-COP/Ghizzardi 2021, Renzini & Andreon 2014). The explanation of the discrepancy as dominated by the stellar component is an accounting decomposition: with MFe,ICM at fixed gas mass matching observations and the simulated stellar mass a factor ~5 higher, Eqs. (1)-(3) force the simulated iron share lower by the same factor. That decomposition is verified by separate empirical comparisons (Figs. 5, 6, 7, Appendix D) rather than assumed. The only definitional element is the side statement that stars are responsible for ICM enrichment, which is built into the simulation's enrichment model; the authors explicitly acknowledge this ('this may sound an obvious outcome of the modelling'). Self-citations to earlier Magneticum papers (Dolag et al. 2017, Biffi et al. 2018, Remus et al. 2017) describe the code and previous consistency checks but are not the evidence for the main claim, which relies on direct comparison with external data. The paper's own sensitivity tests (BCG apertures, Appendix D) show the size of the stellar-mass offset depends on definitions, but this is a systematic caveat, not circularity. Score 2 reflects the minor self-definitional consistency statement only.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central iron-share calculation depends on the simulation's sub-grid model of star formation and chemical enrichment (which fixes how much iron stars make and eject), on the X-ray selection of the gas, and on the observational comparison samples. No new physical entity is introduced, and no parameter is fitted to the iron share data in this paper. The main non-trivial input is the assumption that the simulated and observed stellar mass budgets are directly comparable despite different ICL treatment.

free parameters (2)
  • Galactic wind velocity (vw) = 350 km/s
    Sub-grid stellar feedback parameter in the Magneticum model; sets the strength of gas ejection and therefore the stellar mass fraction and metal retention or expulsion in clusters. Inherited from prior simulation calibration (Springel & Hernquist 2003; Tornatore et al. 2010), not fitted in this paper.
  • Star formation density threshold = 0.1 cm^-3
    Gas density above which gas becomes eligible for star formation in the multi-phase model; affects the amount and location of stellar mass and, indirectly, the iron share. Fixed model choice, not fitted here.
assumptions (4)
  • domain assumption The stellar evolution and chemical enrichment model of Tornatore et al. (2007) with yields by Thielemann et al. (2003), Woosley & Weaver (1995), van den Hoek & Groenewegen (1997) and the single-degenerate SNIa scenario accurately tracks iron production and release.
    The simulated MFe,ICM and MFe,star are computed from these nucleosynthesis inputs; if the yields or SNIa rates are wrong, the iron production and hence the iron share change. These are external astrophysical assumptions, not derived in the paper.
  • domain assumption The hot, X-ray-selected gas component (T > 0.3 keV, non-star-forming, cold fraction < 10%) represents the observationally relevant ICM.
    The paper uses this selection to define the ICM iron mass. It verifies that this component comprises more than 95% of the gas mass, but the remaining cold gas can hold iron and is excluded.
  • domain assumption The observational datasets (Ghizzardi et al. 2021, Renzini & Andreon 2014) provide accurate benchmarks for stellar mass, gas mass, and iron mass.
    The iron conundrum discrepancy is measured against these datasets; if their stellar masses are systematically underestimated or their X-ray iron masses overestimated, the size of the discrepancy changes.
  • domain assumption The Chabrier IMF and the stellar mass-to-light conversions used in the comparison are appropriate.
    The IMF sets the number of supernovae and metal yields; the paper notes that switching IMFs changes stellar masses by only about 20%, so this is a minor but still present assumption.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The full iron budget in simulated galaxy clusters: The chemistry between gas and stars." pith.science (2026). https://pith.science/paper/6DWOVFT7

@misc{pith2026250507936,
  author       = {Pith},
  title        = {Pith review of: The full iron budget in simulated galaxy clusters: The chemistry between gas and stars},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6DWOVFT7}},
  note         = {Machine review of arXiv:2505.07936}
}
read the original abstract

Heavy chemical elements such as iron in the intra-cluster medium (ICM) of galaxy clusters are a signpost of the interaction between the gas and stellar components. Observations of the ICM metallicity in present-day massive systems, however, pose a challenge to the underlying assumption that the cluster galaxies have produced the amount of iron that enriches the ICM. We evaluate the iron share between ICM and stars within simulated galaxy clusters with the twofold aim of investigating the origin of possible differences with respect to observational findings and of shedding light on the observed excess of iron on the ICM with respect to expectations based on the observed stellar population. We evaluated the iron mass in gas and stars in a sample of 448 simulated systems with masses M500 > 1e14 Msun at z=0.07. These were extracted from the high-resolution (352 cMpc/h)^3 volume of the Magneticum cosmological hydrodynamical simulations. We compared our results with observational data of low-redshift galaxy clusters. The iron share in simulated clusters features a shallow dependence on the total mass, and its value is close to unity on average. In the most massive simulated systems, the iron share is thus smaller than observational values by almost an order of magnitude. The dominant contribution to this difference is related to the stellar component, whereas the chemical properties of the ICM agree well overall with the observations. We find larger stellar mass fractions in simulated massive clusters, which in turn yield higher stellar iron masses, than in observational data. Consistently with the modelling, we confirm that the stellar content within simulated present-day massive systems causes the metal enrichment in the ICM. It will be crucial to alleviate the stellar mass discrepancy between simulations and observations to definitely assess the iron budget in galaxy clusters.

Figures

Figures reproduced from arXiv: 2505.07936 by the authors.

Figure 1
Figure 1. Iron share between ICM and stellar component as a func [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Comparison of the iron share from simulations and ob [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 4
Figure 4. Relation between iron mass in gas (grey asterisks) and [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Iron mass in the ICM as a function of gas mass (left) and total mass (right) within [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 7
Figure 7. Figure 7: Scaling relations between stellar mass and total halo mass [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Comparison between the simulated ICM mass-weighted iron abundance [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: Effective iron yield as a function of cluster mass, within R500. Simulation data are marked by blue asterisks, and com￾pared to observational data by Renzini & Andreon (2014) (black crosses) and Ghizzardi et al. (2021) (black open circles). observations is therefore pa…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. The youth of the intracluster medium. I. A non-parametric characterisation of the gas and electron number density profiles of $z \simeq 2$ protoclusters

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    At z≈2, Magneticum protoclusters show moderate self-similarity deviations, double-β density profiles, and hot ionised gas dominant only beyond ~0.1–0.5 R500c, with clear mass, merger and AGN trends.

Reference graph

Works this paper leans on

157 extracted references · 54 canonical work pages · cited by 1 Pith paper

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  3. [3]

    Akritas , M. G. & Bershady , M. A. 1996, , 470, 706

    1996

  4. [4]

    2010, , 407, 263

    Andreon , S. 2010, , 407, 263

    2010

  5. [5]

    2012 a , , 548, A83

    Andreon , S. 2012 a , , 548, A83

    2012

  6. [6]

    2012 b , , 546, A6

    Andreon , S. 2012 b , , 546, A6

    2012

  7. [7]

    2015, , 575, A108

    Andreon , S. 2015, , 575, A108

    2015

  8. [8]

    2023, , 675, A188

    Angelinelli , M., Ettori , S., Dolag , K., Vazza , F., & Ragagnin , A. 2023, , 675, A188

    2023

Show all 157 references
  1. [9]

    1992, , 254, 49

    Arnaud , M., Rothenflug , R., Boulade , O., Vigroux , L., & Vangioni-Flam , E. 1992, , 254, 49

    1992

  2. [10]

    J., & Scott , P

    Asplund , M., Grevesse , N., Sauval , A. J., & Scott , P. 2009, , 47, 481

    2009

  3. [11]

    2023, , 524, 5391

    Ayromlou , M., Nelson , D., & Pillepich , A. 2023, , 524, 5391

    2023

  4. [12]

    E., Bulbul , E., Ghirardini , V., et al

    Bahar , Y. E., Bulbul , E., Ghirardini , V., et al. 2024, , 691, A188

    2024

  5. [13]

    M., Barnes , D

    Bah \'e , Y. M., Barnes , D. J., Dalla Vecchia , C., et al. 2017, , 470, 4186

    2017

  6. [14]

    2012, , 537, A142

    Baldi , A., Ettori , S., Molendi , S., et al. 2012, , 537, A142

    2012

  7. [15]

    2020, , 642, A37

    Bassini , L., Rasia , E., Borgani , S., et al. 2020, , 642, A37

    2020

  8. [16]

    M., Murante , G., Arth , A., et al

    Beck , A. M., Murante , G., Arth , A., et al. 2016, , 455, 2110

    2016

  9. [17]

    S., Conroy , C., & Wechsler , R

    Behroozi , P. S., Conroy , C., & Wechsler , R. H. 2010, , 717, 379

    2010

  10. [18]

    Bell , E. F. & de Jong , R. S. 2001, , 550, 212

    2001

  11. [19]

    F., McIntosh , D

    Bell , E. F., McIntosh , D. H., Katz , N., & Weinberg , M. D. 2003, , 149, 289

    2003

  12. [20]

    2013, , 428, 1395

    Biffi , V., Dolag , K., & B \"o hringer , H. 2013, , 428, 1395

    2013

  13. [21]

    2018 a , , 481, 2213

    Biffi , V., Dolag , K., & Merloni , A. 2018 a , , 481, 2213

    2018

  14. [22]

    H., et al

    Biffi , V., Dolag , K., Reiprich , T. H., et al. 2022, , 661, A17

    2022

  15. [23]

    2018 b , , 214, 123

    Biffi , V., Mernier , F., & Medvedev , P. 2018 b , , 214, 123

    2018

  16. [24]

    2017, , 468, 531

    Biffi , V., Planelles , S., Borgani , S., et al. 2017, , 468, 531

    2017

  17. [25]

    E., Bregman , J

    Blackwell , A. E., Bregman , J. N., & Snowden , S. L. 2022, , 927, 104

    2022

  18. [26]

    2024, , 533, 2656

    Braspenning , J., Schaye , J., Schaller , M., et al. 2024, , 533, 2656

    2024

  19. [27]

    N., Anderson , M

    Bregman , J. N., Anderson , M. E., & Dai , X. 2010, , 716, L63

    2010

  20. [28]

    L., Bah \'e , Y

    Brough , S., Ahad , S. L., Bah \'e , Y. M., et al. 2024, , 528, 771

    2024

  21. [29]

    2021, , 508, 3365

    Buck , T., Rybizki , J., Buder , S., et al. 2021, , 508, 3365

    2021

  22. [30]

    2003, , 115, 763

    Chabrier , G. 2003, , 115, 763

    2003

  23. [31]

    David , L. P. & Nulsen , P. E. J. 2008, , 689, 837

    2008

  24. [32]

    2022, , 511, 2544

    de Graaff , A., Trayford , J., Franx , M., et al. 2022, , 511, 2544

    2022

  25. [33]

    2004, , 419, 7

    De Grandi , S., Ettori , S., Longhetti , M., & Molendi , S. 2004, , 419, 7

    2004

  26. [34]

    & Molendi , S

    De Grandi , S. & Molendi , S. 2001, , 551, 153

    2001

  27. [35]

    de Oliveira , N. O. L., Jim \'e nez-Teja , Y., & Dupke , R. 2022, , 512, 1916

    2022

  28. [36]

    & Aly , H

    Dehnen , W. & Aly , H. 2012, , 425, 1068

    2012

  29. [37]

    2005, , 433, 604

    Di Matteo , T., Springel , V., & Hernquist , L. 2005, , 433, 604

    2005

  30. [38]

    2009, , 399, 497

    Dolag , K., Borgani , S., Murante , G., & Springel , V. 2009, , 399, 497

    2009

  31. [39]

    2004, , 606, L97

    Dolag , K., Jubelgas , M., Springel , V., Borgani , S., & Rasia , E. 2004, , 606, L97

    2004

  32. [40]

    2017, Galaxies, 5, 35

    Dolag , K., Mevius , E., & Remus , R.-S. 2017, Galaxies, 5, 35

    2017

  33. [41]

    2010, , 405, 1544

    Dolag , K., Murante , G., & Borgani , S. 2010, , 405, 1544

    2010

  34. [42]

    M., et al

    Dolag , K., Remus , R.-S., Valenzuela , L. M., et al. 2025, , submitted, arXiv:2504.01061

    2025

  35. [43]

    & Stasyszyn , F

    Dolag , K. & Stasyszyn , F. 2009, , 398, 1678

    2009

  36. [44]

    2005, , 364, 753

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

    2005

  37. [45]

    2017, Astronomische Nachrichten, 338, 293

    Eckert , D., Ettori , S., Pointecouteau , E., et al. 2017, Astronomische Nachrichten, 338, 293

    2017

  38. [46]

    Eckert , D., Gaspari , M., Gastaldello , F., Le Brun , A. M. C., & O'Sullivan , E. 2021, Universe, 7, 142

    2021

  39. [47]

    2019, , 621, A40

    Eckert , D., Ghirardini , V., Ettori , S., et al. 2019, , 621, A40

    2019

  40. [48]

    2015, , 578, A46

    Ettori , S., Baldi , A., Balestra , I., et al. 2015, , 578, A46

    2015

  41. [49]

    2019, , 621, A39

    Ettori , S., Ghirardini , V., Eckert , D., et al. 2019, , 621, A39

    2019

  42. [50]

    2017, , 836, 110

    Ezer , C., Bulbul , E., Nihal Ercan , E., et al. 2017, , 836, 110

    2017

  43. [51]

    2010, , 401, 1670

    Fabjan , D., Borgani , S., Tornatore , L., et al. 2010, , 401, 1670

    2010

  44. [52]

    A., Katz , N., Weinberg , D

    Fardal , M. A., Katz , N., Weinberg , D. H., & Dav \'e , R. 2007, , 379, 985

    2007

  45. [53]

    M., Mantz , A

    Flores , A. M., Mantz , A. B., Allen , S. W., et al. 2021, , 507, 5195

    2021

  46. [54]

    2008, , 60, S343

    Fujita , Y., Tawa , N., Hayashida , K., et al. 2008, , 60, S343

    2008

  47. [55]

    Gallazzi , A., Brinchmann , J., Charlot , S., & White , S. D. M. 2008, , 383, 1439

    2008

  48. [56]

    2011, , 411, 349

    Gaspari , M., Melioli , C., Brighenti , F., & D'Ercole , A. 2011, , 411, 349

    2011

  49. [57]

    2021, Universe, 7, 208

    Gastaldello , F., Simionescu , A., Mernier , F., et al. 2021, Universe, 7, 208

    2021

  50. [58]

    2014, , 570, A117

    Ghizzardi , S., De Grandi , S., & Molendi , S. 2014, , 570, A117

    2014

  51. [59]

    2021, , 646, A92

    Ghizzardi , S., Molendi , S., van der Burg , R., et al. 2021, , 646, A92

    2021

  52. [60]

    H., Sivanandam , S., Zabludoff , A

    Gonzalez , A. H., Sivanandam , S., Zabludoff , A. I., & Zaritsky , D. 2013, , 778, 14

    2013

  53. [61]

    H., Zaritsky , D., & Zabludoff , A

    Gonzalez , A. H., Zaritsky , D., & Zabludoff , A. I. 2007, , 666, 147

    2007

  54. [62]

    2005, , 441, 1055

    Greggio , L. 2005, , 441, 1055

    2005

  55. [63]

    Gunn , J. E. & Gott , J. Richard, I. 1972, , 176, 1

    1972

  56. [64]

    J., Dolag , K., & Liu , J

    Gupta , N., Saro , A., Mohr , J. J., Dolag , K., & Liu , J. 2017, , 469, 3069

    2017

  57. [65]

    & Madau , P

    Haardt , F. & Madau , P. 2001, in Clusters of Galaxies and the High Redshift Universe Observed in X-rays, ed. D. M. Neumann & J. T. V. Tran , 64

    2001

  58. [66]

    A., Puchwein , E., & Sijacki , D

    Henden , N. A., Puchwein , E., & Sijacki , D. 2020, , 498, 2114

    2020

  59. [67]

    A., Hunter , D

    Herrmann , K. A., Hunter , D. A., Zhang , H.-X., & Elmegreen , B. G. 2016, , 152, 177

    2016

  60. [68]

    2014, , 442, 2304

    Hirschmann , M., Dolag , K., Saro , A., et al. 2014, , 442, 2304

    2014

  61. [69]

    T., Shao , Z., Cui , W., et al

    Hough , R. T., Shao , Z., Cui , W., et al. 2024, , 532, 476

    2024

  62. [70]

    D., Akritas , M

    Isobe , T., Feigelson , E. D., Akritas , M. G., & Babu , G. J. 1990, , 364, 104

    1990

  63. [71]

    1986, , 222, 323

    Kaiser , N. 1986, , 222, 323

    1986

  64. [72]

    Kirkpatrick , C. C. & McNamara , B. R. 2015, , 452, 4361

    2015

  65. [73]

    C., McNamara , B

    Kirkpatrick , C. C., McNamara , B. R., & Cavagnolo , K. W. 2011, , 731, L23

    2011

  66. [74]

    A., Montes , M., et al

    Kluge , M., Hatch , N. A., Montes , M., et al. 2024, , accepted, arXiv:2405.13503

    2024 arXiv

  67. [75]

    2006, , 653, 1145

    Kobayashi , C., Umeda , H., Nomoto , K., Tominaga , N., & Ohkubo , T. 2006, , 653, 1145

    2006

  68. [76]

    M., Dunkley , J., et al

    Komatsu , E., Smith , K. M., Dunkley , J., et al. 2011, , 192, 18

    2011

  69. [77]

    V., Vikhlinin , A

    Kravtsov , A. V., Vikhlinin , A. A., & Meshcheryakov , A. V. 2018, Astronomy Letters, 44, 8

    2018

  70. [78]

    F., Schulze , F., et al

    Kudritzki , R.-P., Teklu , A. F., Schulze , F., et al. 2021, , 910, 87

    2021

  71. [79]

    F., Zhang , Y.-Y., Reiprich , T

    Lagan \'a , T. F., Zhang , Y.-Y., Reiprich , T. H., & Schneider , P. 2011, , 743, 13

    2011

  72. [80]

    & Molendi , S

    Leccardi , A. & Molendi , S. 2008, , 487, 461

    2008

  73. [81]

    2020, , 637, A58

    Liu , A., Tozzi , P., Ettori , S., et al. 2020, , 637, A58

    2020

  74. [82]

    2001, , 557, 573

    Loewenstein , M. 2001, , 557, 573

    2001

  75. [83]

    2006, , 648, 230

    Loewenstein , M. 2006, , 648, 230

    2006

  76. [84]

    2013, , 773, 52

    Loewenstein , M. 2013, , 773, 52

    2013

  77. [85]

    2010, , 407, 1003

    Maio , U., Ciardi , B., Dolag , K., Tornatore , L., & Khochfar , S. 2010, , 407, 1003

    2010

  78. [86]

    B., Allen , S

    Mantz , A. B., Allen , S. W., Morris , R. G., et al. 2017, , 472, 2877

    2017

  79. [87]

    & Graur , O

    Maoz , D. & Graur , O. 2017, , 848, 25

    2017

  80. [88]

    2024, , 689, A7

    Marini , I., Popesso , P., Lamer , G., et al. 2024, , 689, A7

    2024

  81. [89]

    2013, , 764, 147

    Matsushita , K., Sakuma , E., Sasaki , T., Sato , K., & Simionescu , A. 2013, , 764, 147

    2013

  82. [90]

    & Recchi , S

    Matteucci , F. & Recchi , S. 2001, , 558, 351

    2001

  83. [91]

    G., Schaye , J., Bower , R

    McCarthy , I. G., Schaye , J., Bower , R. G., et al. 2011, , 412, 1965

    2011

  84. [92]

    2016, , 826, 124

    McDonald , M., Bulbul , E., de Haan , T., et al. 2016, , 826, 124

    2016

  85. [93]

    & Biffi , V

    Mernier , F. & Biffi , V. 2022, in Handbook of X-ray and Gamma-ray Astrophysics, ed. C. Bambi & A. Santangelo (Springer Living Reference Work, ISBN: 978-981-16-4544-0, id. 12), 12

    2022

  86. [94]

    2018 a , , 214, 129

    Mernier , F., Biffi , V., Yamaguchi , H., et al. 2018 a , , 214, 129

    2018

  87. [95]

    S., et al

    Mernier , F., de Plaa , J., Kaastra , J. S., et al. 2017, , 603, A80

    2017

  88. [96]

    2018 b , , 478, L116

    Mernier , F., de Plaa , J., Werner , N., et al. 2018 b , , 478, L116

    2018

  89. [97]

    2018 c , , 480, L95

    Mernier , F., Werner , N., de Plaa , J., et al. 2018 c , , 480, L95

    2018

  90. [98]

    Mihos , J. C. 2019, arXiv e-prints, arXiv:1909.09456

    2019 arXiv

  91. [99]

    D., Lacey , C

    Mitchell , P. D., Lacey , C. G., Baugh , C. M., & Cole , S. 2013, , 435, 87

    2013

  92. [100]

    Mitchell , P. D. & Schaye , J. 2022, , 511, 2948

    2022

  93. [101]

    2016, , 586, A32

    Molendi , S., Eckert , D., De Grandi , S., et al. 2016, , 586, A32

    2016

  94. [102]

    2024, , 685, A88

    Molendi , S., Ghizzardi , S., De Grandi , S., et al. 2024, , 685, A88

    2024

  95. [103]

    & Trujillo , I

    Montes , M. & Trujillo , I. 2018, , 474, 917

    2018

  96. [104]

    J., Heath , C., & Workman , J

    Morsony , B. J., Heath , C., & Workman , J. C. 2014, , 441, 2134

    2014

  97. [105]

    M., et al

    Munshi , F., Governato , F., Brooks , A. M., et al. 2013, , 766, 56

    2013

  98. [106]

    2024, , 686, A157

    Nelson , D., Pillepich , A., Ayromlou , M., et al. 2024, , 686, A157

    2024

  99. [107]

    O'Rourke , D. J. P., Shabala , S. S., & Alexander , P. 2011, , 418, 2145

    2011

  100. [108]

    P., Vrtilek , J

    O'Sullivan , E., Giacintucci , S., David , L. P., Vrtilek , J. M., & Raychaudhury , S. 2011, , 411, 1833

    2011

  101. [109]

    T., et al

    Padawer-Blatt , A., Shao , Z., Hough , R. T., et al. 2025, Universe, 11, 47

    2025

  102. [110]

    & Matteucci , F

    Padovani , P. & Matteucci , F. 1993, , 416, 26

    1993

  103. [111]

    A., Kay , S

    Pearce , F. A., Kay , S. T., Barnes , D. J., Bah \'e , Y. M., & Bower , R. G. 2021, , 507, 1606

    2021

  104. [112]

    Peebles , P. J. E. 1980, The large-scale structure of the universe

    1980

  105. [113]

    2018, , 475, 648

    Pillepich , A., Nelson , D., Hernquist , L., et al. 2018, , 475, 648

    2018

  106. [114]

    H., Kriek , M., Feldmann , R., et al

    Price , S. H., Kriek , M., Feldmann , R., et al. 2017, , 844, L6

    2017

  107. [115]

    2019, , 486, 4001

    Ragagnin , A., Dolag , K., Moscardini , L., Biviano , A., & D'Onofrio , M. 2019, , 486, 4001

    2019

  108. [116]

    2022, , 665, A16

    Ragagnin , A., Meneghetti , M., Bassini , L., et al. 2022, , 665, A16

    2022

  109. [117]

    L., Ferraro , M

    Ragone-Figueroa , C., Granato , G. L., Ferraro , M. E., et al. 2018, , 479, 1125

    2018

  110. [118]

    2025, , submitted

    Rasia , E., Tripodi , R., Borgani , S., et al. 2025, , submitted

    2025

  111. [119]

    2005, , 359, 1041

    Rebusco , P., Churazov , E., B \"o hringer , H., & Forman , W. 2005, , 359, 1041

    2005

  112. [120]

    Remus , R.-S., Dolag , K., & Hoffmann , T. L. 2017, Galaxies, 5, 49

    2017

  113. [121]

    2004, in Clusters of Galaxies: Probes of Cosmological Structure and Galaxy Evolution, ed

    Renzini , A. 2004, in Clusters of Galaxies: Probes of Cosmological Structure and Galaxy Evolution, ed. J. S. Mulchaey , A. Dressler , & A. Oemler , 260

    2004

  114. [122]

    & Andreon , S

    Renzini , A. & Andreon , S. 2014, , 444, 3581

    2014

  115. [123]

    1993, , 419, 52

    Renzini , A., Ciotti , L., D'Ercole , A., & Pellegrini , S. 1993, , 419, 52

    1993

  116. [124]

    2024, , 683, A57

    Rihtar s i c , G., Biffi , V., Fabjan , D., & Dolag , K. 2024, , 683, A57

    2024

  117. [125]

    2022, , 516, 3068

    Sarkar , A., Su , Y., Truong , N., et al. 2022, , 516, 3068

    2022

  118. [126]

    2020, , 637, A34

    Sartoris , B., Biviano , A., Rosati , P., et al. 2020, , 637, A34

    2020

  119. [127]

    S., Biffi , V., et al

    Scheck , D., Sanders , J. S., Biffi , V., et al. 2023, , 670, A33

    2023

  120. [128]

    2018, , 480, 4636

    Schulze , F., Remus , R.-S., Dolag , K., et al. 2018, , 480, 4636

    2018

  121. [129]

    J., Gavazzi , R., et al

    Shuntov , M., McCracken , H. J., Gavazzi , R., et al. 2022, , 664, A61

    2022

  122. [130]

    W., Mantz , A., et al

    Simionescu , A., Allen , S. W., Mantz , A., et al. 2011, Science, 331, 1576

    2011

  123. [131]

    2015, , 811, L25

    Simionescu , A., Werner , N., Urban , O., et al. 2015, , 811, L25

    2015

  124. [132]

    2005, , 364, 1105

    Springel , V. 2005, , 364, 1105

    2005

  125. [133]

    2005, , 361, 776

    Springel , V., Di Matteo , T., & Hernquist , L. 2005, , 361, 776

    2005

  126. [134]

    & Hernquist , L

    Springel , V. & Hernquist , L. 2003, , 339, 289

    2003

  127. [135]

    Springel , V., White , S. D. M., Tormen , G., & Kauffmann , G. 2001, , 328, 726

    2001

  128. [136]

    2020, , 892, 34

    Starikova , S., Vikhlinin , A., Kravtsov , A., et al. 2020, , 892, 34

    2020

  129. [137]

    F., Remus , R.-S., Dolag , K., & Burkert , A

    Teklu , A. F., Remus , R.-S., Dolag , K., & Burkert , A. 2017, , 472, 4769

    2017

  130. [138]

    2003, in From Twilight to Highlight: The Physics of Supernovae, ed

    Thielemann , F.-K., Argast , D., Brachwitz , F., et al. 2003, in From Twilight to Highlight: The Physics of Supernovae, ed. W. Hillebrandt & B. Leibundgut , 331

    2003

  131. [139]

    2007, , 382, 1050

    Tornatore , L., Borgani , S., Dolag , K., & Matteucci , F. 2007, , 382, 1050

    2007

  132. [140]

    2004, , 349, L19

    Tornatore , L., Borgani , S., Matteucci , F., Recchi , S., & Tozzi , P. 2004, , 349, L19

    2004

  133. [141]

    2010, , 402, 1911

    Tornatore , L., Borgani , S., Viel , M., & Springel , V. 2010, , 402, 1911

    2010

  134. [142]

    2019, , 484, 2896

    Truong , N., Rasia , E., Biffi , V., et al. 2019, , 484, 2896

    2019

  135. [143]

    W., Simionescu , A., & Mantz , A

    Urban , O., Werner , N., Allen , S. W., Simionescu , A., & Mantz , A. 2017, , 470, 4583

    2017

  136. [144]

    W., & B \"o hringer , H

    Urban , O., Werner , N., Simionescu , A., Allen , S. W., & B \"o hringer , H. 2011, , 414, 2101

    2011

  137. [145]

    van den Hoek , L. B. & Groenewegen , M. A. T. 1997, , 123, 305

    1997

  138. [146]

    van der Burg , R. F. J., Hoekstra , H., Muzzin , A., et al. 2015, , 577, A19

    2015

  139. [147]

    1977, , 56, 473

    Vigroux , L. 1977, , 56, 473

    1977

  140. [148]

    2016, , 455, 4183

    Vincenzo , F., Matteucci , F., Belfiore , F., & Maiolino , R. 2016, , 455, 4183

    2016

  141. [149]

    2023, , 669, A34

    Vladutescu-Zopp , S., Biffi , V., & Dolag , K. 2023, , 669, A34

    2023

  142. [150]

    2018, , 474, 2073

    Vogelsberger , M., Marinacci , F., Torrey , P., et al. 2018, , 474, 2073

    2018

  143. [151]

    Werner , N., Urban , O., Simionescu , A., & Allen , S. W. 2013, , 502, 656

    2013

  144. [152]

    White , S. D. M. & Rees , M. J. 1978, , 183, 341

    1978

  145. [153]

    Wiersma , R. P. C., Schaye , J., & Smith , B. D. 2009, , 393, 99

    2009

  146. [154]

    Woosley , S. E. & Weaver , T. A. 1995, , 101, 181

    1995

  147. [155]

    M., Thomas , P

    Yates , R. M., Thomas , P. A., & Henriques , B. M. B. 2017, , 464, 3169

    2017

  148. [156]

    Zibetti , S., White , S. D. M., Schneider , D. P., & Brinkmann , J. 2005, , 358, 949

    2005

  149. [157]

    E., Biffi , V., et al

    ZuHone , J., Bahar , Y. E., Biffi , V., et al. 2023, , 675, A150

    2023

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.