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Dynamics of low-mass black hole seeds in the BRAHMA simulations using subgrid-dynamical friction: Impact on merger-driven black hole growth in the high redshift Universe

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Subgrid dynamical friction cuts early black hole seed merger rates by a factor of 4-10, the paper contends.

desk verdict First BRAHMA light-seed runs with subgrid DF show repositioning overestimates z>5 merger rates; the qualitative conclusion holds, but the headline 100–1000 yr^-1 rates rest on a hand-tuned dynamical seed mass and should be treated as conditional. read the letter →

arxiv 2506.09184 v1 pith:5FJSJDDI submitted 2025-06-10 astro-ph.GA

Aklant K. Bhowmick , Laura Blecha , Luke Z. Kelley , Aneesh Sivasankaran , Paul Torrey , Rainer Weinberger , Nianyi Chen , Mark Vogelsberger
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Lars Hernquist Priyamvada Natarajan
This is my paper
classification astro-ph.GA
keywords blackholeseedsdynamicalfrictioncosmologicalhydrodynamicsimulationshigh-redshiftmergersLISAgravitationalwaveratesintermediate-massholesBRAHMAmerger-drivengrowth
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 argues that unresolved dynamical friction governs whether low-mass black hole seeds sink to the centers of their small high-redshift halos and merge. Using a subgrid dynamical-friction prescription in the BRAHMA cosmological simulations, it finds that seeds do sink and merge on timescales of roughly 100-1000 megayears after their host halos merge, yielding z>5 merger rates of 100-1000 events per year across four seeding models. Those rates are a factor of 4-10 lower than the same simulations with black holes artificially pinned to halo centers. The paper concludes that merger-driven growth assembles these seeds to about $10^{4}$-$10^{5}$ solar masses by z~5, with gas accretion contributing less than 2 percent of the mass, and that LISA can use these events to distinguish seeding mechanisms.

What carries the argument

The load-bearing mechanism is the M23 subgrid dynamical-friction estimator, a momentum-conserving sum over background mass elements (Eq. 5) that requires no free parameters and reduces to the Chandrasekhar formula under idealized conditions, applied together with artificially enhanced dynamical seed masses (3-64 times the true seed mass) to suppress numerical heating, and the MERGE_SOFTENING_BOUND merger criterion, which requires pairs to be within two gravitational softening lengths and to be gravitationally bound. This combination determines how far seeds sink, how long binary inspiral takes, and which pairs are counted as mergers.

What would settle it

Run the same [9 Mpc]^3 boxes at a resolution where dark-matter particles are lighter than the seed mass and use the true seed mass in the gravity calculation; if the z>5 merger rate then falls below roughly 10 per year, the artificially enhanced dynamical mass is driving the predicted rates rather than real physics.

Watch

Extended reading notes

Core claim

The central discovery is that the way a simulation treats black hole dynamics below the resolution scale changes the predicted early Universe merger history by an order of magnitude. Adding the Ma et al. 2023 subgrid dynamical-friction estimator lets a significant fraction of ~2.$2x10^{3}$ solar-mass seeds sink to subhalo centers by z~5 and become gravitationally bound pairs at separations of ~0.2-0.4 kpc, where they can merge. Removing both repositioning and subgrid friction leaves most seeds wandering and mergers rare, while the old repositioning scheme merges seeds promptly at kiloparsec separations and overproduces high-redshift events. Across four seed models, the authors find z>5 merger rates of about 100-1000 per year, with peak rates shifting to lower redshift than under repositioning, and these mergers dominate seed growth while gas accretion remains negligible below roughly $10^{5}$ solar masses.

Load-bearing premise

The subgrid dynamical-friction force, computed with seeds artificially made 24 times heavier for the force calculation, accurately captures the real drag that ~$10^{3}$ solar-mass seeds would feel in halos of ~$10^{6}$-$10^{7}$ solar masses.

Editorial extensions

If this is right

  • If the paper is right, the standard practice of pinning black holes to potential minima overestimates z>5 seed merger rates by factors of 4-10, so LISA event-rate forecasts must include unresolved dynamical friction.
  • Under subgrid dynamical friction, different seed models predict 100-1000 seed mergers per year at z>5, giving LISA a realistic chance of detecting seed-mass events during its lifetime.
  • Merger-driven growth builds seeds to 10^4-10^5 solar masses by z~5 with less than 2 percent of mass from gas accretion, implying steep mass functions in the intermediate-mass range that electromagnetic observatories cannot easily probe.
  • The paper treats its rates as upper limits to actual gravitational-wave events because inspiral below 0.2-0.4 kpc, loss-cone scattering, and gravitational-wave recoil are not modeled.
  • Because z>15 merger rates are suppressed by factors of 10-100 relative to repositioning, early-Universe seed mergers are much rarer than previously estimated but still numerous enough to be observable.

Reading between the lines

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

  • If sub-softening dynamical friction contributes significantly, the true sinking efficiency may be higher than modeled here, pushing the rates toward the paper's upper end rather than the lower end.
  • The enhanced dynamical seed mass can be read physically as a nuclear star cluster; if such clusters are rare around z>5 seeds, the simulated merger rates would drop, which is a testable assumption for future small-scale simulations.
  • A LISA non-detection at the predicted rate would disfavor the most lenient seeding model or require that most 0.2-0.4 kpc pairs fail to reach gravitational-wave coalescence.
  • Applying the same dynamics treatment to heavy direct-collapse seeds would show whether the 4-10 x suppression factor persists across seed channels, which would sharpen the seeding discrimination power of LISA.
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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 a new suite of BRAHMA cosmological hydrodynamic simulations that replace the previous BH-repositioning scheme with the Ma et al. (2023) subgrid dynamical-friction model. It studies the dynamics of ~2e3 M_sun BH seeds in (4.5 Mpc)^3 and (9 Mpc)^3 boxes at z>5, comparing three dynamics treatments (repositioning, subgrid DF, and purely natural dynamics), three merger criteria, four seed models, and dynamical seed masses ranging from 3 to 64 M_seed. The main findings are that subgrid DF is required for seeds to sink to halo centers and become gravitationally bound, that post-halo-merger BH merger timescales are roughly 50-1000 Myr, that z>5 merger rates are reduced by a factor of about 4-10 relative to the repositioning scheme, and that the different seed models predict merger rates of about 100-1000 yr^-1 at z~5, with implications for LISA event rates.

Significance. This is a valuable and timely study: it addresses a known limitation of the original BRAHMA predictions and provides a well-controlled comparison of BH dynamics treatments while holding seeding and galaxy physics fixed. The qualitative conclusion that BH repositioning overestimates prompt high-redshift seed mergers is well supported by the internal comparisons, and the relative suppression factor of roughly 4-10 is robust across the tested dynamical seed masses. The study also benefits from systematic tests of merger criteria and seed models, and it is transparent about many caveats. The absolute LISA-relevant rates, however, are conditioned on an ad hoc enhanced dynamical seed mass and should be read as upper limits with a substantial, only partially quantified systematic uncertainty.

major comments (3)
  1. [§2.2.2, §3.4.3, §3.4.4 (Eqs. 4–5, Fig. 14, Fig. 15)] The headline absolute rates in the abstract and in Figure 15 are not independent of the uncalibrated enhanced dynamical mass M_dyn_seed. In the default BRAHMA-9 boxes, M_dyn_seed = 24 M_seed corresponds to roughly 2.2 dark-matter particle masses, while the M23 model is stated to work best when the BH mass is about 10 times the background particle mass; the actual seed mass is 2.2e3 M_sun, below the DM particle mass resolution. Figure 14 shows that changing M_dyn_seed from 64 to 3 M_seed alters the z>5 merger counts by roughly a factor of three (1223 versus 403), and the fiducial BRAHMA-9 value lies in the middle of this range. Because the quoted 100-1000 yr^-1 rates are extracted from this parameter, the manuscript should either calibrate M_dyn_seed with an idealized sinking test or present the rates as a conditional band with the M_dyn_seed dependence made explicit. The relative suppression factor of 4-10 relative to repositioning is, by contrast, robust across the tested range and is not affected by this concern.
  2. [§3.4.4, §4.3, Fig. 15] The merger rates are plotted as events per year and are quoted without an explicit volume normalization or uncertainty budget. Because the production volumes are only (9 Mpc)^3 and Section 4.3 itself quotes a cosmic-variance factor of about two, the range 100-1000 yr^-1 across seed models cannot be taken as a precise prediction. I ask for error bands or at minimum an explicit statement in the figure caption and abstract that the rates carry at least this systematic uncertainty.
  3. [§4.2, Summary bullet 5] The paper suggests that the enhanced dynamical seed mass may physically correspond to seeds embedded in nuclear star clusters, but no nuclear star cluster model is implemented. An extended, tidally stripped cluster does not necessarily exert the same point-mass dynamical friction as a more massive point particle, so this interpretation should be labeled as speculative rather than as a modeled process. This matters because the absolute rates are sensitive to the dynamical mass, as noted in the first major comment.
minor comments (5)
  1. [Abstract, §3.4.4] The phrase 'merger rates between ~100-1000 events per year at z>5' is ambiguous: in the text the rates are evaluated at z~5. Please state explicitly whether the quoted number is a differential rate at z~5 or a cumulative rate integrated over z>5.
  2. [Fig. 14, §3.4.3] The left panels of Figure 14 show cumulative merger counts (1223, 663, and 403) while the text refers to them as 'merge rates'; please clarify the axes, the normalization, and the relation to the rates in Figure 15.
  3. [§2.2.1, Eq. (4)] The BH mass in Eq. (4) is written as M_bh, but the calculations use M_dyn_seed; this substitution should be stated explicitly where the formula is introduced.
  4. [Throughout] The naming convention is inconsistent: the text and Figure 1 use both DF_DISCRETE and DISCRETE_DF for the same dynamics model; please standardize.
  5. [Various] There are several typographical errors, including 'consequentual' in Section 2, 'uncertainity' and 'scattening' in Section 4.3, and 'Thompson scattering' for 'Thomson scattering' near Eq. (3).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the merger-rate predictions are simulation outputs from an external subgrid DF estimator, and the M_dyn_seed sensitivity is a transparent modeling caveat rather than a circular step.

full rationale

The paper's central dynamics ingredient is the M23 subgrid dynamical-friction estimator (Eqs. 4-5), which is an external, parameter-free formula that the authors apply to their simulated BH populations; it is not derived from, nor fitted to, the merger rates that are later quoted. The enhanced dynamical seed mass M_dyn_seed is an input parameter introduced in Section 2.2.2 to mitigate numerical heating, and the paper explicitly quantifies its effect in Section 3.4.3 (Figure 14), showing that z>5 merger counts vary from 1223 to 403 as M_dyn_seed is varied from 64 to 3 M_seed. The fiducial BRAHMA-9 predictions then use M_dyn_seed = 24 M_seed and are explicitly presented as conditional upper limits (Section 3.4.4 and Section 4.3). This is a robustness and calibration concern, not a circular reduction: no parameter is fit to the headline 100-1000 yr^-1 rates, and the factor-of-4-10 suppression versus repositioning is reported as a range across the tested M_dyn_seed values rather than being imposed by construction. Self-citations to earlier BRAHMA work supply the seeding prescriptions and initial conditions, but those are established prior inputs, not results manufactured to force the merger-rate conclusion. The M23 citation includes a co-author, but the model is published, externally testable, and compared with independent works such as Chen et al. (2022) and Genina et al. (2024), so no load-bearing self-citation chain is present. The paper's own caveats about missing sub-softening impact parameters, unresolved sub-kpc hardening, and unmodeled NSC fractions are stated limitations that affect the physical interpretation, not evidence that the derivation is equivalent to its inputs.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central quantitative claim depends primarily on the ad hoc enhanced dynamical seed mass, which is a free parameter rather than a derived physical quantity. No new physical entities are postulated. The remaining assumptions are standard or domain-specific modeling choices, mostly inherited from prior BRAHMA and TNG work.

free parameters (1)
  • Dynamical seed mass M_dyn_seed = 24 M_seed for fiducial BRAHMA-9 boxes; 3, 8, 64 M_seed in tests
    Chosen by hand to prevent numerical heating of BHs below the DM particle mass. Directly controls the strength of the subgrid DF force and changes z>5 merger rates by a factor of ~3 across the tested range.
assumptions (4)
  • domain assumption M23 subgrid DF estimator (Eq. 5) correctly describes unresolved dynamical friction in a softened N-body potential for BH masses below the background particle mass.
    The paper applies the Ma et al. 2023 model without the double-counting correction and without accounting for sub-softening impact parameters, relying on Genina et al. 2024 tests that indicate DF is not naturally resolved at these resolutions.
  • domain assumption The BRAHMA gas-based seed formation criteria (dense and metal-poor gas mass and halo mass thresholds) produce a representative population of ~10^3 M_sun seeds.
    Seed thresholds are inherited from prior BRAHMA work (Bhowmick et al. 2021) and are varied across four models; the paper does not re-derive or validate them against observations.
  • domain assumption Merging BH pairs at separations of 2 times the gravitational softening length with a gravitational binding check is a valid proxy for eventual coalescence.
    The simulations do not model loss-cone scattering, circumbinary gas, or GW emission below 0.2-0.4 kpc, so the quoted merger rates are upper limits as the paper acknowledges.
  • domain assumption The TNG galaxy formation model (feedback, star formation, Bondi accretion) adequately describes the low-mass halos and gas at z>5 for the purposes of seed dynamics.
    The ISM is treated with an effective equation of state, and the authors note TNG-style models underproduce z>12 galaxies compared to JWST; this could affect seed formation environments.

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Cite this review

Pith. "Pith review of Dynamics of low-mass black hole seeds in the BRAHMA simulations using subgrid-dynamical friction: Impact on merger-driven black hole growth in the high redshift Universe." pith.science (2026). https://pith.science/paper/5FJSJDDI

@misc{pith2026250609184,
  author       = {Pith},
  title        = {Pith review of: Dynamics of low-mass black hole seeds in the BRAHMA simulations using subgrid-dynamical friction: Impact on merger-driven black hole growth in the high redshift Universe},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5FJSJDDI}},
  note         = {Machine review of arXiv:2506.09184}
}
abstract

We analyze the dynamics of low-mass black hole (BH) seeds in the high-redshift ($z\gtrsim5$) Universe using a suite of $[4.5~\mathrm{Mpc}]^3$ and $[9~\mathrm{Mpc}]^3$ BRAHMA cosmological hydrodynamic simulations. The simulations form seeds with mass $M_{\mathrm{seed}}=2.2\times10^3~M_{\odot}$ in halos that exceed critical thresholds of dense & metal-poor gas mass ($5-150~M_{\mathrm{seed}}$) and the halo mass ($1000-10000~M_{\mathrm{seed}}$). While the initial BRAHMA boxes pinned the BHs to the halo centers, here we implement a sub-grid dynamical friction (DF) model. We also compare simulations where the BH is allowed to wander without the added DF. We investigate the spatial and velocity offsets of BHs in their host subhalos, as well as BH merger rates. We find that subgrid DF is crucial to ensure that a significant fraction of BHs effectively sink to halo centers by $z\sim5$, thereby enabling them to get gravitationally bound and merge with other BHs at separations close to the spatial resolution ($\sim0.2-0.4~\rm kpc$) of the simulation. For the BHs that merge, the associated merger time scales lag between $\sim100-1000~\mathrm{Myr}$ after their host halos merge. Compared to predictions using BH repositioning, the overall $z\gtrsim5$ BH merger rates under subgrid DF decrease by a factor of $\sim4-10$. Under subgrid DF, the different seed models predict merger rates between $\sim100-1000$ events per year at $z\gtrsim5$. These mergers dominate early BH growth, assembling BHs up to $\sim10^4-10^5~M_{\odot}$ by $z\sim5$, wherein $\lesssim2~\%$ of their mass is assembled via gas accretion. Our results highlight the promise for constraining seeding mechanisms using gravitational waves from future facilities such as the Laser Interferometer Space Antenna.

Figures

Figures reproduced from arXiv: 2506.09184 by the authors.

Figure 1
Figure 1. Full summary of the simulation suite: Between the different seed models, dynamics treatments, dynamical seed masses and merging criteria, there are a total of 14 simulation boxes. The top row shows the default resolution [4.5 Mpc]3 boxes which are used to quantify the impact of different dynamics treatments and merging criteria for a fixed seed model and dynamical seed mass. REPOSITIONING refers to boxes that use BH… view at source ↗
Figure 2
Figure 2. Leftmost panel shows the comoving number density of new seeds formed at different redshifts, for four different boxes assuming different seed models. The middle panel shows the distance dbirth between the seed birth site and the halo center (the densest gas cell), plotted against the metal-poor fraction of star-forming gas in the halo, fsf,mp ≡ Msf,mp/Msf, for seed-forming halos (M˜sfmp, M˜h = 5, 10000) at redshifts… view at source ↗
Figure 3
Figure 3. The number density of BHs (top) and the fraction of BHs residing inside halos (bottom) as a function of red￾shift using the SM5_FOF1000 seed model. Each line indicates a different BH dynamics model (left) or BH seed mass (right) as indicated in the legends. Removing REPOSITIONING leads to ∼ 15% of BHs wandering outside halos at z ≳ 20. How￾ever, by z ∼ 7, almost all BHs are found to be inside halos. Adding the subgr… view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: The grey histograms show the DM distributions of a select few subhalos in our simulations (at z = 9). Their BHs are shown as red circles whose sizes are proportional to their masses (in log units). Each column corresponds to a different treatment for BH dynamics, namel…
Figure 5
Figure 5. Figure 5: BH multiplicity as a function of subhalo mass (top panel) and halo mass (bottom panel) at z = 9 for three BH dynamics models. The multiplicities increase when the merg￾ing efficiency decreases, with REPOSITIONING producing the lowest multiplicities and ALL_NATURAL prod…
Figure 6
Figure 6. Figure 6: Spatial distributions of BHs inside subhalos: Distributions of BH offset distances (doff ) defined as the distance between the most massive BH in a subhalo and potential minima of the subhalo. Note that the contributions from BHs are removed in the potential minima cal…
Figure 7
Figure 7. Figure 7: Velocity distributions of BHs inside subhalos: Distributions of BH velocities (most massive BH in the subhalo) relative to the center of mass (of all DM particles) velocities of their host subhalos. The 1st and 2nd rows show the default resolution boxes, wherein the la…
Figure 8
Figure 8. Figure 8: Illustrative examples of mergers of BHs and their host subhalos under different dynamics treatments (using MERGE_SOFTENING_BOUND). In each of the larger panels, the solid lines show the trajectories of the merging BHs wherein the color gradient shows the time evolution…
Figure 9
Figure 9. Figure 9: Evolution of the relative distances of 20 merging BH pairs since their host halos merged, for different BH dynamics treatments as well as the criteria used for merging BHs. The top row shows simulations that use REPOSITIONING and merge BHs within Rhsml. For the remaini…
Figure 10
Figure 10. Figure 10: Merger time-scales (time between BH mergers and host halo mergers) of the BH pairs in simulations with different treatments for BH dynamics as well as different merging criteria. The left panels show simulations that use REPOSITIONING. For the remaining panels wherein…
Figure 11
Figure 11. Figure 11: Predicted merger rates of 2.2 × 103 M⊙ seeds with Mdyn seed = 64 Mseed in simulations with differ￾ent treatments of BH dynamics and merging criteria. The left, middle and right panels correspond to MERGE_HSML, MERGE_SOFTENING and MERGE_SOFTENING_BOUND. In each panel, …
Figure 12
Figure 12. Figure 12: Distributions of DM masses (left) and stellar masses (right) of the host halos of the BH merger events for the REPOSITIONING, ALL_NATURAL and DISCRETE_DF boxes (with Mdyn seed = 64 Mseed) using MERGE_SOFTENING_BOUND. For the DISCRETE_DF boxes, most mergers occur in ∼ …
Figure 13
Figure 13. Figure 13: Left and middle panels show BH merger time-scales plotted against the redshift of the host halo merger and the initial binary separation (at the time of halo merger) respectively. Blue, orange and green points show Mdyn seed = 3, 8 & 64 Mseed respectively. Green dashe…
Figure 14
Figure 14. Figure 14: Predicted merger rates of 2.2×103 M⊙ seeds with Mdyn seed ∼ 3, 8 & 64 Mseed respectively. The blue, orange and green lines are from the higher resolution DISCRETE_DF boxes that use MERGE_SOFTENING_BOUND. The black line shows the prediction using REPOSITIONING. In the …
Figure 15
Figure 15. Figure 15: Merger rates for all four seed models. The left panel shows the REPOSITIONING model and the right panel shows the DISCRETE_DF model together with the MERGE_SOFTENING_BOUND merger criterion. For the DISCRETE_DF boxes, we apply Mdyn seed = 24 Mseed. In the left panel, d…
Figure 16
Figure 16. Figure 16: For the four seed models in the previous figure, we show the fractional growth in BH mass that occurs due to gas accretion instead of BH-BH mergers. We plot these as 2D histograms as a function of BH mass for BH populations at z = 5. The initial BH growth is dominated…
Figure 17
Figure 17. Figure 17: Here we compare results for the default resolution and the higher resolution boxes for the SM5_FOF1000 seed model and the DISCRETE_DF dynamics model with MERGE_SOFTENING_BOUND and Mdyn seed = 64 Mseed. The left panels show the seed formation rates and the right panels…

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

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Reference graph

Works this paper leans on

110 extracted references · 4 canonical work pages · cited by 1 Pith paper

  1. [1]

    P., Abbott, R., Adhikari, R., et al

    Abbott, B. P., Abbott, R., Adhikari, R., et al. 2009, Reports on Progress in Physics, 72, 076901, doi: 10.1088/0034-4885/72/7/076901

    doi:10.1088/0034-4885/72/7/076901 2009

  2. [2]

    D., Abraham, S., et al

    Abbott, R., Abbott, T. D., Abraham, S., et al. 2020, ApJL, 900, L13, doi: 10.3847/2041-8213/aba493

    doi:10.3847/2041-8213/aba493 2020

  3. [3]

    2014, Science, 345, 1330, doi: 10.1126/science.1251053

    Alexander, T., & Natarajan, P. 2014, Science, 345, 1330, doi: 10.1126/science.1251053

    doi:10.1126/science.1251053 2014

  4. [4]

    2017, arXiv e-prints, arXiv:1702.00786

    Amaro-Seoane, P., Audley, H., Babak, S., et al. 2017, arXiv e-prints, arXiv:1702.00786. https://arxiv.org/abs/1702.00786 Angl´ es-Alc´ azar, D., Dav´ e, R., Faucher-Gigu` ere, C.-A.,¨Ozel, F., & Hopkins, P. F. 2017a, MNRAS, 464, 2840, doi: 10.1093/mnras/stw2565 Angl´ es-Alc´ azar, D., Faucher-Gigu` ere, C.-A., Quataert, E., et al. 2017b, MNRAS, 472, L109,...

    arXiv 2017

  5. [5]

    J., & Natarajan, P

    Armitage, P. J., & Natarajan, P. 2002, ApJL, 567, L9, doi: 10.1086/339770 Bah´ e, Y. M., Schaye, J., Schaller, M., et al. 2022, MNRAS, 516, 167, doi: 10.1093/mnras/stac1339

    doi:10.1086/339770 2002

  6. [6]

    C., & Monaco, P

    Banik, N., Tan, J. C., & Monaco, P. 2019, MNRAS, 483, 3592, doi: 10.1093/mnras/sty3298

    doi:10.1093/mnras/sty3298 2019

  7. [7]

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

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

    arXiv 2012

  8. [8]

    1986, Nature, 324, 446, doi: 10.1038/324446a0

    Barnes, J., & Hut, P. 1986, Nature, 324, 446, doi: 10.1038/324446a0

    doi:10.1038/324446a0 1986

Show all 110 references
  1. [9]

    C., & Silk, J

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

    2023 doi

  2. [11]

    2011, ApJ, 742, 13, doi: 10.1088/0004-637X/742/1/13 28

    Bellovary, J., Volonteri, M., Governato, F., et al. 2011, ApJ, 742, 13, doi: 10.1088/0004-637X/742/1/13 28

    2011 doi

  3. [12]

    M., Hayoune, S., Chafla, K., et al

    Bellovary, J. M., Hayoune, S., Chafla, K., et al. 2021, MNRAS, 505, 5129, doi: 10.1093/mnras/stab1665

    2021 doi

  4. [13]

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

    Bhowmick, A. K., Blecha, L., Torrey, P., et al. 2022a, 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

    2021 doi

  5. [14]

    K., Blecha, L., Ni, Y., et al

    Bhowmick, A. K., Blecha, L., Ni, Y., et al. 2022b, MNRAS, 516, 138, doi: 10.1093/mnras/stac2238

    doi

  6. [15]

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

    Bhowmick, A. K., Blecha, L., Torrey, P., et al. 2024b, MNRAS, 531, 4311, doi: 10.1093/mnras/stae1386 —. 2024c, MNRAS, 533, 1907, doi: 10.1093/mnras/stae1819 —. 2025, arXiv e-prints, arXiv:2411.19332, doi: 10.48550/arXiv.2411.19332

    1907 doi arXiv

  7. [16]

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

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

    2003 doi

  8. [17]

    C., et al

    Cammelli, V., Monaco, P., Tan, J. C., et al. 2025, MNRAS, 536, 851, doi: 10.1093/mnras/stae2663

    2025 doi

  9. [18]

    2022, ApJ, 926, 205, doi: 10.3847/1538-4357/ac332d C ¸ atmabacak, O., Feldmann, R., Angl´ es-Alc´ azar, D., et al

    Cappelluti, N., Hasinger, G., & Natarajan, P. 2022, ApJ, 926, 205, doi: 10.3847/1538-4357/ac332d C ¸ atmabacak, O., Feldmann, R., Angl´ es-Alc´ azar, D., et al. 2022, MNRAS, 511, 506, doi: 10.1093/mnras/stac040

    2022 doi

  10. [19]

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

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

    2003 doi

  11. [20]

    2023, ApJ, 946, 51, doi: 10.3847/1538-4357/acbea6

    Chadayammuri, U., Bogd´ an,´A., Ricarte, A., & Natarajan, P. 2023, ApJ, 946, 51, doi: 10.3847/1538-4357/acbea6

    2023 doi

  12. [21]

    1943, ApJ, 97, 255, doi: 10.1086/144517

    Chandrasekhar, S. 1943, ApJ, 97, 255, doi: 10.1086/144517

    1943 doi

  13. [22]

    D., et al

    Chen, N., Mukherjee, D., Matteo, T. D., et al. 2024, The Open Journal of Astrophysics, 7, 28, doi: 10.33232/001c.116179

    2024 doi

  14. [23]

    2022, MNRAS, 510, 531, doi: 10.1093/mnras/stab3411

    Chen, N., Ni, Y., Tremmel, M., et al. 2022, MNRAS, 510, 531, doi: 10.1093/mnras/stab3411

    2022 doi

  15. [24]

    2024, ApJ, 977, 200, doi: 10.3847/1538-4357/ad9561

    Natarajan, P. 2024, ApJ, 977, 200, doi: 10.3847/1538-4357/ad9561

    2024 doi

  16. [25]

    M., & Cornell, J

    Choquette, J., Cline, J. M., & Cornell, J. M. 2019, JCAP, 2019, 036, doi: 10.1088/1475-7516/2019/07/036

    2019 doi

  17. [26]

    2024, A&A, 692, A81, doi: 10.1051/0004-6361/202450021

    Damiano, A., Valentini, M., Borgani, S., et al. 2024, A&A, 692, A81, doi: 10.1051/0004-6361/202450021

    2024 doi

  18. [27]

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

    doi

  19. [28]

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

    doi

  20. [29]

    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

  21. [30]

    M., Shiralilou, B., et al

    Dayal, P., Rossi, E. M., Shiralilou, B., et al. 2019, MNRAS, 486, 2336, doi: 10.1093/mnras/stz897

    2019 doi

  22. [31]

    2020, MNRAS, 491, 4973, doi: 10.1093/mnras/stz3309 Di Matteo, T., Khandai, N., DeGraf, C., et al

    DeGraf, C., & Sijacki, D. 2020, MNRAS, 491, 4973, doi: 10.1093/mnras/stz3309 Di Matteo, T., Khandai, N., DeGraf, C., et al. 2012, ApJL, 745, L29, doi: 10.1088/2041-8205/745/2/L29 Di Matteo, T., Ni, Y., Chen, N., et al. 2023, MNRAS, 525, 1479, doi: 10.1093/mnras/stad2198 Dong-P...

    2020 doi arXiv

  23. [32]

    2016, MNRAS, 463, 3948, doi: 10.1093/mnras/stw2265

    Dubois, Y., Peirani, S., Pichon, C., et al. 2016, MNRAS, 463, 3948, doi: 10.1093/mnras/stw2265

    2016 doi

  24. [33]

    2018, MP-Gadget/MP-Gadget: A tag for getting a DOI, FirstDOI, Zenodo, doi: 10.5281/zenodo.1451799

    Pedersen, C. 2018, MP-Gadget/MP-Gadget: A tag for getting a DOI, FirstDOI, Zenodo, doi: 10.5281/zenodo.1451799

    2018 doi

  25. [34]

    A., et al

    Feng, Y., Di-Matteo, T., Croft, R. A., et al. 2016, MNRAS, 455, 2778, doi: 10.1093/mnras/stv2484

    2016 doi

  26. [35]

    L., Woosley, S

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

    2001 doi

  27. [36]

    2024, MNRAS, 534, 957, doi: 10.1093/mnras/stae2144 Grant Roberts, M., Braff, L., Garg, A., et al

    Genina, A., Springel, V., & Rantala, A. 2024, MNRAS, 534, 957, doi: 10.1093/mnras/stae2144 Grant Roberts, M., Braff, L., Garg, A., et al. 2025, JCAP, 2025, 060, doi: 10.1088/1475-7516/2025/01/060

    2024 doi

  28. [37]

    2017, MNRAS, 468, 3935, doi: 10.1093/mnras/stx666

    Habouzit, M., Volonteri, M., & Dubois, Y. 2017, MNRAS, 468, 3935, doi: 10.1093/mnras/stx666

    2017 doi

  29. [38]

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

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

    2016 doi

  30. [40]

    2025, arXiv e-prints, arXiv:2503.23710, doi: 10.48550/arXiv.2503.23710

    Jiang, F., Jia, Z., Zheng, H., et al. 2025, arXiv e-prints, arXiv:2503.23710, doi: 10.48550/arXiv.2503.23710

    2025 doi

  31. [41]

    2022, MNRAS, 511, 4005, doi: 10.1093/mnras/stab3710

    Kannan, R., Garaldi, E., Smith, A., et al. 2022, MNRAS, 511, 4005, doi: 10.1093/mnras/stab3710

    2022 doi

  32. [42]

    2023, MNRAS, 524, 2594, doi: 10.1093/mnras/stac3743

    Kannan, R., Springel, V., Hernquist, L., et al. 2023, MNRAS, 524, 2594, doi: 10.1093/mnras/stac3743

    2023 doi

  33. [43]

    H., & Hernquist, L

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

    1996 doi

  34. [44]

    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

  35. [45]

    Z., Blecha, L., & Hernquist, L

    Kelley, L. Z., Blecha, L., & Hernquist, L. 2017, MNRAS, 464, 3131, doi: 10.1093/mnras/stw2452

    2017 doi

  36. [46]

    2015, MNRAS, 450, 1349, doi: 10.1093/mnras/stv627

    Khandai, N., Di Matteo, T., Croft, R., et al. 2015, MNRAS, 450, 1349, doi: 10.1093/mnras/stv627

    2015 doi

  37. [47]

    Y., Rubin, S

    Khlopov, M. Y., Rubin, S. G., & Sakharov, A. S. 2005, Astroparticle Physics, 23, 265, doi: 10.1016/j.astropartphys.2004.12.002

    2005 doi

  38. [48]

    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

  39. [49]

    2021, MNRAS, 505, 172, doi: 10.1093/mnras/stab1205

    Lapiner, S., Dekel, A., & Dubois, Y. 2021, MNRAS, 505, 172, doi: 10.1093/mnras/stab1205

    2021 doi

  40. [50]

    2024, arXiv e-prints, arXiv:2410.07856, doi: 10.48550/arXiv.2410.07856 29

    Trebitsch, M. 2024, arXiv e-prints, arXiv:2410.07856, doi: 10.48550/arXiv.2410.07856 29

    2024 doi

  41. [51]

    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

  42. [52]

    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

  43. [53]

    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

  44. [54]

    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

  45. [55]

    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

  46. [56]

    F., Ma, X., et al

    Ma, L., Hopkins, P. F., Ma, X., et al. 2021, MNRAS, 508, 1973, doi: 10.1093/mnras/stab2713

    2021 doi

  47. [57]

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

    2001 doi

  48. [58]

    W., Feinberg, L

    McElwain, M. W., Feinberg, L. D., Perrin, M. D., et al. 2023, PASP, 135, 058001, doi: 10.1088/1538-3873/acada0

    2023 doi

  49. [59]

    2013, Classical and Quantum Gravity, 30, 244005, doi: 10.1088/0264-9381/30/24/244005

    Merritt, D. 2013, Classical and Quantum Gravity, 30, 244005, doi: 10.1088/0264-9381/30/24/244005

    2013 doi

  50. [60]

    Meshveliani, T., Zavala, J., & Lovell, M. R. 2023, PhRvD, 107, 083010, doi: 10.1103/PhysRevD.107.083010

    2023 doi

  51. [61]

    2024, arXiv e-prints, arXiv:2409.19095, doi: 10.48550/arXiv.2409.19095

    Matteo, T. 2024, arXiv e-prints, arXiv:2409.19095, doi: 10.48550/arXiv.2409.19095

    2024 doi arXiv

  52. [62]

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

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

    2021 doi

  53. [63]

    2019a, Computational Astrophysics and Cosmology, 6, 2, doi: 10.1186/s40668-019-0028-x

    Nelson, D., Springel, V., Pillepich, A., et al. 2019a, Computational Astrophysics and Cosmology, 6, 2, doi: 10.1186/s40668-019-0028-x

    doi

  54. [64]

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

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

    doi

  55. [65]

    2024, arXiv e-prints, arXiv:2409.10666, doi: 10.48550/arXiv.2409.10666

    Ni, Y., Chen, N., Zhou, Y., et al. 2024, arXiv e-prints, arXiv:2409.10666, doi: 10.48550/arXiv.2409.10666

    2024 doi arXiv

  56. [66]

    2022, MNRAS, 513, 670, doi: 10.1093/mnras/stac351

    Ni, Y., Di Matteo, T., Bird, S., et al. 2022, MNRAS, 513, 670, doi: 10.1093/mnras/stac351

    2022 doi

  57. [67]

    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

  58. [68]

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

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

    2016 doi

  59. [69]

    P., et al

    Pakmor, R., Springel, V., Coles, J. P., et al. 2023, MNRAS, 524, 2539, doi: 10.1093/mnras/stac3620

    2023 doi

  60. [70]

    2024, MNRAS, 532, 4681, doi: 10.1093/mnras/stae1712

    Partmann, C., Naab, T., Rantala, A., et al. 2024, MNRAS, 532, 4681, doi: 10.1093/mnras/stae1712

    2024 doi

  61. [71]

    2019, MNRAS, 486, 101, doi: 10.1093/mnras/stz822

    Pfister, H., Volonteri, M., Dubois, Y., Dotti, M., & Colpi, M. 2019, MNRAS, 486, 101, doi: 10.1093/mnras/stz822

    2019 doi

  62. [72]

    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

  63. [73]

    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

  64. [74]

    E., Condon, J

    Reines, A. E., Condon, J. J., Darling, J., & Greene, J. E. 2020, ApJ, 888, 36, doi: 10.3847/1538-4357/ab4999

    2020 doi

  65. [75]

    S., Kara, E

    Reynolds, C. S., Kara, E. A., Mushotzky, R. F., et al. 2023, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 12678, UV, X-Ray, and Gamma-Ray Space Instrumentation for Astronomy XXIII, ed. O. H. Siegmund & K. Hoadley, 126781E, doi: 10.1117/1...

    2023 doi

  66. [76]

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

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

    2018 doi

  67. [77]

    2021, MNRAS, 503, 6098, doi: 10.1093/mnras/stab866

    Quinn, T. 2021, MNRAS, 503, 6098, doi: 10.1093/mnras/stab866

    2021 doi

  68. [78]

    G., Sakharov, A

    Rubin, S. G., Sakharov, A. S., & Khlopov, M. Y. 2001, Soviet Journal of Experimental and Theoretical Physics, 92, 921, doi: 10.1134/1.1385631

    2001 doi

  69. [79]

    2021, MNRAS, 506, 613, doi: 10.1093/mnras/stab1737

    Sassano, F., Schneider, R., Valiante, R., et al. 2021, MNRAS, 506, 613, doi: 10.1093/mnras/stab1737

    2021 doi

  70. [80]

    Z., et al

    Sayeb, M., Blecha, L., Kelley, L. Z., et al. 2021, MNRAS, 501, 2531, doi: 10.1093/mnras/staa3826

    2021 doi

  71. [81]

    A., Bower, R

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

    2015 doi

  72. [82]

    2007, MNRAS, 377, 1711, doi: 10.1111/j.1365-2966.2007.11734.x

    Sesana, A., Volonteri, M., & Haardt, F. 2007, MNRAS, 377, 1711, doi: 10.1111/j.1365-2966.2007.11734.x

    2007

  73. [83]

    2025, arXiv e-prints, arXiv:2504.00075, doi: 10.48550/arXiv.2504.00075

    Shen, T., Shen, X., Xiao, H., Vogelsberger, M., & Jiang, F. 2025, arXiv e-prints, arXiv:2504.00075, doi: 10.48550/arXiv.2504.00075

    2025 doi arXiv

  74. [84]

    2015, MNRAS, 452, 575, doi: 10.1093/mnras/stv1340

    Sijacki, D., Vogelsberger, M., Genel, S., et al. 2015, MNRAS, 452, 575, doi: 10.1093/mnras/stv1340

    2015 doi

  75. [85]

    Singh, J., Monaco, P., & Tan, J. C. 2023, MNRAS, 525, 969, doi: 10.1093/mnras/stad2346

    2023 doi

  76. [86]

    2023, MNRAS, 522, 2707, doi: 10.1093/mnras/stad1131

    Siwek, M., Weinberger, R., & Hernquist, L. 2023, MNRAS, 522, 2707, doi: 10.1093/mnras/stad1131

    2023 doi

  77. [87]

    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

  78. [88]

    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

  79. [89]

    2023, MNRAS, 518, 4672, doi: 10.1093/mnras/stac3169

    Izquierdo-Villalba, D. 2023, MNRAS, 518, 4672, doi: 10.1093/mnras/stac3169

    2023 doi

  80. [90]

    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

  81. [91]

    2003, MNRAS, 339, 289, doi: 10.1046/j.1365-8711.2003.06206.x 30

    Springel, V., & Hernquist, L. 2003, MNRAS, 339, 289, doi: 10.1046/j.1365-8711.2003.06206.x 30

    2003

  82. [92]

    C., Singh, J., Cammelli, V., et al

    Tan, J. C., Singh, J., Cammelli, V., et al. 2024, arXiv e-prints, arXiv:2412.01828, doi: 10.48550/arXiv.2412.01828

    2024 doi arXiv

  83. [93]

    2015, MNRAS, 448, 1835, doi: 10.1093/mnras/stv139

    Taylor, P., & Kobayashi, C. 2015, MNRAS, 448, 1835, doi: 10.1093/mnras/stv139

    2015 doi

  84. [94]

    A., Bell, E

    Terrazas, B. A., Bell, E. F., Pillepich, A., et al. 2020, MNRAS, 493, 1888, doi: 10.1093/mnras/staa374

    2020 doi

  85. [95]

    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

  86. [96]

    Tremmel, M., Governato, F., Volonteri, M., & Quinn, T. R. 2015, MNRAS, 451, 1868, doi: 10.1093/mnras/stv1060

    2015 doi

  87. [97]

    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

  88. [98]

    2023, MNRAS, 519, 4753, doi: 10.1093/mnras/stac3768

    Trinca, A., Schneider, R., Maiolino, R., et al. 2023, MNRAS, 519, 4753, doi: 10.1093/mnras/stac3768

    2023 doi

  89. [99]

    2022, MNRAS, 511, 616, doi: 10.1093/mnras/stac062

    Trinca, A., Schneider, R., Valiante, R., et al. 2022, MNRAS, 511, 616, doi: 10.1093/mnras/stac062

    2022 doi

  90. [100]

    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

  91. [101]

    2020, Nature Reviews Physics, 2, 42, doi: 10.1038/s42254-019-0127-2

    Vogelsberger, M., Marinacci, F., Torrey, P., & Puchwein, E. 2020, Nature Reviews Physics, 2, 42, doi: 10.1038/s42254-019-0127-2

    2020 doi

  92. [102]

    2014a, Nature, 509, 177, doi: 10.1038/nature13316 —

    Vogelsberger, M., Genel, S., Springel, V., et al. 2014a, Nature, 509, 177, doi: 10.1038/nature13316 —. 2014b, MNRAS, 444, 1518, doi: 10.1093/mnras/stu1536

    doi

  93. [103]

    2016, MNRAS, 460, 2979, doi: 10.1093/mnras/stw1123

    Volonteri, M., Dubois, Y., Pichon, C., & Devriendt, J. 2016, MNRAS, 460, 2979, doi: 10.1093/mnras/stw1123

    2016 doi

  94. [104]

    S., et al

    Volonteri, M., Pfister, H., Beckmann, R. S., et al. 2020, MNRAS, 498, 2219, doi: 10.1093/mnras/staa2384

    2020 doi

  95. [105]

    2025, arXiv e-prints, arXiv:2502.13241, doi: 10.48550/arXiv.2502.13241

    Weinberger, R., Bhowmick, A., Blecha, L., et al. 2025, arXiv e-prints, arXiv:2502.13241, doi: 10.48550/arXiv.2502.13241

    2025 doi arXiv

  96. [106]

    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

  97. [107]

    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

  98. [108]

    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

  99. [109]

    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

  100. [110]

    2024, Science China Physics, Mechanics, and Astronomy, 67, 109512, doi: 10.1007/s11433-024-2433-3

    Yuan, G.-W., Lei, L., Wang, Y.-Z., et al. 2024, Science China Physics, Mechanics, and Astronomy, 67, 109512, doi: 10.1007/s11433-024-2433-3

    2024 doi

  101. [111]

    2024, arXiv e-prints, arXiv:2409.19914, doi: 10.48550/arXiv.2409.19914

    Zhou, Y., Mukherjee, D., Chen, N., et al. 2024, arXiv e-prints, arXiv:2409.19914, doi: 10.48550/arXiv.2409.19914

    2024 doi arXiv

  102. [112]

    2024, arXiv e-prints, arXiv:2411.03448, doi: 10.48550/arXiv.2411.03448

    Ziparo, F., Gallerani, S., & Ferrara, A. 2024, arXiv e-prints, arXiv:2411.03448, doi: 10.48550/arXiv.2411.03448

    2024 doi arXiv

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