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The relationship between galaxy size and halo properties: Insights from the IllustrisTNG simulations and differential clustering

T0 review · 2 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read In the TNG simulations, the size-based differential clustering of galaxies is produced mainly by satellite galaxies rather than by correlations between galaxy size and halo spin or concentration.

desk verdict Direct measurement of size-based differential clustering in TNG300, mostly a satellite effect; the interpretation is plausible but rests on the least-resolved satellites. read the letter →

arxiv 2502.03679 v1 pith:4C2ILBMA submitted 2025-02-05 astro-ph.GA

Rachel S. Somerville , Austen Gabrielpillai , Boryana Hadzhiyska , Shy Genel This is my paper · ORCID
classification astro-ph.GA
keywords galaxysizesdarkmatterhaloshalospinconcentrationsatellitegalaxiesclusteringdifferentialIllustrisTNG
topics Dark Matter
open problems Dark Matter
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

This paper asks why galaxies of the same stellar mass come in widely different radial sizes, and whether those differences are imprinted by the dark matter halos that host them. Using the IllustrisTNG suite of cosmological simulations and matching dark-matter-only runs, it looks for correlations between the ratio of galaxy radius to halo virial radius and halo spin, concentration, and formation time. It finds that size correlates with formation time and with concentration only for very concentrated halos, but not with spin at low redshift. The central discovery is that the observed tendency for small galaxies to cluster more strongly than large ones at fixed mass appears in TNG300, yet it is not driven by these halo-size correlations: when satellites are removed, the signal largely disappears. This would resolve a reported tension between observations and simulation-based size-halo relations by pointing to satellite galaxies as the main source of the clustering difference.

What carries the argument

The load-bearing objects are the ratio $r_{\rm gal}/R_{\rm vir}$, the projected two-point correlation function $w_p(r_p)$ computed for large and small galaxy samples, and the decomposition of that clustering into central and satellite contributions. To avoid baryonic contamination of halo properties, the analysis uses halo catalogs from dark-matter-only runs with matched initial conditions, linked to the full-physics galaxies by a bijective matching procedure. The decisive step is recomputing $w_p$ after removing satellite galaxies: the size-based clustering ratio returns near unity, which isolates satellites as the mechanism carrying the signal.

What would settle it

Repeat the large-versus-small clustering ratio at $z=0$ for central galaxies only in a higher-resolution cosmological simulation of the same volume, or with satellite sizes recomputed at higher particle resolution: if the signal remains significantly below unity, or if the satellite sizes change enough to alter the ratio, the claim that satellites drive the signal would be overturned.

Watch

Extended reading notes

Core claim

The paper's central claim is that in the IllustrisTNG300 simulation, the scale-dependent differential clustering of small versus large galaxies at fixed stellar mass is produced almost entirely by satellite galaxies, and is not a direct consequence of correlations between galaxy size and secondary halo properties such as spin or concentration. At $z=0$, TNG reproduces the observed pattern, with large galaxies less clustered than small ones and a stronger signal on small scales, but nearly all of the signal vanishes when the sample is restricted to central galaxies. The correlations the paper does measure, a positive $r_{\rm gal}/R_{\rm vir}$-concentration trend above $c \sim 16$ and a correlation with halo formation time, are confined to central galaxies and cannot account for the clustering pattern. The authors interpret this as support for a simple picture in which a satellite's size is set at the time its halo reached peak mass, so satellites appear small because their halos formed and stopped growing in a denser, earlier universe.

Load-bearing premise

The argument depends on the assumption that low-mass satellite galaxies in TNG300 are modeled faithfully enough, in size, abundance, and clustering, that the measured satellite contribution is trustworthy rather than a numerical artifact of limited resolution.

Editorial extensions

If this is right

  • If the satellite-driven interpretation holds, the observed size-dependent clustering does not require a direct physical link between galaxy radius and halo spin or concentration, so the apparent disagreement between observations and simulation-based size-halo correlations is resolved.
  • The same mechanism predicts that size-based differential clustering should weaken with cosmic time and reverse in sign by $z \sim 3$, as the central galaxy population becomes relatively more clustered.
  • Future measurements of size-dependent clustering from wide-area imaging surveys can test whether the signal indeed comes from the satellite population, since satellites should also show distinctive color and environmental signatures.
  • Empirical models that assign galaxy sizes using only the halo's peak-mass radius, as in the earlier satellite model, can reproduce the TNG differential clustering without invoking secondary halo properties.

Reading between the lines

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

  • If the signal is truly satellite-dominated, size-selected clustering becomes a practical probe of satellite quenching and environmental stripping, not of halo spin, in surveys where color and size are measured together.
  • Because the conclusion leans on low-mass satellites in TNG300, which may be under-resolved, a natural next test is to repeat the central-only decomposition in a higher-resolution simulation or with subhalo-rich dark-matter-only models; if better-resolved satellites shift either their sizes or their abundance, the size of the satellite contribution could change.
  • The weak reversal seen for central galaxies at high redshift suggests that size and halo mass are not entirely independent, and this residual correlation could become measurable in larger volumes.
  • One could test the mechanism directly by predicting, from the same size assignment rule, the satellite size distribution and its dependence on host-centric distance, and comparing it with future wide-field imaging.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The paper investigates correlations between the ratio of galaxy size to host halo virial radius (r_gal/R_vir) and halo spin, concentration, and formation time using the IllustrisTNG suite (TNG50, TNG100, TNG300) together with matched dark-matter-only simulations from which halo properties are measured. It reports essentially no correlation with spin at z=0, a positive correlation with NFW concentration only for c_NFW >~16, and a positive correlation with formation time. The paper then measures the projected two-point correlation function of large versus small galaxies in stellar mass bins in TNG300 and compares directly with SDSS measurements from Behroozi et al. (2022). The simulation qualitatively reproduces the observed scale-dependent differential clustering, but the signal nearly disappears when only central galaxies are considered, leading the authors to conclude that the differential clustering is driven primarily by satellite galaxies rather than by correlations between galaxy size and secondary halo properties.

Significance. If the satellite-driven interpretation is correct, the paper resolves the tension raised by Behroozi et al. (2022): size-dependent clustering need not imply a direct physical link between galaxy size and halo spin or concentration, and the Hearin et al. (2019) picture is supported. The paper provides the first direct measurement of size-based differential clustering in a cosmological hydrodynamic simulation, and it makes falsifiable high-redshift predictions for forthcoming surveys. Notable strengths are the use of halo properties from matched dark-matter-only runs to avoid baryonic contamination of halo parameters, direct comparison with observational clustering measurements, and the use of jackknife error estimates. The results are emergent measurements from an existing simulation suite and are not obtained by fitting model parameters to the target clustering signal, so circularity is not a concern.

major comments (2)
  1. [Sec. 4.3; Fig. 8] The central claim that 'nearly all' of the size-based differential clustering arises from satellites is not robust to the resolution limitations that the paper itself acknowledges. The lowest stellar mass bin (9.75 < log M*/M_sun < 10.25) includes satellites whose stellar masses correspond to roughly 100 stellar particles, and Sec. 4.3 states that such systems may have too few particles or too low spatial resolution to accurately simulate tidal and ram pressure stripping. The central-only clustering in Fig. 8 is a null measurement with large jackknife errors, so the wording that the signal has 'almost entirely disappeared' is stronger than the data support. The authors should demonstrate resolution robustness, for example by recomputing the all-galaxy and central-only clustering in TNG100 (which has higher mass resolution, albeit smaller volume) or by removing the lowest mass bin and showing that the satellite-driven conclusion is unchanged.
  2. [Sec. 3.2; Fig. 3] The critical concentration threshold c ~ 16 is identified post hoc from the same data and is not supported by any statistical test. The paper does not report confidence intervals on the r_gal/R_vir versus c_NFW relation in the high-concentration regime, nor does it test whether the change in slope at c ~ 16 is significant rather than a binning artifact. The subsequent interpretation in Sec. 4.1 that the upturn is driven by stripped or splashback halos with non-monotonic mass accretion histories is plausible but not quantitatively demonstrated. The authors should add a quantitative significance analysis, e.g., Spearman correlation coefficients in concentration bins with bootstrap errors, and state clearly whether the threshold is a fitted parameter or a descriptive summary of the data.
minor comments (5)
  1. [Sec. 2.4, Eq. (2)] The summation over line-of-sight separations in the estimator for w_p(r) does not explicitly include the bin width Delta pi; as written, the projected correlation function is missing the usual multiplicative factor and the units are not transparent.
  2. [Sec. 3.2, first paragraph] The phrase 'at lower correlations log c_NFW <~ 1.2' should read 'at lower concentrations'.
  3. [Sec. 4.2] There is a typo in the list of B22 models: 'dicfferent' should be 'different'.
  4. [Sec. 5, conclusions] The summary statement that 'central galaxies with larger radii tend to be hosted by halos of larger mass and higher concentration' is not fully consistent with Fig. 10, where at z=0-1 the concentration difference between large and small centrals appears only in the highest stellar mass bin.
  5. [Abstract and Sec. 2.2] The abstract says 'galaxy formation history and environment ... plays an important role'; the verb should agree with the plural compound subject ('play'). Also, the spelling 'bijective' with a special character should be rendered as 'bijective' for accessibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the correlations and clustering measurements are emergent outputs of IllustrisTNG, compared against external SDSS/B22 data without fitting the target signal.

full rationale

The paper's derivation chain is self-contained in the sense required here: it does not fit any parameter to the differential clustering signal it claims to predict. Galaxy sizes, halo properties, and clustering are all measured from the IllustrisTNG simulation outputs, with halo properties taken from matched dark-matter-only rockstar runs (Gabrielpillai et al. 2021). The size-halo correlations in Sec. 3.2 are direct measurements of r_gal/R_vir against spin, concentration, and formation time; none of these quantities is defined in terms of the others. The differential clustering in Sec. 3.3 is computed with the standard projected correlation function estimator, splitting galaxies by whether their size is above or below the median in each stellar mass bin; this is a measurement, not a constructed prediction. The central claim that satellites drive the signal is based on the central-only version of the same statistic (Fig. 8), which is a subsample decomposition rather than an identity forced by construction. The comparison with Behroozi et al. (2022) uses external SDSS observations and external empirical models, and no TNG parameter is calibrated to reproduce those clustering measurements. The paper's own caveat that TNG300 satellites may be under-resolved (Sec. 4.3) is a robustness/correctness concern, not a circularity: it affects whether the emergent satellite attribution is physically reliable, but it does not make any equation or claim equivalent to its inputs by definition. Self-citations (e.g., Gabrielpillai et al. 2021 for the bijective matching catalogs, Pillepich et al. 2019 for stellar-mass rescaling) supply tools and corrections, not the target result, and the cited matching work is a separate catalog product. No circular step can be exhibited with a specific equation-to-equation reduction, so the appropriate finding is no significant circularity with score 0.

Assumptions & free parameters 2 free parameters · 6 assumptions · 0 invented entities

The paper is an observational-style analysis of an existing simulation suite, so it contributes no new equations or entities. Its claims rest on the TNG subgrid model, the dark-matter-only to full-physics matching, the NFW profile assumption, and two hand-selected numbers: the redshift-dependent mass rescaling factors and the c~16 concentration break.

free parameters (2)
  • TNG300 stellar mass rescaling factors = 1.4 at z=0; 1.3 at z=1; 1.1 at z=2; 1.0 at z=3
    Adopted from Pillepich et al. (2019), these multiply TNG300 stellar masses before binning (Sec. 3.1); they change which galaxies are classified as large or small and are not re-derived here.
  • Critical concentration threshold c~16 = ~16 (log10 c ~1.2)
    The boundary separating no correlation from positive correlation between rgal/Rvir and cNFW is read off Fig. 3 by eye, not estimated by a formal change-point analysis (Sec. 3.2).
assumptions (6)
  • domain assumption The IllustrisTNG subgrid baryonic model predicts realistic galaxy sizes and stellar masses at z=0-3.
    All correlations and clustering claims inherit the simulation's subgrid treatment of star formation, stellar feedback, and AGN feedback (Sec. 2.1 and Sec. 4.3).
  • domain assumption Dark-matter-only halo properties describe the same halos as in the full-physics runs after bijective matching.
    Halo properties are measured in TNG Dark with rockstar and assigned to full-physics subhalos via bijective matching (Sec. 2.2); matching is validated mainly for centrals in Gabrielpillai et al. (2021).
  • domain assumption NFW profile fits provide reliable halo concentrations, including for stripped or splashback halos.
    Concentration is computed from rockstar's NFW fit (Sec. 2.2 and Appendix A), yet the high-concentration trend in Fig. 3 is driven by halos with non-monotonic mass accretion histories that may not be NFW-like (Sec. 4.1).
  • domain assumption Bryan and Norman (1998) mass and radius definitions and Peebles (1969) spin parameter are the appropriate halo conventions.
    Adopted from previous literature (Sec. 2.2 and Table A1); changes in convention could shift rgal/Rvir normalizations and trends.
  • ad hoc to paper Redshift-dependent stellar mass rescaling factors from Pillepich et al. (2019) correct resolution trends in TNG300.
    Applied in Sec. 3.1 without deriving or re-fitting them; they affect mass bin membership and the large versus small split.
  • standard math Jackknife resampling over 27 subboxes gives unbiased uncertainty estimates for wp.
    Statistical estimator used in Sec. 2.4; standard but assumes subboxes are independent enough.

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Cite this review

Pith. "Pith review of The relationship between galaxy size and halo properties: Insights from the IllustrisTNG simulations and differential clustering." pith.science (2026). https://pith.science/paper/4C2ILBMA

@misc{pith2026250203679,
  author       = {Pith},
  title        = {Pith review of: The relationship between galaxy size and halo properties: Insights from the IllustrisTNG simulations and differential clustering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4C2ILBMA}},
  note         = {Machine review of arXiv:2502.03679}
}
abstract

The physical origin of the radial sizes of galaxies and how galaxy sizes are correlated with the properties of their host dark matter halos is an open question in galaxy formation. In observations, the large-scale clustering of galaxies selected by stellar mass is significantly different for large and small galaxies, and Behroozi et al. (2022) showed that these results are in tension with some of the correlations between galaxy size and halo properties in the literature. We analyze the IllustrisTNG suite of large volume cosmological hydrodynamic simulations along with dark matter only simulations with matched initial conditions. We investigate correlations between the ratio of galaxy size to halo virial radius ($r_{\rm gal}/R_{\rm vir}$) and halo spin, concentration, and formation time at redshift 0-3. We find a significant correlation between $r_{\rm gal}/R_{\rm vir}$ and concentration, but only above a critical value $c \simeq 16$, and we also find a correlation between $r_{\rm gal}/R_{\rm vir}$ and halo formation time. We suggest that galaxy formation history and environment, in addition to halo properties at a given output time, play an important role in shaping galaxy size. In addition, we directly measure size-based differential clustering in the TNG300 simulation and compare directly with the observational results. We find significant scale-dependent size-based differential clustering in TNG, in qualitative agreement with observations. However, correlations between $r_{\rm gal}/R_{\rm vir}$ and secondary halo properties are not the drivers of the differential clustering in the simulations; instead, we find that most of this signal in TNG arises from satellite galaxies.

Figures

Figures reproduced from arXiv: 2502.03679 by the authors.

Figure 1
Figure 1. The size-mass relation for all three TNG boxes at z = 0. The first three panels from left to right show the median scaling relationship for central (dashed) and for satellite (dotted) and the 16-84 percentile region (as a shaded area) and individual galaxies (grey shaded regions) for TNG50 (green), TNG100 (orange), and TNG300 (purple). The fourth panel shows the median and 16-84 percentile areas for all three boxes.… view at source ↗
Figure 2
Figure 2. The size-mass relation for all three TNG boxes at (top) z = 1, (middle) z = 2, and (bottom) z = 3. Panels are as described in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Ratio of galaxy radius to halo radius, 𝑟gal/𝑅vir vs. (left) spin, (middle) halo concentration, and (right) halo formation time at 𝑧 = 0 for TNG50 (green), TNG100 (orange), and TNG300 (purple). The black line in the middle panel shows the fit to the correlation reported by Jiang et al. (2019), where the thicker solid line indicates the approximate range of concentrations represented in the simulations used in that st… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Ratio of galaxy radius to halo radius 𝑟gal/𝑅vir vs. (left) spin and (right) concentration for (top) z = 1, (middle) z = 2, and (bottom) z = 3. See [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: The 2-point correlation function as a function of projected distance for all galaxies (central and satellites combined) in TNG300 (purple), SDSS (grey), and the concentration based model from B22 (red) for three different stellar mass bins (three columns) at 𝑧 = 0. We …
Figure 6
Figure 6. Figure 6: The 2-point correlation function for large (teal) and small (magenta) galaxies (central and satellites combined) for three different stellar mass bins at redshifts 𝑧 = 1 (top), 𝑧 = 2 (middle), and 𝑧 = 3 (bottom), for the TNG300 simulation volume. numbers of galaxies; i…
Figure 7
Figure 7. Figure 7: The ratio of the 2-point correlation functions for large vs. small galaxies, for central and satellite galaxies combined, for three different stellar mass bins at 𝑧 = 1 (top), 𝑧 = 2 (middle), and 𝑧 = 3 (bottom). TNG predicts that size-based differential clustering flip…
Figure 8
Figure 8. Figure 8: The 2-point correlation function as a function of projected distance for central galaxies only in TNG300 (purple) at 𝑧 = 0. We show the results for large (top) and small (middle) galaxies, as well as the ratio between the two (bottom). The plotted results are for centr…
Figure 9
Figure 9. Figure 9: (Left) halo mass, (middle) halo concentration, and (right) halo spin residuals vs. galaxy size residuals for TNG50 (green), TNG100(orange), and TNG300 (purple). All residuals are functions of galaxy stellar mass. Annotated on each panel is Spearman’s correlation coeffi…
Figure 10
Figure 10. Figure 10: Distribution of the halo concentration 𝑐nfw in the three stellar mass bins for TNG300 bijective matched central galaxies for z = 0, 1, 2, and 3, separated into populations of small and large galaxies. In the two lower stellar mass bins, the distributions are quite simi…
Figure 11
Figure 11. Figure 11: Distribution of 𝑚vir in the three stellar mass bins for TNG300 bijective matched central galaxies for z = 0, 1, 2, and 3, separated into populations of small and large galaxies. Large galaxies live in systematically more massive halos in most stellar mass and redshift …

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Reference graph

Works this paper leans on

74 extracted references · 20 canonical work pages · cited by 2 Pith papers

  1. [1]

    V., 2016, @doi [ ] 10.3847/0004-637X/824/2/79 , http://adsabs.harvard.edu/abs/2016ApJ...824...79A 824, 79

    Agertz O., Kravtsov A. V., 2016, @doi [ ] 10.3847/0004-637X/824/2/79 , http://adsabs.harvard.edu/abs/2016ApJ...824...79A 824, 79

    doi:10.3847/0004-637x/824/2/79 2016

  2. [2]

    Ahumada R., et al., 2020, @doi [ ] 10.3847/1538-4365/ab929e , https://ui.adsabs.harvard.edu/abs/2020ApJS..249....3A 249, 3

    doi:10.3847/1538-4365/ab929e 2020

  3. [3]

    Barden M., et al., 2005, @doi [ ] 10.1086/497679 , http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2005ApJ...635..959B&db_key=AST 635, 959

    doi:10.1086/497679 2005

  4. [4]

    S., Conroy C., Wechsler R

    Behroozi P. S., Conroy C., Wechsler R. H., 2010, @doi [ ] 10.1088/0004-637X/717/1/379 , http://adsabs.harvard.edu/abs/2010ApJ...717..379B 717, 379

    doi:10.1088/0004-637x/717/1/379 2010

  5. [5]

    S., Wechsler R

    Behroozi P. S., Wechsler R. H., Wu H.-Y., Busha M. T., Klypin A. A., Primack J. R., 2013a, , 763, 18

    work page

  6. [6]

    S., Wechsler R

    Behroozi P. S., Wechsler R. H., Conroy C., 2013b, @doi [ ] 10.1088/0004-637X/770/1/57 , http://adsabs.harvard.edu/abs/2013ApJ...770...57B 770, 57

    doi:10.1088/0004-637x/770/1/57

  7. [7]

    P., 2022, @doi [ ] 10.1093/mnras/stab3193 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.2800B 509, 2800

    Behroozi P., Hearin A., Moster B. P., 2022, @doi [ ] 10.1093/mnras/stab3193 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.2800B 509, 2800

    doi:10.1093/mnras/stab3193 2022

  8. [9]

    R., Faber S

    Blumenthal G. R., Faber S. M., Primack J. R., Rees M. J., 1985, @doi [ ] 10.1038/313072a0 , https://ui.adsabs.harvard.edu/abs/1985Natur.313...72B 313, 72

    work page doi:10.1038/313072a0 1985

Show all 74 references
  1. [10]

    Blumenthal G., Faber S., Flores R., Primack J., 1986, , 301, 27

    1986

  2. [11]

    Bose S., Eisenstein D. J., Hernquist L., Pillepich A., Nelson D., Marinacci F., Springel V., Vogelsberger M., 2019, @doi [ ] 10.1093/mnras/stz2546 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.5693B 490, 5693

    2019 doi

  3. [12]

    Brinchmann J., Charlot S., White S. D. M., Tremonti C., Kauffmann G., Heckman T., Brinkmann J., 2004, @doi [ ] 10.1111/j.1365-2966.2004.07881.x , https://ui.adsabs.harvard.edu/abs/2004MNRAS.351.1151B 351, 1151

    2004

  4. [13]

    L., Norman M

    Bryan G. L., Norman M. L., 1998, @doi [ ] 10.1086/305262 , http://adsabs.harvard.edu/abs/1998ApJ...495...80B 495, 80

    1998 doi

  5. [14]

    Chabrier G., 2003, @doi [ ] 10.1086/374879 , https://ui.adsabs.harvard.edu/abs/2003ApJ...586L.133C 586, L133

    2003 doi

  6. [15]

    S., Ostriker J

    Choi E., Somerville R. S., Ostriker J. P., Naab T., Hirschmann M., 2018, @doi [ ] 10.3847/1538-4357/aae076 , https://ui.adsabs.harvard.edu/abs/2018ApJ...866...91C 866, 91

    2018 doi

  7. [16]

    E., Zennaro M., 2021, @doi [ ] 10.1093/mnras/stab1170 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504.5205C 504, 5205

    Contreras S., Angulo R. E., Zennaro M., 2021, @doi [ ] 10.1093/mnras/stab1170 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504.5205C 504, 5205

    2021 doi

  8. [17]

    J., Jonsson P., Primack J

    Covington M., Dekel A., Cox T. J., Jonsson P., Primack J. R., 2008, , 384, 94

    2008

  9. [18]

    D., Primack J

    Covington M. D., Primack J. R., Porter L. A., Croton D. J., Somerville R. S., Dekel A., 2011, @doi [ ] 10.1111/j.1365-2966.2011.18926.x , http://adsabs.harvard.edu/abs/2011MNRAS.tmp.1029C p. 1029

    2011

  10. [19]

    J., Spergel D

    Dalcanton J. J., Spergel D. N., Summers F. J., 1997, @doi [ ] 10.1086/304182 , https://ui.adsabs.harvard.edu/abs/1997ApJ...482..659D 482, 659

    1997 doi

  11. [20]

    Desmond H., Mao Y.-Y., Wechsler R., Crain R., Schaye J., 2017, @doi [ ] 10.1093/mnrasl/slx093 , 471, L11

    2017 doi

  12. [21]

    A., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2010.17555.x , http://adsabs.harvard.edu/abs/2011MNRAS.410.1660D 410, 1660

    Dutton A. A., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2010.17555.x , http://adsabs.harvard.edu/abs/2011MNRAS.410.1660D 410, 1660

    2011

  13. [22]

    F., Kere s D., Chan T

    El-Badry K., Wetzel A., Geha M., Hopkins P. F., Kere s D., Chan T. K., Faucher-Gigu \`e re C.-A., 2016, @doi [ ] 10.3847/0004-637X/820/2/131 , https://ui.adsabs.harvard.edu/abs/2016ApJ...820..131E 820, 131

    2016 doi

  14. [23]

    C., et al., 2004, , http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2004ApJ...600L.107F&db_key=AST 600, L107

    Ferguson H. C., et al., 2004, , http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2004ApJ...600L.107F&db_key=AST 600, L107

    2004

  15. [24]

    Furlong M., et al., 2017, @doi [ ] 10.1093/mnras/stw2740 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465..722F 465, 722

    2017 doi

  16. [25]

    S., Genel S., Rodriguez-Gomez V., Pandya V., Yung L

    Gabrielpillai A., Somerville R. S., Genel S., Rodriguez-Gomez V., Pandya V., Yung L. Y. A., Hernquist L., 2021, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2021arXiv211103077G p. arXiv:2111.03077

    2021 arXiv

  17. [26]

    M., Hernquist L., Vogelsberger M., Snyder G

    Genel S., Fall S. M., Hernquist L., Vogelsberger M., Snyder G. F., Rodriguez-Gomez V., Sijacki D., Springel V., 2015, @doi [ ] 10.1088/2041-8205/804/2/L40 , 804, L40

    2015 doi

  18. [27]

    Genel S., et al., 2018, @doi [ ] 10.1093/mnras/stx3078 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474.3976G 474, 3976

    2018 doi

  19. [28]

    N., 2020, @doi [ ] 10.1093/mnras/staa623 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.5506H 493, 5506

    Hadzhiyska B., Bose S., Eisenstein D., Hernquist L., Spergel D. N., 2020, @doi [ ] 10.1093/mnras/staa623 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.5506H 493, 5506

    2020 doi

  20. [29]

    S., Gabrielpillai A., Bose S., Eisenstein D., Hernquist L., 2021, @doi [ ] 10.1093/mnras/stab2564 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508..698H 508, 698

    Hadzhiyska B., Liu S., Somerville R. S., Gabrielpillai A., Bose S., Eisenstein D., Hernquist L., 2021, @doi [ ] 10.1093/mnras/stab2564 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508..698H 508, 698

    2021 doi

  21. [30]

    Hearin A., Behroozi P., Kravtsov A., Moster B., 2019, @doi [ ] 10.1093/mnras/stz2251 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.1805H 489, 1805

    2019 doi

  22. [31]

    Hernquist L., 1990, @doi [ ] 10.1086/168845 , https://ui.adsabs.harvard.edu/abs/1990ApJ...356..359H 356, 359

    1990 doi

  23. [32]

    F., Cox T

    Hopkins P. F., Cox T. J., Younger J. D., Hernquist L., 2009, @doi [ ] 10.1088/0004-637X/691/2/1168 , http://adsabs.harvard.edu/abs/2009ApJ...691.1168H 691, 1168

    2009 doi

  24. [33]

    Huang K.-H., et al., 2017, @doi [ ] 10.3847/1538-4357/aa62a6 , https://ui.adsabs.harvard.edu/abs/2017ApJ...838....6H 838, 6

    2017 doi

  25. [34]

    K., Cranmer M., Melchior P., Ho S., Somerville R

    Jespersen C. K., Cranmer M., Melchior P., Ho S., Somerville R. S., Gabrielpillai A., 2022, @doi [ ] 10.3847/1538-4357/ac9b18 , https://ui.adsabs.harvard.edu/abs/2022ApJ...941....7J 941, 7

    2022 doi

  26. [35]

    Jiang F., et al., 2019, @doi [ ] 10.1093/mnras/stz1952 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.4801J 488, 4801

    2019 doi

  27. [36]

    S., 2023, @doi [ ] 10.1093/mnras/stad178 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.1630K 520, 1630

    Karmakar T., Genel S., Somerville R. S., 2023, @doi [ ] 10.1093/mnras/stad178 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.520.1630K 520, 1630

    2023 doi

  28. [37]

    V., 2013, @doi [ ] 10.1088/2041-8205/764/2/L31 , http://adsabs.harvard.edu/abs/2013ApJ...764L..31K 764, L31

    Kravtsov A. V., 2013, @doi [ ] 10.1088/2041-8205/764/2/L31 , http://adsabs.harvard.edu/abs/2013ApJ...764L..31K 764, L31

    2013 doi

  29. [38]

    Lange R., et al., 2015, @doi [ ] 10.1093/mnras/stu2467 , http://adsabs.harvard.edu/abs/2015MNRAS.447.2603L 447, 2603

    2015 doi

  30. [39]

    Marinacci F., et al., 2018, @doi [ ] 10.1093/mnras/sty2206 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.5113M 480, 5113

    2018 doi

  31. [40]

    Meert A., Vikram V., Bernardi M., 2015, @doi [ ] 10.1093/mnras/stu2333 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.446.3943M 446, 3943

    2015 doi

  32. [41]

    J., Mao S., White S

    Mo H. J., Mao S., White S. D. M., 1998, @doi [ ] 10.1046/j.1365-8711.1998.01227.x , https://ui.adsabs.harvard.edu/abs/1998MNRAS.295..319M 295, 319

    1998

  33. [42]

    Morishita T., et al., 2024, @doi [ ] 10.3847/1538-4357/ad1404 , https://ui.adsabs.harvard.edu/abs/2024ApJ...963....9M 963, 9

    2024 doi

  34. [43]

    P., Somerville R

    Moster B. P., Somerville R. S., Maulbetsch C., van den Bosch F. C., Macci \`o A. V., Naab T., Oser L., 2010, @doi [ ] 10.1088/0004-637X/710/2/903 , http://adsabs.harvard.edu/abs/2010ApJ...710..903M 710, 903

    2010 doi

  35. [44]

    P., Naab T., White S

    Moster B. P., Naab T., White S. D. M., 2013, @doi [ ] 10.1093/mnras/sts261 , http://adsabs.harvard.edu/abs/2013MNRAS.428.3121M 428, 3121

    2013 doi

  36. [45]

    H., Ostriker J

    Naab T., Johansson P. H., Ostriker J. P., 2009, , http://adsabs.harvard.edu/abs/2009ApJ...699L.178N 699, 178

    2009

  37. [46]

    P., et al., 2018, @doi [ ] 10.1093/mnras/sty618 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1206N 477, 1206

    Naiman J. P., et al., 2018, @doi [ ] 10.1093/mnras/sty618 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1206N 477, 1206

    2018 doi

  38. [47]

    F., Frenk C

    Navarro J. F., Frenk C. S., White S. D. M., 1997, , 490, 493

    1997

  39. [48]

    Nelson D., et al., 2018, @doi [ ] 10.1093/mnras/stx3040 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475..624N 475, 624

    2018 doi

  40. [49]

    Nelson D., et al., 2019a, @doi [Computational Astrophysics and Cosmology] 10.1186/s40668-019-0028-x , https://ui.adsabs.harvard.edu/abs/2019ComAC...6....2N 6, 2

    doi

  41. [50]

    Nelson D., et al., 2019b, @doi [ ] 10.1093/mnras/stz2306 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.3234N 490, 3234

    doi

  42. [51]

    P., Naab T., Johansson P

    Oser L., Ostriker J. P., Naab T., Johansson P. H., Burkert A., 2010, @doi [ ] 10.1088/0004-637X/725/2/2312 , https://ui.adsabs.harvard.edu/abs/2010ApJ...725.2312O 725, 2312

    2010 doi

  43. [52]

    Peebles P. J. E., 1969, , http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=1969ApJ...155..393P&db_key=AST 155, 393

    1969

  44. [53]

    Pillepich A., et al., 2018, @doi [ ] 10.1093/mnras/stx2656 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.4077P 473, 4077

    2018 doi

  45. [54]

    Pillepich A., et al., 2019, @doi [ ] 10.1093/mnras/stz2338 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.3196P 490, 3196

    2019 doi

  46. [55]

    A., Somerville R

    Porter L. A., Somerville R. S., Primack J. R., Johansson P. H., 2014, @doi [ ] 10.1093/mnras/stu1434 , http://adsabs.harvard.edu/abs/2014MNRAS.444..942P 444, 942

    2014 doi

  47. [56]

    Ravindranath S., et al., 2004, , http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2004ApJ...604L...9R&db_key=AST 604, L9

    2004

  48. [57]

    V., Navarro J

    Sales L. V., Navarro J. F., Schaye J., Dalla Vecchia C., Springel V., Haas M. R., Helmi A., 2009, @doi [ ] 10.1111/j.1745-3933.2009.00725.x , http://adsabs.harvard.edu/abs/2009MNRAS.399L..64S 399, L64

    2009

  49. [58]

    Shen S., et al., 2003, , http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2003MNRAS.343..978S&db_key=AST 343, 978

    2003

  50. [59]

    Shibuya T., Ouchi M., Harikane Y., 2015, @doi [ ] 10.1088/0067-0049/219/2/15 , https://ui.adsabs.harvard.edu/abs/2015ApJS..219...15S 219, 15

    2015 doi

  51. [60]

    S., et al., 2018, @doi [ ] 10.1093/mnras/stx2040 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.2714S 473, 2714

    Somerville R. S., et al., 2018, @doi [ ] 10.1093/mnras/stx2040 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.2714S 473, 2714

    2018 doi

  52. [61]

    Springel V., 2010, @doi [ ] 10.1111/j.1365-2966.2009.15715.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.401..791S 401, 791

    2010

  53. [62]

    Springel V., Hernquist L., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06206.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.339..289S 339, 289

    2003

  54. [63]

    Springel V., White S. D. M., Tormen G., Kauffmann G., 2001, @doi [ ] 10.1046/j.1365-8711.2001.04912.x , https://ui.adsabs.harvard.edu/abs/2001MNRAS.328..726S 328, 726

    2001

  55. [64]

    Springel V., et al., 2018, @doi [ ] 10.1093/mnras/stx3304 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475..676S 475, 676

    2018 doi

  56. [65]

    F., Remus R.-S., Dolag K., Beck A

    Teklu A. F., Remus R.-S., Dolag K., Beck A. M., Burkert A., Schmidt A. S., Schulze F., Steinborn L. K., 2015, @doi [ ] 10.1088/0004-637X/812/1/29 , 812, 29

    2015 doi

  57. [66]

    V., White S

    \"U bler H., Naab T., Oser L., Aumer M., Sales L. V., White S. D. M., 2014, @doi [ ] 10.1093/mnras/stu1275 , http://adsabs.harvard.edu/abs/2014MNRAS.443.2092U 443, 2092

    2014 doi

  58. [67]

    Vogelsberger M., et al., 2014a, @doi [ ] 10.1093/mnras/stu1536 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.444.1518V 444, 1518

    doi

  59. [68]

    Vogelsberger M., et al., 2014b, @doi [ ] 10.1038/nature13316 , https://ui.adsabs.harvard.edu/abs/2014Natur.509..177V 509, 177

    doi

  60. [69]

    Weinberger R., et al., 2017, @doi [ ] 10.1093/mnras/stw2944 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.3291W 465, 3291

    2017 doi

  61. [70]

    White S. D. M., Rees M. J., 1978, @doi [ ] 10.1093/mnras/183.3.341 , https://ui.adsabs.harvard.edu/abs/1978MNRAS.183..341W 183, 341

    1978 doi

  62. [71]

    S., Grand R

    Yang H., Gao L., Frenk C. S., Grand R. J. J., Guo Q., Liao S., Shao S., 2023, @doi [ ] 10.1093/mnras/stac3335 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.5253Y 518, 5253

    2023 doi

  63. [72]

    Zavala J., et al., 2016, @doi [ ] 10.1093/mnras/stw1286 , http://adsabs.harvard.edu/abs/2016MNRAS.460.4466Z 460, 4466

    2016 doi

  64. [73]

    Zjupa J., Springel V., 2017, @doi [ ] 10.1093/mnras/stw2945 , http://adsabs.harvard.edu/abs/2017MNRAS.466.1625Z 466, 1625

    2017 doi

  65. [74]

    van der Wel A., et al., 2014, @doi [ ] 10.1088/0004-637X/788/1/28 , http://adsabs.harvard.edu/abs/2014ApJ...788...28V 788, 28

    2014 doi

  66. [75]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.stat...

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.