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REVIEW 3 major objections 5 minor 39 references

The hidden satellites of massive galaxies and quasars at high-redshift

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Stellar radiation can hide up to 60% of the satellites around $z\approx6$ quasars, simulations suggest.

desk verdict Clean differential simulation with a plausible mechanism, but the headline 40–60% satellite reduction is not backed by the paper's own mean counts. read the letter →

arxiv 1909.01360 v1 pith:VAEANSK7 submitted 2019-09-03 astro-ph.GA astro-ph.CO

Tiago Costa , Joakim Rosdahl , Taysun Kimm This is my paper · ORCID
classification astro-ph.GAastro-ph.CO
keywords galaxyformationsatellitegalaxieshigh-redshiftquasarsstellarradiativefeedbacktidaldisruptionradiation-hydrodynamicsimulationscosmologicalzoom-inz=6massivehaloes
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

The paper aims to establish that ordinary starlight, not just supernovae, can dramatically reduce the number of satellite galaxies around the most massive galaxies at redshift $z\approx6$, including the likely hosts of bright quasars. In two otherwise identical radiation-hydrodynamic simulations of a $\sim 2\times10^{12}\,M_\odot$ halo, turning on stellar radiative feedback lowers the mean number of $M_\star > 10^7\,M_\odot$ systems inside the virial radius from about 102 to 73, a 40--60% drop, while leaving counts outside the virial radius almost unchanged. The proposed cause is that radiation acts within about a million years of star formation, before the 10-million-year supernova delay, puffing up young dwarf galaxies and making them less tightly bound; the host's tidal field then shreds them sooner. A sympathetic reader should care because deep JWST observations of $z>6$ quasars may find surprisingly few faint satellites, and that deficit could be mistaken for evidence of a lighter dark-matter halo rather than recognized as a fingerprint of early stellar feedback.

What carries the argument

The load-bearing object is a matched pair of cosmological zoom-in radiation-hydrodynamic simulations of the same $\approx 2.4\times10^{12}\,M_\odot$ halo at $z=6$, identical except that one tracks stellar radiative feedback (ionizing and non-ionizing radiation, dust radiation pressure, and photo-heating) and the other does not. The mechanism is a timing-and-binding chain: radiation from young stars acts essentially immediately, while supernovae are delayed by 10 Myr, so radiation curbs the collapse of gas in low-mass progenitors and leaves them less tightly bound; when these dwarfs fall into the host's deep potential well, where peak stellar circular velocities exceed $700\,\mathrm{km\,s^{-1}}$, tidal forces disrupt them on a shorter timescale than in the no-radiation run, converting many of them into diffuse stellar material rather than surviving cores.

What would settle it

Deep JWST imaging of a statistically meaningful sample of $z\approx6$ quasar hosts would settle it: if the number of satellites with $M_\star > 10^7\,M_\odot$ within roughly one virial radius is not tens of percent lower than simulations without stellar radiation predict, or if the predicted diffuse stellar envelope is absent, the central claim is falsified.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that stellar radiative feedback sets the satellite population of a massive high-redshift galaxy by acting before supernovae. Including photo-ionization, photo-heating, and radiation pressure produces galaxies that are slightly less massive and distinctly less concentrated: at early times the mean peak stellar circular velocity of galaxies in haloes above $10^{10}\,M_\odot$ drops from about 65 to $35\,\mathrm{km\,s^{-1}}$. The same tidal forces that strip dark matter from infalling dwarfs then destroy their stellar cores as well, so that many systems that survive as compact clumps of mass $10^7$--$10^9\,M_\odot$ in a supernova-only run are completely dispersed in the radiative run. The result is a 40--60% reduction in satellites with $M_\star > 10^7\,M_\odot$ within the virial radius, a nearly unchanged population outside it, and a more diffuse, smoother central galaxy with an effective radius larger by a factor $\lesssim 3$.

Load-bearing premise

The quantitative result rests on treating a single simulated halo, with AGN feedback deliberately switched off, as representative of real $z\approx6$ quasar environments; if that halo is atypical, or if AGN radiation or subgrid choices change how tightly bound dwarf progenitors are, the 40--60% amplitude could change even if the direction of the effect is robust.

Editorial extensions

If this is right

  • JWST-class imaging of $z>6$ quasar fields should find noticeably fewer $M_\star > 10^7\,M_\odot$ satellites inside the virial radius than supernova-only galaxy formation models predict.
  • The missing satellites should reappear as diffuse stellar light, tidal streams, and an extended stellar envelope out to roughly 10 kpc around the quasar host.
  • Surviving satellites in these extreme environments should be preferentially compact, irregular, low-mass cores, since extended systems are shredded first.
  • A low satellite count around a quasar would not by itself imply a light host halo; the same halo mass yields different counts depending on when early feedback acted.
  • The suppression should be weaker around less massive or lower-redshift galaxies, where tidal fields are weaker.

Reading between the lines

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

  • The paper leaves implicit that the same loosen-then-shred mechanism should be testable as a function of host mass: if the host halo is less massive, the suppression should weaken, so satellite counts could become a quantitative probe of both halo mass and early feedback strength.
  • I would extend the argument to quasar radiation: since the paper deliberately turns AGN feedback off, adding quasar radiation could amplify or alter the suppression, a direct and testable next step rather than a result of the present runs.
  • A further inference is that present-day massive galaxies may retain the signature of this process in their diffuse stellar haloes, built partly from dwarf progenitors whose binding energy was lowered by reionization-era starlight before they were tidally shredded.
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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

3 major / 5 minor

Summary. The paper presents two cosmological zoom-in radiation-hydrodynamic simulations of a rare z=6 halo with Mvir≈2.4×10^12 Msun, differing only in whether stellar radiative feedback is included (SN vs SN+RT). The authors report that stellar radiation makes satellite progenitors less tightly bound before supernovae, so that they are more easily tidally disrupted once they enter the massive halo. Consequently, the number of satellites and stellar clumps with Mstar>10^7 Msun inside the virial radius is claimed to drop by 40–60%, and the central galaxy becomes more extended and diffuse (Reff increases by a factor ≲3). The paper argues that high-redshift quasar hosts may therefore show anomalously few luminous satellites, a prediction testable with JWST.

Significance. If the central claim holds, the paper identifies a genuinely important mechanism: the timing of stellar radiative feedback relative to supernovae can control satellite survival in massive high-z haloes, with direct observational consequences for quasar environments. The experimental design is a strength: two simulations that differ only in the inclusion of stellar radiative feedback, with galaxies identified by two independent methods (halo-based and stellar-particle-based), and the qualitative difference in satellite counts is visible in both. The paper also provides a plausible causal chain (lower binding energy at z≈7–8, shorter tidal disruption timescale) supported by particle-ID matching between the runs. The prediction of a diffuse stellar envelope and reduced satellite counts around z>6 quasars is falsifiable with upcoming JWST data. The main weaknesses are that the quantitative amplitude rests on a single halo realization and on numerical choices that are not tested for convergence, and that the reported 40–60% figure does not follow from the paper's own cumulative counts.

major comments (3)
  1. [Section 3, Fig. 2 and Abstract] The headline quantitative claim is not supported by the paper's own numbers. The caption to Fig. 2 reports mean cumulative counts within Rvir of 102±8 (SN) vs 73±5 (SN+RT) for Mstar>10^7 Msun, which is a reduction of 29/102≈28%, not 40–60%. For Mstar>10^8 Msun the counts are 26±2 vs 16±2, a 38% reduction. The text's own description, 'an excess of ≈30 massive clumps' and '≈10', corresponds to these same 28% and 38% reductions. The abstract states 'drops by up to 60%' and the text states '≈40%−60% reduction', for which the only basis appears to be the method-(i) halo-based count of 6 vs 2 massive satellites, a different galaxy census from the cumulative counts quoted in the same section. This is an internal arithmetic inconsistency in a load-bearing claim, and the headline number must be corrected or explicitly re-derived.
  2. [Section 2 and Section 4, 'second most massive halo' and 'larger number of massive z=6 haloes'] The quantitative amplitude of the satellite suppression rests on a single halo realization: the second most massive halo in a 500 h^-1 Mpc box at z=6. The paper acknowledges the need for larger samples in Section 4, but does not provide any estimate of cosmic variance from even one additional halo, nor any resolution convergence test. Since the minimum cell size is 40 pc (Section 2) and the result depends on resolving the internal structure of 10^10 Msun subhaloes (which are only marginally resolved in a 40 pc cell), the 40–60% amplitude could be substantially altered at higher resolution. The direction of the effect is likely robust, but the amplitude central to the paper's abstract is not demonstrated to be converged or representative.
  3. [Section 2, 'We therefore exclude radiation and mechanical feedback from AGN'] The simulations deliberately exclude AGN feedback, yet the paper frames the target halo as a likely quasar host. While this choice is justified for isolating stellar radiative feedback, it leaves open the possibility that AGN radiation or outflows, if switched on at earlier times, could either amplify or erase the reported suppression. The discussion in Section 4 acknowledges this only qualitatively ('it remains possible for the process outlined here to be amplified'). The paper should state more clearly that the prediction for real quasar hosts is conditional on the unknown AGN onset time and strength.
minor comments (5)
  1. [Abstract] The abstract contains a duplicated word: 'the strength of stellar radiative feedback and and can be anomalously low'.
  2. [Section 3, fourth paragraph] The sentence 'the total stellar mass in SN+RT is at most times lower by20−40%' is awkwardly worded; 'at most times' should be 'at most times' or 'typically lower by 20–40%'.
  3. [Section 3, Fig. 2 caption] The caption states 'the number of galaxies within the halo drops by up to 40%' while the text and abstract quote '40%−60%' and 'up to 60%'. These should be made consistent.
  4. [Table 1] The table header 'T able 1' contains a spacing typo.
  5. [General] The redshift range in Fig. 2 is described as 'between 6.1<z<6', which reverses the inequality direction; it likely means 6<z<6.1.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the satellite suppression is a differential simulation output, not a fitted or self-referential prediction.

full rationale

This paper reports a controlled simulation experiment: two otherwise identical Ramses-RT runs, SN and SN+RT, differ only in whether stellar radiative feedback is included. The satellite counts, stellar masses, half-mass radii, and clump populations are output diagnostics of the simulations, not fitted parameters. The causal claim that radiation makes progenitors less bound and therefore more tidally disruptable is supported by tracing the same stellar particles from z≈8 to z=6 and showing that matched systems disperse in SN+RT while surviving as cores in SN (Fig. 3); this is an internal consistency check, not an input assumption. The cited code and subgrid models (Rosdahl et al. 2013/2015, Kimm et al. 2015/2017) were developed in prior work with overlapping authors, but they are shared by both runs and are not calibrated to the satellite counts or to the 40–60% suppression, so they are contextual rather than load-bearing self-citations. No step in the derivation chain reduces a 'prediction' by construction to a fitted parameter or to a self-citation. Separately, the abstract's 'up to 60%' and Section 3's '40–60%' are not supported by the paper's own cumulative numbers (102±8 → 73±5 is ≈28%; 26±2 → 16±2 is ≈38%); this is an internal arithmetic and consistency issue, not a circularity, and it does not change the circularity score.

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

The central claim inherits several modeling choices from the Ramses-RT code and from prior feedback implementations. No parameter was fitted to the 40-60% satellite reduction, so the ledger is short on fitted degrees of freedom; the main burden is representativeness and numerical convergence, not post-hoc tuning.

free parameters (6)
  • Reduced speed of light fraction = 0.03c
    Chosen to make radiative transfer tractable; adopted from Rosdahl et al. 2015 convergence tests. The satellite disruption rate could depend on this approximation.
  • Single-scattering dust opacity normalization = 10^3 (Z/Zsun) cm^2 g^-1
    Sets radiation pressure on dust in the single-scattering regime and affects how much momentum radiation deposits before supernovae.
  • Multi-scattering dust opacity normalization = 10 (Z/Zsun) cm^2 g^-1
    Sets radiation pressure in the multi-scattering regime; contributes to puffing up satellite progenitors.
  • Minimum hydro cell size = 40 pc
    Numerical resolution; the survival of small tidally disrupted cores and clump masses is likely resolution sensitive.
  • Star formation efficiency prescription = Variable, Kimm et al. 2017 model
    Controls when stars form and hence the timing of radiative feedback relative to supernovae; central to the mechanism.
  • AdaptaHop galaxy identification thresholds = Method i: rho_TH=80, NSPH=32, NHOP=16, fPoisson=4; Method ii: rho_TH=200, NSPH=20, NHOP=20, fPoisson=4
    Choice of halo finder parameters determines which systems are counted as satellites; the reported reduction could depend on these thresholds.
assumptions (6)
  • domain assumption Ramses-RT correctly solves coupled radiation hydrodynamics, gravity, and N-body dynamics for this regime.
    The mechanism relies on the code's treatment of photoionization, photoheating, and radiation pressure; not formally verified, but code is published and used in prior works (Rosdahl et al. 2013, 2015).
  • domain assumption Reduced speed of light 0.03c is converged for the quantities used here.
    Paper cites Appendix D of Rosdahl et al. 2015 for convergence of stellar mass, morphology, outflow, and ISM properties; satellite destruction timescales are a further extrapolation.
  • domain assumption The external UV background of Faucher-Giguere et al. 2009 is appropriate at z=6.
    The background ionizes gas in small progenitor halos and affects their binding and star formation.
  • domain assumption The Kimm et al. 2015 supernova and Kimm et al. 2017 star formation subgrid models are accurate for dwarf progenitors.
    The relative timing of radiation and supernovae sets how puffy the satellite progenitors become.
  • ad hoc to paper The simulated halo is representative of z=6 quasar hosts.
    Only one halo is simulated; the authors explicitly call for larger samples to quantify cosmic variance (Section 4).
  • domain assumption AGN feedback can be neglected without changing the stellar-radiation effect on satellites.
    Real quasar hosts at z=6 harbor supermassive black holes; AGN radiation and outflows are excluded by design, which could alter satellite survival and gas reservoirs.

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Cite this review

Pith. "Pith review of The hidden satellites of massive galaxies and quasars at high-redshift." pith.science (2026). https://pith.science/paper/VAEANSK7

@misc{pith2026190901360,
  author       = {Pith},
  title        = {Pith review of: The hidden satellites of massive galaxies and quasars at high-redshift},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VAEANSK7}},
  note         = {Machine review of arXiv:1909.01360}
}
abstract

Using cosmological, radiation-hydrodynamic simulations targeting a rare $\approx \, 2 \times 10^{12} \, \rm M_\odot$ halo at $z \, = \, 6$, we show that the number counts and internal properties of satellite galaxies within the massive halo are sensitively regulated by a combination of local stellar radiative feedback and strong tidal forces. Radiative feedback operates before the first supernova explosions erupt and results in less tightly-bound galaxies. Satellites are therefore more vulnerable to tidal stripping when they accrete onto the main progenitor and are tidally disrupted on a significantly shorter timescale. Consequently, the number of satellites with $M_{\rm \star} > 10^{7} \, \rm M_\odot$ within the parent system's virial radius drops by up to $60 \%$ with respect to an identical simulation performed without stellar radiative feedback. Radiative feedback also impacts the central galaxy, whose effective radius increases by a factor $\lesssim 3$ due to the presence of a more extended and diffuse stellar component. We suggest that the number of satellites in the vicinity of massive high-redshift galaxies is an indication of the strength of stellar radiative feedback and and can be anomalously low in the extreme cosmic environments of high-redshift quasars.

Figures

Figures reproduced from arXiv: 1909.01360 by the authors.

Figure 1
Figure 1. Mass-weighted entropy within a cubic volume of side length 500kpc centred on the most massive galaxy at z = 6 in the simulation without stellar radiative feedback (top, left panel) and with stellar radiation (bottom, left panel). The black circle marks the halo’s virial radius. By suppressing star formation, stellar ra￾diation leads to weaker supernova-driven outflows, explaining the lower entropies seen in the simu… view at source ↗
Figure 2
Figure 2. Top: Average number of satellites per logarithmic stel￾lar mass within Rvir (filled histograms) and in the radius range Rvir < R < 2Rvir (open and hatched histograms), as obtained by averaging over all simulations snapshots between z = 6.1 and z = 6. Violet histograms show the satellite number count in the simulation without stellar radiative feedback, while the orange and hatched histograms give the result for the … view at source ↗
Figure 3
Figure 3. We select three systems with Mvir ∼ 1010 M at z = 8, matching them between the simulations without- and with radiative feedback. We track the stellar particles within 30% Rvir forward in time and show the resulting spatial distribution at z = 7 (first column), z = 6.5 (second column) and z = 6 (third column) in SN (top row) and SN+RT (bottom row). We provide the peak stellar circular velocity next to each system, ta… view at source ↗

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Works this paper leans on

39 extracted references · 4 canonical work pages

  1. [1]

    arXiv:1904.02723

    Agertz O., et al., 2019, \, (submitted), https://ui.adsabs.harvard.edu/abs/2019arXiv190402723A p. arXiv:1904.02723

    arXiv 2019

  2. [2]

    Aubert D., Pichon C., Colombi S., 2004, @doi [ ] 10.1111/j.1365-2966.2004.07883.x , https://ui.adsabs.harvard.edu/abs/2004MNRAS.352..376A 352, 376

    arXiv 2004

  3. [3]

    Ba \ n ados E., et al., 2018, @doi [ ] 10.1038/nature25180 , https://ui.adsabs.harvard.edu/\#abs/2018Natur.553..473B 553, 473

    doi:10.1038/nature25180 2018

  4. [4]

    Balmaverde B., et al., 2017, @doi [ ] 10.1051/0004-6361/201730683 , http://adsabs.harvard.edu/abs/2017A

    doi:10.1051/0004-6361/201730683 2017

  5. [5]

    C., Volonteri M., Rees M

    Begelman M. C., Volonteri M., Rees M. J., 2006, @doi [ ] 10.1111/j.1365-2966.2006.10467.x , http://adsabs.harvard.edu/abs/2006MNRAS.370..289B 370, 289

    arXiv 2006

  6. [6]

    S., Wechsler R

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

    doi:10.1088/0004-637x/770/1/57 2013

  7. [7]

    Biernacki P., Teyssier R., 2018, @doi [ ] 10.1093/mnras/sty216 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.5688B 475, 5688

    doi:10.1093/mnras/sty216 2018

  8. [8]

    B., et al., 2018, @doi [ ] 10.3847/1538-4357/aae396 , http://adsabs.harvard.edu/abs/2018ApJ...867..153C 867, 153

    Champagne J. B., et al., 2018, @doi [ ] 10.3847/1538-4357/aae396 , http://adsabs.harvard.edu/abs/2018ApJ...867..153C 867, 153

    work page doi:10.3847/1538-4357/aae396 2018

Show all 39 references
  1. [9]

    G., 2014, @doi [ ] 10.1093/mnras/stu101 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.439.2146C 439, 2146

    Costa T., Sijacki D., Trenti M., Haehnelt M. G., 2014, @doi [ ] 10.1093/mnras/stu101 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.439.2146C 439, 2146

    2014 doi

  2. [10]

    G., 2015, @doi [ ] 10.1093/mnrasl/slu193 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448L..30C 448, L30

    Costa T., Sijacki D., Haehnelt M. G., 2015, @doi [ ] 10.1093/mnrasl/slu193 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448L..30C 448, L30

    2015 doi

  3. [11]

    G., 2018, @doi [ ] 10.1093/mnras/sty1514 , http://adsabs.harvard.edu/abs/2018MNRAS.479.2079C 479, 2079

    Costa T., Rosdahl J., Sijacki D., Haehnelt M. G., 2018, @doi [ ] 10.1093/mnras/sty1514 , http://adsabs.harvard.edu/abs/2018MNRAS.479.2079C 479, 2079

    2018 doi

  4. [12]

    Decarli R., et al., 2017, @doi [ ] 10.1038/nature22358 , https://ui.adsabs.harvard.edu/abs/2017Natur.545..457D 545, 457

    2017 doi

  5. [13]

    Di Matteo T., Khandai N., DeGraf C., Feng Y., Croft R. A. C., Lopez J., Springel V., 2012, @doi [ ] 10.1088/2041-8205/745/2/L29 , https://ui.adsabs.harvard.edu/\#abs/2012ApJ...745L..29D 745, L29

    2012 doi

  6. [14]

    J., 1988, @doi [ ] 10.1093/mnras/230.1.5P , https://ui.adsabs.harvard.edu/\#abs/1988MNRAS.230P...5E 230, 5p

    Efstathiou G., Rees M. J., 1988, @doi [ ] 10.1093/mnras/230.1.5P , https://ui.adsabs.harvard.edu/\#abs/1988MNRAS.230P...5E 230, 5p

    1988 doi

  7. [15]

    Fan X., et al., 2001, @doi [ ] 10.1086/324111 , https://ui.adsabs.harvard.edu/\#abs/2001AJ....122.2833F 122, 2833

    2001 doi

  8. [16]

    Faucher-Gigu \`e re C.-A., Lidz A., Zaldarriaga M., Hernquist L., 2009, @doi [ ] 10.1088/0004-637X/703/2/1416 , http://adsabs.harvard.edu/abs/2009ApJ...703.1416F 703, 1416

    2009 doi

  9. [17]

    S., Dubois Y., Peirani S., Pichon C., Devriendt J., 2019, @doi [ ] 10.1093/mnras/stz2105 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.tmp.2120H p

    Habouzit M., Volonteri M., Somerville R. S., Dubois Y., Peirani S., Pichon C., Devriendt J., 2019, @doi [ ] 10.1093/mnras/stz2105 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.tmp.2120H p. 2120

    2019 doi

  10. [18]

    F., Grudic M

    Hopkins P. F., Grudic M. Y., Wetzel A. R., Keres D., Gaucher-Giguere C.-A., Ma X., Murray N., Butcher N., 2018, \, (submitted), http://adsabs.harvard.edu/abs/2018arXiv181112462H

    2018

  11. [19]

    M., Glover S

    Kannan R., Marinacci F., Simpson C. M., Glover S. C. O., Hernquist L., 2018, \, (submitted), http://adsabs.harvard.edu/abs/2018arXiv181201614K

    2018

  12. [20]

    arXiv:1905.11414

    Katz H., et al., 2019, \, (submitted), https://ui.adsabs.harvard.edu/abs/2019arXiv190511414K p. arXiv:1905.11414

    2019 arXiv

  13. [21]

    Kim S., et al., 2009, @doi [ ] 10.1088/0004-637X/695/2/809 , http://adsabs.harvard.edu/abs/2009ApJ...695..809K 695, 809

    2009 doi

  14. [22]

    Kimm T., Cen R., Devriendt J., Dubois Y., Slyz A., 2015, @doi [ ] 10.1093/mnras/stv1211 , http://adsabs.harvard.edu/abs/2015MNRAS.451.2900K 451, 2900

    2015 doi

  15. [23]

    Kimm T., Katz H., Haehnelt M., Rosdahl J., Devriendt J., Slyz A., 2017, @doi [ ] 10.1093/mnras/stx052 , http://adsabs.harvard.edu/abs/2017MNRAS.466.4826K 466, 4826

    2017 doi

  16. [24]

    Kimm T., Haehnelt M., Blaizot J., Katz H., Michel-Dansac L., Garel T., Rosdahl J., Teyssier R., 2018, @doi [ ] 10.1093/mnras/sty126 , http://adsabs.harvard.edu/abs/2018MNRAS.475.4617K 475, 4617

    2018 doi

  17. [25]

    Lupi A., Volonteri M., Decarli R., Bovino S., Silk J., Bergeron J., 2019, @doi [ ] 10.1093/mnras/stz1959 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.4004L 488, 4004

    2019 doi

  18. [26]

    Mandelker N., Dekel A., Ceverino D., DeGraf C., Guo Y., Primack J., 2017, @doi [ ] 10.1093/mnras/stw2358 , http://adsabs.harvard.edu/abs/2017MNRAS.464..635M 464, 635

    2017 doi

  19. [27]

    P., Naab T., White S

    Moster B. P., Naab T., White S. D. M., 2018, @doi [ ] 10.1093/mnras/sty655 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1822M 477, 1822

    2018 doi

  20. [28]

    Peters T., et al., 2017, @doi [ ] 10.1093/mnras/stw3216 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466.3293P 466, 3293

    2017 doi

  21. [29]

    Rosdahl J., Teyssier R., 2015, @doi [ ] 10.1093/mnras/stv567 , http://adsabs.harvard.edu/abs/2015MNRAS.449.4380R 449, 4380

    2015 doi

  22. [30]

    Rosdahl J., Blaizot J., Aubert D., Stranex T., Teyssier R., 2013, @doi [ ] 10.1093/mnras/stt1722 , http://adsabs.harvard.edu/abs/2013MNRAS.436.2188R 436, 2188

    2013 doi

  23. [31]

    Rosdahl J., Schaye J., Teyssier R., Agertz O., 2015, @doi [ ] 10.1093/mnras/stv937 , http://adsabs.harvard.edu/abs/2015MNRAS.451...34R 451, 34

    2015 doi

  24. [32]

    Rosdahl J., et al., 2018, @doi [ ] 10.1093/mnras/sty1655 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479..994R 479, 994

    2018 doi

  25. [33]

    G., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15452.x , https://ui.adsabs.harvard.edu/\#abs/2009MNRAS.400..100S 400, 100

    Sijacki D., Springel V., Haehnelt M. G., 2009, @doi [ ] 10.1111/j.1365-2966.2009.15452.x , https://ui.adsabs.harvard.edu/\#abs/2009MNRAS.400..100S 400, 100

    2009

  26. [34]

    Teyssier R., 2002, @doi [ ] 10.1051/0004-6361:20011817 , https://ui.adsabs.harvard.edu/abs/2002A&A...385..337T 385, 337

    2002 doi

  27. [35]

    Trakhtenbrot B., Lira P., Netzer H., Cicone C., Maiolino R., Shemmer O., 2017, @doi [ ] 10.3847/1538-4357/836/1/8 , https://ui.adsabs.harvard.edu/\#abs/2017ApJ...836....8T 836, 8

    2017 doi

  28. [36]

    Tweed D., Devriendt J., Blaizot J., Colombi S., Slyz A., 2009, @doi [ ] 10.1051/0004-6361/200911787 , https://ui.adsabs.harvard.edu/abs/2009A&A...506..647T 506, 647

    2009 doi

  29. [37]

    J., 2006, @doi [ ] 10.1086/507444 , http://adsabs.harvard.edu/abs/2006ApJ...650..669V 650, 669

    Volonteri M., Rees M. J., 2006, @doi [ ] 10.1086/507444 , http://adsabs.harvard.edu/abs/2006ApJ...650..669V 650, 669

    2006 doi

  30. [38]

    Wu X.-B., et al., 2015, @doi [ ] 10.1038/nature14241 , https://ui.adsabs.harvard.edu/\#abs/2015Natur.518..512W 518, 512

    2015 doi

  31. [39]

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