Pith. sign in

REVIEW 3 major objections 1 minor 96 references

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

T0 review · 3 major / 1 minor · reviewed 2026-06-25 · grok-4.3

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

desk verdict TNG50 finds a mass-dependent reversal in CGM metallicity with assembly history plus higher cold-gas angular momentum in late-forming low-mass halos, but environment correlation is uncontrolled. read the letter →

arxiv 2606.24735 v1 pith:UROOJ3YN submitted 2026-06-23 astro-ph.GA

classification astro-ph.GA
keywords circumgalacticmediumhaloassemblyhistorygalaxyformationCGMmetallicitytimestellarmassspecificstarrateIllustrisTNG
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 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.

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.

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.

Watch

Extended reading notes

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.

Load-bearing premise

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

Editorial extensions

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.

Reading between the lines

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.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

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 · score 0.0 of 10

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.

Assumptions & free parameters 2 free parameters · 1 assumptions · 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.
assumptions (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.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The dependence of Circumgalactic Medium properties on halo assembly histories in the IllustrisTNG simulations." pith.science (2026). https://pith.science/paper/UROOJ3YN

@misc{pith2026260624735,
  author       = {Pith},
  title        = {Pith review of: The dependence of Circumgalactic Medium properties on halo assembly histories in the IllustrisTNG simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UROOJ3YN}},
  note         = {Machine review of arXiv:2606.24735}
}
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.

Figures

Figures reproduced from arXiv: 2606.24735 by the authors.

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, respectively… view at source ↗
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 percentile ra… view at source ↗
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 ↗
Figures from the paper (9 more)
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 form…
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]
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 in…
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]
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, …
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:…
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]
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 differ…
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 degre…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

96 extracted references · 96 canonical work pages

  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]
  3. [3]

    doi:10.1046/j.1365-8711.2003.06504.x , 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, title ZOMG - I

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

  6. [6]

    A Universal Angular Momentum Profile for Galactic Halos

    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

Show all 96 references
  1. [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

  2. [10]

    J., Crain, R

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

  3. [11]

    J., Crain, R

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

  4. [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

  5. [13]

    2006, MNRAS, 368, 2, doi: 10.1111/j.1365-2966.2006.10145.x Faucher-Gigu` ere, C.-A., & Oh, S

    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

  6. [14]

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

  7. [15]

    B., Werk, J

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

  8. [16]

    2025, MNRAS, 537, 3543, doi: 10.1093/mnras/staf255

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

  9. [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

  10. [18]

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

  11. [19]

    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

  12. [20]

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

  13. [21]

    C., Froning, C

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

  14. [22]

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

  15. [24]

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

  16. [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

  17. [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

  18. [27]

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

  19. [28]

    F., Wetzel, A., Kereˇ s, D., et al

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

  20. [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

  21. [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

  22. [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

  23. [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

  24. [33]

    C., Howk, J

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

  25. [34]

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

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

  26. [35]

    J., & Chen, H.-W

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

  27. [36]

    H., Mo, H

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

  28. [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

  29. [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

  30. [39]

    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

  31. [40]

    C., Fanson, J., Schiminovich, D., et al

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

  32. [41]

    D., Schaye, J., Bower, R

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

  33. [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

  34. [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

  35. [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

  36. [45]

    L., Kereˇ s, D., Faucher-Gigu` ere, C.-A., et al

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

  37. [46]

    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

  38. [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

  39. [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

  40. [49]

    2015b, Astronomy and Computing, 13, 12, doi: 10.1016/j.ascom.2015.09.003

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

  41. [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

  42. [51]

    2018b, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040

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

  43. [52]

    2019, MNRAS, 490, 3234, doi: 10.1093/mnras/stz2306

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

  44. [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

  45. [54]

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

  46. [55]

    D., & Dav´ e, R

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

  47. [56]

    D., Dav´ e, R., Kereˇ s, D., et al

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

  48. [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

  49. [58]

    D., Davies, J

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

  50. [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

  51. [60]

    2018a, MNRAS, 473, 4077, doi: 10.1093/mnras/stx2656 17

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

  52. [61]

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

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

  53. [62]

    2019, MNRAS, 490, 3196, doi: 10.1093/mnras/stz2338

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

  54. [63]

    2021, A&A, 647, A1, doi: 10.1051/0004-6361/202039313

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

  55. [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

  56. [65]

    X., Werk, J

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

  57. [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

  58. [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

  59. [68]

    A., Bower, R

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

  60. [69]

    2025, arXiv e-prints, arXiv:2508.21126, doi: 10.48550/arXiv.2508.21126

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

  61. [70]

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

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

  62. [71]

    2018, MNRAS, 475, 676, doi: 10.1093/mnras/stx3304

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

  63. [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

  64. [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

  65. [74]

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

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

  66. [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

  67. [76]

    S., & Dopita, M

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

  68. [77]

    A., et al

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

  69. [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

  70. [79]

    S., & Werk, J

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

  71. [80]

    K., et al

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

  72. [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

  73. [82]

    Vijayaraghavan, R., & Ricker, P. M. 2013, MNRAS, 435, 2713, doi: 10.1093/mnras/stt1485

  74. [83]

    2013, MNRAS, 436, 3031, doi: 10.1093/mnras/stt1789

    Vogelsberger, M., Genel, S., Sijacki, D., et al. 2013, MNRAS, 436, 3031, doi: 10.1093/mnras/stt1789

  75. [84]

    A., Stinson, G

    Wang, L., Dutton, A. A., Stinson, G. S., et al. 2015, MNRAS, 454, 83, doi: 10.1093/mnras/stv1937

  76. [85]

    2022, MNRAS, 509, 3148, doi: 10.1093/mnras/stab3167

    Wang, S., Xu, D., Lu, S., et al. 2022, MNRAS, 509, 3148, doi: 10.1093/mnras/stab3167

  77. [86]

    H., Bullock, J

    Wechsler, R. H., Bullock, J. S., Primack, J. R., Kravtsov, A. V., & Dekel, A. 2002, ApJ, 568, 52, doi: 10.1086/338765

  78. [87]

    2017, MNRAS, 465, 3291, doi: 10.1093/mnras/stw2944

    Weinberger, R., Springel, V., Hernquist, L., et al. 2017, MNRAS, 465, 3291, doi: 10.1093/mnras/stw2944

  79. [88]

    C., Brinkman, B., Canizares, C., et al

    Weisskopf, M. C., Brinkman, B., Canizares, C., et al. 2002, PASP, 114, 1, doi: 10.1086/338108

  80. [89]

    K., Prochaska, J

    Werk, J. K., Prochaska, J. X., Tumlinson, J., et al. 2014, ApJ, 792, 8, doi: 10.1088/0004-637X/792/1/8

  81. [90]

    K., Prochaska, J

    Werk, J. K., Prochaska, J. X., Cantalupo, S., et al. 2016, ApJ, 833, 54, doi: 10.3847/1538-4357/833/1/54

  82. [91]

    Wiersma, R. P. C., Schaye, J., & Smith, B. D. 2009, MNRAS, 393, 99, doi: 10.1111/j.1365-2966.2008.14191.x

  83. [92]

    H., & Putman, M

    Yoon, J. H., & Putman, M. E. 2013, ApJL, 772, L29, doi: 10.1088/2041-8205/772/2/L29

  84. [93]

    G., Adelman, J., Anderson, Jr., J

    York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579, doi: 10.1086/301513

  85. [94]

    2018, ApJ, 853, 84, doi: 10.3847/1538-4357/aaa54a

    Zehavi, I., Contreras, S., Padilla, N., et al. 2018, ApJ, 853, 84, doi: 10.3847/1538-4357/aaa54a

  86. [95]

    2025, A&A, 693, A197, doi: 10.1051/0004-6361/202452273

    Zhang, Y., Comparat, J., Ponti, G., et al. 2025, A&A, 693, A197, doi: 10.1051/0004-6361/202452273

  87. [96]

    2020, MNRAS, 499, 768, doi: 10.1093/mnras/staa2607

    Zinger, E., Pillepich, A., Nelson, D., et al. 2020, MNRAS, 499, 768, doi: 10.1093/mnras/staa2607

  88. [97]

    Zjupa, J., & Springel, V. 2017, MNRAS, 466, 1625, doi: 10.1093/mnras/stw2945 18 APPENDIX A.PERMUTATION TEST Permutation test is a classical non-parametric statistical tool and has also been used in astronomical researches to assess whether two samples are statistically disting...

Pith tools

Reviewed June 25, 2026 · model on record in the stance chip above.