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The buildup of strongly barred galaxies in the TNG100 simulation

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read In the TNG100 simulation, strong stellar bars form in early-assembled discs and quench their galaxies' centres shortly after appearing.

desk verdict First population-level look at strong bars in TNG100; descriptive results hold, but the bar-quenching causal claim is under-identified by the single-variable control sample. read the letter →

arxiv 1908.00547 v3 pith:E4XZYM34 submitted 2019-08-01 astro-ph.GA astro-ph.CO

Yetli Rosas-Guevara , Silvia Bonoli , Massimo Dotti , Tommaso Zana , Dylan Nelson , Annalisa Pillepich , Luis C. Ho , David Izquierdo-Villalba
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Lars Hernquist Ruediger Pakmor
This is my paper · ORCID
classification astro-ph.GAastro-ph.CO
keywords galaxybarsbarquenchingstarformationdiscevolutioncosmologicalhydrodynamicalsimulationAGNfeedbackTNG100stellarmassassembly
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 uses the TNG100 cosmological simulation to argue that strong stellar bars are not randomly placed features: they grow in disc galaxies that assembled their dominant disc early, and their appearance is followed by a rapid shutdown of central star formation. Among 270 massive disc galaxies at redshift zero, 40 per cent are barred and 22 per cent host strong bars, with the strong-bar fraction rising with stellar mass and with older bars found in more massive galaxies. The strong bars form between redshift 0.5 and 1.5, during a phase of enhanced star formation and black-hole growth, and after the bar settles the star-formation efficiency inside its radius drops steeply while mass-matched unbarred galaxies keep forming stars. The paper reads this as evidence for bar quenching, possibly helped by nuclear feedback from an early-grown black hole. The result connects an observable internal structure, the bar, to a possible route for shutting down star formation in massive galaxies.

What carries the argument

The central tool is the second Fourier harmonic amplitude $A_2$ of the face-on stellar surface density, whose peak sets the bar strength and whose peak radius sets the bar length; a strong bar is a stable structure with $A_2$ at least 0.3 and a constant phase inside that radius. The comparison clock is the normalized time since bar formation, $t_{\rm norm}$, which is zero when the bar becomes stable and one at $z=0$; this allows the histories of barred and unbarred galaxies to be aligned at the same stage of bar evolution. A control sample of unbarred galaxies matched by $z=0$ stellar mass supplies the counterfactual baseline.

What would settle it

A decisive run would switch off black-hole feedback in the same simulation: if strong bars still quench their central regions shortly after formation, the bar alone suffices; if the central gas keeps forming stars, the quenching attributed to the bar is actually driven by the black hole. Observational confirmation would come from resolved maps of central star formation in galaxies caught just after bar formation at redshift around 0.5 to 1, which should show the star-formation efficiency dropping first inside the bar radius.

Watch

Extended reading notes

Core claim

Within the TNG100 cosmological simulation, strong stellar bars are the product of a specific assembly history: the galaxy must build a dominant, dynamically cool disc while its bulge is still small, and it must do so early, between redshift 1.5 and 0.5. These galaxies show enhanced star formation and black-hole accretion while the bar is being built, then a rapid drop in star-formation efficiency inside the bar radius once the bar settles. The nuclear region becomes gas-poor and quenched well before $z=0$, while unbarred galaxies matched in $z=0$ stellar mass continue forming stars on the main sequence. The paper interprets this sequence as bar quenching: the bar torques gas inward, exhausts the central cold gas, and shuts off star formation, with early black-hole feedback acting as a supporting rather than the primary cause.

Load-bearing premise

The comparison treats unbarred galaxies matched only by stellar mass at $z=0$ as the control for what barred galaxies would look like without a bar, so if the two populations already differ in halo mass, environment, bulge size, or early black-hole growth, the later differences in gas and star formation cannot be attributed to the bar alone.

Editorial extensions

If this is right

  • The cosmic bar fraction should be a clock for when massive discs became dynamically cold: more massive galaxies should host older bars and quench earlier, as the paper finds.
  • A sharp drop in star-formation efficiency inside the bar after formation implies that bars are a plausible internal quenching channel even without changes in large-scale environment.
  • At fixed final stellar mass, the barred and unbarred populations encode different assembly histories: barred galaxies built their stars and black holes early, unbarred galaxies grew later and more gradually.
  • Since merger histories are similar for the two populations, interactions are unlikely to be the main trigger of strong bars in this mass range; the early disc and small bulge are the decisive conditions.
  • The parallel early growth of black holes and later central quenching suggests AGN feedback and bar formation may work together, with the bar establishing the conditions for its own nuclear shutoff.

Reading between the lines

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

  • If the control sample were matched on halo mass, environment, and early assembly history as well as stellar mass, the causal attribution to the bar could be cleaner; until then, pre-existing differences between the populations remain a candidate explanation for the quenching.
  • A direct model-variation experiment is implied: simulations without black-hole feedback should produce fewer or weaker strong bars if early AGN feedback is what keeps bulges small, and barred galaxies in those runs would reveal whether the bar alone can quench.
  • The same bar-detection method could be applied to every snapshot to produce a predicted cosmic bar fraction curve, giving high-redshift surveys a quantitative target to confirm or refute the early-formation scenario.
  • If bars are later destroyed by mergers or buckling, the model predicts that central star formation can re-ignite only if fresh gas is available, which connects this work to studies of bar lifetimes.
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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

4 major / 5 minor

Summary. The paper analyzes strong bars in 270 z=0 disc galaxies from TNG100 with stellar masses 10^10.4-10^11 Msun. It identifies 107 barred galaxies (59 strong, 48 weak) via Fourier decomposition and finds that the barred fraction increases with stellar mass, barred galaxies have lower gas-to-stellar mass ratios and lower sSFRs than unbarred ones, and strong bars form mostly at 0.5<z<1.5 in galaxies with early-built discs. Tracking progenitor histories, the authors compare strong-barred galaxies with unbarred controls matched by z=0 stellar mass and report a rapid drop in central star formation after bar formation, which they interpret as evidence for bar quenching, possibly assisted by nuclear feedback.

Significance. If the causal interpretation holds, this is an important cosmological confirmation of bar-driven quenching and a demonstration that early disc assembly and nuclear feedback jointly set the bar population in a full hydrodynamical simulation. The paper is valuable for its descriptive measurements: bar fractions, bar lengths, mass trends, gas content, and comparison with observed local samples, all in a state-of-the-art cosmological run. The bar-age definition using stability conditions and the explicit treatment of merger histories are careful features. However, the central causal claim about bar quenching is not uniquely supported by the current comparison setup, as detailed below.

major comments (4)
  1. [§2.5 and §4.3 (Figs 12, 15)] The control sample of unbarred galaxies is matched to strong-barred galaxies only by stellar mass at z=0 (§2.5). At tnorm=0, the epoch of bar formation, the strong-barred sample is already offset from the controls by up to ~0.4 dex in stellar mass and has lower bulge-to-total ratio and more massive SMBHs (§4.3, Figs 12 and 15). Because each of these pre-existing differences is independently associated with earlier quenching in TNG, the observed post-bar decline in central SFR and SFEdr/SFEgal (Figs 11 and 13) is equally compatible with strong bars forming preferentially in galaxies already on a path to early quenching. The within-bar versus outside-bar contrast in Fig. 13 is suggestive, but the same aperture in unbarred controls also declines, so the differential timing is not by itself decisive. Please add a control matched at tbar on stellar mass, B/T, and BH mass (or a propensity-score/conditional analysis) and show that the quenching signal survives.
  2. [§5.3] Only bars that survive to z=0 enter the sample, and the paper itself notes in §5.3 that a large population of early bars that are subsequently destroyed is expected. Bar destruction through gas inflows, buckling, or interactions is plausibly correlated with the same central gas consumption that the paper attributes to the bar, so survivor bias can exaggerate the apparent post-bar quenching signal. Since this selection effect is acknowledged but not quantified, the central causal reading ("indicative of bar quenching") is weakened; at minimum the authors should test how many bars form and dissolve in TNG100 and whether their host properties differ, or restrict the causal claim to the survivor population.
  3. [§5.2 and Fig. 15] Fig. 15 shows that strongly barred galaxies have systematically higher SMBH masses and higher kinetic-mode energy rates than unbarred controls at all times, and the specific BH accretion rate and central sSFR follow similar evolutions. This is as consistent with early BH growth and feedback creating the conditions for bar formation and quenching as with the bar being the primary quenching agent. The statement that AGN feedback plays a "secondary, or at least ancillary" role is therefore not established by the presented evidence; it is a speculation, as the authors themselves note. Please either supply a quantitative decomposition (e.g., gas depletion timescales, torque estimates, or a comparison with a TNG variant without BH feedback) or explicitly present the causal hierarchy as an open question rather than a conclusion.
  4. [§4.4, Fig. 13] The SFE comparison relies on median tracks from 59 strong bars divided into three mass bins, but the paper does not report the number of galaxies contributing to each median bin or any confidence interval for the difference between barred and unbarred samples. With roughly 20 objects per bin, the apparent rapid drop after tnorm=0 could be driven by individual outliers. Please report per-bin sample sizes and uncertainty estimates (e.g., bootstrap confidence intervals or a significance test for the differential drop in SFEdr/SFEgal).
minor comments (5)
  1. [§2.4, Eq. (4)] The typeset definition of tnorm is ambiguous; if the intended definition is (tbar - tlookback)/tbar, please write it explicitly with parentheses.
  2. [§2.3] The phase-constancy condition "Φ< rbar const" needs a precise tolerance; currently it is unclear how strictly the phase must be constant within rbar.
  3. [Fig. 11] The axis label of the inset panel is missing; the inset shows logarithmic SFR but the reader must infer the quantity.
  4. [Fig. 9 caption] The caption states "sSFR∼10^12 yr^-1" for the passive galaxy; the exponent should presumably be negative (10^-12 yr^-1).
  5. [Throughout] Several inline ranges such as "M∗ 10^10.4−11M⊙" are missing the exponent notation and are hard to read; please format these consistently as 10^10.4−10^11 M⊙.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: bar detection and formation-time definitions are applied to independent simulation outputs, and the headline quenching signal is not a fitted function of the bar indicator.

full rationale

This paper is a simulation-analysis and measurement study, not a derivation in which an input is renamed as a prediction. Bars are identified by Fourier A2 amplitudes with thresholds (A2,max >= 0.2 barred and >= 0.3 strong; Section 2.3), and bar age is defined by the lookback time at which A2,max first satisfies a stability condition (Section 2.3, conditions i and ii). The quantities presented as results—sSFR, gas-to-stellar mass ratio, SFR at 2/5/10 kpc apertures, SFEdr/SFEgal, BH mass, and kinetic BH feedback—are independent outputs of the TNG100 simulation and are not constructed from the A2 threshold or from the z=0 mass matching used to build the control sample. The use of the same A2 diagnostic for classification and for setting tbar is a measurement convention, not a circular derivation, because the subsequent time evolution of SFR, gas, and BH properties is a separate observable. The control sample is matched only by z=0 stellar mass (Section 2.5), and the paper itself reports that barred galaxies already differ at tnorm=0 in stellar mass, B/T, and BH mass (Section 4.3, Fig. 12; Section 5.1, Fig. 15); that is a causal-identification limitation—pre-existing differences could drive later quenching—rather than a circular reduction, since the post-bar SFR drop is not algebraically implied by the matching variable. Self-citations to Bonoli et al. (2016), Spinoso et al. (2017), and Zana et al. (2018a,b, 2019) are used for motivation and discussion, not as load-bearing evidence for the TNG100 results, and the z=0 bar fractions, bar lengths, and gas-content trends are checked against external observational samples (Barazza et al. 2008; Nair et al. 2010; Gadotti 2011; Cervantes-Sodi et al. 2015; Gavazzi et al. 2015). No equation in the paper reduces a claimed prediction to a fitted parameter or to the bar indicator by construction, so there is no significant circularity.

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

The central claims rest on the fidelity of the TNG100 simulation and on hand-chosen thresholds for bar classification. No new physical entities are introduced. The thresholds and the control-sample matching are the main levers that could shift the results.

free parameters (5)
  • A2_bar_detection_threshold = 0.2
    Hand-chosen cutoff for a galaxy to be classified as barred (Section 2.3). Bar fraction and sample size depend on this value.
  • A2_strong_bar_threshold = 0.3
    Hand-chosen cutoff separating strong from weak bars (Section 2.3). The central sample of 59 strong bars depends on this value.
  • rbar_minimum = 1 kpc
    Minimum bar length to avoid gravitational softening (Section 2.3). Affects which bars are counted.
  • bar_age_stability_delta = 0.4 in A2 over ~300 Myr
    Criterion (ii) in Section 2.3 defines the bar formation time as when A2 fluctuations are below this threshold. The inferred bar ages and the tnorm alignment depend on this choice.
  • SFE_normalization_aperture = 15 kpc
    The star formation efficiency inside/outside the bar is normalized by the galaxy SFE within 15 kpc (Section 4.4). This choice affects the SFE ratio but not the main trend.
assumptions (5)
  • domain assumption TNG100 subgrid physics (star formation, stellar winds, AGN feedback) provides a sufficiently realistic model of galaxy formation.
    The entire analysis uses TNG100 as a proxy for real galaxies. The paper cites calibration papers (Pillepich et al. 2018a, Weinberger et al. 2017) rather than justifying this assumption internally.
  • domain assumption The kinematic decomposition of Genel et al. (2015) correctly assigns stellar particles to disc and bulge components.
    Section 2.2 uses this decomposition to select disc-dominated galaxies and to measure D/T and B/T. If the decomposition is wrong, the sample selection is biased.
  • domain assumption Fourier A2 amplitude and phase constancy identify real bars rather than transient distortions.
    Section 2.3 defines bars with A2,max >= 0.2 and constant phase. This is a standard method, but the paper assumes it is a faithful tracer of a physical bar in the simulation.
  • domain assumption The SubLink merger tree correctly tracks progenitor galaxies across snapshots.
    Section 4.5 uses merger histories and progenitor properties. Errors in tree connections would affect the inferred formation timelines.
  • standard math Fourier decomposition is a mathematically valid representation of the stellar surface density.
    Equations 2 and 3 assume a Fourier expansion of the face-on density. This is standard mathematics, no independent justification required.

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Pith. "Pith review of The buildup of strongly barred galaxies in the TNG100 simulation." pith.science (2026). https://pith.science/paper/E4XZYM34

@misc{pith2026190800547,
  author       = {Pith},
  title        = {Pith review of: The buildup of strongly barred galaxies in the TNG100 simulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E4XZYM34}},
  note         = {Machine review of arXiv:1908.00547}
}
abstract

We analyse the properties of strongly barred disc galaxies using the TNG100 simulation, a cosmological hydrodynamical realisation of the IllustrisTNG suite. We identify 270 disc galaxies at $z=0$ in the stellar mass range $M_{*}=10^{10.4-11}M_{\odot}$, of which 40 per cent are barred. Of the detected bars, more than half are strong. We find that the fraction of barred galaxies increases with stellar mass, in agreement with observational results. Strongly barred galaxies exhibit, overall, lower gas-to-stellar mass ratios compared to unbarred galaxies. The majority of barred galaxies are quenched (sSFR $\sim10^{-11.7} $yr$^{-1}$), whereas unbarred galaxies continue to be active (sSFR $\sim10^{-10.3}$yr$^{-1}$) on the main sequence of star-forming galaxies. We explore the evolution of strongly barred and unbarred galaxies to investigate their formation and quenching histories. We find that strong bars form between $0.5< z< 1.5$, with more massive galaxies hosting older bars. Strong bars form in galaxies with an early-established prominent disc component, undergoing periods of enhanced star formation and black hole accretion, possibly assisted by cosmological inflows. Unbarred galaxies, on the other hand, assemble most of their mass and disc component at late times. The nuclear region of strongly barred galaxies quenches shortly after bar formation, while unbarred galaxies remain active across time. Our findings are indicative of bar quenching, possibly assisted by nuclear feedback processes. We conclude that the cosmological environment, together with small scale feedback processes, determine the chances of a galaxy to form a bar and to rapidly quench its central region.

Figures

Figures reproduced from arXiv: 1908.00547 by the authors.

Figure 1
Figure 1. Distribution of BT and DT for TNG100 galaxies calculated within an aperture of 10R50,∗.The black solid lines show the distribution of the parent-disc galaxies while the blue and orange solid lines correspond to the galaxies with strong and weak bars in the parent-disc galaxies, respectively. The vertical solid lines indicate the median values in the BT and DT distributions for the parent disc galaxies, the vertical … view at source ↗
Figure 2
Figure 2. An example of a strongly barred and an unbarred galaxy with the same stellar mass (∼ 1010.7M ) in the TNG100 simulation. Top left: In the upper panel, the A2 profile of the Fourier decomposition of the face-on stellar surface density (green curve) and its cumulative distribution, A2,tot. In the lower panel, the corresponding profiles of the phase, as defined in equations 2 and 3. The vertical solid line indicates th… view at source ↗
Figure 3
Figure 3. The evolution of the strong bar shown in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Representation of the construction of the control sam￾ple of unbarred galaxies at z 0. For each z 0 strongly barred galaxy with a stellar mass M, we select a sample of unbarred galaxies with a stellar mass between M −∆M and M ∆M at z 0. We compare the properties of the…
Figure 5
Figure 5. Figure 5: Top panel: The bar strength distribution for the par￾ent disc-dominated galaxies in TNG100. Bottom panel: The dis￾tribution of the bar lengths, rbar (solid lines), for weak and strong bar samples. rbar 1 − 6 kpc). Interestingly, Algorry et al. (2017), using a barred sa…
Figure 6
Figure 6. Figure 6: Left panel: The bar fraction as a function of stellar mass for the barred galaxy sample in the TNG100 simulation, and separated into weak and strong bars. The markers show observational estimates from Cervantes-Sodi et al. (2015) (CS15, triangles), who use late-type ga…
Figure 7
Figure 7. Figure 7: The bar lengths in TNG100 barred galaxies as a func￾tion of r90,∗ where r90,∗ is the radius containing 90 per cent of the total light. Symbols are colour-coded according to the stellar mass of the host galaxy. Stars represent weak bars and triangles strong bars. Observ…
Figure 8
Figure 8. Figure 8: Left panel: The sSFR as a function of stellar mass for TNG100 galaxies. The solid lines and markers represent the median relation for weak, strong and no bars, as specified in the legend. Scattered symbols correspond to each galaxy. Grey contours and the diffuse densit…
Figure 9
Figure 9. Figure 9: The evolution of a strongly barred galaxy with a final stellar mass ∼ 1010.7M at z 0 in the TNG100 simulation. Each row shows the time evolution of different galaxy components via face-on surface density maps of stars (top panels), gas (middle panels) and instantaneous…
Figure 10
Figure 10. Figure 10: The evolution of an unbarred galaxy at z 0 in the TNG100 simulation. The stellar mass at z 0 is similar to the one from strongly barred galaxy shown in [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Top and Middle panels: The evolution of the bar properties in strongly barred and unbarred galaxies at z 0 in the TNG100 simulation, in terms of tnorm, as defined in eq. 4, in three bins of stellar mass as labelled in each column. On average, the bar strength and leng…
Figure 12
Figure 12. Figure 12: The evolution of properties of unbarred and strongly barred galaxies at z 0 in the TNG100 simulations, in three bins of stellar mass as indicated in each column. Top row: The evolution of BT and DT of strongly barred (solid lines) and unbarred galaxies (dashed lines) …
Figure 13
Figure 13. Figure 13: The median evolution of the normalised star formation efficiency, SFEdrSFEgal, inside (blue lines) and outside the bar (yellow lines), in strongly barred (solid lines) and unbarred galaxies (dashed lines) at z 0 , in three bins of stellar mass as indicated in each pan…
Figure 14
Figure 14. Figure 14: Mergers histories of z 0 strongly barred (blue lines) and unbarred galaxies (green lines). The left part of the plot shows the fraction of galaxies that experience at least one major merger at times prior to bar formation (−0.25 < tnorm < 0, left histograms) and at ti…
Figure 15
Figure 15. Figure 15: The evolution of SMBHs. From left to right increases with stellar mass. First row: The median growth of SMBHs as a function of time hosted by z 0 strongly barred (solid lines) and z 0 unbarred galaxies (dashed lines) in the TNG100 simulation. Second row: The median en…

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Works this paper leans on

96 extracted references · 37 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    G., Navarro J

    Abadi M. G., Navarro J. F., Steinmetz M., Eke V. R., 2003, ApJ, 597, 21

    2003

  3. [3]

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

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

    2017

  4. [4]

    Athanassoula E., 1992, MNRAS, 259, 328

    1992

  5. [5]

    Athanassoula E., 1992b, MNRAS, 259, 345

  6. [6]

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

    2002

  7. [7]

    2003, MNRAS, 341, 1179 (A03)

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

    2003

  8. [8]

    C., Dehnen W., 2005, MNRAS, 363, 496

    Athanassoula E., Lambert J. C., Dehnen W., 2005, MNRAS, 363, 496

    2005

Show all 96 references
  1. [9]

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

    2013

  2. [10]

    D., Jogee S., Marinova I., 2008, ApJ, 675, 1194

    Barazza F. D., Jogee S., Marinova I., 2008, ApJ, 675, 1194

    2008

  3. [11]

    H., 2007, ApJ, 666, 189

    Berentzen I., Shlosman I., Martinez-Valpuesta I., Heller C. H., 2007, ApJ, 666, 189

    2007

  4. [12]

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

    2016

  5. [13]

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

    2015

  6. [14]

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

    2017

  7. [15]

    Cheung E., et al., 2013, ApJ, 779, 162

    2013

  8. [16]

    H., 1981, A&A, 96, 164

    Combes F., Sanders R. H., 1981, A&A, 96, 164

    1981

  9. [17]

    Consolandi G., 2016, A&A, 595, 67

    2016

  10. [18]

    P., Carollo C

    Debattista V. P., Carollo C. M., Mayer L., Moore B., 2004, ApJ, 604, L93

    2004

  11. [19]

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

    2005

  12. [20]

    E., Martig M., James P

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

    2019 arXiv

  13. [21]

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

    2019

  14. [22]

    Englmaier P., Shlosman I., 2000, ApJ, 528, 677

    2000

  15. [23]

    Erwin P., 2018, MNRAS, 474, 5372

    2018

  16. [24]

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

    2015

  17. [25]

    Fragkoudi F., Athanassoula E., Bosma A., 2016, MNRAS, 462, L41

    2016

  18. [26]

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

    2009

  19. [27]

    A., 2009, MNRAS, 393, 1531

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

    2009

  20. [28]

    A., 2011, MNRAS, 415, 3308

    Gadotti D. A., 2011, MNRAS, 415, 3308

    2011

  21. [29]

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

    2015

  22. [30]

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

    2014

  23. [31]

    M., Hernquist, L., et al., 2015, , 804, L40

    Genel, S., Fall, S. M., Hernquist, L., et al., 2015, , 804, L40

    2015

  24. [32]

    Genel S., et al., 2018, MNRAS, 474, 3976

    2018

  25. [33]

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

    2011

  26. [34]

    T., 2019, arXiv, arXiv:1907.06910

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

    2019 arXiv

  27. [35]

    Huertas-Company M., et al., 2019, arXiv, arXiv:1903.07625

    2019 arXiv

  28. [36]

    Izquierdo-Villalba D., Bonoli S., Spinoso D., Rosas-Guevara Y., Henriques B. M. B., Hernandez-Monteagudo C., 2019, arXiv e-prints, arXiv:1901.10490

    2019 arXiv

  29. [37]

    A., Percival S

    James P. A., Percival S. M., 2016, MNRAS, 457, 917

    2016

  30. [38]

    Jogee S., Scoville N., Kenney J. D. P., 2005, ApJ, 630, 837

    2005

  31. [39]

    S., Chung H., Lee G.-H., Park C., Cervantes Sodi B., Kim S

    Kim E., Hwang H. S., Chung H., Lee G.-H., Park C., Cervantes Sodi B., Kim S. S., 2017, ApJ, 845, 93

    2017

  32. [40]

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

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

    2018

  33. [41]

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

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

    1996

  34. [42]

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

    2012

  35. [43]

    D., Combes F., 2018, A&A, 609, A60

    Khoperskov S., Haywood M., Di Matteo P., Lehnert M. D., Combes F., 2018, A&A, 609, A60

    2018

  36. [44]

    Laurikainen E., Salo H., Buta R., 2004, ApJ, 607, 103

    2004

  37. [45]

    R., Shapley A

    Law D. R., Shapley A. E., Steidel C. C., Reddy N. A., Christensen C. R., Erb D. K., 2012, Natur, 487, 338

    2012

  38. [46]

    J., Sparke L

    Maciejewski W., Teuben P. J., Sparke L. S., Stone J. M., 2002, MNRAS, 329, 502

    2002

  39. [47]

    Maciejewski W., 2004, MNRAS, 354, 892

    2004

  40. [48]

    Marinacci F., Pakmor R., Springel V., 2014, MNRAS, 437, 1750

    2014

  41. [49]

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

    2018

  42. [50]

    Martinez-Valpuesta I., Shlosman I., Heller C., 2006, ApJ, 637, 214

    2006

  43. [51]

    Martinez-Valpuesta I., Aguerri J., Gonzalez-Garcia C., 2016, Galaxies, 4, 7

    2016

  44. [52]

    L., et al., 2010, MNRAS, 405, 783

    Masters K. L., et al., 2010, MNRAS, 405, 783

    2010

  45. [53]

    L., et al., 2012, MNRAS, 424, 2180

    Masters K. L., et al., 2012, MNRAS, 424, 2180

    2012

  46. [54]

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

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

    2011

  47. [55]

    Mayer L., Wadsley J., 2004, MNRAS, 347, 277

    2004

  48. [56]

    Mendez-Abreu J., Sanchez-Janssen R., Aguerri J. A. L., Corsini E. M., Zarattini S., 2012, ApJ, 761, L6

    2012

  49. [57]

    A., 1994, ApJ, 425, 551

    Merritt D., Sellwood J. A., 1994, ApJ, 425, 551

    1994

  50. [58]

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

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

    2018

  51. [59]

    B., Abraham R

    Nair P. B., Abraham R. G., 2010, ApJ, 714, L260

    2010

  52. [60]

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

    2015

  53. [61]

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

  54. [62]

    Nelson D., et al., 2019, arXiv, arXiv:1902.05554

    2019 arXiv

  55. [63]

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

  56. [64]

    A., Sellwood J

    Norman C. A., Sellwood J. A., Hasan H., 1996, ApJ, 462, 114

    1996

  57. [65]

    Planck Collaboration 2016, A& A, 594, A13

    2016

  58. [66]

    Perez I., Sanchez-Blazquez P., Zurita A., 2009, A&A, 495, 775

    2009

  59. [67]

    P., Peebles P

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

    1973

  60. [68]

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

  61. [69]

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

  62. [70]

    Pillepich A., et al., 2019, arXiv e-prints, arXiv:1902.05553

    2019 arXiv

  63. [71]

    L., 2019, MNRAS, 483, 2721

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

    2019

  64. [72]

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

    2015

  65. [73]

    Rodriguez-Gomez V., et al., 2019, MNRAS, 483, 4140

    2019

  66. [74]

    J., Garc \' a-G \'o mez C., 2007, A&A, 472, 63

    Romero-G \'o mez M., Athanassoula E., Masdemont J. J., Garc \' a-G \'o mez C., 2007, A&A, 472, 63

    2007

  67. [75]

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

    2012

  68. [76]

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

    2015

  69. [77]

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

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

    1993

  70. [78]

    A., Merritt D., 1994, ApJ, 425, 530

    Sellwood J. A., Merritt D., 1994, ApJ, 425, 530

    1994

  71. [79]

    Sheth K., et al., 2008, ApJ, 675, 1141

    2008

  72. [80]

    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

  73. [81]

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

    2015

  74. [82]

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

    2017

  75. [83]

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

    2001

  76. [84]

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

    2003

  77. [85]

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

    2005

  78. [86]

    Springel V., 2010, MNRAS, 401, 791

    2010

  79. [87]

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

    2018

  80. [88]

    Tacchella S., et al., 2019, arXiv e-prints, arXiv:1904.12860

    2019 arXiv

  81. [89]

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

    2013

  82. [90]

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

  83. [91]

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

  84. [92]

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

    2017

  85. [93]

    Weinberger R., et al., 2018, MNRAS, 479, 4056

    2018

  86. [94]

    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

  87. [95]

    R., Mayer L., Haardt F., Shen S., Bonoli S., 2018b, MNRAS, 479, 5214

    Zana T., Dotti M., Capelo P. R., Mayer L., Haardt F., Shen S., Bonoli S., 2018b, MNRAS, 479, 5214

  88. [96]

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

    2019

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.