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arxiv: 2604.24836 · v1 · submitted 2026-04-27 · 🌌 astro-ph.GA

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The Thermodynamic and Kinematic Evolution of Circumgalactic Gas around z=1 in the IllustrisTNG model

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Pith reviewed 2026-05-08 02:27 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords circumgalactic mediumgalactic feedbackmultiphase gasthermodynamic evolutionkinematic evolutiontracer particlescosmological simulationion phases
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The pith

Circumgalactic gas evolves into distinct cold and warm-hot phases within 500 million years regardless of initial position.

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

The paper follows individual gas parcels around galaxies at redshift one by tracing their paths forward in time. It shows that the gas mixes rapidly between different temperatures and densities. Within about 500 million years the gas settles into cold material close to the galaxy and warmer material farther out, no matter where it began. Feedback from the galaxy itself drives most of this change by heating and expelling gas that has cooled and fallen inward. The result implies that the region around galaxies is continually reset on short timescales.

Core claim

Gas in the circumgalactic medium mixes between different temperature and density phases quickly and within approximately 500 million years evolves into distinct cold (around 10,000 K) and warm-hot (around 300,000 K) phases at small and large distances from the galaxy, respectively, regardless of its initial distance from the galaxy center. This is largely driven by feedback from the galaxy, which heats and ejects cold gas that had previously cooled and accreted toward and occasionally into the galaxy from the outer regions.

What carries the argument

Monte Carlo tracer particles that follow the gas parcels in a Lagrangian manner to record changes in their temperature, density, and velocity over time.

If this is right

  • Autocorrelations of kinematic quantities take approximately 400 million years to fully decorrelate from initial values.
  • Gas associated with O VI remains in its narrow temperature-density range hundreds of millions of years longer than gas associated with Mg II or C IV.
  • Observations of different ions can therefore sample circumgalactic gas at different points in its evolutionary cycle even when the gas occupies the same location.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Surveys that measure multiple ions at once may be able to separate recently processed gas from older gas in the same halo.
  • Galaxy formation models will need to incorporate this rapid phase separation when calculating how much fresh gas reaches the central galaxy.
  • The fact that final phase depends little on starting radius suggests feedback mixes the entire circumgalactic region on timescales shorter than a typical orbital period.

Load-bearing premise

The simulation's treatment of gas cooling, mixing, and feedback from the galaxy accurately reflects real physical processes.

What would settle it

Observations that find kinematic properties of circumgalactic gas remain correlated with their starting conditions for much longer than 400 million years, or that show no clear separation into cold inner and warm-hot outer phases, would falsify the central result.

Figures

Figures reproduced from arXiv: 2604.24836 by Daniel DeFelippis, Greg L. Bryan, Shy Genel.

Figure 1
Figure 1. Figure 1: The instantaneous (top) and cumulative (bot￾tom) percentage of the z = 1 CGM that ever enters the galaxy as a function of time for all six halos in our sample. The thick purple line is halo5819 (see view at source ↗
Figure 2
Figure 2. Figure 2: The temperature distribution of CGM gas in halo5819 with an initial radius between 27.5 and 32.5 kpc (left), between 67.5 and 72.5 kpc (middle), and between 107.5 and 112.5 kpc (right) at z = 1 as a function of time. All distances are in physical coordinates. In the top row, the color shows the number of tracers, which is proportional to the mass, in each temperature bin at any given time. The second, thir… view at source ↗
Figure 3
Figure 3. Figure 3: Entropy (left column), pressure (middle column), and density (right column) distributions of CGM gas from the same halo as view at source ↗
Figure 4
Figure 4. Figure 4: Temperature distributions of CGM gas at an initial radius of 30 ± 2.5 kpc as defined in view at source ↗
Figure 5
Figure 5. Figure 5: The change in radius of tracers in the z = 1 CGM of halo5819 as a function of their initial radial velocities. Tracers are separated by whether their initial temperatures are above (top row) or below (bottom row) 3×104 K. Each column shows a different amount of time after z = 1. By design, the tracers cannot move horizontally across panels. The orange lines show the change in radius after the associated el… view at source ↗
Figure 6
Figure 6. Figure 6: The mean autocorrelations of the radial veloc￾ities and specific angular momenta of z = 1 CGM tracers from halo5819 starting at three different initial radii. Trac￾ers are separated by whether their initial temperatures are above (top panel) or below (bottom panel) 3 × 104 K. The 1σ spread for j at r0 = 30 kpc is shown in both panels and is comparable to all other radii and to that of vr. clear path of tra… view at source ↗
Figure 7
Figure 7. Figure 7: The fraction of all z = 1 CGM gas from halo5819 associated with three different ions as a function of time, separated by being initially associated with Mgii (left), Civ (middle), and Ovi (right). By construction, these fractions sum to 1 at the initial time because the gas is initially selected to be associated with one ion, but at later times they do not necessarily sum to 1 because some gas evolves outs… view at source ↗
Figure 8
Figure 8. Figure 8: A complement to view at source ↗
Figure 9
Figure 9. Figure 9: The location of CGM gas from halo5819 with an initial radius of ≈ 30 kpc (top), ≈ 70 kpc (middle), and ≈ 110 kpc (bottom) at z = 1 as a function of time separated by the observed phase of that gas. The coldest and densest gas (Mgii–like; left panel) is always seen closer to the galaxy, while warmer and more diffuse phases (Civ–like and Ovi–like; middle and right panels, respectively) appear as gas moves ou… view at source ↗
Figure 10
Figure 10. Figure 10: Median entropy radial profiles of the warm–hot (solid lines) and cold (dashed lines) phases of the CGM for our sample of six halos at z = 1. The 1σ spread for halo5819 is shown and is comparable to the other halos. The warm– hot phase has a relatively flat entropy profile in the CGM of all halos beyond ≈ 40 kpc, while the cold phase’s profile varies by ∼ 1 dex in the same radial range. and occur over long… view at source ↗
Figure 11
Figure 11. Figure 11: Temperature (top) and entropy (bottom) evolution of three tracers from halo5819 colored by their radius and pressure, demonstrating the variability in the behavior of individual tracers compared to the population studied in the rest of the paper. REFERENCES Anand, A., Nelson, D., & Kauffmann, G. 2021, MNRAS, 504, 65, doi: 10.1093/mnras/stab871 Armillotta, L., Fraternali, F., & Marinacci, F. 2016, MNRAS, 4… view at source ↗
read the original abstract

The circumgalactic medium (CGM) is known to contain multiphase gas in various stages of evolution and interaction with the galaxy. In order to characterize its detailed behavior on short timescales, we use a subregion of the TNG100 cosmological simulation to study the evolution of the $z=1$ CGM around six galaxies in $10^{11.5}-10^{12}$ $M_{\odot}$ halos at a high time cadence of $\approx2$ Myr. We use Monte Carlo tracer particles to follow this CGM gas forward in time in a Lagrangian way and determine how its thermodynamic and kinematic properties change. We find that CGM gas mixes between different temperature and density phases quickly and within $\approx500$ Myr evolves into distinct cold ($T\approx10^4$ $\rm{K}$) and warm-hot ($T\approx10^{5.5}$ $\rm{K}$) phases at small and large distances from the galaxy, respectively, regardless of its initial ($z=1$) halo-centric radius. This is largely driven by feedback from the galaxy, which heats and ejects cold gas that had previously cooled and accreted toward and occasionally into the galaxy from the outer CGM. We see signatures of this process in autocorrelations of kinematic quantities, which take $\approx400$ Myr to fully decorrelate from their initial values, suggesting a timescale over which feedback disrupts and reprocesses CGM gas. We also examine gas in narrow temperature and density ranges associated with commonly observed ions and find that gas that is O VI-like stays in its phase for hundreds of Myr longer than gas that is Mg II-like or C IV-like, suggesting that CGM observations of different species could probe gas in different evolutionary states, even if the gas is cospatial.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript analyzes the short-timescale thermodynamic and kinematic evolution of CGM gas at z=1 using Monte Carlo tracer particles in a high-cadence (~2 Myr) subregion of the TNG100 simulation. It follows gas around six galaxies in 10^{11.5}-10^{12} M_⊙ halos and reports that, independent of initial halo-centric radius, the gas mixes rapidly and separates within ~500 Myr into cold (T≈10^4 K) phases at small radii and warm-hot (T≈10^{5.5} K) phases at large radii, driven by galactic feedback that heats and ejects previously accreted cold gas. Kinematic autocorrelations decorrelate on ~400 Myr timescales, and ion-associated phases (O VI-like) persist longer than Mg II-like or C IV-like phases.

Significance. If the central results hold, the work is significant for constraining the multiphase structure and dynamical processing of the CGM on observationally relevant timescales. The Lagrangian high-cadence tracer approach is a strength, enabling direct measurement of mixing, ejection, and phase persistence without circularity in the reported quantities. The finding that different ions probe distinct evolutionary states even when cospatial has direct implications for absorption-line studies.

major comments (3)
  1. [Methods and Results] The central claim that CGM gas reaches distinct cold/warm-hot phases within ≈500 Myr independent of initial radius rests on the specific subgrid cooling, star formation, wind, and AGN prescriptions plus the Monte Carlo tracer advection in TNG. No resolution variations, feedback-parameter sweeps, or tracer-convergence tests at the ~2 Myr cadence are reported, so it is unclear whether the decorrelation and phase-separation timescales are robust or set by numerical mixing and wind-launching efficiency.
  2. [Results] The sample comprises only six galaxies in a narrow halo-mass range (10^{11.5}-10^{12} M_⊙). The assertion that the radial phase separation occurs 'regardless of its initial (z=1) halo-centric radius' therefore lacks statistical power; a larger sample or explicit variation across halo mass and environment is needed to support the generality of the result within the model.
  3. [Results] The phase definitions (cold T≈10^4 K, warm-hot T≈10^{5.5} K, and narrow bins for O VI-, Mg II-, and C IV-like gas) are load-bearing for the ion-persistence and mixing claims, yet no sensitivity tests to bin boundaries or to the precise temperature-density cuts are provided.
minor comments (2)
  1. [Abstract and Results] The abstract states that autocorrelations 'take ≈400 Myr to fully decorrelate,' but the precise definition of 'fully decorrelate' (e.g., dropping below a threshold or reaching noise level) and the exact kinematic quantities used should be stated explicitly in the main text.
  2. [Figures] Figure captions and axis labels for the autocorrelation functions and phase-evolution plots should include the number of tracer particles per galaxy and the precise time sampling to allow readers to assess numerical robustness.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their positive evaluation of the work's significance and the Lagrangian tracer approach. We address each major comment below with clarifications and indicate revisions to the manuscript where they strengthen the presentation without altering the core findings.

read point-by-point responses
  1. Referee: [Methods and Results] The central claim that CGM gas reaches distinct cold/warm-hot phases within ≈500 Myr independent of initial radius rests on the specific subgrid cooling, star formation, wind, and AGN prescriptions plus the Monte Carlo tracer advection in TNG. No resolution variations, feedback-parameter sweeps, or tracer-convergence tests at the ~2 Myr cadence are reported, so it is unclear whether the decorrelation and phase-separation timescales are robust or set by numerical mixing and wind-launching efficiency.

    Authors: We agree that the reported timescales are specific to the IllustrisTNG subgrid model and the Monte Carlo tracer implementation in the high-cadence TNG100 subregion. The ~2 Myr cadence was selected to resolve short-term evolution while the tracer particles minimize artificial mixing compared to Eulerian methods. Explicit resolution or feedback-parameter variations were not performed because the study utilizes an existing high-output-frequency subregion rather than new runs. In the revised manuscript we will add an expanded discussion of model limitations, potential numerical influences on mixing rates, and the fact that the results characterize behavior within this widely used simulation framework. revision: partial

  2. Referee: [Results] The sample comprises only six galaxies in a narrow halo-mass range (10^{11.5}-10^{12} M_⊙). The assertion that the radial phase separation occurs 'regardless of its initial (z=1) halo-centric radius' therefore lacks statistical power; a larger sample or explicit variation across halo mass and environment is needed to support the generality of the result within the model.

    Authors: The sample of six galaxies in the stated mass range is indeed limited and was chosen to permit the computationally expensive high time cadence required for Lagrangian tracking of individual gas parcels. The statement that phase separation occurs independent of initial radius refers to the consistent behavior seen across the tracked particles in these systems. We will revise the text to qualify this claim explicitly as applying to the galaxies studied here, to avoid implying broader statistical generality, and to note that extending the sample would require additional high-cadence simulation outputs. revision: yes

  3. Referee: [Results] The phase definitions (cold T≈10^4 K, warm-hot T≈10^{5.5} K, and narrow bins for O VI-, Mg II-, and C IV-like gas) are load-bearing for the ion-persistence and mixing claims, yet no sensitivity tests to bin boundaries or to the precise temperature-density cuts are provided.

    Authors: The temperature and density ranges follow standard literature conventions for multiphase CGM gas and common ions. While we did not include explicit sensitivity tests in the original analysis, the main conclusions on rapid mixing and differential ion-phase lifetimes arise from the overall thermodynamic trajectories rather than the precise edges of the bins. In the revised manuscript we will add a short justification of the adopted cuts together with a brief demonstration that the reported timescales are insensitive to modest shifts in the boundaries. revision: partial

Circularity Check

0 steps flagged

No circularity: all results are direct measurements from simulation snapshots and tracer histories

full rationale

The paper analyzes CGM gas evolution by following Monte Carlo tracers forward in time within the existing TNG100 simulation at ~2 Myr cadence. Reported quantities (phase separation into cold/warm-hot components within ~500 Myr independent of initial radius, kinematic autocorrelation decorrelation on ~400 Myr, ion-specific phase persistence times) are computed directly from the snapshot data and tracer particle histories. No equations, fitted parameters, or self-citations are used to define or predict these outputs in terms of themselves; the analysis contains no self-definitional steps, no 'predictions' that reduce to input fits, and no load-bearing uniqueness theorems or ansatzes imported from prior author work. The central claims are empirical measurements within the model, not tautological derivations.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The analysis depends on the fidelity of the IllustrisTNG subgrid feedback and cooling modules plus the accuracy of Monte Carlo tracers for Lagrangian gas tracking; no new entities are postulated.

free parameters (2)
  • halo mass range
    Galaxies selected in 10^11.5-10^12 M_sun halos at z=1
  • time cadence = 2 Myr
    High-cadence output of approximately 2 Myr chosen for short-timescale tracking
axioms (1)
  • domain assumption IllustrisTNG subgrid physics and numerical resolution sufficiently capture CGM mixing, cooling, and feedback on Myr timescales
    Invoked implicitly when interpreting tracer evolution as physical rather than numerical.

pith-pipeline@v0.9.0 · 5643 in / 1552 out tokens · 70487 ms · 2026-05-08T02:27:32.085874+00:00 · methodology

discussion (0)

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Works this paper leans on

99 extracted references · 99 canonical work pages · 2 internal anchors

  1. [1]

    2021, MNRAS, 504, 65, doi: 10.1093/mnras/stab871

    Anand, A., Nelson, D., & Kauffmann, G. 2021, MNRAS, 504, 65, doi: 10.1093/mnras/stab871

  2. [2]

    2016, MNRAS, 462, 4157, doi: 10.1093/mnras/stw1930

    Armillotta, L., Fraternali, F., & Marinacci, F. 2016, MNRAS, 462, 4157, doi: 10.1093/mnras/stw1930

  3. [3]

    2023, MNRAS, 524, 4091, doi: 10.1093/mnras/stad2152

    Barbani, F., Pascale, R., Marinacci, F., et al. 2023, MNRAS, 524, 4091, doi: 10.1093/mnras/stad2152

  4. [4]

    S., Sharma, P., Dutta, A., & Nath, B

    Bisht, M. S., Sharma, P., Dutta, A., & Nath, B. B. 2025, MNRAS, 542, 1573, doi: 10.1093/mnras/staf1319 Bouch´ e, N., Hohensee, W., Vargas, R., et al. 2012, MNRAS, 426, 801, doi: 10.1111/j.1365-2966.2012.21114.x Bouch´ e, N. F., Wendt, M., Zabl, J., et al. 2025, A&A, 694, A67, doi: 10.1051/0004-6361/202451093

  5. [5]

    doi:10.1086/305262 , eprint =

    Bryan, G. L., & Norman, M. L. 1998, ApJ, 495, 80, doi: 10.1086/305262

  6. [6]

    A., Clegg A

    Chevalier, R. A., & Clegg, A. W. 1985, Nature, 317, 44, doi: 10.1038/317044a0

  7. [7]

    , keywords =

    Choudhury, P. P., Sharma, P., & Quataert, E. 2019, MNRAS, 488, 3195, doi: 10.1093/mnras/stz1857

  8. [8]

    J., Rudie, G

    Cooper, T. J., Rudie, G. C., Chen, H.-W., et al. 2021, MNRAS, 508, 4359, doi: 10.1093/mnras/stab2869

  9. [9]

    2021, ApJ, 918, 83, doi: 10.3847/1538-4357/ac0e8e

    Das, S., Mathur, S., Gupta, A., & Krongold, Y. 2021, ApJ, 918, 83, doi: 10.3847/1538-4357/ac0e8e

  10. [10]

    2019, ApJL, 882, L23, doi: 10.3847/2041-8213/ab3b09

    Das, S., Mathur, S., Nicastro, F., & Krongold, Y. 2019, ApJL, 882, L23, doi: 10.3847/2041-8213/ab3b09 Dav´ e, R., Angl´ es-Alc´ azar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827, doi: 10.1093/mnras/stz937

  11. [11]

    F., Genel, S., et al

    DeFelippis, D., Bouch´ e, N. F., Genel, S., et al. 2021, ApJ, 923, 56, doi: 10.3847/1538-4357/ac2cbf

  12. [12]

    L., & Fall, S

    DeFelippis, D., Genel, S., Bryan, G. L., & Fall, S. M. 2017, ApJ, 841, 16, doi: 10.3847/1538-4357/aa6dfc

  13. [13]

    , keywords =

    DeFelippis, D., Genel, S., Bryan, G. L., et al. 2020, ApJ, 895, 17, doi: 10.3847/1538-4357/ab8a4a 20

  14. [14]

    , keywords =

    Dutta, A., Bisht, M. S., Sharma, P., et al. 2024, MNRAS, 531, 5117, doi: 10.1093/mnras/stae977

  15. [15]

    2022, MNRAS, 510, 3561, doi: 10.1093/mnras/stab3653

    Dutta, A., Sharma, P., & Nelson, D. 2022, MNRAS, 510, 3561, doi: 10.1093/mnras/stab3653

  16. [16]

    2020, MNRAS, 499, 5022, doi: 10.1093/mnras/staa3147

    Dutta, R., Fumagalli, M., Fossati, M., et al. 2020, MNRAS, 499, 5022, doi: 10.1093/mnras/staa3147

  17. [17]

    Faerman, Y., Sternberg, A., & McKee, C. F. 2017, ApJ, 835, 52, doi: 10.3847/1538-4357/835/1/52 —. 2020, ApJ, 893, 82, doi: 10.3847/1538-4357/ab7ffc Faucher-Gigu` ere, C.-A., & Oh, S. P. 2023, ARA&A, 61, 131, doi: 10.1146/annurev-astro-052920-125203

  18. [18]

    2017, MNRAS, 470, L39, doi: 10.1093/mnrasl/slx072

    Fielding, D., Quataert, E., Martizzi, D., & Faucher-Gigu` ere, C.-A. 2017a, MNRAS, 470, L39, doi: 10.1093/mnrasl/slx072

  19. [19]

    Fielding, D., Quataert, E., McCourt, M., & Thompson, T. A. 2017b, MNRAS, 466, 3810, doi: 10.1093/mnras/stw3326

  20. [20]

    B., & Bryan, G

    Fielding, D. B., & Bryan, G. L. 2022, ApJ, 924, 82, doi: 10.3847/1538-4357/ac2f41

  21. [21]

    and Tonnesen, Stephanie and DeFelippis, Daniel and Li, Miao and Su, Kung-Yi and Bryan, Greg L

    Fielding, D. B., Tonnesen, S., DeFelippis, D., et al. 2020, ApJ, 903, 32, doi: 10.3847/1538-4357/abbc6d

  22. [22]

    C., Emami, R., Somerville, R

    Forbes, J. C., Emami, R., Somerville, R. S., et al. 2023, ApJ, 948, 107, doi: 10.3847/1538-4357/acb53e

  23. [23]

    2013, MNRAS, 435, 1426, doi: 10.1093/mnras/stt1383

    Genel, S., Vogelsberger, M., Nelson, D., et al. 2013, MNRAS, 435, 1426, doi: 10.1093/mnras/stt1383

  24. [24]

    Ram Pressure Stripping in Clusters: Gravity Can Bind the

    Ghosh, R., Dutta, A., & Sharma, P. 2024, MNRAS, 531, 3445, doi: 10.1093/mnras/stae1345

  25. [25]

    Grand, R. J. J., van de Voort, F., Zjupa, J., et al. 2019, MNRAS, 490, 4786, doi: 10.1093/mnras/stz2928

  26. [26]

    Gronke, M., & Oh, S. P. 2018, MNRAS, 480, L111, doi: 10.1093/mnrasl/sly131

  27. [27]

    2020, MNRAS, 494, 3581, doi: 10.1093/mnras/staa902

    Hafen, Z., Faucher-Gigu` ere, C.-A., Angl´ es-Alc´ azar, D., et al. 2020, MNRAS, 494, 3581, doi: 10.1093/mnras/staa902

  28. [28]

    Quasars Probing Galaxies. I. Signatures of Gas Accretion at Redshift z ≈ 0.2∗ †

    Ho, S. H., Martin, C. L., Kacprzak, G. G., & Churchill, C. W. 2017, ApJ, 835, 267, doi: 10.3847/1538-4357/835/2/267

  29. [29]

    H., Martin, C

    Ho, S. H., Martin, C. L., & Turner, M. L. 2019, ApJ, 875, 54, doi: 10.3847/1538-4357/ab0ec2

  30. [30]

    J., Miller, M

    Hodges-Kluck, E. J., Miller, M. J., & Bregman, J. N. 2016, ApJ, 822, 21, doi: 10.3847/0004-637X/822/1/21

  31. [31]

    2022, MNRAS, 509, 6091, doi: 10.1093/mnras/stab3363

    Huang, S., Katz, N., Cottle, J., et al. 2022, MNRAS, 509, 6091, doi: 10.1093/mnras/stab3363

  32. [32]

    Fielding, D. B. 2024, ApJ, 972, 148, doi: 10.3847/1538-4357/ad5965

  33. [33]

    B., Smith, B

    Hummels, C. B., Smith, B. D., Hopkins, P. F., et al. 2019, ApJ, 882, 156, doi: 10.3847/1538-4357/ab378f

  34. [34]

    Hunter, J. D. 2007, Computing in Science Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

  35. [35]
  36. [36]

    W., Kruijssen, J

    Keller, B. W., Kruijssen, J. M. D., & Wadsley, J. W. 2020, MNRAS, 493, 2149, doi: 10.1093/mnras/staa380

  37. [37]

    J., Jenkins, A., Deason, A., et al

    Kelly, A. J., Jenkins, A., Deason, A., et al. 2022, MNRAS, 514, 3113, doi: 10.1093/mnras/stac1019

  38. [38]

    Kim, C.-G., Kim, J.-G., Gong, M., & Ostriker, E. C. 2023, ApJ, 946, 3, doi: 10.3847/1538-4357/acbd3a

  39. [39]

    Kwak, K., & Shelton, R. L. 2010, ApJ, 719, 523, doi: 10.1088/0004-637X/719/1/523

  40. [40]

    2020, ApJ, 897, 97, doi: 10.3847/1538-4357/ab989a

    Lan, T.-W. 2020, ApJ, 897, 97, doi: 10.3847/1538-4357/ab989a

  41. [41]

    J., & Remming, I

    Liang, C. J., & Remming, I. 2020, MNRAS, 491, 5056, doi: 10.1093/mnras/stz3403

  42. [42]

    L., Li, Y., Li, M., & Fielding, D

    Lochhaas, C., Bryan, G. L., Li, Y., Li, M., & Fielding, D. 2020, MNRAS, 493, 1461, doi: 10.1093/mnras/staa358

  43. [43]

    S., et al

    Lochhaas, C., Tumlinson, J., Peeples, M. S., et al. 2023, ApJ, 948, 43, doi: 10.3847/1538-4357/acbb06

  44. [44]

    V., Melchior, A.-L., & Zolotukhin, I

    Marinacci, F., Binney, J., Fraternali, F., et al. 2010, MNRAS, 404, 1464, doi: 10.1111/j.1365-2966.2010.16352.x

  45. [45]

    2012, MNRAS, 420, 1825, doi: 10.1111/j.1365-2966.2011.19805.x

    Marinacci, F., Fraternali, F., Nipoti, C., et al. 2011, MNRAS, 415, 1534, doi: 10.1111/j.1365-2966.2011.18810.x

  46. [46]

    2019, MNRAS, 489, 4233, doi: 10.1093/mnras/stz2391

    Springel, V. 2019, MNRAS, 489, 4233, doi: 10.1093/mnras/stz2391

  47. [47]

    2018, MNRAS, 480, 5113, doi: 10.1093/mnras/sty2206

    Marinacci, F., Vogelsberger, M., Pakmor, R., et al. 2018, MNRAS, 480, 5113, doi: 10.1093/mnras/sty2206

  48. [48]

    2024, MNRAS, 527, 5093, doi: 10.1093/mnras/stad3497

    Gupta, A. 2024, MNRAS, 527, 5093, doi: 10.1093/mnras/stad3497

  49. [49]

    McCourt, M., Sharma, P., Quataert, E., & Parrish, I. J. 2012, MNRAS, 419, 3319, doi: 10.1111/j.1365-2966.2011.19972.x

  50. [50]

    P., Pillepich, A., Springel, V., et al

    Naiman, J. P., Pillepich, A., Springel, V., et al. 2018, MNRAS, 477, 1206, doi: 10.1093/mnras/sty618

  51. [51]

    First results from the IllustrisTNG simulations: the galaxy color bimodality

    Nelson, D., Pillepich, A., Springel, V., et al. 2018, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040 —. 2019, MNRAS, 490, 3234, doi: 10.1093/mnras/stz2306

  52. [52]

    2020, Monthly Notices of the Royal Astronomical Society, 498, 2391, doi: 10.1093/mnras/staa2419

    Nelson, D., Sharma, P., Pillepich, A., et al. 2020, MNRAS, 498, 2391, doi: 10.1093/mnras/staa2419

  53. [53]

    M., Churchill, C

    Nielsen, N. M., Churchill, C. W., & Kacprzak, G. G. 2013a, ApJ, 776, 115, doi: 10.1088/0004-637X/776/2/115

  54. [54]

    Murphy, M. T. 2013b, ApJ, 776, 114, doi: 10.1088/0004-637X/776/2/114

  55. [55]

    M., Churchill, C

    Nielsen, N. M., Churchill, C. W., Kacprzak, G. G., Murphy, M. T., & Evans, J. L. 2015, ApJ, 812, 83, doi: 10.1088/0004-637X/812/1/83 21

  56. [56]

    Oppenheimer, B. D. 2018, MNRAS, 480, 2963, doi: 10.1093/mnras/sty1918

  57. [57]

    D., Segers, M., Schaye, J., Richings, A

    Oppenheimer, B. D., Segers, M., Schaye, J., Richings, A. J., & Crain, R. A. 2018, MNRAS, 474, 4740, doi: 10.1093/mnras/stx2967

  58. [58]

    B., Bryan, G

    Pandya, V., Fielding, D. B., Bryan, G. L., et al. 2023, ApJ, 956, 118, doi: 10.3847/1538-4357/acf3ea

  59. [59]

    S., Corlies, L., Tumlinson, J., et al

    Peeples, M. S., Corlies, L., Tumlinson, J., et al. 2019, ApJ, 873, 129, doi: 10.3847/1538-4357/ab0654

  60. [60]

    Perez, F., & Granger, B. E. 2007, Computing in Science Engineering, 9, 21, doi: 10.1109/MCSE.2007.53 P´ eroux, C., Nelson, D., van de Voort, F., et al. 2020, MNRAS, 499, 2462, doi: 10.1093/mnras/staa2888

  61. [61]

    2018a, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112

    Pillepich, A., Nelson, D., Hernquist, L., et al. 2018a, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112

  62. [62]

    2018b, MNRAS, 473, 4077, doi: 10.1093/mnras/stx2656

    Pillepich, A., Springel, V., Nelson, D., et al. 2018b, MNRAS, 473, 4077, doi: 10.1093/mnras/stx2656 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2016, A&A, 594, A13, doi: 10.1051/0004-6361/201525830

  63. [63]

    , keywords =

    Prasad, D., Sharma, P., & Babul, A. 2015, ApJ, 811, 108, doi: 10.1088/0004-637X/811/2/108

  64. [64]

    C., et al

    Qu, Z., Chen, H.-W., Rudie, G. C., et al. 2022, MNRAS, 516, 4882, doi: 10.1093/mnras/stac2528 —. 2023, MNRAS, 524, 512, doi: 10.1093/mnras/stad1886

  65. [65]

    2024, MNRAS, 528, 3320, doi: 10.1093/mnras/stae237 Ramos-Mart´ ınez, M., G´ omez, G

    Ramesh, R., & Nelson, D. 2024, MNRAS, 528, 3320, doi: 10.1093/mnras/stae237

  66. [66]

    arXiv e-prints , keywords =

    Ramesh, R., Nelson, D., Fielding, D., & Br¨ uggen, M. 2026, arXiv e-prints, arXiv:2602.23416, doi: 10.48550/arXiv.2602.23416

  67. [67]

    2023, MNRAS, 522, 1843, doi: 10.1093/mnras/stad1104

    Rathjen, T.-E., Naab, T., Walch, S., et al. 2023, MNRAS, 522, 1843, doi: 10.1093/mnras/stad1104

  68. [68]

    , keywords =

    Rudie, G. C., Steidel, C. C., Trainor, R. F., et al. 2012, ApJ, 750, 67, doi: 10.1088/0004-637X/750/1/67

  69. [69]

    C., Nath, B

    Sarkar, K. C., Nath, B. B., Sharma, P., & Shchekinov, Y. 2015, MNRAS, 448, 328, doi: 10.1093/mnras/stu2760

  70. [70]

    A., Bower, R

    Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521, doi: 10.1093/mnras/stu2058

  71. [71]

    E., & Mao, S

    Schneider, E. E., & Mao, S. A. 2024, ApJ, 966, 37, doi: 10.3847/1538-4357/ad2e8a

  72. [72]

    E., Robertson B

    Schneider, E. E., & Robertson, B. E. 2018, ApJ, 860, 135, doi: 10.3847/1538-4357/aac329

  73. [73]

    F., Zabl, J., et al

    Schroetter, I., Bouch´ e, N. F., Zabl, J., et al. 2021, MNRAS, 506, 1355, doi: 10.1093/mnras/stab1447

  74. [74]

    2025, MNRAS, 541, 2471, doi: 10.1093/mnras/staf1066

    Shah, H., van de Voort, F., Seta, A., & Federrath, C. 2025, MNRAS, 541, 2471, doi: 10.1093/mnras/staf1066

  75. [75]

    2012, MNRAS, 423, 600, doi: 10.1111/j.1365-2966.2012.20901.x

    Sharma, P., McCourt, M., Parrish, I. J., & Quataert, E. 2012, MNRAS, 427, 1219, doi: 10.1111/j.1365-2966.2012.22050.x

  76. [76]

    2019, MNRAS, 484, 2632, doi: 10.1093/mnras/stz098

    Shimizu, I., Todoroki, K., Yajima, H., & Nagamine, K. 2019, MNRAS, 484, 2632, doi: 10.1093/mnras/stz098

  77. [77]

    C., Fielding, D

    Smith, M. C., Fielding, D. B., Bryan, G. L., et al. 2024, MNRAS, 527, 1216, doi: 10.1093/mnras/stad3168

  78. [78]

    Sobacchi, E., & Sormani, M. C. 2019, MNRAS, 486, 205, doi: 10.1093/mnras/stz792

  79. [79]

    Klessen, R. S. 2018, MNRAS, 481, 3370, doi: 10.1093/mnras/sty2500

  80. [80]

    2024, A&A, 691, A259, doi: 10.1051/0004-6361/202450544

    Sparre, M., Pfrommer, C., & Puchwein, E. 2024, A&A, 691, A259, doi: 10.1051/0004-6361/202450544

Showing first 80 references.