pith. sign in

arxiv: 2606.24735 · v1 · pith:UROOJ3YNnew · submitted 2026-06-23 · 🌌 astro-ph.GA

The dependence of Circumgalactic Medium properties on halo assembly histories in the IllustrisTNG simulations

Pith reviewed 2026-06-25 23:29 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords circumgalactic mediumhalo assembly historygalaxy formationCGM metallicityhalo formation timestellar massspecific star formation rateIllustrisTNG
0
0 comments X

The pith

Early-forming halos host galaxies with higher stellar mass and metallicity but lower CGM gas mass and star formation rates at z=0.

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

The paper examines how the assembly history of dark matter halos influences the properties of the circumgalactic medium and the galaxies they contain in the IllustrisTNG simulations. Halos are split into early- and late-forming populations according to the redshift at which they reach half their final mass. Across mass ranges from 10^10.5 to 10^12.5 solar masses, early-forming halos are found to contain more massive, metal-rich galaxies with reduced CGM gas and lower specific star formation rates today. The metallicity trend reverses in the highest mass bin, and the differences in gas content develop after the formation time, driven by differing merger activity rather than initial accretion.

Core claim

Halos classified as early-forming exhibit galaxies with higher stellar mass and metallicity, lower CGM gas mass, and lower sSFR at z=0. Early-forming halos below 10^12 solar masses show higher CGM gas-phase metallicities, but the trend reverses in the 10^12-12.5 bin. Fresh accretion into the CGM is insensitive to assembly history while late-forming systems experience more wet mergers. CGM gas masses are similar at the formation time, so the observed differences arise afterward. In lower-mass halos the cold CGM gas in late-forming systems carries higher specific angular momentum and greater rotational support.

What carries the argument

Halo formation time, defined as the epoch when a halo reaches half its z=0 mass, used to divide populations into early- and late-forming.

If this is right

  • Early-forming halos contain galaxies that have converted more gas into stars by z=0.
  • Late-forming halos retain larger CGM gas reservoirs and sustain higher specific star formation rates.
  • CGM metallicity is higher around early-forming halos below 10^12 solar masses but lower in the 10^12-12.5 range.
  • Differences in CGM gas properties emerge after the formation time through post-assembly processes.
  • Late-forming lower-mass halos show cold CGM gas with higher specific angular momentum and stronger rotational support.

Where Pith is reading between the lines

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

  • The mass-dependent reversal in CGM metallicity may indicate a shift in the balance between enrichment and dilution processes around group-scale halos.
  • Accounting for halo assembly history could reduce scatter when comparing CGM observations across different galaxy samples.
  • The kinematic differences suggest assembly history influences how angular momentum is delivered to the CGM in lower-mass systems.
  • Proxies for formation time in observations, such as galaxy color or concentration, could be tested against these simulation trends.

Load-bearing premise

That the half-mass formation time cleanly separates assembly-history effects from correlated factors such as environment or merger timing.

What would settle it

A direct measurement showing no difference in present-day stellar mass or CGM gas mass between early- and late-forming halos of the same mass would falsify the claimed dependence.

Figures

Figures reproduced from arXiv: 2606.24735 by Houzun Chen, Xi Kang, Yiyuan Zhang.

Figure 1
Figure 1. Figure 1: The median mass accretion histories for ‘early’ and ‘late’ halos. In each panel, the blue curve corresponds to halos with earlier assembly histories (‘early’), while orange curve represents those with later assembly histories (‘late’). Shaded regions indicate the 16-84th percentile range. More￾over, solid and dashed black lines show the specific accretion history for individual ‘early’ and ‘late’ halos, re… view at source ↗
Figure 2
Figure 2. Figure 2: The global baryonic properties of the halo. From left to right, the panels show the total stellar mass, the baryon fraction, and the specific star formation rate at z = 0, respectively. In each panel, the blue symbols mark the median values of halos with earlier assembly histories (‘early’), while orange symbols represent those with later assembly histories (‘late’). The shaded regions show the 16-84th per… view at source ↗
Figure 3
Figure 3. Figure 3: Total and phase-resolved mass of CGM gas. Clockwise from the top-left, the panels show the total CGM gas mass, and the masses of the cold, hot, and warm phases. Symbols follow the same conventions as in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The radial profiles of the density for the different gas phases, the curves represent the median value. From left to right, the panels show the cold, warm, and hot phases. Different colors indicate different halo masses and different line styles represent distinct formation time, specifically, solid lines correspond to early-forming halos, while dotted lines represent late-forming ones [PITH_FULL_IMAGE:fi… view at source ↗
Figure 5
Figure 5. Figure 5: The metallicity of the gas on galaxy and CGM scales. The left panel shows the metallicity in twice half stellar mass radius and the right panel presents the metallcity of the CGM gas. Symbols follow the same conventions as in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The radial distribution of the metallicity. The solid line shows the median of ‘early’ halos while the dotted line shows the median of ‘late’ halos. The shaded regions represent the 16-84th percentile. et al. 2025), or even expel part of it beyond the virial ra￾dius into the surrounding environment (J. Suresh et al. 2015). This could also help to explain the flatter metal￾licity profile at larger radii in … view at source ↗
Figure 7
Figure 7. Figure 7: The mass comes from different origins. From left to right, the panels present the mass comes from fresh accretion, intergalactic tranfer, and merger-driven infall. Symbols follow the same conventions as in [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The median radial profiles of the specific angular momentum and Bullock spin parameter for halos with different assembly histories. The upper row shows the specific angular momentum, together with the comparison between the predicted and measured early-to-late ratios, while the second row shows the Bullock spin parameter. In each row, the left panel corresponds to the cold phase and the right panel to the … view at source ↗
Figure 9
Figure 9. Figure 9: The mass flux at different radii. From left to right, panels show the inflow, outflow, and net mass flux, respectively. Positive values indicate outflows, while negative values indicate inflows. Colors and line styles follow the same convention as in [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: The gas mass in the CGM of each halo sample at its formation time. Symbols follow the same conventions as in [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 10
Figure 10. Figure 10: Different environments of ‘early’ and ‘late’ ha￾los in each mass bin. Top-panel: Normalized over-density. Symbols show the medians, the shaded regions indicate the 16-84th percentile ranges of the full distributions. Bottom– panel: Fraction of halos residing in different cosmic-web types (node, filament, wall, and void). Within each halo– mass bin, the left and right stacked bars correspond to the ‘early’… view at source ↗
Figure 13
Figure 13. Figure 13: The difference in CGM gas mass between z = 0 and the formation time. Blue and orange histograms denote ‘early’ and ‘late’ halos, respectively. The vertical dashed line separates net mass growth from mass loss. duction at fixed halo mass. This tends to reduce the degree of metal enrichment, especially in the inner halo. However, the larger CGM gas reservoir maintained by continued gas supply, together with… view at source ↗
read the original abstract

While halo mass is the dominant factor shaping the embedded galaxies, the properties of the circumgalactic medium (CGM) also depend on halo assembly history. To investigate this, we calculate the formation times for TNG50 halos with masses between $10^{10.5}$ and $10^{12.5} M_\odot$, classifying them into `early-' and `late-forming' populations. It is found that across all mass bins, early-formed halos generally host galaxies with higher stellar mass and higher metallicity, with lower CGM gas mass and lower specific star formation rate (sSFR) at $z\sim0$. For the CGM metallicity, `early' halos with masses below $10^{12}\mathrm{M_\odot}$ show systematically higher gas phase metallicities, whereas in the $10^{12-12.5}\mathrm{M_\odot}$ bin the trend reverses. When examining the origins of the CGM gas, it is found that fresh accretion is insensitive to assembly history, whereas the `late' galaxies experience more wet mergers. These differences in gas properties arise from processes after the formation time, given that the CGM gas masses show no significant differences at formation time. Finally, our analysis of CGM kinematics shows that for halos below $10^{12}\mathrm{M_\odot}$, the cold gas in late-forming halos carries higher specific angular momentum and simply has a higher degree of rotational support, while the same properties in the $10^{12-12.5}\mathrm{M_\odot}$ bin shows no significant dependence on assembly history.

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 / 1 minor

Summary. The paper uses the IllustrisTNG50 simulation to examine how circumgalactic medium (CGM) and galaxy properties depend on halo assembly history. Halos in the mass range 10^{10.5}–10^{12.5} M_⊙ are classified as early- or late-forming based on the redshift when they reach half their z=0 mass. The central claim is that early-forming halos host galaxies with higher stellar mass and metallicity but lower CGM gas mass and sSFR at z=0; CGM metallicity trends reverse above 10^{12} M_⊙. Differences are attributed to post-formation processes, with late-forming halos experiencing more wet mergers while fresh accretion is insensitive to assembly history. Kinematic differences in cold CGM gas are reported only below 10^{12} M_⊙.

Significance. If the reported trends are robust to environment and formation-time definition, the work would demonstrate that assembly history imprints on CGM properties beyond halo mass, with implications for models of gas accretion, mergers, and feedback. The paper receives credit for tracing CGM gas origins directly in the simulation (fresh accretion vs. wet mergers) and for noting that CGM mass differences are absent at formation time, which supports a post-formation origin.

major comments (3)
  1. [Methods / abstract] The classification into early- and late-forming populations (abstract and methods) relies on the half-mass formation time without reported tests of robustness to alternative definitions (e.g., 20% or 80% mass assembly time). This is load-bearing for the central claim that differences arise from assembly history.
  2. [Results / discussion of origins] No control or regression for local environment (density, tidal field, or neighbor count) is described despite the known correlation between formation time and environment within mass bins. This leaves open the possibility that reported CGM and metallicity differences (including the reversal at 10^{12}–10^{12.5} M_⊙) trace external factors rather than internal assembly, as noted in the stress-test concern.
  3. [Abstract / Results] The abstract and results sections provide no sample sizes per mass bin, no error estimation or bootstrap uncertainties on the reported trends, and no assessment of selection biases in the mass bins. This prevents quantitative evaluation of the strength of the early/late differences.
minor comments (1)
  1. [Abstract] Notation for mass bins (e.g., 10^{12-12.5} M_⊙) should be standardized to 10^{12}–10^{12.5} M_⊙ for clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive report and the recognition of the paper's contributions in tracing CGM gas origins. We address each major comment below, agreeing where revisions are needed to strengthen the manuscript.

read point-by-point responses
  1. Referee: [Methods / abstract] The classification into early- and late-forming populations (abstract and methods) relies on the half-mass formation time without reported tests of robustness to alternative definitions (e.g., 20% or 80% mass assembly time). This is load-bearing for the central claim that differences arise from assembly history.

    Authors: The half-mass formation time is the conventional definition used throughout the assembly bias literature, but we agree that explicit robustness checks would strengthen the central claim. In the revised manuscript we will add a dedicated subsection presenting the key trends (stellar mass, CGM gas mass, metallicity, sSFR) recomputed with 20 % and 80 % mass assembly times; we expect the qualitative early/late differences and the metallicity reversal to persist, but will report any quantitative changes. revision: yes

  2. Referee: [Results / discussion of origins] No control or regression for local environment (density, tidal field, or neighbor count) is described despite the known correlation between formation time and environment within mass bins. This leaves open the possibility that reported CGM and metallicity differences (including the reversal at 10^{12}–10^{12.5} M_⊙) trace external factors rather than internal assembly, as noted in the stress-test concern.

    Authors: We acknowledge the well-known correlation between formation time and environment. Our analysis is performed in narrow halo-mass bins, which already mitigates much of the mass-driven environmental variation, and the absence of CGM-mass differences at the formation redshift supports a post-formation origin. Nevertheless, a direct environmental control was not performed. In revision we will add a short discussion quantifying the typical environmental differences between early- and late-forming halos in our sample and will test whether the reported trends remain after a simple density-matched subsampling; if the trends weaken, we will state this limitation explicitly. revision: partial

  3. Referee: [Abstract / Results] The abstract and results sections provide no sample sizes per mass bin, no error estimation or bootstrap uncertainties on the reported trends, and no assessment of selection biases in the mass bins. This prevents quantitative evaluation of the strength of the early/late differences.

    Authors: We agree that sample sizes, uncertainties, and bias assessment are required for quantitative interpretation. The revised manuscript will report the number of early- and late-forming halos in each of the three mass bins, add bootstrap or jackknife error estimates to all median trends shown in the figures, and include a brief paragraph discussing possible selection biases arising from the TNG50 volume and the mass-bin boundaries. revision: yes

Circularity Check

0 steps flagged

No circularity: direct simulation population comparisons

full rationale

The paper defines halo formation time as the redshift when M_halo(z) first reaches 0.5 * M_halo(z=0), splits the TNG50 sample into early/late populations within fixed z=0 mass bins, and reports measured differences in stellar mass, metallicity, CGM gas mass, sSFR, merger rates, and kinematics. These are empirical statistics extracted from the simulation snapshots; no equations, fitted parameters, or self-citations reduce the reported trends to the inputs by construction. The classification is a conventional definition and does not presuppose the CGM differences it is used to test. The analysis is therefore self-contained against the simulation data.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The analysis rests on the accuracy of the TNG50 baryonic model and on standard but arbitrary choices for halo classification; no new entities are introduced.

free parameters (2)
  • Halo mass range
    Halos restricted to 10^{10.5}–10^{12.5} M_⊙; the boundaries are chosen by the authors to span the regime of interest.
  • Formation-time definition
    Halos labeled early or late according to when they reach half their z=0 mass; the half-mass threshold is a conventional but non-unique choice.
axioms (1)
  • domain assumption The IllustrisTNG subgrid physics produce CGM properties representative enough of the real universe to support conclusions about assembly-history dependence.
    All reported differences are extracted from the simulation without cross-checks against independent observational datasets mentioned in the abstract.

pith-pipeline@v0.9.1-grok · 5831 in / 1484 out tokens · 31401 ms · 2026-06-25T23:29:49.543780+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

96 extracted references · 96 canonical work pages · 13 internal anchors

  1. [1]

    Alpaslan, M., & Tinker, J. L. 2021, MNRAS, 505, 5403, doi: 10.1093/mnras/stab1591 Angl´ es-Alc´ azar, D., Faucher-Gigu` ere, C.-A., Kereˇ s, D., et al. 2017, MNRAS, 470, 4698, doi: 10.1093/mnras/stx1517 Aragon Calvo, M. A., Neyrinck, M. C., & Silk, J. 2019, The Open Journal of Astrophysics, 2, 7, doi: 10.21105/astro.1697.07881

  2. [2]

    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

  3. [3]

    , keywords =

    Birnboim, Y., & Dekel, A. 2003, MNRAS, 345, 349, doi: 10.1046/j.1365-8711.2003.06955.x

  4. [4]

    X., Tumlinson, J., et al

    Bordoloi, R., Prochaska, J. X., Tumlinson, J., et al. 2018, ApJ, 864, 132, doi: 10.3847/1538-4357/aad8ac

  5. [5]

    2017, MNRAS, 469, 594, doi: 10.1093/mnras/stx873

    Garaldi, E. 2017, MNRAS, 469, 594, doi: 10.1093/mnras/stx873

  6. [6]

    S., Dekel, A., Kolatt, T

    Bullock, J. S., Dekel, A., Kolatt, T. S., et al. 2001, ApJ, 555, 240, doi: 10.1086/321477

  7. [7]

    N., Tripp, T

    Burchett, J. N., Tripp, T. M., Bordoloi, R., et al. 2016, ApJ, 832, 124, doi: 10.3847/0004-637X/832/2/124

  8. [8]

    COLIBRE: calibrating subgrid feedback in cosmological simulations that include a cold gas phase

    Chaikin, E., Schaye, J., Schaller, M., et al. 2025, arXiv e-prints, arXiv:2509.04067, doi: 10.48550/arXiv.2509.04067

  9. [9]

    L., Thom, C., Prochaska, J

    Cooksey, K. L., Thom, C., Prochaska, J. X., & Chen, H.-W. 2010, ApJ, 708, 868, doi: 10.1088/0004-637X/708/1/868

  10. [10]

    J., Crain, R

    Davies, J. J., Crain, R. A., McCarthy, I. G., et al. 2019, MNRAS, 485, 3783, doi: 10.1093/mnras/stz635

  11. [11]

    , keywords =

    Davies, J. J., Crain, R. A., Oppenheimer, B. D., & Schaye, J. 2020, MNRAS, 491, 4462, doi: 10.1093/mnras/stz3201

  12. [12]

    J., Crain, R

    Davies, J. J., Crain, R. A., & Pontzen, A. 2021, MNRAS, 501, 236, doi: 10.1093/mnras/staa3643 de S´ a-Freitas, C., Gon¸ calves, T. S., de Carvalho, R. R., et al. 2022, MNRAS, 509, 3889, doi: 10.1093/mnras/stab3230

  13. [13]

    Monthly Notices of the Royal Astronomical Society , volume =

    Dekel, A., & Birnboim, Y. 2006, MNRAS, 368, 2, doi: 10.1111/j.1365-2966.2006.10145.x Faucher-Gigu` ere, C.-A., & Oh, S. P. 2023, ARA&A, 61, 131, doi: 10.1146/annurev-astro-052920-125203

  14. [14]

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

  15. [15]

    B., Werk, J

    Ford, A. B., Werk, J. K., Dav´ e, R., et al. 2016, MNRAS, 459, 1745, doi: 10.1093/mnras/stw595

  16. [16]

    , keywords =

    Genel, S. 2025, MNRAS, 537, 3543, doi: 10.1093/mnras/staf255

  17. [17]

    2004, PASJ, 56, 29, doi: 10.1093/pasj/56.1.29 16 Gal´ arraga-Espinosa, D., Garaldi, E., & Kauffmann, G

    Fujita, Y. 2004, PASJ, 56, 29, doi: 10.1093/pasj/56.1.29 16 Gal´ arraga-Espinosa, D., Garaldi, E., & Kauffmann, G. 2023, A&A, 671, A160, doi: 10.1051/0004-6361/202244935

  18. [18]

    Gao, L., Springel, V., & White, S. D. M. 2005, MNRAS, 363, L66, doi: 10.1111/j.1745-3933.2005.00084.x

  19. [19]

    Following the Flow: Tracer Particles in Astrophysical Fluid Simulations , shorttitle =

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

  20. [20]

    Grand, R. J. J., G´ omez, F. A., Marinacci, F., et al. 2017, MNRAS, 467, 179, doi: 10.1093/mnras/stx071

  21. [21]

    , keywords =

    Green, J. C., Froning, C. S., Osterman, S., et al. 2012, ApJ, 744, 60, doi: 10.1088/0004-637X/744/1/6010.1086/141956

  22. [22]

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

  23. [24]

    Dutton, A. A. 2017b, MNRAS, 464, 2796, doi: 10.1093/mnras/stw2539

  24. [25]

    2017, MNRAS, 469, 2292, doi: 10.1093/mnras/stx952

    Hafen, Z., Faucher-Gigu` ere, C.-A., Angl´ es-Alc´ azar, D., et al. 2017, MNRAS, 469, 2292, doi: 10.1093/mnras/stx952

  25. [26]

    2019, MNRAS, 488, 1248, doi: 10.1093/mnras/stz1773

    Hafen, Z., Faucher-Gigu` ere, C.-A., Angl´ es-Alc´ azar, D., et al. 2019, MNRAS, 488, 1248, doi: 10.1093/mnras/stz1773

  26. [27]

    Hahn, O., Porciani, C., Dekel, A., & Carollo, C. M. 2009, MNRAS, 398, 1742, doi: 10.1111/j.1365-2966.2009.15271.x

  27. [28]

    F., et al., 2018, @doi [ ] 10.1093/mnras/sty1690 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480..800H 480, 800

    Hopkins, P. F., Wetzel, A., Kereˇ s, D., et al. 2018, MNRAS, 480, 800, doi: 10.1093/mnras/sty1690

  28. [29]

    B., Bryan, G

    Hummels, C. B., Bryan, G. L., Smith, B. D., & Turk, M. J. 2013, MNRAS, 430, 1548, doi: 10.1093/mnras/sts702

  29. [30]

    2001, A&A, 365, L1, doi: 10.1051/0004-6361:20000036 Kereˇ s, D., Katz, N., Weinberg, D

    Jansen, F., Lumb, D., Altieri, B., et al. 2001, A&A, 365, L1, doi: 10.1051/0004-6361:20000036 Kereˇ s, D., Katz, N., Weinberg, D. H., & Dav´ e, R. 2005, MNRAS, 363, 2, doi: 10.1111/j.1365-2966.2005.09451.x

  30. [31]

    M., Fox, A

    Lehner, N., O’Meara, J. M., Fox, A. J., et al. 2014, ApJ, 788, 119, doi: 10.1088/0004-637X/788/2/119

  31. [32]

    C., Tripp, T

    Lehner, N., Howk, J. C., Tripp, T. M., et al. 2013, ApJ, 770, 138, doi: 10.1088/0004-637X/770/2/138

  32. [33]

    C., Howk, J

    Lehner, N., Berek, S. C., Howk, J. C., et al. 2020, ApJ, 900, 9, doi: 10.3847/1538-4357/aba49c

  33. [34]

    2020, Astronomische Nachrichten, 341, 177, doi: 10.1002/asna.202023775

    Li, J.-T. 2020, Astronomische Nachrichten, 341, 177, doi: 10.1002/asna.202023775

  34. [35]

    J., & Chen, H.-W

    Liang, C. J., & Chen, H.-W. 2014, MNRAS, 445, 2061, doi: 10.1093/mnras/stu1901

  35. [36]

    H., Mo, H

    Lim, S. H., Mo, H. J., Wang, H., & Yang, X. 2016, MNRAS, 455, 499, doi: 10.1093/mnras/stv2282

  36. [37]

    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

  37. [38]

    2022, MNRAS, 509, 2707, doi: 10.1093/mnras/stab3169

    Lu, S., Xu, D., Wang, S., et al. 2022, MNRAS, 509, 2707, doi: 10.1093/mnras/stab3169

  38. [39]

    First results from the IllustrisTNG simulations: radio haloes and magnetic fields

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

  39. [40]

    The Galaxy Evolution Explorer: A Space Ultraviolet Survey Mission

    Martin, D. C., Fanson, J., Schiminovich, D., et al. 2005, ApJL, 619, L1, doi: 10.1086/426387

  40. [41]

    , keywords =

    Mitchell, P. D., Schaye, J., Bower, R. G., & Crain, R. A. 2020, MNRAS, 494, 3971, doi: 10.1093/mnras/staa938

  41. [42]

    D., Chaves-Montero, J., Artale, M

    Montero-Dorta, A. D., Chaves-Montero, J., Artale, M. C., & Favole, G. 2021, MNRAS, 508, 940, doi: 10.1093/mnras/stab2556

  42. [43]

    F., Ribeiro, A

    Morell, D. F., Ribeiro, A. L. B., de Carvalho, R. R., et al. 2020, MNRAS, 494, 3317, doi: 10.1093/mnras/staa881

  43. [44]

    2025, ApJ, 990, 98, doi: 10.3847/1538-4357/addf46

    Morgan, J., Bailin, J., & Anderson, A. 2025, ApJ, 990, 98, doi: 10.3847/1538-4357/addf46

  44. [45]

    L., Kere s D., Faucher-Gigu \`e re C.-A., Hopkins P

    Muratov, A. L., Kereˇ s, D., Faucher-Gigu` ere, C.-A., et al. 2015, MNRAS, 454, 2691, doi: 10.1093/mnras/stv2126

  45. [46]

    MNRAS , author =

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

  46. [47]

    2015a, MNRAS, 448, 59, doi: 10.1093/mnras/stv017

    Nelson, D., Genel, S., Vogelsberger, M., et al. 2015a, MNRAS, 448, 59, doi: 10.1093/mnras/stv017

  47. [48]

    2013, MNRAS, 429, 3353, doi: 10.1093/mnras/sts595

    Nelson, D., Vogelsberger, M., Genel, S., et al. 2013, MNRAS, 429, 3353, doi: 10.1093/mnras/sts595

  48. [49]

    and Pillepich, A

    Nelson, D., Pillepich, A., Genel, S., et al. 2015b, Astronomy and Computing, 13, 12, doi: 10.1016/j.ascom.2015.09.003

  49. [50]

    2018a, MNRAS, 477, 450, doi: 10.1093/mnras/sty656

    Nelson, D., Kauffmann, G., Pillepich, A., et al. 2018a, MNRAS, 477, 450, doi: 10.1093/mnras/sty656

  50. [51]

    MNRAS , author =

    Nelson, D., Pillepich, A., Springel, V., et al. 2018b, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040

  51. [52]

    First Results from the TNG50 Simulation: Galactic outflows driven by supernovae and black hole feedback

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

  52. [53]

    2020, MNRAS, 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. [54]

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

  54. [55]

    Revisiting the analysis of H data cubes for 97 galaxies

    Oppenheimer, B. D., & Dav´ e, R. 2008, MNRAS, 387, 577, doi: 10.1111/j.1365-2966.2008.13280.x

  55. [56]

    P., Cole, S., Frenk, C

    Oppenheimer, B. D., Dav´ e, R., Kereˇ s, D., et al. 2010, MNRAS, 406, 2325, doi: 10.1111/j.1365-2966.2010.16872.x

  56. [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

  57. [58]

    , keywords =

    Oppenheimer, B. D., Davies, J. J., Crain, R. A., et al. 2020, MNRAS, 491, 2939, doi: 10.1093/mnras/stz3124

  58. [59]

    S., Werk, J

    Peeples, M. S., Werk, J. K., Tumlinson, J., et al. 2014, ApJ, 786, 54, doi: 10.1088/0004-637X/786/1/54

  59. [60]

    Simulating Galaxy Formation with the IllustrisTNG Model

    Pillepich, A., Springel, V., Nelson, D., et al. 2018a, MNRAS, 473, 4077, doi: 10.1093/mnras/stx2656 17

  60. [61]

    MNRAS , author =

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

  61. [62]

    , keywords =

    Pillepich, A., Nelson, D., Springel, V., et al. 2019, MNRAS, 490, 3196, doi: 10.1093/mnras/stz2338

  62. [63]

    , keywords =

    Predehl, P., Andritschke, R., Arefiev, V., et al. 2021, A&A, 647, A1, doi: 10.1051/0004-6361/202039313

  63. [64]

    2011, ApJ, 740, 91, doi: 10.1088/0004-637X/740/2/91

    Cooksey, K. 2011, ApJ, 740, 91, doi: 10.1088/0004-637X/740/2/91

  64. [65]

    X., Werk, J

    Prochaska, J. X., Werk, J. K., Worseck, G., et al. 2017, ApJ, 837, 169, doi: 10.3847/1538-4357/aa6007

  65. [66]

    2023a, MNRAS, 518, 5754, doi: 10.1093/mnras/stac3524

    Ramesh, R., Nelson, D., & Pillepich, A. 2023a, MNRAS, 518, 5754, doi: 10.1093/mnras/stac3524

  66. [67]

    2023b, MNRAS, 522, 1535, doi: 10.1093/mnras/stad951

    Ramesh, R., Nelson, D., & Pillepich, A. 2023b, MNRAS, 522, 1535, doi: 10.1093/mnras/stad951

  67. [68]

    MNRAS , author =

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

  68. [69]

    The COLIBRE project: cosmological hydrodynamical simulations of galaxy formation and evolution

    Schaye, J., Chaikin, E., Schaller, M., et al. 2025, arXiv e-prints, arXiv:2508.21126, doi: 10.48550/arXiv.2508.21126

  69. [70]

    , keywords =

    Springel, V. 2010, MNRAS, 401, 791, doi: 10.1111/j.1365-2966.2009.15715.x

  70. [71]

    MNRAS , author =

    Springel, V., Pakmor, R., Pillepich, A., et al. 2018, MNRAS, 475, 676, doi: 10.1093/mnras/stx3304

  71. [72]

    2019, MNRAS, 488, 2549, doi: 10.1093/mnras/stz1859

    Stern, J., Fielding, D., Faucher-Gigu` ere, C.-A., & Quataert, E. 2019, MNRAS, 488, 2549, doi: 10.1093/mnras/stz1859

  72. [73]

    2020, MNRAS, 492, 6042, doi: 10.1093/mnras/staa198

    Stern, J., Fielding, D., Faucher-Gigu` ere, C.-A., & Quataert, E. 2020, MNRAS, 492, 6042, doi: 10.1093/mnras/staa198

  73. [74]

    2016, MNRAS, 458, 1510, doi: 10.1093/mnras/stw332

    Leauthaud, A. 2016, MNRAS, 458, 1510, doi: 10.1093/mnras/stw332

  74. [75]

    2015, MNRAS, 448, 895, doi: 10.1093/mnras/stu2762

    Suresh, J., Bird, S., Vogelsberger, M., et al. 2015, MNRAS, 448, 895, doi: 10.1093/mnras/stu2762

  75. [76]

    ApJS , author =

    Sutherland, R. S., & Dopita, M. A. 1993, ApJS, 88, 253, doi: 10.1086/191823

  76. [77]

    A., et al

    Tojeiro, R., Eardley, E., Peacock, J. A., et al. 2017, MNRAS, 470, 3720, doi: 10.1093/mnras/stx1466

  77. [78]

    2020, MNRAS, 494, 549, doi: 10.1093/mnras/staa685

    Truong, N., Pillepich, A., Werner, N., et al. 2020, MNRAS, 494, 549, doi: 10.1093/mnras/staa685

  78. [79]

    S., & Werk, J

    Tumlinson, J., Peeples, M. S., & Werk, J. K. 2017, ARA&A, 55, 389, doi: 10.1146/annurev-astro-091916-055240

  79. [80]

    K., et al

    Tumlinson, J., Thom, C., Werk, J. K., et al. 2011, Science, 334, 948, doi: 10.1126/science.1209840

  80. [81]

    K., et al

    Tumlinson, J., Thom, C., Werk, J. K., et al. 2013, ApJ, 777, 59, doi: 10.1088/0004-637X/777/1/59 van de Voort, F., Bah´ e, Y. M., Bower, R. G., et al. 2017, MNRAS, 466, 3460, doi: 10.1093/mnras/stw3356

Showing first 80 references.