Pith. sign in

REVIEW 1 major objections 2 cited by

Supermassive Black Hole Assembly from Heavy Seeds with Dynamical Friction in the BRAHMA Simulations: Implications for JWST, LISA, and the Local Universe

T0 review · 1 major / 0 minor · reviewed 2026-06-27 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Lenient heavy black hole seed models in BRAHMA simulations produce merger rates above 100 per year and near-unity occupation fractions in galaxies down to 10 million solar masses, while strict models yield rates around 1 per year and fracti

desk verdict Lenient heavy-seed models in the new BRAHMA runs produce merger rates above 100 per year and near-unity occupation fractions, versus roughly 1 per year and low fractions for the strict model, but the subgrid dynamical friction implementation is the main uncertainty. read the letter →

arxiv 2606.12851 v1 pith:UFXSR75M submitted 2026-06-11 astro-ph.GA

Aklant K. Bhowmick , Laura Blecha , Paul Torrey , Luke Zoltan Kelley , Rachel S. Somerville , Rainer Weinberger , Priyamvada Natarajan , Tiziana Di Matteo
show 3 more authors
Lars Hernquist Mark Vogelsberger Alex M. Garcia
This is my paper · ORCID
classification astro-ph.GA
keywords supermassiveblackholesheavyseedsdynamicalfrictioncosmologicalsimulationsholemergersoccupationfractionJWSTLISA
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 compares two lenient heavy-seed prescriptions, in which black hole seeds of 10,000 or 100,000 solar masses form in every halo containing sufficient dense and metal-poor gas, against a strict prescription that adds further direct-collapse constraints. These prescriptions are implemented inside the BRAHMA cosmological simulations together with a subgrid dynamical friction model that governs how seeds sink and merge. By redshift 5 the lenient cases produce overmassive black holes whose luminosities overlap with those inferred for JWST sources, while by the present day they also generate high merger rates and high occupation fractions even in low-mass galaxies. The differences imply that measurements of merger rates and local occupation fractions can distinguish the dominant seeding pathways.

What carries the argument

The subgrid dynamical friction model combined with lenient versus strict criteria for forming 10,000 and 100,000 solar-mass seeds inside halos that contain sufficient dense and metal-poor gas, run inside the BRAHMA cosmological simulations.

What would settle it

A measurement of the present-day black hole occupation fraction in galaxies with stellar mass near 10 million solar masses that is either near 100 percent or below 10 percent would distinguish the lenient from the strict seed models.

Watch

Extended reading notes

Core claim

In the BRAHMA simulations, lenient seed models in which all halos with sufficient dense and metal-poor gas form 10,000 and 100,000 solar-mass seeds generate by z approximately 5 multiple systems with black-hole-to-stellar-mass ratios at or above 0.01 that reach luminosities of 10^43 to 10^45 erg per second, while at z equals 0 they produce merger rates of at least 100 per year and occupation fractions near unity even for galaxies with stellar masses below 10 million solar masses; the strict seed model, which forms 100,000 solar-mass seeds only under additional direct-collapse conditions, instead produces merger rates of only about 1 per year and occupation fractions below 10 percent for gala

Load-bearing premise

The subgrid dynamical friction model and the specific criteria defining sufficient dense and metal-poor gas for lenient seeding versus additional direct-collapse constraints for strict seeding accurately represent unresolved high-redshift astrophysical processes.

Editorial extensions

If this is right

  • Lenient production of 100,000 solar-mass seeds produces multiple overmassive systems with black-hole-to-stellar-mass ratios at or above 0.01 inside galaxies that host 100 million to 1 billion solar-mass black holes.
  • All three seed models produce black-hole-to-stellar-mass relations broadly consistent with the local universe for galaxies above 1 billion solar masses by redshift 5.
  • The lenient scenarios also generate systems near the upper envelope of the observed local scatter in the black-hole-to-stellar-mass relation.
  • Future gravitational-wave event rates and measurements of local black hole occupation fractions will constrain the dominant pathways for high-redshift black hole assembly.

Reading between the lines

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

  • If observations favor the lenient models, then mergers contribute a larger fraction of black hole growth at high redshift than they do under strict seeding.
  • The overmassive black holes produced at redshift 5 in lenient models suggest that seeding alone, without requiring sustained super-Eddington accretion, can account for some JWST detections.
  • High occupation fractions in present-day dwarf galaxies would favor lenient seeding even if the strict model matches other observables.
  • The efficiency of the dynamical friction model in driving seed mergers sets the scale of the predicted rate differences between the two classes of models.
Share X Bluesky LinkedIn Reddit HN

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

1 major / 0 minor

Summary. The manuscript presents results from the BRAHMA cosmological simulations investigating the assembly of supermassive black holes from heavy seeds under different seed formation prescriptions coupled with a subgrid dynamical friction model. It reports that lenient seed models, allowing seed formation in halos with sufficient dense and metal-poor gas for ~10^4 and ~10^5 M_sun seeds, lead to high BH merger rates of ≳100 yr^{-1} and near-unity local occupation fractions even in low-mass galaxies (M_* ≲ 10^7 M_⊙). In contrast, a strict seed model with additional direct-collapse constraints yields merger rates of ~1 yr^{-1} and occupation fractions ≲10% for M_* ≲ 10^8 M_⊙. The models produce M_*-M_BH relations consistent with local observations for massive galaxies, with lenient models also producing overmassive BHs.

Significance. If the results hold, they demonstrate that variations in seed prescriptions can lead to dramatically different predictions for observable quantities like merger rates and BH occupation fractions, providing potential constraints from JWST, LISA, and local universe observations. The work highlights the sensitivity of high-redshift BH assembly to seeding criteria.

major comments (1)
  1. [Abstract (subgrid dynamical friction model and seed criteria)] The central claims regarding the distinction in merger rates (≳100 vs ~1 yr^{-1}) and occupation fractions (near-unity vs ≲10%) between lenient and strict models rely on the subgrid dynamical friction implementation and the specific seed formation criteria. The provided text reports simulation outputs but provides no details on validation, convergence tests, or error quantification for the subgrid DF model or the 'dense & metal-poor gas' thresholds, making the quantitative predictions model-dependent without independent checks against resolved high-z calculations.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for their constructive comments on our manuscript. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract (subgrid dynamical friction model and seed criteria)] The central claims regarding the distinction in merger rates (≳100 vs ~1 yr^{-1}) and occupation fractions (near-unity vs ≲10%) between lenient and strict models rely on the subgrid dynamical friction implementation and the specific seed formation criteria. The provided text reports simulation outputs but provides no details on validation, convergence tests, or error quantification for the subgrid DF model or the 'dense & metal-poor gas' thresholds, making the quantitative predictions model-dependent without independent checks against resolved high-z calculations.

    Authors: We agree that the manuscript would benefit from expanded details on the subgrid DF model and seed criteria. Section 2 describes the DF implementation (following the formulation validated in our prior BRAHMA papers) and the seed thresholds (motivated by dense, metal-poor gas conditions for direct-collapse scenarios). In the revision we will add an explicit paragraph summarizing the referenced validation and convergence tests from those works, plus a short discussion of threshold sensitivity. We will also note the model dependence of the quantitative rates more prominently. Direct new comparisons to fully resolved high-z calculations remain outside the scope of this large-volume study. revision: partial

standing simulated objections not resolved
  • Independent checks against resolved high-z calculations for the subgrid DF model and seed thresholds

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; simulation outputs are direct consequences of input prescriptions

full rationale

The paper reports merger rates and occupation fractions as direct outputs from BRAHMA cosmological simulations run with two classes of heavy-seed prescriptions (lenient vs strict) plus a subgrid dynamical friction model. These quantities are generated by evolving the specified initial conditions and subgrid rules; they do not reduce via the paper's equations to quantities fitted to JWST, LISA, or local-universe data, nor do they rely on self-definitional loops, load-bearing self-citations, or ansatzes smuggled from prior author work. The central claims remain independent simulation predictions under the stated model variations.

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

The central claims rest on parameterized subgrid prescriptions for seed formation and dynamical friction whose specific thresholds and implementations are chosen to represent different physical scenarios rather than derived from first principles.

free parameters (2)
  • Seed mass
    The paper varies discrete seed masses of approximately 10^4 and 10^5 solar masses as part of the heavy-seed model prescriptions.
  • Formation criteria thresholds
    Lenient versus strict conditions on gas density, metallicity, and additional direct-collapse constraints are chosen model parameters.
assumptions (2)
  • standard math Standard Lambda-CDM cosmological framework
    The BRAHMA simulations are cosmological and assume the standard background cosmology.
  • domain assumption Subgrid models for unresolved gas physics, star formation, and black hole dynamics
    The simulations rely on parameterized prescriptions for physics below the resolution scale, including the dynamical friction implementation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Supermassive Black Hole Assembly from Heavy Seeds with Dynamical Friction in the BRAHMA Simulations: Implications for JWST, LISA, and the Local Universe." pith.science (2026). https://pith.science/paper/UFXSR75M

@misc{pith2026260612851,
  author       = {Pith},
  title        = {Pith review of: Supermassive Black Hole Assembly from Heavy Seeds with Dynamical Friction in the BRAHMA Simulations: Implications for JWST, LISA, and the Local Universe},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UFXSR75M}},
  note         = {Machine review of arXiv:2606.12851}
}
abstract

The JWST discoveries of supermassive black holes (BHs) at $z \gtrsim 5$ may provide key insights into their seeding origins. Using new $[18{-}72~\rm Mpc]^3$ BRAHMA cosmological simulations, we investigate how variations in heavy-seed prescriptions, coupled with a subgrid dynamical friction model, shape BH populations at $z \sim 5$ and $z \sim 0$. We consider two "lenient'' seed models, in which all halos containing sufficient dense & metal-poor gas form $\sim10^4$ and $\sim10^5~M_{\odot}$ seeds, and a "strict'' seed model, in which $\sim10^5 M_{\odot}$ seeds form only under additional constraints motivated by direct collapse black hole formation. By $z \sim 5$, all models produce $M_*-M_{\rm BH}$ relations broadly consistent with the observed local Universe for $M_*\gtrsim10^9~M_{\odot}$ galaxies, but only the lenient scenarios generate systems near the upper envelope of the observed local scatter. In galaxies hosting $M_{\rm BH} \sim 10^8$-$10^9~M_{\odot}$ BHs, lenient production of $\sim10^5~M_{\odot}$ seeds also produces multiple overmassive systems with $M_{\rm BH}/M_* \gtrsim 0.01$. Although their growth is dominated by seeding and mergers, these systems reach luminosities of $\sim10^{43}$-$10^{45}\mathrm{erg s^{-1}}$, comparable to those inferred for JWST-detected BHs. As a key observational signature, the lenient seed models yield merger rates of $\gtrsim100\mathrm{yr^{-1}}$ and near-unity local BH occupation fractions even in galaxies with $M_* \lesssim 10^7~M_{\odot}$. In contrast, the strict seed model produces merger rates of only $\sim1\mathrm{yr^{-1}}$ and local occupation fractions of $\lesssim10\%$ for galaxies with $M_* \lesssim 10^8~M_{\odot}$. Future gravitational-wave event rates and measurements of local BH occupation fractions will therefore provide strong constraints on the dominant pathways responsible for high-redshift BH assembly.

Figures

Figures reproduced from arXiv: 2606.12851 by the authors.

Figure 1
Figure 1. Visualizations of the boxes at the final snapshots, illustrating the implementation of the Lenient-Heavy and Strict-Heavy seed models. The larger panels correspond to SUITE-z5 at z = 5, and the inset panels correspond to SUITE-z0 at z = 0. The left halves of the 2D maps show the gas density field, which smoothly transitions to the gas metallicity field on the right halves. Since all simulations with the same volume … view at source ↗
Figure 2
Figure 2. Evolution of the BH number density for our three different seed models. The leftmost panel shows the overall number density, whereas middle and right panels show the number density of ≳ 105 M⊙ and ≳ 106 M⊙ BHs respectively. In the rightmost panel, the black marker shows the range of observational constraints obtained by integrating the range of z = 0 BHMF measurements compiled by Porras-Valverde et al. (2026) and sh… view at source ↗
Figure 3
Figure 3. Similar to the previous figure, but here we com￾pare the number density evolution produced by our subgrid￾DF based BRAHMA-18-D5 boxes vs. prior results using reposi￾tioning (Bhowmick et al. 2025a) for our Lenient-Heavy and Strict-Heavy seed models. Under subgrid-DF, there is a substantial delay in the assembly of ≳ 106 M⊙ BHs com￾pared to respositioning. possible in the BRAHMA simulations. However, these high resolu… view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: For the DGB simulations, we show the fractional contribution to BH growth from seeding and mergers as opposed to gas accretion. For the different seed models, this is shown as a function of BH mass for populations at z = 0 (left) and z = 5 (right). In the early phase o…
Figure 5
Figure 5. Figure 5: BHMFs predicted by different seeding models at z = 5 (top row) and z = 0 (bottom row). Thick lines show the DGB predictions, while thin lines show the calibrated ESD predictions. The dashed lines indicate the repositioning-based predictions for the Lenient-Heavy seed m…
Figure 7
Figure 7. Figure 7: Predictions for the M∗–MBH relation for different seeding models. We focus exclusively on central BHs, plotting the mass of the most massive BH in each halo against the total stellar mass. Each panel corresponds to a given seeding model; smaller circles show DGB boxes …
Figure 8
Figure 8. Figure 8: Bolometric luminosity versus BH mass for the overmassive z = 5 BHs in the Lenient-Heavy boxes. Blue and orange symbols correspond to BHs in the ESD and DGB boxes, respectively. The error bars indicate luminos￾ity variability, estimated as the median values of the max￾i…
Figure 9
Figure 9. Figure 9: Similar to the [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: Delay times between BH mergers and their corresponding host galaxy mergers in the Lenient-Heavy, Strict-Heavy, and Lenient-LowMass seed models within the DGB boxes. From left to right, the panels show the delay time as a function of halo merger redshift, total BH mass…
Figure 11
Figure 11. Figure 11: BH–BH merger rates predicted by our simulations for the three different seed models. The left panel shows the total merger rates, the middle panel includes only mergers between BHs with masses ≳ 105 M⊙, and the right panel shows mergers between BHs with masses ≳ 106 M…
Figure 12
Figure 12. Figure 12: BH occupation fractions at z = 5 (top) and z = 0 (bottom) predicted by our DGB simulations. Solid lines show the predictions for the different seed models. Dotted lines show predictions from previous BRAHMA boxes that used BH repositioning (Bhowmick et al. 2024b, 2025…
Figure 13
Figure 13. Figure 13: Left Panel: For the DGB simulations, we plot the galaxy masses and redshifts at which descendant BHs with Mbh = MESD = 8 Mseed assemble. Each data point (filled circle) corresponds to an assembly event traced along galaxy merger trees. In the Lenient-LowMass seed mode…
Figure 14
Figure 14. Figure 14: Comparison of the two-point correlation function predicted by the DGB (solid) and ESD (dashed) simulations for BHs with Mbh > MESD at z = 0 (left) and z = 5 (right). Because the clustering signal can depend on simulation volume—since larger volumes sample more diverse…
Figure 15
Figure 15. Figure 15: Calibration of the contribution from unresolved minor mergers in the stochastic seed model: The y-axis shows the mass growth due to unresolved minor mergers (∆Munresolved minor ) per resolved merger, in units of the ESD mass (MESD). This quantity is added to the remna…
Figure 16
Figure 16. Figure 16: Validation of the stochastic seed model: Comparison of the BH number density evolution (leftmost panel) and the BHMFs at z = 5 & 0 (remaining three panels) between the DGB (solid) and ESD (dashed) simulations. Importantly, both DGB and ESD simulations have the same vo…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How to raise a supermassive black hole: interpreting early JWST AGN with the AESOPICA simulations

    astro-ph.GA 2026-07 conditional novelty 6.0 of 10

    A systematic simulation suite with a mock JWST survey shows early overmassive black holes can arise from any seed mass under fast accretion, and broad-line selection explains much of the observed overmassiveness.

  2. Black Hole Occupation Fraction: Dependence on Black Hole Mass Threshold, Environment, Resolution and Redshift

    astro-ph.GA 2026-06 unverdicted novelty 5.0 of 10

    Black hole occupation fraction rises with stellar mass but its normalization, shape, and redshift trend depend strongly on BH mass threshold, central vs satellite galaxies, simulation box, resolution, and sampled popu...

Reference graph

Works this paper leans on

146 extracted references · 146 canonical work pages · cited by 2 Pith papers

  1. [1]

    Dunlop, J. S. 2013, MNRAS, 432, 3438, doi: 10.1093/mnras/stt696

    work page doi:10.1093/mnras/stt696 2013

  2. [2]

    M., et al

    Agazie, G., Anumarlapudi, A., Archibald, A. M., et al. 2023, ApJL, 951, L8, doi: 10.3847/2041-8213/acdac6

    work page doi:10.3847/2041-8213/acdac6 2023

  3. [3]

    and Coil, A

    Aird, J., Coil, A. L., Georgakakis, A., et al. 2015, MNRAS, 451, 1892, doi: 10.1093/mnras/stv1062

    work page doi:10.1093/mnras/stv1062 2015

  4. [4]

    , keywords =

    Akins, H. B., Casey, C. M., Lambrides, E., et al. 2025, ApJ, 991, 37, doi: 10.3847/1538-4357/ade984

    work page doi:10.3847/1538-4357/ade984 2025

  5. [5]

    Science , archivePrefix = "arXiv", eprint =

    Alexander, T., & Natarajan, P. 2014, Science, 345, 1330, doi: 10.1126/science.1251053 Alonso-´Alvarez, G., Cline, J. M., & Dewar, C. 2024, PhRvL, 133, 021401, doi: 10.1103/PhysRevLett.133.021401

    work page doi:10.1126/science.1251053 2014

  6. [6]

    Laser Interferometer Space Antenna

    Amaro-Seoane, P., Audley, H., Babak, S., et al. 2017, arXiv e-prints, arXiv:1702.00786, doi: 10.48550/arXiv.1702.00786

    work page Pith review arXiv doi:10.48550/arxiv.1702.00786 2017

  7. [7]

    T., Jahnke, K., Onoue, M., et al

    Andika, I. T., Jahnke, K., Onoue, M., et al. 2024, A&A, 685, A25, doi: 10.1051/0004-6361/202349025 Ba˜ nados, E., Venemans, B. P., Mazzucchelli, C., et al. 2018, Nature, 553, 473, doi: 10.1038/nature25180

    work page doi:10.1051/0004-6361/202349025 2024

  8. [8]

    MNRAS , author =

    Barausse, E. 2012, MNRAS, 423, 2533, doi: 10.1111/j.1365-2966.2012.21057.x

    work page doi:10.1111/j.1365-2966.2012.21057.x 2012

Show all 146 references
  1. [9]

    L., & Aird, J

    Barlow-Hall, C. L., & Aird, J. 2025, arXiv e-prints, arXiv:2506.16145, doi: 10.48550/arXiv.2506.16145

    2025 doi

  2. [10]

    1986, Nature, 324, 446, doi: 10.1038/324446a0 Ba˜ nados, E., Venemans, B

    Barnes, J., & Hut, P. 1986, Nature, 324, 446, doi: 10.1038/324446a0 Ba˜ nados, E., Venemans, B. P., Decarli, R., et al. 2016, The Astrophysical Journal Supplement Series, 227, 11, doi: 10.3847/0067-0049/227/1/11

    1986 doi

  3. [11]

    C., & Silk, J

    Begelman, M. C., & Silk, J. 2023, MNRAS, 526, L94, doi: 10.1093/mnrasl/slad124

    2023 doi

  4. [12]

    C., Volonteri, M., & Rees, M

    Begelman, M. C., Volonteri, M., & Rees, M. J. 2006, MNRAS, 370, 289, doi: 10.1111/j.1365-2966.2006.10467.x

    2006 doi

  5. [13]

    K., et al

    Bernardi, M., Meert, A., Sheth, R. K., et al. 2013, MNRAS, 436, 697, doi: 10.1093/mnras/stt1607

    2013 doi

  6. [14]

    K., Blecha, L., Torrey, P., et al

    Bhowmick, A. K., Blecha, L., Torrey, P., et al. 2022, MNRAS, 510, 177, doi: 10.1093/mnras/stab3439 —. 2024a, MNRAS, 529, 3768, doi: 10.1093/mnras/stae780 —. 2021, MNRAS, 507, 2012, doi: 10.1093/mnras/stab2204 —. 2024b, MNRAS, 533, 1907, doi: 10.1093/mnras/stae1819 —. 2024c, MN...

    2022 doi

  7. [15]

    K., Blecha, L., Kelley, L

    Bhowmick, A. K., Blecha, L., Kelley, L. Z., et al. 2025b, ApJ, 991, 81, doi: 10.3847/1538-4357/adf96b

    doi

  8. [16]

    K., Blecha, L., Torrey, P., et al

    Bhowmick, A. K., Blecha, L., Torrey, P., et al. 2026, ApJ, 997, 187, doi: 10.3847/1538-4357/ae2607 Bogd´ an,´A., Goulding, A. D., Natarajan, P., et al. 2024, Nature Astronomy, 8, 126, doi: 10.1038/s41550-023-02111-9

    2026 doi

  9. [17]

    2003, ApJ, 596, 34, doi: 10.1086/377529

    Bromm, V., & Loeb, A. 2003, ApJ, 596, 34, doi: 10.1086/377529

    2003 doi

  10. [18]

    R., Simons, R

    Brooks, M., Trump, J. R., Simons, R. C., et al. 2026, ApJ, 1002, 129, doi: 10.3847/1538-4357/ae5652

    2026 doi

  11. [19]

    J., Natarajan, P., Baldassare, V

    Burke, C. J., Natarajan, P., Baldassare, V. F., & Geha, M. 2024, Multi-wavelength constraints on the local black hole occupation fraction. https://arxiv.org/abs/2410.11177

    2024

  12. [20]

    J., Natarajan, P., Baldassare, V

    Burke, C. J., Natarajan, P., Baldassare, V. F., & Geha, M. 2025, ApJ, 978, 77, doi: 10.3847/1538-4357/ad94d9 31

    2025 doi

  13. [21]

    2025, MNRAS, 542, 2597, doi: 10.1093/mnras/staf1362

    Cenci, E., & Habouzit, M. 2025, MNRAS, 542, 2597, doi: 10.1093/mnras/staf1362

    2025 doi

  14. [22]

    2003, PASP, 115, 763, doi: 10.1086/376392

    Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392

    2003 doi

  15. [23]

    2024, ApJ, 969, 93, doi: 10.3847/1538-4357/ad4966

    Cho, H., & Woo, J.-H. 2024, ApJ, 969, 93, doi: 10.3847/1538-4357/ad4966

    2024 doi

  16. [24]

    2026, arXiv e-prints, arXiv:2601.04955, doi: 10.48550/arXiv.2601.04955

    Springel, V. 2026, arXiv e-prints, arXiv:2601.04955, doi: 10.48550/arXiv.2601.04955

    2026 doi

  17. [25]

    Das, A., Schleicher, D. R. G., Basu, S., & Boekholt, T. C. N. 2021a, MNRAS, doi: 10.1093/mnras/stab1428

    doi

  18. [26]

    Das, A., Schleicher, D. R. G., Leigh, N. W. C., & Boekholt, T. C. N. 2021b, MNRAS, 503, 1051, doi: 10.1093/mnras/stab402

    doi

  19. [27]

    B., Miller, M

    Davies, M. B., Miller, M. C., & Bellovary, J. M. 2011, ApJL, 740, L42, doi: 10.1088/2041-8205/740/2/L42

    2011 doi

  20. [28]

    S., & White, S

    Davis, M., Efstathiou, G., Frenk, C. S., & White, S. D. M. 1985, ApJ, 292, 371, doi: 10.1086/163168

    1985 doi

  21. [29]

    2020, MNRAS, 491, 4973, doi: 10.1093/mnras/stz3309

    DeGraf, C., & Sijacki, D. 2020, MNRAS, 491, 4973, doi: 10.1093/mnras/stz3309

    2020 doi

  22. [30]

    C., Chowdhury, D

    Dekel, A., Stone, N. C., Chowdhury, D. D., et al. 2025, A&A, 695, A97, doi: 10.1051/0004-6361/202452393

    2025 doi

  23. [31]

    Durodola, E., Pacucci, F., & Hickox, R. C. 2024, arXiv e-prints, arXiv:2406.10329, doi: 10.48550/arXiv.2406.10329 —. 2025, ApJ, 985, 169, doi: 10.3847/1538-4357/adced2 EPTA Collaboration, InPTA Collaboration, Antoniadis, J., et al. 2024, A&A, 685, A94, doi: 10.1051/0004-6361/202347433

    2024 doi

  24. [32]

    K., Lupton, R

    Fan, X., Narayanan, V. K., Lupton, R. H., et al. 2001, AJ, 122, 2833, doi: 10.1086/324111

    2001 doi

  25. [33]

    P., et al

    Fei, Q., Fujimoto, S., Naidu, R. P., et al. 2025, arXiv e-prints, arXiv:2509.20452, doi: 10.48550/arXiv.2509.20452

    2025 doi

  26. [34]

    S., & Sagunski, L

    Fischer, M. S., & Sagunski, L. 2024, A&A, 690, A299, doi: 10.1051/0004-6361/202451304

    2024 doi

  27. [35]

    L., Woosley, S

    Fryer, C. L., Woosley, S. E., & Heger, A. 2001, ApJ, 550, 372, doi: 10.1086/319719

    2001 doi

  28. [36]

    D., Greene, J

    Goulding, A. D., Greene, J. E., Setton, D. J., et al. 2023, ApJL, 955, L24, doi: 10.3847/2041-8213/acf7c5

    2023 doi

  29. [37]

    E., Strader, J., & Ho, L

    Greene, J. E., Strader, J., & Ho, L. C. 2020, ARA&A, 58, 257, doi: 10.1146/annurev-astro-032620-021835

    2020 doi

  30. [38]

    E., Labbe, I., Goulding, A

    Greene, J. E., Labbe, I., Goulding, A. D., et al. 2023, arXiv e-prints, arXiv:2309.05714, doi: 10.48550/arXiv.2309.05714 —. 2024, ApJ, 964, 39, doi: 10.3847/1538-4357/ad1e5f

    2023 doi

  31. [39]

    E., Setton, D

    Greene, J. E., Setton, D. J., Furtak, L. J., et al. 2025, arXiv e-prints, arXiv:2509.05434, doi: 10.48550/arXiv.2509.05434

    2025 doi

  32. [40]

    2016, MNRAS, 463, 529, doi: 10.1093/mnras/stw1924

    Peirani, S. 2016, MNRAS, 463, 529, doi: 10.1093/mnras/stw1924

    2016 doi

  33. [41]

    S., et al

    Habouzit, M., Li, Y., Somerville, R. S., et al. 2021, MNRAS, 503, 1940, doi: 10.1093/mnras/stab496

    2021 doi

  34. [42]

    2011, MNRAS, 415, 2101, doi: 10.1111/j.1365-2966.2011.18820.x

    Hahn, O., & Abel, T. 2011, MNRAS, 415, 2101, doi: 10.1111/j.1365-2966.2011.18820.x

    2011 doi

  35. [43]

    N., Maiolino, R., Juodˇ zbalis, I., et al

    Hainline, K. N., Maiolino, R., Juodˇ zbalis, I., et al. 2025, ApJ, 979, 138, doi: 10.3847/1538-4357/ad9920

    2025 doi

  36. [44]

    2023, ApJ, 959, 39, doi: 10.3847/1538-4357/ad029e Hern´ andez-Y´ evenes, J., Nagar, N., Arratia, V., & Jarrett, T

    Harikane, Y., Zhang, Y., Nakajima, K., et al. 2023, ApJ, 959, 39, doi: 10.3847/1538-4357/ad029e Hern´ andez-Y´ evenes, J., Nagar, N., Arratia, V., & Jarrett, T. H. 2024, MNRAS, 531, 4503, doi: 10.1093/mnras/stae1372

    2023 doi

  37. [45]

    F., Richards, G

    Hopkins, P. F., Richards, G. T., & Hernquist, L. 2007, ApJ, 654, 731, doi: 10.1086/509629

    2007 doi

  38. [46]

    2026, arXiv e-prints, arXiv:2603.17967

    Iani, E., Rinaldi, P., Torralba, A., et al. 2026, arXiv e-prints, arXiv:2603.17967. https://arxiv.org/abs/2603.17967

    2026

  39. [47]

    Jeon, J., Bromm, V., Liu, B., & Finkelstein, S. L. 2024, arXiv e-prints, arXiv:2402.18773, doi: 10.48550/arXiv.2402.18773 —. 2025, ApJ, 979, 127, doi: 10.3847/1538-4357/ad9f3a

    2024 doi

  40. [48]

    D., Fan, X., et al

    Jiang, L., McGreer, I. D., Fan, X., et al. 2016, ApJ, 833, 222, doi: 10.3847/1538-4357/833/2/222

    2016 doi

  41. [49]

    L., Kocevski, D

    Jones, B. L., Kocevski, D. D., Pacucci, F., et al. 2025, arXiv e-prints, arXiv:2510.07376, doi: 10.48550/arXiv.2510.07376 Juodˇ zbalis, I., Maiolino, R., Baker, W. M., et al. 2024, Nature, 636, 594, doi: 10.1038/s41586-024-08210-5

    2025 doi

  42. [50]

    H., & Hernquist, L

    Katz, N., Weinberg, D. H., & Hernquist, L. 1996, ApJS, 105, 19, doi: 10.1086/192305

    1996 doi

  43. [51]

    2017, MNRAS, 467, 4739, doi: 10.1093/mnras/stx126

    Kaviraj, S., Laigle, C., Kimm, T., et al. 2017, MNRAS, 467, 4739, doi: 10.1093/mnras/stx126

    2017 doi

  44. [52]

    K., Weinberger, R., et al

    Kho, J., Bhowmick, A. K., Weinberger, R., et al. 2026, Learning the Universe at High Redshifts: Impact of Accretion Modeling on Early Black Hole Growth. https://arxiv.org/abs/2606.10036

    2026 arXiv

  45. [53]

    2024, A&A, 691, A52, doi: 10.1051/0004-6361/202348857

    Killi, M., Watson, D., Brammer, G., et al. 2024, A&A, 691, A52, doi: 10.1051/0004-6361/202348857

    2024 doi

  46. [54]

    D., Onoue, M., Inayoshi, K., et al

    Kocevski, D. D., Onoue, M., Inayoshi, K., et al. 2023, ApJL, 954, L4, doi: 10.3847/2041-8213/ace5a0

    2023 doi

  47. [55]

    D., Finkelstein, S

    Kocevski, D. D., Finkelstein, S. L., Barro, G., et al. 2024, arXiv e-prints, arXiv:2404.03576, doi: 10.48550/arXiv.2404.03576

    2024 doi

  48. [56]

    2023, ApJL, 957, L7, doi: 10.3847/2041-8213/ad037a

    Kokorev, V., Fujimoto, S., Labbe, I., et al. 2023, ApJL, 957, L7, doi: 10.3847/2041-8213/ad037a

    2023 doi

  49. [57]

    I., Greene, J

    Kokorev, V., Caputi, K. I., Greene, J. E., et al. 2024a, ApJ, 968, 38, doi: 10.3847/1538-4357/ad4265 32

    doi

  50. [58]

    2024b, ApJ, 975, 178, doi: 10.3847/1538-4357/ad7d03

    Kokorev, V., Chisholm, J., Endsley, R., et al. 2024b, ApJ, 975, 178, doi: 10.3847/1538-4357/ad7d03

    doi

  51. [59]

    Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511, doi: 10.1146/annurev-astro-082708-101811 Kov´ acs, O. E., Bogd´ an,´A., Natarajan, P., et al. 2024, ApJL, 965, L21, doi: 10.3847/2041-8213/ad391f

    2013 doi

  52. [60]

    2023, PhRvD, 108, 083012, doi: 10.1103/PhysRevD.108.083012

    Kritos, K., Berti, E., & Silk, J. 2023, PhRvD, 108, 083012, doi: 10.1103/PhysRevD.108.083012

    2023 doi

  53. [61]

    2020, MNRAS, 498, 5652, doi: 10.1093/mnras/staa2276

    Kroupa, P., Subr, L., Jerabkova, T., & Wang, L. 2020, MNRAS, 498, 5652, doi: 10.1093/mnras/staa2276

    2020 doi

  54. [62]

    E., Bezanson, R., et al

    Labbe, I., Greene, J. E., Bezanson, R., et al. 2025, ApJ, 978, 92, doi: 10.3847/1538-4357/ad3551

    2025 doi

  55. [63]

    L., Garofali, K., et al

    Lambrides, E., Larson, R. L., Garofali, K., et al. 2026, Nature Astronomy, doi: 10.1038/s41550-026-02813-w

    2026 doi

  56. [64]

    L., Finkelstein, S

    Larson, R. L., Finkelstein, S. L., Kocevski, D. D., et al. 2023, ApJL, 953, L29, doi: 10.3847/2041-8213/ace619

    2023 doi

  57. [65]

    R., Tremaine, S., Richstone, D., & Faber, S

    Lauer, T. R., Tremaine, S., Richstone, D., & Faber, S. M. 2007, ApJ, 670, 249, doi: 10.1086/522083

    2007 doi

  58. [66]

    2025a, arXiv e-prints, arXiv:2502.05048, doi: 10.48550/arXiv.2502.05048

    Li, J., Shen, Y., & Zhuang, M.-Y. 2025a, arXiv e-prints, arXiv:2502.05048, doi: 10.48550/arXiv.2502.05048

    doi

  59. [67]

    D., Shen, Y., et al

    Li, J., Silverman, J. D., Shen, Y., et al. 2025b, ApJ, 981, 19, doi: 10.3847/1538-4357/ada603

    doi

  60. [68]

    C., & Chen, C.-H

    Li, R., Ho, L. C., & Chen, C.-H. 2025c, arXiv e-prints, arXiv:2505.12867, doi: 10.48550/arXiv.2505.12867

    doi

  61. [69]

    R., & Ma, C.-P

    Liepold, E. R., & Ma, C.-P. 2024, ApJL, 971, L29, doi: 10.3847/2041-8213/ad66b8

    2024 doi

  62. [70]

    2006, MNRAS, 371, 1813, doi: 10.1111/j.1365-2966.2006.10801.x —

    Lodato, G., & Natarajan, P. 2006, MNRAS, 371, 1813, doi: 10.1111/j.1365-2966.2006.10801.x —. 2007, MNRAS, 377, L64, doi: 10.1111/j.1745-3933.2007.00304.x

    2006 doi

  63. [71]

    2018, MNRAS, 476, 3523, doi: 10.1093/mnras/sty362

    Luo, Y., Ardaneh, K., Shlosman, I., et al. 2018, MNRAS, 476, 3523, doi: 10.1093/mnras/sty362

    2018 doi

  64. [72]

    2020, MNRAS, 492, 4917, doi: 10.1093/mnras/staa153

    Luo, Y., Shlosman, I., Nagamine, K., & Fang, T. 2020, MNRAS, 492, 4917, doi: 10.1093/mnras/staa153

    2020 doi

  65. [73]

    2014, MNRAS, 442, 3616, doi: 10.1093/mnras/stu1120

    Lupi, A., Colpi, M., Devecchi, B., Galanti, G., & Volonteri, M. 2014, MNRAS, 442, 3616, doi: 10.1093/mnras/stu1120

    2014 doi

  66. [74]

    2024, A&A, 689, A128, doi: 10.1051/0004-6361/202451249

    Mazzucchelli, C. 2024, A&A, 689, A128, doi: 10.1051/0004-6361/202451249

    2024 doi

  67. [75]

    F., Kelley, L

    Ma, L., Hopkins, P. F., Kelley, L. Z., & Faucher-Gigu` ere, C.-A. 2023, MNRAS, 519, 5543, doi: 10.1093/mnras/stad036

    2023 doi

  68. [76]

    Madau, P., & Rees, M. J. 2001, ApJL, 551, L27, doi: 10.1086/319848

    2001 doi

  69. [77]

    2023, arXiv e-prints, arXiv:2308.01230, doi: 10.48550/arXiv.2308.01230

    Maiolino, R., Scholtz, J., Curtis-Lake, E., et al. 2023, arXiv e-prints, arXiv:2308.01230, doi: 10.48550/arXiv.2308.01230

    2023 doi

  70. [78]

    2024a, Nature, 627, 59, doi: 10.1038/s41586-024-07052-5

    Maiolino, R., Scholtz, J., Witstok, J., et al. 2024a, Nature, 627, 59, doi: 10.1038/s41586-024-07052-5

    doi

  71. [79]

    2024b, A&A, 691, A145, doi: 10.1051/0004-6361/202347640

    Maiolino, R., Scholtz, J., Curtis-Lake, E., et al. 2024b, A&A, 691, A145, doi: 10.1051/0004-6361/202347640

    doi

  72. [80]

    2018, ApJS, 237, 5, doi: 10.3847/1538-4365/aac724

    Matsuoka, Y., Iwasawa, K., Onoue, M., et al. 2018, ApJS, 237, 5, doi: 10.3847/1538-4365/aac724

    2018 doi

  73. [81]

    2019, ApJL, 872, L2, doi: 10.3847/2041-8213/ab0216

    Matsuoka, Y., Onoue, M., Kashikawa, N., et al. 2019, ApJL, 872, L2, doi: 10.3847/2041-8213/ab0216

    2019 doi

  74. [82]

    Z., et al

    Matt, C., G¨ ultekin, K., Kelley, L. Z., et al. 2026, ApJ, 997, 188, doi: 10.3847/1538-4357/ae2480

    2026 doi

  75. [83]

    P., Brammer, G., et al

    Matthee, J., Naidu, R. P., Brammer, G., et al. 2024, ApJ, 963, 129, doi: 10.3847/1538-4357/ad2345

    2024 doi

  76. [84]

    R., Zwick, L., & Di Matteo, T

    Mayer, L., Capelo, P. R., Zwick, L., & Di Matteo, T. 2024, ApJ, 961, 76, doi: 10.3847/1538-4357/ad11cf

    2024 doi

  77. [85]

    A., & Prole, L

    Mehta, D., Regan, J. A., & Prole, L. 2024, The Open Journal of Astrophysics, 7, 107, doi: 10.33232/001c.126629

    2024 doi

  78. [86]

    H., Regan, J

    Mehta, D. H., Regan, J. A., & Prole, L. 2026, Nature Astronomy, doi: 10.1038/s41550-025-02767-5

    2026 doi

  79. [87]

    2008, MNRAS, 388, 1011, doi: 10.1111/j.1365-2966.2008.13472.x

    Merloni, A., & Heinz, S. 2008, MNRAS, 388, 1011, doi: 10.1111/j.1365-2966.2008.13472.x

    2008 doi

  80. [88]

    2024, ApJL, 966, L30, doi: 10.3847/2041-8213/ad3c2a

    Mezcua, M., Pacucci, F., Suh, H., Siudek, M., & Natarajan, P. 2024, ApJL, 966, L30, doi: 10.3847/2041-8213/ad3c2a

    2024 doi

  81. [89]

    P., Gallo, E., Greene, J

    Miller, B. P., Gallo, E., Greene, J. E., et al. 2015, ApJ, 799, 98, doi: 10.1088/0004-637X/799/1/98

    2015 doi

  82. [90]

    J., Warren, S

    Mortlock, D. J., Warren, S. J., Venemans, B. P., et al. 2011, Nature, 474, 616, doi: 10.1038/nature10159

    2011 doi

  83. [91]

    P., Matthee, J., Katz, H., et al

    Naidu, R. P., Matthee, J., Katz, H., et al. 2025, arXiv e-prints, arXiv:2503.16596, doi: 10.48550/arXiv.2503.16596

    2025 doi arXiv

  84. [92]

    2021, MNRAS, 501, 1413, doi: 10.1093/mnras/staa3724

    Natarajan, P. 2021, MNRAS, 501, 1413, doi: 10.1093/mnras/staa3724

    2021 doi

  85. [93]

    2017, ApJ, 838, 117, doi: 10.3847/1538-4357/aa6330

    Natarajan, P., Pacucci, F., Ferrara, A., et al. 2017, ApJ, 838, 117, doi: 10.3847/1538-4357/aa6330

    2017 doi

  86. [94]

    2024, ApJL, 960, L1, doi: 10.3847/2041-8213/ad0e76

    Natarajan, P., Pacucci, F., Ricarte, A., et al. 2024, ApJL, 960, L1, doi: 10.3847/2041-8213/ad0e76

    2024 doi

  87. [95]

    2012, MNRAS, 422, 2051, doi: 10.1111/j.1365-2966.2012.20708.x

    Natarajan, P., & Volonteri, M. 2012, MNRAS, 422, 2051, doi: 10.1111/j.1365-2966.2012.20708.x

    2012 doi

  88. [96]

    D., Seth, A

    Nguyen, D. D., Seth, A. C., Neumayer, N., et al. 2019, ApJ, 872, 104, doi: 10.3847/1538-4357/aafe7a

    2019 doi

  89. [97]

    2023, ApJL, 942, L17, doi: 10.3847/2041-8213/aca9d3

    Onoue, M., Inayoshi, K., Ding, X., et al. 2023, ApJL, 942, L17, doi: 10.3847/2041-8213/aca9d3

    2023 doi

  90. [98]

    2025, ApJ, 994, 40, doi: 10.3847/1538-4357/ae1619

    Pacucci, F., Hernquist, L., & Fujii, M. 2025, ApJ, 994, 40, doi: 10.3847/1538-4357/ae1619

    2025 doi

  91. [99]

    2023, ApJL, 957, L3, doi: 10.3847/2041-8213/ad0158

    Pacucci, F., Nguyen, B., Carniani, S., Maiolino, R., & Fan, X. 2023, ApJL, 957, L3, doi: 10.3847/2041-8213/ad0158

    2023 doi

  92. [100]

    2011, MNRAS, 418, 1392, doi: 10.1111/j.1365-2966.2011.19591.x

    Pakmor, R., Bauer, A., & Springel, V. 2011, MNRAS, 418, 1392, doi: 10.1111/j.1365-2966.2011.19591.x

    2011 doi

  93. [101]

    2016, MNRAS, 462, 2603, doi: 10.1093/mnras/stw1761 33

    Springel, V. 2016, MNRAS, 462, 2603, doi: 10.1093/mnras/stw1761 33

    2016 doi

  94. [102]

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

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

    doi

  95. [103]

    2018b, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112 Planck Collaboration, Ade, P

    Pillepich, A., Nelson, D., Hernquist, L., et al. 2018b, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2016, A&A, 594, A13, doi: 10.1051/0004-6361/201525830

    2016 doi

  96. [104]

    J., Ricarte, A., Natarajan, P., et al

    Porras-Valverde, A. J., Ricarte, A., Natarajan, P., et al. 2026, ApJ, 998, 48, doi: 10.3847/1538-4357/ae2fb1

    2026 doi

  97. [105]

    R., Regan, J

    Prole, L. R., Regan, J. A., Mehta, D., et al. 2025, arXiv e-prints, arXiv:2511.09640, doi: 10.48550/arXiv.2511.09640 Ramos Padilla, A. F., Ashby, M. L. N., Smith, H. A., et al. 2020, MNRAS, 499, 4325, doi: 10.1093/mnras/staa2813

    2025 doi

  98. [106]

    L., McMahon, R

    Reed, S. L., McMahon, R. G., Martini, P., et al. 2017, MNRAS, 468, 4702, doi: 10.1093/mnras/stx728

    2017 doi

  99. [107]

    A., Wise, J

    Regan, J. A., Wise, J. H., Woods, T. E., et al. 2020, The Open Journal of Astrophysics, 3, 15, doi: 10.21105/astro.2008.08090

    2020 doi

  100. [108]

    E., & Volonteri, M

    Reines, A. E., & Volonteri, M. 2015, ApJ, 813, 82, doi: 10.1088/0004-637X/813/2/82

    2015 doi

  101. [109]

    2018, MNRAS, 481, 3278, doi: 10.1093/mnras/sty2448

    Ricarte, A., & Natarajan, P. 2018, MNRAS, 481, 3278, doi: 10.1093/mnras/sty2448

    2018 doi

  102. [110]

    P., et al

    Rusakov, V., Watson, D., Nikopoulos, G. P., et al. 2026, Nature, 649, 574, doi: 10.1038/s41586-025-09900-4

    2026 doi

  103. [111]

    F., Davies, F

    Schindler, J.-T., Hennawi, J. F., Davies, F. B., et al. 2025, Nature Astronomy, 9, 1732, doi: 10.1038/s41550-025-02660-1

    2025 doi

  104. [112]

    2023, MNRAS, 526, 3250, doi: 10.1093/mnras/stad2503

    Schneider, R., Valiante, R., Trinca, A., et al. 2023, MNRAS, 526, 3250, doi: 10.1093/mnras/stad2503

    2023 doi

  105. [113]

    E., & Greene, J

    Schutte, Z., Reines, A. E., & Greene, J. E. 2019, ApJ, 887, 245, doi: 10.3847/1538-4357/ab35dd

    2019 doi

  106. [114]

    T., & Haiman, Z

    Scoggins, M. T., & Haiman, Z. 2024, MNRAS, 531, 4584, doi: 10.1093/mnras/stae1449

    2024 doi

  107. [115]

    T., Haiman, Z., & Wise, J

    Scoggins, M. T., Haiman, Z., & Wise, J. H. 2023, MNRAS, 519, 2155, doi: 10.1093/mnras/stac3715

    2023 doi

  108. [116]

    L., & Haiman, Z

    Shang, C., Bryan, G. L., & Haiman, Z. 2010, MNRAS, 402, 1249, doi: 10.1111/j.1365-2966.2009.15960.x

    2010 doi

  109. [117]

    F., Faucher-Gigu` ere, C.-A., et al

    Shen, X., Hopkins, P. F., Faucher-Gigu` ere, C.-A., et al. 2020, MNRAS, 495, 3252, doi: 10.1093/mnras/staa1381

    2020 doi

  110. [118]

    2008, MNRAS, 385, 1443, doi: 10.1111/j.1365-2966.2008.12922.x

    Smith, B., Sigurdsson, S., & Abel, T. 2008, MNRAS, 385, 1443, doi: 10.1111/j.1365-2966.2008.12922.x

    2008 doi

  111. [119]

    D., Regan, J

    Smith, B. D., Regan, J. A., Downes, T. P., et al. 2018, MNRAS, 480, 3762, doi: 10.1093/mnras/sty2103

    2018 doi

  112. [120]

    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

    2010 doi

  113. [121]

    2003, MNRAS, 339, 289, doi: 10.1046/j.1365-8711.2003.06206.x

    Springel, V., & Hernquist, L. 2003, MNRAS, 339, 289, doi: 10.1046/j.1365-8711.2003.06206.x

    2003 doi

  114. [122]

    Springel, V., White, S. D. M., Tormen, G., & Kauffmann, G. 2001, MNRAS, 328, 726, doi: 10.1046/j.1365-8711.2001.04912.x

    2001 doi

  115. [123]

    Sugimura, K., Omukai, K., & Inoue, A. K. 2014, MNRAS, 445, 544, doi: 10.1093/mnras/stu1778

    2014 doi

  116. [124]

    J., Finkelstein, S

    Taylor, A. J., Finkelstein, S. L., Kocevski, D. D., et al. 2024, arXiv e-prints, arXiv:2409.06772, doi: 10.48550/arXiv.2409.06772

    2024 doi

  117. [125]

    2014, MNRAS, 442, 2751, doi: 10.1093/mnras/stu983

    Taylor, P., & Kobayashi, C. 2014, MNRAS, 442, 2751, doi: 10.1093/mnras/stu983

    2014 doi

  118. [126]

    2014, MNRAS, 438, 1985, doi: 10.1093/mnras/stt2295

    Torrey, P., Vogelsberger, M., Genel, S., et al. 2014, MNRAS, 438, 1985, doi: 10.1093/mnras/stt2295

    2014 doi

  119. [127]

    2017, MNRAS, 470, 1121, doi: 10.1093/mnras/stx1160

    Tremmel, M., Karcher, M., Governato, F., et al. 2017, MNRAS, 470, 1121, doi: 10.1093/mnras/stx1160

    2017 doi

  120. [128]

    2024, arXiv e-prints, arXiv:2412.14248, doi: 10.48550/arXiv.2412.14248

    Trinca, A., Valiante, R., Schneider, R., et al. 2024, arXiv e-prints, arXiv:2412.14248, doi: 10.48550/arXiv.2412.14248

    2024 doi

  121. [129]

    P., Verdoes Kleijn, G

    Venemans, B. P., Verdoes Kleijn, G. A., Mwebaze, J., et al. 2015, MNRAS, 453, 2259, doi: 10.1093/mnras/stv1774

    2015 doi

  122. [130]

    2018, ApJL, 865, L9, doi: 10.3847/2041-8213/aadf3a

    Visbal, E., & Haiman, Z. 2018, ApJL, 865, L9, doi: 10.3847/2041-8213/aadf3a

    2018 doi

  123. [131]

    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

    2013 doi

  124. [132]

    2014, MNRAS, 444, 1518, doi: 10.1093/mnras/stu1536

    Vogelsberger, M., Genel, S., Springel, V., et al. 2014, MNRAS, 444, 1518, doi: 10.1093/mnras/stu1536

    2014 doi

  125. [133]

    2018, ApJL, 869, L9, doi: 10.3847/2041-8213/aaf1d2 —

    Wang, F., Yang, J., Fan, X., et al. 2018, ApJL, 869, L9, doi: 10.3847/2041-8213/aaf1d2 —. 2021, ApJL, 907, L1, doi: 10.3847/2041-8213/abd8c6

    2018 doi

  126. [134]

    2020, ApJS, 248, 32, doi: 10.3847/1538-4365/ab908c

    Weinberger, R., Springel, V., & Pakmor, R. 2020, ApJS, 248, 32, doi: 10.3847/1538-4365/ab908c

    2020 doi

  127. [135]

    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

    2017 doi

  128. [136]

    2018, MNRAS, 479, 4056, doi: 10.1093/mnras/sty1733

    Weinberger, R., Springel, V., Pakmor, R., et al. 2018, MNRAS, 479, 4056, doi: 10.1093/mnras/sty1733

    2018 doi

  129. [137]

    J., Delorme, P., Reyl´ e, C., et al

    Willott, C. J., Delorme, P., Reyl´ e, C., et al. 2010, AJ, 139, 906, doi: 10.1088/0004-6256/139/3/906

    2010 doi

  130. [138]

    H., Regan, J

    Wise, J. H., Regan, J. A., O’Shea, B. W., et al. 2019, Nature, 566, 85, doi: 10.1038/s41586-019-0873-4

    2019 doi

  131. [139]

    2025, ApJL, 993, L48, doi: 10.3847/2041-8213/ae14d4

    Wu, Z., Cen, R., & Teyssier, R. 2025, ApJL, 993, L48, doi: 10.3847/2041-8213/ae14d4

    2025 doi

  132. [140]

    H., & Norman, M

    Xu, H., Wise, J. H., & Norman, M. L. 2013, ApJ, 773, 83, doi: 10.1088/0004-637X/773/2/83

    2013 doi

  133. [141]

    2023, Research in Astronomy and Astrophysics, 23, 075024, doi: 10.1088/1674-4527/acdfa5

    Xu, H., Chen, S., Guo, Y., et al. 2023, Research in Astronomy and Astrophysics, 23, 075024, doi: 10.1088/1674-4527/acdfa5

    2023 doi

  134. [142]

    2019, AJ, 157, 236, doi: 10.3847/1538-3881/ab1be1

    Yang, J., Wang, F., Fan, X., et al. 2019, AJ, 157, 236, doi: 10.3847/1538-3881/ab1be1

    2019 doi

  135. [143]

    R., Boresta, M., et al

    Zana, T., Capelo, P. R., Boresta, M., et al. 2026, A&A, 708, A7, doi: 10.1051/0004-6361/202557006

    2026 doi

  136. [144]

    2023, MNRAS, 518, 2123, doi: 10.1093/mnras/stac2633

    Zhang, H., Behroozi, P., Volonteri, M., et al. 2023, MNRAS, 518, 2123, doi: 10.1093/mnras/stac2633

    2023 doi

  137. [145]

    2026, ApJL, 1000, L19, doi: 10.3847/2041-8213/ae4bd0 34

    Zhang, S., Liu, B., Bromm, V., & K¨ uhnel, F. 2026, ApJL, 1000, L19, doi: 10.3847/2041-8213/ae4bd0 34

    2026 doi

  138. [146]

    2025, JCAP, 2025, 040, doi: 10.1088/1475-7516/2025/04/040

    Ziparo, F., Gallerani, S., & Ferrara, A. 2025, JCAP, 2025, 040, doi: 10.1088/1475-7516/2025/04/040

    2025 doi

Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.