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REVIEW 2 major objections 5 minor 112 references

Galaxy formation physics behind bar formation: A view from cosmological hydrodynamical simulations

T0 review · 2 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Supernova feedback, not black-hole feedback, sets whether a Milky Way-like disc grows a bar.

desk verdict Transparent feedback-variation study whose criteria-failure result is worth citing, but whose causal claim outruns a one-seed-per-model design. read the letter →

arxiv 2411.16876 v2 pith:5LEQRNFA submitted 2024-11-25 astro-ph.GA

Yetli Rosas-Guevara , Silvia Bonoli , Ewald Puchwein , Massimo Dotti , Sergio Contreras This is my paper · ORCID
classification astro-ph.GA
keywords galacticbarssupernovafeedbackAGNdiscstabilityzoom-insimulationscosmologicalhydrodynamicsMilkyWayanaloguesbarinstabilitycriteria
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

Galactic bars are the elongated stellar structures seen in most disc galaxies, including the Milky Way. The paper asks which galaxy-formation physics decides whether such a bar forms, and it argues that the answer is largely the strength of supernova feedback. In a suite of 42 zoom-in cosmological resimulations of six Milky Way-sized galaxies selected because the parent TNG50 simulation gave each a strong bar, the authors vary only the energy injected per supernova and the efficiency of quasar-mode black hole feedback. They report that supernova feedback alone changes whether and when bars appear: stronger winds delay or suppress bars, while the black-hole variations only alter the bar's strength and length. The same runs show that the classic disc-instability criteria used to predict bars fail exactly in the extreme cases where a massive compact bulge is present.

What carries the argument

The central objects are three analytic disc-instability diagnostics — the Toomre parameter $Q_T=\kappa\sigma_R/(3.36G\Sigma)$, the ELN ratio $\epsilon_{\rm ELN}=v_{\rm max}/(G M_{\rm disc}/r_{\rm disc})^{1/2}$, and the MMW spin criterion $\lambda_{\rm MMW}$ — together with a controlled zoom-in resimulation design in which only the supernova wind energy parameter $e_w$ or the quasar-mode feedback efficiency $\epsilon_{f,\rm high}$ is changed. The criteria are used to judge whether a simulated disc should be bar-unstable, and the feedback variations isolate which channel changes that judgement. Bars are identified by Fourier decomposition of the face-on stellar surface density, with strength $A_2$ and length set by the constant-phase radius.

What would settle it

A decisive test would be to re-run the same six haloes with several independent random seeds for both the TNG50-like and strong-wind models: if any strong-wind realisation forms a stable bar while the matching TNG50-like run does not, the claimed suppression would not hold up. An observational falsifier would be to find Milky Way analogues with extended, low-velocity-dispersion discs that still host strong bars at the same rate as compact discs, contrary to the stability picture.

Watch

Extended reading notes

Core claim

The paper's central claim is that, for Milky Way-like discs, bar formation is regulated by supernova feedback strength, while quasar-mode black hole feedback plays at most a secondary role. Each of six haloes is re-simulated in seven model variants covering five wind strengths (no wind, weaker, TNG50-like, medium, strong) and two black hole feedback variants, and the outcome is that only the supernova wind strength shifts bar formation time or suppresses the bar outright. Stronger winds delay disc assembly and produce less massive, more extended discs with lower radial velocity dispersion, which resist bar instability; the strongest winds stop bar formation entirely. At the opposite extreme, no wind leads to early runaway star formation and a massive compact bulge that also prevents a bar. The paper further argues that the Toomre, ELN, and MMW criteria correctly mark the bar-forming cases at the time of bar formation, but in the no-wind model ELN and MMW incorrectly predict instability while Toomre predicts stability for the weak-wind model that does form a bar, and the authors attribute these failures to the criteria not accounting for the central bulge.

Load-bearing premise

The load-bearing premise is that the six selected haloes, chosen because the parent simulation made each one strongly barred, are a representative sample, so that differences between feedback models trace the changed physics rather than random stochastic scatter in the zoom-in re-simulations.

Editorial extensions

If this is right

  • Bar formation time in Milky Way-like galaxies should be earlier for weaker supernova-driven winds and suppressed entirely for the strongest winds, so observed bar fractions can encode the average supernova feedback strength.
  • Quasar-mode black hole feedback can be varied without changing whether a bar forms; its main effect is on bar strength and length.
  • The ELN and MMW criteria are not by themselves reliable predictors of bar formation when a massive compact bulge is present; a bulge-concentration condition is needed.
  • Disc-dominated morphology is robust across all feedback models, so a massive disc alone does not guarantee that a bar will form.
  • The statistics of barred galaxies can serve as a constraint on subgrid galaxy-formation models, since different feedback strengths give different bar fractions and formation redshifts.

Reading between the lines

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

  • The authors do not state this, but the result implies that surveys measuring bar fractions as a function of disc size and stellar mass could act as an observational probe of feedback strength in real galaxies.
  • The failure pattern of the three criteria suggests a simple repair: adding a bulge-concentration term to ELN and MMW should recover the no-wind case, and this could be tested directly on the same simulations.
  • Because stochasticity removed one of six bars even in the TNG50-like run, a stronger test of the feedback effect would come from re-running the same haloes with multiple random seeds; the authors flag this limitation, and seed-averaged comparisons would settle whether the feedback effect is real.
  • If the supernova-dominated picture is correct, high-redshift barred galaxies detected by JWST may preferentially be systems with weak feedback or low bulge concentration, a pattern that future high-redshift bar surveys could check.
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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

2 major / 5 minor

Summary. The paper resimulates six TNG50 Milky Way-mass barred disc galaxies with the Arepo code and seven variants of the TNG feedback model, varying the galactic wind energy parameter e_w and the quasar-mode BH feedback efficiency ε_f,high. The authors find that stronger supernova feedback delays disc assembly and suppresses bar formation, with the strongest wind model producing stable discs, while the no-wind model fails to form a bar because of a massive compact bulge. Quasar BH feedback variations do not change whether a bar forms but do change bar strength and length. The paper evaluates the Toomre, ELN, and MMW instability criteria at bar formation and shows they correctly predict bar presence/absence except in the no-wind and weak-wind cases, which they attribute to the missing effect of a central bulge.

Significance. The study is a clean controlled parameter experiment on a well-characterised simulation sample and provides a useful test of analytic bar-instability criteria under controlled variations of subgrid physics. The explicit discussion of stochasticity in Section 5.4 is honest, and the suite of 42 zoom-in runs is a valuable resource. If the causal attribution is confirmed with additional realizations, the conclusion that SN feedback strength is the dominant regulator of bar formation in MW-mass systems would constrain subgrid models. However, as presented, the central causal claim is not yet separated from run-to-run noise, which limits the strength of the conclusions that can be drawn.

major comments (2)
  1. [Sec. 5.4; Abstract] The central claim that supernova feedback strength causally delays and can prevent bar formation is underdetermined by the single realization per (halo, model). The paper's own Section 5.4 states that subgrid stochasticity can affect the radial velocity dispersion and disc extent, which are exactly the quantities entering Q_T, ε_ELN, and λ_MMW (Figs. 4, 5, 8), and that one of six control galaxies loses its bar in the TNG50-like model. With one run per model, the binary bar/no-bar outcomes across adjacent wind models (WW 6/6, TNG50-like 5/6, MW 1/6, SW 0/6, NW 0/6) are not statistically distinguishable from noise: for example, the Clopper-Pearson 95% intervals for 1/6 and 5/6 overlap. Please either provide multiple stochastic realizations (different random seeds) for at least the models near the bar/no-bar boundary, or temper the abstract and Section 6 to say the effect is seen 'in our realizations' and justify quantitatively why the trend exceeds stochastic scatter.
  2. [Sec. 3.2; Abstract] The statement in the Abstract that quasar-mode BH feedback 'does not affect bar formation' is not consistent with the text in Section 3.2 that 'the bar is present in all simulations, but the bar formation time varies dramatically' across the BH models. If 'formation' is meant as the presence of a bar, the claim should be reworded to 'does not affect whether a bar forms'; if it includes formation time, the statement is internally contradictory. Given the stochastic scatter conceded in Section 5.4, this dramatic variation also needs to be assessed against run-to-run noise before a null effect on formation is claimed.
minor comments (5)
  1. [Eq. (5)] The mass-loading relation in Eq. (5) appears dimensionally incorrect: η_w = 2 v_w^2 e_w (1−τ_w) should read η_w = 2 e_w (1−τ_w)/v_w^2, consistent with the text describing available wind energy divided by wind kinetic energy.
  2. [Sec. 4.2] In the sentence beginning 'For the galaxies in the models that form a bar', the expression '(λ_MMW/λ_crit) ∼< 12' is presumably a typo for '(λ_MMW/λ_crit) < 1'; as written, it is not meaningful for the stability threshold.
  3. [Sec. 5.3 and Sec. 6] The acronyms 'WWM' and 'ELT' appear where 'MMW' and 'ELN' are intended (Section 5.3 and the summary bullet list); please correct.
  4. [Sec. 5.4] The sentence 'however, the radial velocity dispersion and the extent of the disc could be' is incomplete; it should end with 'affected'.
  5. [Sec. 2.1; Abstract] The Abstract should state explicitly that the sample is preselected to be barred in TNG50, so the conclusions concern galaxies that form bars under the TNG50 model; the limitation is only stated in Section 5.4.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity found: the central causal claims follow from controlled variations of external subgrid parameters, and the instability criteria are independent literature diagnostics rather than fitted outputs.

full rationale

The paper's main claim is that increasing supernova feedback energy (ew from 0 to 7.2) delays disc assembly and suppresses bar formation, while quasar-mode black-hole feedback (epsilon_f,high) affects bar strength and length but not formation. This is an intervention study: ew and epsilon_f,high are fixed model parameters varied across 42 zoom-in runs, and none is fitted to the six target galaxies. Bar presence or absence is determined by a standard Fourier A2 criterion with threshold 0.2, which is independent of the Toomre, ELN, and MMW instability criteria taken from the literature. Evaluating those criteria on the same simulations is a post-hoc diagnostic test, not a derivation of bar outcome from the criteria themselves. The only self-citations are the RG20/RG22 bar catalogue and identification method used to select the parent TNG50 sample; these are method and data references to a public simulation and do not carry the causal argument. The acknowledged stochasticity and the single-realization design are validity concerns about separating systematic feedback effects from chaotic divergence, not circularity: they do not make any equation reduce to its inputs. No circular step meeting the quoted-reduction standard is present.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new particles, forces, or physical entities, and it does not fit constants to the target galaxies. The conclusions rest on subgrid model choices, e_w and epsilon_f,high, the faithfulness of the zoom-in ICs, and the literature criteria. The only hand-set numeric input in the analysis is cNFW=10 for lambda_crit, which the authors checked for robustness.

free parameters (3)
  • e_w (galactic wind energy parameter) = 0 (NW), 1.8 (WW), 3.6 (TNG50-like), 5.4 (MW), 7.2 (SW)
    Hand-selected strengths of supernova-driven winds. The paper's central claim that SN feedback delays or suppresses bar formation is a monotonic response to this parameter.
  • epsilon_f,high (quasar-mode BH feedback efficiency) = 0.1 in standard models, 0.05 in BHlowEff
    Varied to test AGN feedback. The conclusion that quasar feedback does not affect bar formation is based on comparing these two values.
  • cNFW (halo concentration for MMW criterion) = 10
    Assumed to compute lambda_crit in Eq. 3 (Section 4.2). The authors state they tested other values with comparable results.
assumptions (5)
  • domain assumption The IllustrisTNG galaxy formation model is an adequate representation of galaxy formation physics for this study.
    Invoked throughout as the baseline subgrid model; the paper varies only the wind energy parameter and BH quasar efficiency.
  • domain assumption The zoom-in initial conditions faithfully reproduce the TNG50 parent environment for the selected haloes.
    Section 2.2 describes the IC generation; the fidelity is assumed, with no explicit convergence test shown.
  • domain assumption The three bar instability criteria (Toomre, ELN, MMW) are appropriate diagnostics of bar formation in these systems.
    Section 4 evaluates the simulated galaxies against these criteria; the paper's failure analysis treats them as meaningful predictors.
  • domain assumption Bar identification via A2,max > 0.2 and constant phase reliably distinguishes bars from transient asymmetries.
    Appendix A.1; the classification of barred versus unbarred is central to the 'criteria fail to forecast' claim.
  • domain assumption Cosmological parameters from Planck Collaboration (2016) are adopted as exact inputs.
    Section 2 lists the cosmology; no sensitivity test is performed.

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Cite this review

Pith. "Pith review of Galaxy formation physics behind bar formation: A view from cosmological hydrodynamical simulations." pith.science (2026). https://pith.science/paper/5LEQRNFA

@misc{pith2026241116876,
  author       = {Pith},
  title        = {Pith review of: Galaxy formation physics behind bar formation: A view from cosmological hydrodynamical simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5LEQRNFA}},
  note         = {Machine review of arXiv:2411.16876}
}
read the original abstract

We present a suite of zoom-in cosmological simulations of Milky Way-like galaxies with a prominent disc component and a strong bar in their centre, based on a subsample of barred galaxies from the TNG50 magneto-hydrodynamic simulation. We modify the physical models that regulate star formation, namely, supernova feedback and black hole quasar feedback, to examine how they affect the disc and bar formation. We find that, independently of the feedback prescriptions, all galaxies show a similar morphology, which is dominant in comparison with the bulge mass. The black hole quasar feedback models used in this study do not affect bar formation, although they can affect the bar strength and length. The energy released by the supernovae causes a delay in the time of bar formation and, in models with the strongest feedback, galaxies form stable discs against bar formation. This could be understood since supernova feedback influences disc and bulge assembly, resulting in discs with lower mass content, radial velocity dispersion and larger size as the supernova feedback strength increases. We study disc stability using three bar instability criteria proposed in the literature. We find that galaxies with varied supernovae and black hole quasar feedback satisfy these criteria at the moment of bar formation, except in extreme cases where the galaxy lacks or has weak supernova feedback. In these models, two of three criteria fail to forecast the existence (or absence) of a bar, probably because they do not account for the influence of a massive and compact bulge. Our findings provide insights into the physical processes behind bar formation and highlight the importance of additional conditions, other than a massive and compact disc that promote bar formation.

Figures

Figures reproduced from arXiv: 2411.16876 by the authors.

Figure 1
Figure 1. Left panel: Dark matter distribution of the TNG50 simulation. Magenta empty circles show the position of the selected haloes of the zoom-in simulations. Right panel: Mocked images in JWST NIRCam F200W, F115W, and F070W filters (face-on) calculated in Nelson et al. 2018, (TNG database) for the TNG50 galaxies that are resimulated. The NIRCam blue channel highlights the young population of the galaxy, and the NIRCam re… view at source ↗
Figure 2
Figure 2. Face-on stellar density maps of two distinct galaxies at z = 0. Top figure shows the wind model variations: each column corresponds to a distinct wind model, with left Col. showing a galaxy without SN feedback and right Col. galaxies with the strongest SN feedback. The bottom figure shows different quasar black hole models but with the same stellar feedback model as TNG50-like as indicated by [PITH_FULL_IMAGE:figur… view at source ↗
Figure 3
Figure 3. Median evolution of the bar and galaxy morphology for simulations with different galactic wind (top) and black hole feedback (bottom) models. Each column corresponds to a different model, as indicated in the figures. From top to bottom rows: the bar strength, bar extent, the stellar mass and the disc-to-total and bulge-to-total mass fraction are shown. The medians and the distribution between 20th and 80th percentil… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Mass and size evolution of discs and bulges. Galactic winds model variations (top) and quasar BH physics model variations (bottom). The last Col. shows the original TNG50. Solid lines and shaded regions represent median values and the 20th to 80th percentile distributi…
Figure 5
Figure 5. Figure 5: Evolution of relevant parameters for bar formation. From top to bottom: ELN criterion (Eq.2), MMW criterion (Eq.3) and Toomre parameter (Eq. 1) for different galactic wind models (top figure) and BH physics models (bottom figure) and the original TNG50. Horizontal dash…
Figure 6
Figure 6. Figure 6: Median evolution of the maximum circular velocity of the Dark Matter halo. Each column represents the variations of galactic winds (upper Fig.) and BH physics models (bottom Fig.) in order from left to right, and vertical solid lines represent the bar formation redshif…
Figure 7
Figure 7. Figure 7: Median evolution of spin parameter, the ratio of the angular momentum of the disc over the halo and the disc mass fraction (solid thicker lines). From left to right, different galactic wind models (top Fig.) and BH physics models (bottom Fig.) are shown. The rightmost …
Figure 8
Figure 8. Figure 8: Evolution of radial velocity dispersion, stellar surface density, epicyclic and angular frequency in a-stellar half-mass radius aperture. From left to right: different galactic wind models (top figure) and BH physics models (bottom figure). The last right Col. correspo…
Figure 9
Figure 9. Figure 9: Bulge-to-disc mass fractions as a function of bulge-to-disc size ratios. Left to right: galactic wind models (top Fig) and BH physics model variations (bottom Fig). Colour code represents the redshift. Horizontal grey dashed lines represent the limit given by Kataria &…

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Works this paper leans on

112 extracted references · 77 canonical work pages

  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]

    G., et al., 2017, MNRAS, 469, 1054

    Algorry D. G., et al., 2017, MNRAS, 469, 1054

    2017

  4. [4]

    E., Hern \'a ndez-Monteagudo C., Contreras S., Zennaro M., 2021, MNRAS, 503, 3596

    Aric \`o G., Angulo R. E., Hern \'a ndez-Monteagudo C., Contreras S., Zennaro M., 2021, MNRAS, 503, 3596

    2021

  5. [5]

    Athanassoula E., Misiriotis A., 2002, MNRAS, 330, 35

    2002

  6. [6]

    2003, MNRAS, 341, 1179 (A03)

    Athanassoula E. 2003, MNRAS, 341, 1179 (A03)

    2003

  7. [7]

    Athanassoula E., 2008, MNRAS, 390, L69

    2008

  8. [8]

    Athanassoula E., Machado R. E. G., Rodionov S. A., 2013, MNRAS, 429, 1949

    2013

Show all 112 references
  1. [9]

    Bi D., Shlosman I., Romano-D \' az E., 2022, MNRAS, 513, 693

    2022

  2. [10]

    Bi D., Shlosman I., Romano-D \' az E., 2022, ApJ, 934, 52

    2022

  3. [11]

    Binney J., Tremaine S., 2008, gady.book

    2008

  4. [12]

    Bird S., Vogelsberger M., Haehnelt M., Sijacki D., Genel S., Torrey P., Springel V., et al., 2014, MNRAS, 445, 2313

    2014

  5. [13]

    Bonoli S., Mayer L., Kazantzidis S., Madau P., Bellovary J., Governato F., 2016, MNRAS, 459, 2603

    2016

  6. [14]

    G., Benson A

    Bower R. G., Benson A. J., Malbon R., Helly J. C., Frenk C. S., Baugh C. M., Cole S., Lacey C. G., 2006, MNRAS, 370, 645

    2006

  7. [15]

    M., Schaye J., Ludlow A

    Borrow J., Schaller M., Bah \'e Y. M., Schaye J., Ludlow A. D., Ploeckinger S., Nobels F. S. J., et al., 2023, MNRAS, 526, 2441

    2023

  8. [16]

    S., Dekel A., Kolatt T

    Bullock J. S., Dekel A., Kolatt T. S., Kravtsov A. V., Klypin A. A., Porciani C., Primack J. R., 2001, ApJ, 555, 240

    2001

  9. [17]

    Cervantes Sodi B., Li C., Park C., 2015, ApJ, 807, 111

    2015

  10. [18]

    Cervantes Sodi B., 2017, ApJ, 835, 80

    2017

  11. [19]

    J., Rajgor S., Myers R., 2008, MNRAS, 386, 909

    Conselice C. J., Rajgor S., Myers R., 2008, MNRAS, 386, 909

    2008

  12. [20]

    Collier A., Shlosman I., Heller C., 2018, MNRAS, 476, 1331

    2018

  13. [21]

    G., Guo Y., Buttitta C., Jogee S., Bagley M

    Costantin L., P \'e rez-Gonz \'a lez P. G., Guo Y., Buttitta C., Jogee S., Bagley M. B., Barro G., et al., 2023, Natur, 623, 499

    2023

  14. [22]

    A., van de Voort F., 2023, ARA&A, 61, 473

    Crain R. A., van de Voort F., 2023, ARA&A, 61, 473

    2023

  15. [23]

    J., Springel V., White S

    Croton D. J., Springel V., White S. D. M., De Lucia G., Frenk S. C., Gao L., Jenkins A. and Kauffmann G. et al. , 2006, MNRAS, 365, 11

    2006

  16. [24]

    Cuomo V., Aguerri J. A. L., Corsini E. M., Debattista V. P., 2020, A&A, 641, A111

    2020

  17. [25]

    Dahari O., 1984, The Astronomical Journal, 89, 966

    1984

  18. [26]

    Jang D., Kim W.-T., 2023, ApJ, 942, 106

    2023

  19. [27]

    P., Mayer L., Carollo C

    Debattista V. P., Mayer L., Carollo C. M., Moore B., Wadsley J., Quinn T., 2006, ApJ, 645, 209

    2006

  20. [28]

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

    2005

  21. [29]

    E., Martig M., James P

    Donohoe-Keyes C. E., Martig M., James P. A., Kraljic K., 2019, arXiv, arXiv:1908.11119

    2019 arXiv

  22. [30]

    Donnari M., et al., 2019, MNRAS, 485, 4817

    2019

  23. [31]

    Efstathiou G., Lake G., Negroponte J., 1982, MNRAS, 199, 1069

    1982

  24. [32]

    Englmaier P., Shlosman I., 2004, ApJL, 617, L115

    2004

  25. [33]

    Fanali R., Dotti M., Fiacconi D., Haardt F., 2015, MNRAS, 454, 3641

    2015

  26. [34]

    Fragkoudi F., Grand R. J. J., Pakmor R., Bl \'a zquez-Calero G., Gargiulo I., Gomez F., Marinacci F., et al., 2020, MNRAS, 494, 5936

    2020

  27. [35]

    Fragkoudi F., Grand R., Pakmor R., G \'o mez F., Marinacci F., Springel V., 2024, arXiv, arXiv:2406.09453

    2024 arXiv

  28. [36]

    Fraser-McKelvie A., Merrifield M., Arag \'o n-Salamanca A., Peterken T., Kraljic K., Masters K., Stark D., et al., 2020, MNRAS, 499, 1116

    2020

  29. [37]

    Faucher-Gigu \`e re C.-A., Lidz A., Zaldarriaga M., Hernquist L., 2009, ApJ, 703, 1416

    2009

  30. [38]

    A., 2009, MNRAS, 393, 1531

    Gadotti D. A., 2009, MNRAS, 393, 1531

    2009

  31. [39]

    Gavazzi G., et al., 2015, A&A, 580, A116

    2015

  32. [40]

    Genel S., et al., 2014, MNRAS, 445, 175

    2014

  33. [41]

    L., Springel V., Hernquist L., Nelson D., Pillepich A., Weinberger R., et al., 2019, ApJ, 871, 21

    Genel S., Bryan G. L., Springel V., Hernquist L., Nelson D., Pillepich A., Weinberger R., et al., 2019, ApJ, 871, 21

    2019

  34. [42]

    T., 2019, arXiv, arXiv:1907.06910

    George K., Subramanian S., Paul K. T., 2019, arXiv, arXiv:1907.06910

    2019 arXiv

  35. [43]

    Ghosh S., Fragkoudi F., Di Matteo P., Saha K., 2023, A&A, 674, A128

    2023

  36. [44]

    Guedes J., Callegari S., Madau P., Mayer L., 2011, ApJ, 742, 76

    2011

  37. [45]

    L., Chen Z., Wise E., Bagley M

    Guo Y., Jogee S., Finkelstein S. L., Chen Z., Wise E., Bagley M. B., Barro G., et al., 2023, ApJL, 945, L10

    2023

  38. [46]

    J., Finkelstein S

    Guo Y., Jogee S., Wise E., Pritchett K., McGrath E. J., Finkelstein S. L., Iyer K. G., et al., 2024,

    2024

  39. [47]

    Grand R. J. J., G \'o mez F. A., Marinacci F., Pakmor R., Springel V., Campbell D. J. R., Frenk C. S., et al., 2017, MNRAS, 467, 179

    2017

  40. [48]

    Henriques B. M. B., White S. D. M., Thomas P. A., Angulo R. E., Guo Q., Lemson G., Springel V., 2013, MNRAS, 431, 3373

    2013

  41. [49]

    Irodotou D., Fragkoudi F., Pakmor R., Grand R. J. J., Gadotti D. A., Costa T., Springel V., et al., 2022, MNRAS, 513, 3768

    2022

  42. [50]

    Izquierdo-Villalba D., Bonoli S., Rosas-Guevara Y., Springel V., White S. D. M., Zana T., Dotti M., et al., 2022, arXiv, arXiv:2203.07734

    2022 arXiv

  43. [51]

    D., Pontzen A., Agertz O., Rey M

    Joshi G. D., Pontzen A., Agertz O., Rey M. P., Read J., Pillepich A., 2024, arXiv, arXiv:2407.00171

    2024 arXiv

  44. [52]

    K., Das M., 2018, MNRAS, 475, 1653

    Kataria S. K., Das M., 2018, MNRAS, 475, 1653

    2018

  45. [53]

    H., Hernquist L., 1996, ApJS, 105, 19

    Katz N., Weinberg D. H., Hernquist L., 1996, ApJS, 105, 19

    1996

  46. [54]

    C., Libeskind N

    Khoperskov S., Minchev I., Steinmetz M., Ratcliffe B., Walcher J. C., Libeskind N. I., 2024, MNRAS, 533, 3975

    2024

  47. [55]

    A., Gadotti D

    Le Conte Z. A., Gadotti D. A., Ferreira L., Conselice C. J., de S \'a -Freitas C., Kim T., Neumann J., et al., 2023, arXiv, arXiv:2309.10038

    2023 arXiv

  48. [56]

    Kraljic K., Bournaud F., Martig M., 2012, ApJ, 757, 60

    2012

  49. [57]

    Long S., Shlosman I., Heller C., 2014, ApJL, 783, L18

    2014

  50. [58]

    Marinacci F., et al., 2018, MNRAS, 480, 5113

    2018

  51. [59]

    L., et al., 2011, MNRAS, 411, 2026

    Masters K. L., et al., 2011, MNRAS, 411, 2026

    2011

  52. [60]

    J., Mao S., White S

    Mo H. J., Mao S., White S. D. M., 1998, MNRAS, 295, 319

    1998

  53. [61]

    P., et al., 2018, MNRAS, 477, 1206

    Naiman J. P., et al., 2018, MNRAS, 477, 1206

    2018

  54. [62]

    F., Benz W., 1991, ApJ, 380, 320

    Navarro J. F., Benz W., 1991, ApJ, 380, 320

    1991

  55. [63]

    Nelson D., et al., 2015, A& C, 13, 12

    2015

  56. [64]

    Nelson D., et al., 2018a, MNRAS, 475, 624

  57. [65]

    Nelson D., et al., 2019a, ComAC, 6, 2

  58. [66]

    Nelson D., Pillepich A., Springel V., Pakmor R., Weinberger R., Genel S., Torrey P., et al., 2019b, MNRAS, 490, 3234

  59. [67]

    D., Dav \'e R., 2006, MNRAS, 373, 1265

    Oppenheimer B. D., Dav \'e R., 2006, MNRAS, 373, 1265

    2006

  60. [68]

    D., Dav \'e R., 2008, MNRAS, 387, 577

    Oppenheimer B. D., Dav \'e R., 2008, MNRAS, 387, 577

    2008

  61. [69]

    P., Peebles P

    Ostriker J. P., Peebles P. J. E., 1973, ApJ, 186, 467

    1973

  62. [70]

    L., 2019, MNRAS, 483, 2721

    Peschken N., okas E. L., 2019, MNRAS, 483, 2721

    2019

  63. [71]

    Pillepich A., et al., 2018a, MNRAS, 473, 4077

  64. [72]

    Pillepich A., et al., 2018b, MNRAS, 475, 648

  65. [73]

    Pillepich A., Nelson D., Springel V., Pakmor R., Torrey P., Weinberger R., Vogelsberger M., et al., 2019, MNRAS, 490, 3196

    2019

  66. [74]

    Pillepich A., Sotillo-Ramos D., Ramesh R., Nelson D., Engler C., Rodriguez-Gomez V., Fournier M., et al., 2023, arXiv, arXiv:2303.16217

    2023 arXiv

  67. [75]

    Planck Collaboration 2016, A& A, 594, A13

    2016

  68. [76]

    Pontzen A., Governato F., 2012, MNRAS, 421, 3464

    2012

  69. [77]

    Rodriguez-Gomez V., et al., 2015, MNRAS, 449, 49

    2015

  70. [78]

    B., Agertz O., Renaud F., 2023, MNRAS, 518, 1002

    Romeo A. B., Agertz O., Renaud F., 2023, MNRAS, 518, 1002

    2023

  71. [79]

    S., Tissera P

    Rosito M. S., Tissera P. B., Pedrosa S. E., Rosas-Guevara Y., 2019, A&A, 629, A37

    2019

  72. [80]

    C., et al., 2020, MNRAS, 491, 2547

    Rosas-Guevara Y., Bonoli S., Dotti M., Zana T., Nelson D., Pillepich A., Ho L. C., et al., 2020, MNRAS, 491, 2547

    2020

  73. [81]

    Rosas-Guevara Y., Bonoli S., Dotti M., Izquierdo-Villalba D., Lupi A., Zana T., Bonetti M., et al., 2022, MNRAS, 512, 5339

    2022

  74. [82]

    Rosas-Guevara Y., Bonoli S., Misa Moreira C., Izquierdo-Villalba D., 2024, A&A, 684, A179

    2024

  75. [83]

    Saha K., Naab T., 2013, MNRAS, 434, 1287

    2013

  76. [84]

    Saha K., Elmegreen B., 2018, ApJ, 858, 24

    2018

  77. [85]

    V., Navarro J

    Sales L. V., Navarro J. F., Theuns T., Schaye J., White S. D. M., Frenk C. S., Crain R. A., et al., 2012, MNRAS, 423, 1544

    2012

  78. [86]

    Scannapieco C., Athanassoula E., 2012, MNRAS, 425, L10

    2012

  79. [87]

    Schaye J., et al., 2015, MNRAS, 446, 521

    2015

  80. [88]

    A., Wilkinson A., 1993, RPPh, 56, 173

    Sellwood J. A., Wilkinson A., 1993, RPPh, 56, 173

    1993

  81. [89]

    A., 2012, ApJ, 751, 44

    Sellwood J. A., 2012, ApJ, 751, 44

    2012

  82. [90]

    M., Elmegreen B

    Sheth K., Melbourne J., Elmegreen D. M., Elmegreen B. G., Athanassoula E., Abraham R. G., Weiner B. J., 2012, ApJ, 758, 136

    2012

  83. [91]

    Sijacki D., et al., 2015, MNRAS, 452, 575

    2015

  84. [92]

    Semczuk M., Dehnen W., Sch \"o nrich R., Athanassoula E., 2024, arXiv, arXiv:2407.11154

    2024 arXiv

  85. [93]

    Spinoso D., Bonoli S., Dotti M., Mayer L., Madau P., Bellovary J., 2017, MNRAS, 465, 3729

    2017

  86. [94]

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

    2001

  87. [95]

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

    2003

  88. [96]

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

    2005

  89. [97]

    Springel V., 2010, MNRAS, 401, 791

    2010

  90. [98]

    ascl:1502.003

    Springel V., 2015, ascl.soft. ascl:1502.003

    2015

  91. [99]

    Springel V., et al., 2018, MNRAS, 475, 676

    2018

  92. [100]

    Suresh J., Bird S., Vogelsberger M., Genel S., Torrey P., Sijacki D., Springel V., et al., 2015, MNRAS, 448, 895

    2015

  93. [101]

    Toomre A., 1964, ApJ, 139, 1217

    1964

  94. [102]

    Vogelsberger M., Genel S., Sijacki D., Torrey P., Springel V., Hernquist L., 2013, MNRAS, 436, 3031

    2013

  95. [103]

    Vogelsberger M., et al., 2014a, Nature, 509, 177

  96. [104]

    Vogelsberger M., et al., 2014b, MNRAS, 444, 1518

  97. [105]

    Vogelsberger M., Marinacci F., Torrey P., Puchwein E., 2020, NatRP, 2, 42

    2020

  98. [106]

    Weinberger R., et al., 2017, MNRAS, 465, 3291

    2017

  99. [107]

    Yurin D., Springel V., 2015, MNRAS, 452, 2367

    2015

  100. [108]

    C., Debattista V

    Zhao D., Du M., Ho L. C., Debattista V. P., Shi J., 2020, ApJ, 904, 170

    2020

  101. [109]

    R., Bonoli S., Haardt F., Mayer L., Spinoso D., 2018a, MNRAS, 473, 2608

    Zana T., Dotti M., Capelo P. R., Bonoli S., Haardt F., Mayer L., Spinoso D., 2018a, MNRAS, 473, 2608

  102. [110]

    Zana T., et al., 2019, MNRAS, 488, 1864

    2019

  103. [111]

    Zana T., Lupi A., Bonetti M., Dotti M., Rosas-Guevara Y., Izquierdo-Villalba D., Bonoli S., et al., 2022, MNRAS, 515, 1524

    2022

  104. [112]

    Zhou Z.-B., Zhu W., Wang Y., Feng L.-L., 2020, ApJ, 895, 92

    2020

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.