pith. sign in

arxiv: 2606.23788 · v1 · pith:TI6C3BTLnew · submitted 2026-06-22 · 🌌 astro-ph.GA · astro-ph.CO· astro-ph.HE· gr-qc

Red vs. Blue: How metallicity shapes black hole dynamics and mergers in dense star clusters

Saloni Agrawal , Kyle Kremer , Michael Zevin , Cailin Plunkett , Elena Gonz\'alez Prieto , Fulya K{\i}ro\u{g}lu , Christopher E. O'Connor , Frederic A. Rasio
show 1 more author
Claire S. Ye
This is my paper

Pith reviewed 2026-06-26 07:57 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.COastro-ph.HEgr-qc
keywords metallicityblack hole mergersstar clustersgravitational wavesdynamical formationglobular clustershierarchical mergers
0
0 comments X

The pith

High-metallicity star clusters produce low-mass black hole mergers matching events like GW241011 and GW241110.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Dense star clusters form gravitational wave sources through dynamical interactions, but low-metallicity models predict black holes more massive than those seen in recent LIGO events such as GW241011 and GW241110. This paper presents Monte Carlo simulations focused on higher-metallicity clusters with [Fe/H] at or above -1, matching the red globular cluster subpopulation. Metallicity alters the black hole mass function, the number of mergers per cluster, retention after natal kicks, the time scale for mass segregation, and the distribution of merger delay times. High-metallicity models specifically yield low-mass hierarchical mergers whose component masses and ratios align with the observed events.

Core claim

High-metallicity cluster models produce low-mass hierarchical mergers consistent with the mass ratios and component masses of GW241011 and GW241110. Metallicity has a significant effect on the mass function of black holes and black hole mergers, the total number of black hole mergers per cluster, black hole retention from natal kicks, the mass segregation time for black-hole-driven cluster dynamics, and the merger delay time distribution.

What carries the argument

Monte Carlo star cluster simulations with refined coverage in metallicity for clusters with [Fe/H] ≥ -1 that track stellar evolution, black hole formation, retention, and dynamical interactions.

If this is right

  • Higher metallicity reduces the masses of black holes formed in clusters.
  • Black hole retention after natal kicks decreases at higher metallicity.
  • The onset of black-hole-driven mass segregation occurs later in high-metallicity clusters.
  • Merger delay time distributions shift with metallicity.
  • Low-mass hierarchical mergers become viable in high-metallicity settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The red versus blue globular cluster subpopulations may contribute differently to the overall gravitational wave merger rate.
  • Future catalogs could reveal trends between merger masses and the metallicity of host galaxies.
  • Population synthesis models for gravitational wave sources should incorporate explicit metallicity variations across cluster environments.

Load-bearing premise

The Monte Carlo code and its input physics accurately capture metallicity-dependent black hole retention and dynamics without dominant systematic biases from untested modeling choices.

What would settle it

A future LIGO-Virgo-KAGRA catalog that shows no population of low-mass hierarchical mergers preferentially associated with high-metallicity environments would falsify the claim that such clusters explain events like GW241011 and GW241110.

Figures

Figures reproduced from arXiv: 2606.23788 by Cailin Plunkett, Christopher E. O'Connor, Claire S. Ye, Elena Gonz\'alez Prieto, Frederic A. Rasio, Fulya K{\i}ro\u{g}lu, Kyle Kremer, Michael Zevin, Saloni Agrawal.

Figure 1
Figure 1. Figure 1: Metallicity and mass distribution of observed and simulated globular clusters. The upper panel shows the probability density (PDF) for metallicity values for the full globular cluster population in Virgo (gray), and separated into metal-rich (red) and metal-poor (blue) clusters (e.g., Peng et al. 2006; Harris 2009; Strader et al. 2011). We also show in black the metallicity distribution for Galactic glob￾u… view at source ↗
Figure 2
Figure 2. Figure 2: Black hole mass function (shown as cumulative distribution) for all black holes formed via stellar collapse in our CMC models with fixed initial conditions, N = 8 × 105 , rv = 1 pc, and Rgc = 8 kpc. The eight colors span our full range in metallicity: −2 ≤ [Fe/H] ≤ 0.1. The gray shaded re￾gion denotes the boundary for the pair-instability mass gap adopted in our models. The mass functions shift toward lowe… view at source ↗
Figure 3
Figure 3. Figure 3: Evolution of various cluster properties with time for the same eight CMC models shown in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Number of total (circles) and in-cluster (triangles) black hole mergers as a function of initial cluster mass across metallicity, showing power-law fits and intrinsic scatter (1σ) for [Fe/H]= [0, −0.5, −1, and − 2]. Although the overall normal￾ization changes (see also [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Merger yield as a function of metallicity across 5 groups of CMC models with varying N and rv. Left panel: Total number of black hole mergers (including linear fits in dashed lines and shaded 1σ uncertainty). Right panel: Total number of black hole mergers normalized by maximum number of black holes retained. Each point is summed over all models at a particular metallicity. Error bars represent Poisson unc… view at source ↗
Figure 6
Figure 6. Figure 6: Secondary versus primary mass for all black hole mergers across metallicities, again showing the same eight models as in [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Mass ratio (q = m2/m1) versus primary mass m1 for all black hole mergers in our highest-metallicity CMC models, from left to right [Fe/H]= [0, −0.25, −0.5]. Here we combine all models in [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

Dense star clusters are a well-established environment for the formation of gravitational wave sources through dynamical interactions. Recent LIGO-Virgo-KAGRA (LVK) events such as GW241011 and GW241110 provide some of the best evidence yet for a dynamical origin. However, their relatively low component masses are in tension with predictions from low-metallicity globular cluster models (which typically produce more massive black holes), hinting that these events may have originated in higher-metallicity environments. Here we present a new set of Monte Carlo star cluster simulations with refined coverage in metallicity, focusing specifically on clusters with [Fe/H] $\geq-1$, similar to the ''red'' globular cluster subpopulation observed in most galaxies. We show that metallicity has a significant effect on the mass function of black holes and black hole mergers, the total number of black hole mergers per cluster, black hole retention from natal kicks, the mass segregation time for black-hole-driven cluster dynamics, and the merger delay time distribution. We also show that high-metallicity cluster models produce low-mass hierarchical mergers consistent with the mass ratios and component masses of GW241011 and GW241110, motivating the importance of high-metallicity clusters in the astrophysical interpretation of future LVK catalogs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript presents Monte Carlo simulations of dense star clusters at metallicities [Fe/H] ≥ −1. It reports that metallicity strongly modulates black-hole mass functions, merger rates per cluster, natal-kick retention, mass-segregation timescales, and merger delay-time distributions. The central result is that the high-metallicity (“red”) models generate low-mass hierarchical mergers whose component masses and mass ratios are consistent with the LVK events GW241011 and GW241110, thereby motivating a dynamical origin for these events in higher-metallicity cluster environments.

Significance. If the modeling assumptions prove robust, the work supplies a concrete astrophysical channel that can reconcile the relatively low masses of certain LVK events with dynamical formation, broadening the set of environments that must be considered when interpreting the growing catalog of binary black-hole mergers.

major comments (3)
  1. [Abstract and §4] Abstract and §4 (results on hierarchical mergers): the statement that high-metallicity models produce mergers “consistent with” GW241011 and GW241110 is presented without any quantitative measure (overlap integral, Kolmogorov–Smirnov statistic, or posterior probability) of the match in component masses and mass ratios; the claim therefore remains qualitative.
  2. [Methods (§2–3)] Methods (§2–3, stellar evolution and kick prescriptions): the reported BH mass function, retention fraction, and consequent low-mass hierarchical population at [Fe/H] ≥ −1 rest on a single set of metallicity-dependent wind, core-collapse, and natal-kick prescriptions inside the Monte Carlo integrator. No sensitivity runs with alternate wind scalings (e.g., updated Vink) or fallback-modulated kicks are shown, even though such changes are known to shift the upper BH mass cutoff by several solar masses and retention by factors of ∼2 at these metallicities.
  3. [§4 and discussion] §4 and discussion: no comparison is made between the simulated high-metallicity merger population and independent observational constraints on high-[Fe/H] clusters (e.g., BH retention inferred from X-ray binaries or dynamical mass-to-light ratios), leaving the weakest modeling assumption untested against data.
minor comments (2)
  1. [Table 1] Table 1 (simulation grid): the metallicity sampling is described only as “[Fe/H] ≥ −1”; explicit bin centers or a list of the discrete values actually run would improve reproducibility.
  2. [Figures] Figure captions: several panels compare “red” versus “blue” subpopulations but do not state the exact [Fe/H] thresholds used to define each subpopulation in the plotted curves.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which have helped us identify areas for improvement. We address each major comment point by point below, indicating revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (results on hierarchical mergers): the statement that high-metallicity models produce mergers “consistent with” GW241011 and GW241110 is presented without any quantitative measure (overlap integral, Kolmogorov–Smirnov statistic, or posterior probability) of the match in component masses and mass ratios; the claim therefore remains qualitative.

    Authors: We agree that the consistency statement is qualitative. In the revised manuscript we will add a quantitative assessment in §4 (and update the abstract accordingly), for example by reporting the fraction of simulated mergers whose component masses and mass ratios lie within the 90% credible intervals of the LVK events and/or by performing a simple two-sample KS test on the relevant distributions. revision: yes

  2. Referee: [Methods (§2–3)] Methods (§2–3, stellar evolution and kick prescriptions): the reported BH mass function, retention fraction, and consequent low-mass hierarchical population at [Fe/H] ≥ −1 rest on a single set of metallicity-dependent wind, core-collapse, and natal-kick prescriptions inside the Monte Carlo integrator. No sensitivity runs with alternate wind scalings (e.g., updated Vink) or fallback-modulated kicks are shown, even though such changes are known to shift the upper BH mass cutoff by several solar masses and retention by factors of ∼2 at these metallicities.

    Authors: The adopted prescriptions follow the standard metallicity-dependent implementations in the Monte Carlo code (Vink wind scaling and fallback-modulated kicks). Performing a full suite of sensitivity runs would require substantial additional computational resources beyond the scope of the present study. We will, however, expand the methods and discussion sections to explicitly discuss the sensitivity of our results to these choices, citing literature on alternate wind and kick models and noting the expected direction of changes. revision: partial

  3. Referee: [§4 and discussion] §4 and discussion: no comparison is made between the simulated high-metallicity merger population and independent observational constraints on high-[Fe/H] clusters (e.g., BH retention inferred from X-ray binaries or dynamical mass-to-light ratios), leaving the weakest modeling assumption untested against data.

    Authors: We will add a dedicated paragraph in the discussion that qualitatively compares our predicted BH retention fractions and merger rates at [Fe/H] ≥ −1 with existing constraints from X-ray binary populations in metal-rich environments. We will also note the current scarcity of direct dynamical mass-to-light ratio measurements for high-metallicity clusters and the limitations this imposes on quantitative tests. revision: yes

Circularity Check

0 steps flagged

No circularity: forward Monte Carlo simulations compared to observations

full rationale

The paper runs Monte Carlo cluster simulations at specified metallicities using standard input physics (stellar evolution, kicks, binary interactions) and reports output distributions of BH masses, retention, merger rates, and delay times. These are compared to GW events for consistency. No equations, fitted parameters, or self-citations reduce any reported match to a quantity defined or fitted from the same data. The derivation chain consists of independent forward modeling whose outputs are not forced by construction to reproduce the target observations.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. Metallicity is treated as an input variable whose effects are explored numerically.

pith-pipeline@v0.9.1-grok · 5814 in / 979 out tokens · 18400 ms · 2026-06-26T07:57:28.553993+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

92 extracted references · 91 canonical work pages · 2 internal anchors

  1. [1]

    G., Abouelfettouh, I., Acernese, F., et al

    Abac, A. G., Abouelfettouh, I., Acernese, F., et al. 2025a, ApJL, 993, L25, doi: 10.3847/2041-8213/ae0c9c —. 2025b, ApJL, 993, L21, doi: 10.3847/2041-8213/ae0d54

    work page doi:10.3847/2041-8213/ae0c9c 2041

  2. [2]

    P., Abbott, R., Abbott, T

    Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2016a, PhRvL, 116, 061102, doi: 10.1103/PhysRevLett.116.061102 —. 2016b, PhRvL, 116, 241103, doi: 10.1103/PhysRevLett.116.241103

    work page doi:10.1103/physrevlett.116.061102

  3. [3]

    D., Abraham, S., et al

    Abbott, R., Abbott, T. D., Abraham, S., et al. 2020, PhRvL, 125, 101102, doi: 10.1103/PhysRevLett.125.101102

    work page doi:10.1103/physrevlett.125.101102 2020

  4. [4]

    J., Bavera, S

    Andrews, J. J., Bavera, S. S., Briel, M., et al. 2025, ApJS, 281, 3, doi: 10.3847/1538-4365/adfb78

    work page doi:10.3847/1538-4365/adfb78 2025

  5. [5]

    2020, PhRvD, 102, 123016, doi: 10.1103/PhysRevD.102.123016

    Antonini, F., & Gieles, M. 2020, PhRvD, 102, 123016, doi: 10.1103/PhysRevD.102.123016

    work page doi:10.1103/physrevd.102.123016 2020

  6. [6]

    2018, MNRAS, 478, 1844, doi: 10.1093/mnras/sty1186

    Askar, A., Arca Sedda, M., & Giersz, M. 2018, MNRAS, 478, 1844, doi: 10.1093/mnras/sty1186

    work page doi:10.1093/mnras/sty1186 2018

  7. [7]

    2017, MNRAS, 464, L36, doi: 10.1093/mnrasl/slw177

    Askar, A., Szkudlarek, M., Gondek-Rosi´ nska, D., Giersz, M., & Bulik, T. 2017, MNRAS, 464, L36, doi: 10.1093/mnrasl/slw177

    work page doi:10.1093/mnrasl/slw177 2017

  8. [8]

    2017, MNRAS, 467, 524, doi: 10.1093/mnras/stw3392

    Banerjee, S. 2017, MNRAS, 467, 524, doi: 10.1093/mnras/stw3392

    work page doi:10.1093/mnras/stw3392 2017

  9. [9]

    Barmby, P., Holland, S., & Huchra, J. P. 2002, AJ, 123, 1937, doi: 10.1086/339667

    work page doi:10.1086/339667 2002

  10. [10]

    L., et al

    Belczynski, K., Bulik, T., Fryer, C. L., et al. 2010, ApJ, 714, 1217, doi: 10.1088/0004-637X/714/2/1217

    work page doi:10.1088/0004-637x/714/2/1217 2010

  11. [11]

    A., et al

    Belczynski, K., Kalogera, V., Rasio, F. A., et al. 2008, ApJS, 174, 223, doi: 10.1086/521026

    work page internal anchor Pith review doi:10.1086/521026 2008

  12. [12]

    2016, A&A, 594, A97, doi: 10.1051/0004-6361/201628980

    Belczynski, K., Heger, A., Gladysz, W., et al. 2016, A&A, 594, A97, doi: 10.1051/0004-6361/201628980

    work page doi:10.1051/0004-6361/201628980 2016

  13. [13]

    1944, MNRAS, 104, 273, doi: 10.1093/mnras/104.5.273

    Bondi, H., & Hoyle, F. 1944, MNRAS, 104, 273, doi: 10.1093/mnras/104.5.273

    work page doi:10.1093/mnras/104.5.273 1944

  14. [14]

    G., & Heggie, D

    Breen, P. G., & Heggie, D. C. 2013, MNRAS, 432, 2779, doi: 10.1093/mnras/stt628

    work page doi:10.1093/mnras/stt628 2013

  15. [15]

    2020, ApJ, 898, 71, doi: 10.3847/1538-4357/ab9d85

    Breivik, K., Coughlin, S., Zevin, M., et al. 2020, ApJ, 898, 71, doi: 10.3847/1538-4357/ab9d85

    work page doi:10.3847/1538-4357/ab9d85 2020

  16. [16]

    P., & Strader, J

    Brodie, J. P., & Strader, J. 2006, ARA&A, 44, 193, doi: 10.1146/annurev.astro.44.051905.092441

    work page doi:10.1146/annurev.astro.44.051905.092441 2006

  17. [17]

    2021, Nature, 589, 29, doi: 10.1038/s41586-020-03059-w

    Burrows, A., & Vartanyan, D. 2021, Nature, 589, 29, doi: 10.1038/s41586-020-03059-w

    work page doi:10.1038/s41586-020-03059-w 2021

  18. [18]

    1985, ApJ, 298, 80, doi: 10.1086/163589

    Casertano, S., & Hut, P. 1985, ApJ, 298, 80, doi: 10.1086/163589

    work page doi:10.1086/163589 1985

  19. [19]

    L., Kalogera, V., & Rasio, F

    Chatterjee, S., Rodriguez, C. L., Kalogera, V., & Rasio, F. A. 2017, ApJL, 836, L26, doi: 10.3847/2041-8213/aa5caa

    work page doi:10.3847/2041-8213/aa5caa 2017

  20. [20]

    Verbunt, F. W. M. 2014, A&A, 563, A83, doi: 10.1051/0004-6361/201322714 12 T able 1.Summary of black hole formation and merger statistics across cluster models. The models indicated with an asterisk are models that were computed as part of the originalCMC Cluster Catalog. For consistency, with Kremer et al. (2020b); Mai et al. (2026), we preserve the same...

    work page doi:10.1051/0004-6361/201322714 2014

  21. [21]

    G., Blakeslee, J

    Cohen, J. G., Blakeslee, J. P., & Ryzhov, A. 1998, ApJ, 496, 808, doi: 10.1086/305429

    work page doi:10.1086/305429 1998

  22. [22]

    M., Casares, J., Mu˜ noz-Darias, T., et al

    Corral-Santana, J. M., Casares, J., Mu˜ noz-Darias, T., et al. 2016, A&A, 587, A61, doi: 10.1051/0004-6361/201527130 Di Carlo, U. N., Giacobbo, N., Mapelli, M., et al. 2019, MNRAS, 487, 2947, doi: 10.1093/mnras/stz1453

    work page doi:10.1051/0004-6361/201527130 2016

  23. [23]

    J., H´ enault-Brunet, V., Gieles, M., & Baumgardt, H

    Dickson, N., Smith, P. J., H´ enault-Brunet, V., Gieles, M., & Baumgardt, H. 2024, MNRAS, 529, 331, doi: 10.1093/mnras/stae470

    work page doi:10.1093/mnras/stae470 2024

  24. [24]

    2023a, MNRAS, 518, 1057, doi: 10.1093/mnras/stac3140

    El-Badry, K., Rix, H.-W., Quataert, E., et al. 2023a, MNRAS, 518, 1057, doi: 10.1093/mnras/stac3140

    work page doi:10.1093/mnras/stac3140

  25. [25]

    2023b, MNRAS, 521, 4323, doi: 10.1093/mnras/stad799

    El-Badry, K., Rix, H.-W., Cendes, Y., et al. 2023b, MNRAS, 521, 4323, doi: 10.1093/mnras/stad799

    work page doi:10.1093/mnras/stad799

  26. [26]

    J., & Vink, J

    Eldridge, J. J., & Vink, J. S. 2006, A&A, 452, 295, doi: 10.1051/0004-6361:20065001

    work page doi:10.1051/0004-6361:20065001 2006

  27. [27]

    , keywords =

    Justham, S. 2019, ApJ, 887, 53, doi: 10.3847/1538-4357/ab518b

    work page doi:10.3847/1538-4357/ab518b 2019

  28. [28]

    M., Sravan, N., Cantrell, A., et al

    Farr, W. M., Sravan, N., Cantrell, A., et al. 2011, ApJ, 741, 103, doi: 10.1088/0004-637X/741/2/103

    work page doi:10.1088/0004-637x/741/2/103 2011

  29. [29]

    E., & Farr, B

    Fishbach, M., Holz, D. E., & Farr, B. 2017, ApJL, 840, L24, doi: 10.3847/2041-8213/aa7045

    work page doi:10.3847/2041-8213/aa7045 2017

  30. [30]

    Fragione, G., Loeb, A., & Rasio, F. A. 2020, ApJL, 902, L26, doi: 10.3847/2041-8213/abbc0a

    work page doi:10.3847/2041-8213/abbc0a 2020

  31. [31]

    J., Bavera, S

    Fragos, T., Andrews, J. J., Bavera, S. S., et al. 2023, ApJS, 264, 45, doi: 10.3847/1538-4365/ac90c1

    work page doi:10.3847/1538-4365/ac90c1 2023

  32. [32]

    L., Belczynski, K., Wiktorowicz, G., et al

    Fryer, C. L., Belczynski, K., Wiktorowicz, G., et al. 2012, ApJ, 749, 91, doi: 10.1088/0004-637X/749/1/91 Gaia Collaboration, Panuzzo, P., Mazeh, T., et al. 2024, A&A, 686, L2, doi: 10.1051/0004-6361/202449763

    work page doi:10.1088/0004-637x/749/1/91 2012

  33. [33]

    Nature Astronomy , volume =

    Gerosa, D., & Fishbach, M. 2021a, Nature Astronomy, 5, 749, doi: 10.1038/s41550-021-01398-w —. 2021b, Nature Astronomy, 5, 749, doi: 10.1038/s41550-021-01398-w

    work page doi:10.1038/s41550-021-01398-w

  34. [34]

    2018, MNRAS, 474, 2959, doi: 10.1093/mnras/stx2933

    Giacobbo, N., Mapelli, M., & Spera, M. 2018, MNRAS, 474, 2959, doi: 10.1093/mnras/stx2933

    work page doi:10.1093/mnras/stx2933 2018

  35. [35]

    2018, MNRAS, 475, L15, doi: 10.1093/mnrasl/slx203

    Giesers, B., Dreizler, S., Husser, T.-O., et al. 2018, MNRAS, 475, L15, doi: 10.1093/mnrasl/slx203

    work page doi:10.1093/mnrasl/slx203 2018

  36. [36]

    2019, A&A, 632, A3, doi: 10.1051/0004-6361/201936203 Gonz´ alez, E., Kremer, K., Chatterjee, S., et al

    Giesers, B., Kamann, S., Dreizler, S., et al. 2019, A&A, 632, A3, doi: 10.1051/0004-6361/201936203 Gonz´ alez, E., Kremer, K., Chatterjee, S., et al. 2021, ApJL, 908, L29, doi: 10.3847/2041-8213/abdf5b Gonz´ alez Prieto, E., Weatherford, N. C., Fragione, G.,

    work page doi:10.1051/0004-6361/201936203 2019

  37. [37]

    Kremer, K., & Rasio, F. A. 2024, ApJ, 969, 29, doi: 10.3847/1538-4357/ad43d6

    work page doi:10.3847/1538-4357/ad43d6 2024

  38. [38]

    Harris, W. E. 1996; 2010 edition, AJ, 112, 1487, doi: 10.1086/118116 —. 2009, ApJ, 703, 939, doi: 10.1088/0004-637X/703/1/939

    work page doi:10.1086/118116 1996

  39. [39]

    Hartmann, D. H. 2003, ApJ, 591, 288, doi: 10.1086/375341

    work page doi:10.1086/375341 2003

  40. [40]

    O., Grindlay, J

    Heinke, C. O., Grindlay, J. E., Lugger, P. M., et al. 2003, ApJ, 598, 501, doi: 10.1086/378885

    work page doi:10.1086/378885 2003

  41. [41]

    S., & Webbink, R

    Hjellming, M. S., & Webbink, R. F. 1987, ApJ, 318, 794, doi: 10.1086/165412

    work page doi:10.1086/165412 1987

  42. [42]

    , keywords =

    Hobbs, G., Lorimer, D. R., Lyne, A. G., & Kramer, M. 2005, MNRAS, 360, 974, doi: 10.1111/j.1365-2966.2005.09087.x

    work page doi:10.1111/j.1365-2966.2005.09087.x 2005

  43. [43]

    Binary Black Hole Mergers from Globular Clusters: The Impact of Globular Cluster Properties , shorttitle =

    Hong, J., Vesperini, E., Askar, A., et al. 2018, MNRAS, 480, 5645, doi: 10.1093/mnras/sty2211

    work page doi:10.1093/mnras/sty2211 2018

  44. [44]

    , keywords =

    Hurley, J. R., Tout, C. A., & Pols, O. R. 2002, MNRAS, 329, 897, doi: 10.1046/j.1365-8711.2002.05038.x Jord´ an, A., Peng, E. W., Blakeslee, J. P., et al. 2009, ApJS, 180, 54, doi: 10.1088/0067-0049/180/1/54

    work page doi:10.1046/j.1365-8711.2002.05038.x 2002

  45. [45]

    Kimball, C., Talbot, C., Berry, C. P. L., et al. 2021, ApJL, 915, L35, doi: 10.3847/2041-8213/ac0aef Kıro˘ glu, F., Kremer, K., Biscoveanu, S., Gonz´ alez Prieto, E., & Rasio, F. A. 2025a, ApJ, 979, 237, doi: 10.3847/1538-4357/ada26b Kıro˘ glu, F., Kremer, K., & Rasio, F. A. 2025b, ApJL, 994, L37, doi: 10.3847/2041-8213/ae1eeb

    work page doi:10.3847/2041-8213/ac0aef 2021

  46. [46]

    2026, in Encyclopedia of Astrophysics, Volume 3, Vol

    Kremer, K. 2026, in Encyclopedia of Astrophysics, Volume 3, Vol. 3, 458–472, doi: 10.1016/B978-0-443-21439-4.00103-6

    work page doi:10.1016/b978-0-443-21439-4.00103-6 2026

  47. [47]

    Rasio, F. A. 2019, ApJ, 871, 38, doi: 10.3847/1538-4357/aaf646

    work page doi:10.3847/1538-4357/aaf646 2019

  48. [48]

    Z., Weatherford, N

    Kremer, K., Rui, N. Z., Weatherford, N. C., et al. 2021, ApJ, 917, 28, doi: 10.3847/1538-4357/ac06d4

    work page doi:10.3847/1538-4357/ac06d4 2021

  49. [49]

    C., Hopkins, P

    Kremer, K., Weatherford, N. C., Hopkins, P. F., Rui, N. Z., & Ye, C. S. 2025, ApJL, 993, L34, doi: 10.3847/2041-8213/ae1233

    work page doi:10.3847/2041-8213/ae1233 2025

  50. [50]

    Rasio, F. A. 2020a, in IAU Symposium, Vol. 351, Star Clusters: From the Milky Way to the Early Universe, ed. A. Bragaglia, M. Davies, A. Sills, & E. Vesperini, 357–366, doi: 10.1017/S1743921319007269

    work page doi:10.1017/s1743921319007269

  51. [51]

    Modeling

    Kremer, K., Ye, C. S., Rui, N. Z., et al. 2020b, ApJS, 247, 48, doi: 10.3847/1538-4365/ab7919

    work page doi:10.3847/1538-4365/ab7919

  52. [52]

    2020c, ApJ, 903, 45, doi: 10.3847/1538-4357/abb945

    Kremer, K., Spera, M., Becker, D., et al. 2020c, ApJ, 903, 45, doi: 10.3847/1538-4357/abb945

    work page doi:10.3847/1538-4357/abb945

  53. [53]

    , keywords =

    Kroupa, P. 2001, MNRAS, 322, 231, doi: 10.1046/j.1365-8711.2001.04022.x

    work page doi:10.1046/j.1365-8711.2001.04022.x 2001

  54. [54]

    J., & Zepf, S

    Kundu, A., Maccarone, T. J., & Zepf, S. E. 2002, ApJL, 574, L5, doi: 10.1086/342353

    work page doi:10.1086/342353 2002

  55. [55]

    J., Zepf, S

    Kundu, A., Maccarone, T. J., Zepf, S. E., & Puzia, T. H. 2003, ApJL, 589, L81, doi: 10.1086/376493 14

    work page doi:10.1086/376493 2003

  56. [56]

    R., Dalessandro, E., et al

    Lanzoni, B., Ferraro, F. R., Dalessandro, E., et al. 2010, ApJ, 717, 653, doi: 10.1088/0004-637X/717/2/653

    work page doi:10.1088/0004-637x/717/2/653 2010

  57. [57]

    J., Kundu, A., Zepf, S

    Maccarone, T. J., Kundu, A., Zepf, S. E., & Rhode, K. L. 2007, Nature, 445, 183, doi: 10.1038/nature05434

    work page doi:10.1038/nature05434 2007

  58. [58]

    J., et al., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13567.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.389..629B 389, 629

    Mackey, A. D., Wilkinson, M. I., Davies, M. B., & Gilmore, G. F. 2008, MNRAS, 386, 65, doi: 10.1111/j.1365-2966.2008.13052.x

    work page doi:10.1111/j.1365-2966.2008.13052.x 2008

  59. [59]

    Shadows of the

    Mai, A., Kremer, K., & Kıro˘ glu, F. 2026, ApJ, 998, 138, doi: 10.3847/1538-4357/ae2de5

    work page doi:10.3847/1538-4357/ae2de5 2026

  60. [60]

    2024, ARA&A, 62, 21, doi: 10.1146/annurev-astro-052722-105936

    Marchant, P., & Bodensteiner, J. 2024, ARA&A, 62, 21, doi: 10.1146/annurev-astro-052722-105936

    work page doi:10.1146/annurev-astro-052722-105936 2024

  61. [61]

    R., de Koter, A., Vink, J

    Mokiem, M. R., de Koter, A., Vink, J. S., et al. 2007, A&A, 473, 603, doi: 10.1051/0004-6361:20077545

    work page doi:10.1051/0004-6361:20077545 2007

  62. [62]

    A., & Umbreit, S

    Morscher, M., Pattabiraman, B., Rodriguez, C., Rasio, F. A., & Umbreit, S. 2015, ApJ, 800, 9, doi: 10.1088/0004-637X/800/1/9 O’Connor, C. E., Kremer, K., Agrawal, S., et al. 2026a, arXiv e-prints, arXiv:2606.14846, doi: 10.48550/arXiv.2606.14846 O’Connor, C. E., Kremer, K., & Rasio, F. A. 2026b, arXiv e-prints, arXiv:2604.02412, doi: 10.48550/arXiv.2604.02412

    work page doi:10.1088/0004-637x/800/1/9 2015

  63. [63]

    2011, ApJS, 192, 3, doi: 10.1088/0067-0049/192/1/3

    Paxton, B., Bildsten, L., Dotter, A., et al. 2011, ApJS, 192, 3, doi: 10.1088/0067-0049/192/1/3

    work page doi:10.1088/0067-0049/192/1/3 2011

  64. [64]

    W., Jord´ an, A., Cˆ ot´ e, P., et al

    Peng, E. W., Jord´ an, A., Cˆ ot´ e, P., et al. 2006, ApJ, 639, 95, doi: 10.1086/498210

    work page doi:10.1086/498210 2006

  65. [65]

    Peters, P. C. 1964, Physical Review, 136, 1224, doi: 10.1103/PhysRev.136.B1224

    work page doi:10.1103/physrev.136.b1224 1964

  66. [66]

    Pooley, D., Lewin, W. H. G., Anderson, S. F., et al. 2003, ApJL, 591, L131, doi: 10.1086/377074 Portegies Zwart, S. F., & McMillan, S. L. W. 2000, ApJL, 528, L17, doi: 10.1086/312422 Portegies Zwart, S. F., McMillan, S. L. W., & Gieles, M. 2010, ARA&A, 48, 431, doi: 10.1146/annurev-astro-081309-130834

    work page doi:10.1086/377074 2003

  67. [67]

    L., Chatterjee, S., & Rasio, F

    Rodriguez, C. L., Chatterjee, S., & Rasio, F. A. 2016, PhRvD, 93, 084029, doi: 10.1103/PhysRevD.93.084029

    work page doi:10.1103/physrevd.93.084029 2016

  68. [68]

    L., Hafen, Z., Grudi´ c, M

    Rodriguez, C. L., Hafen, Z., Grudi´ c, M. Y., et al. 2023, MNRAS, 521, 124, doi: 10.1093/mnras/stad578

    work page doi:10.1093/mnras/stad578 2023

  69. [69]

    and Zevin, Michael and

    Rodriguez, C. L., Zevin, M., Amaro-Seoane, P., et al. 2019, PhRvD, 100, 043027, doi: 10.1103/PhysRevD.100.043027

    work page doi:10.1103/physrevd.100.043027 2019

  70. [70]

    L., Weatherford, N

    Rodriguez, C. L., Weatherford, N. C., Coughlin, S. C., et al. 2022, ApJS, 258, 22, doi: 10.3847/1538-4365/ac2edf

    work page doi:10.3847/1538-4365/ac2edf 2022

  71. [71]

    2018, PhRvD, 97, 103014, doi: 10.1103/PhysRevD.97.103014

    Samsing, J. 2018, PhRvD, 97, 103014, doi: 10.1103/PhysRevD.97.103014

    work page doi:10.1103/physrevd.97.103014 2018

  72. [72]

    J., Kremer, K., Rodriguez, C

    Samsing, J., D’Orazio, D. J., Kremer, K., Rodriguez, C. L., & Askar, A. 2020, PhRvD, 101, 123010, doi: 10.1103/PhysRevD.101.123010

    work page doi:10.1103/physrevd.101.123010 2020

  73. [73]

    Science , archivePrefix = "arXiv", eprint =

    Sana, H., de Mink, S. E., de Koter, A., et al. 2012, Science, 337, 444, doi: 10.1126/science.1223344

    work page doi:10.1126/science.1223344 2012

  74. [74]

    2017, MNRAS, 470, 4739, doi: 10.1093/mnras/stx1576

    Spera, M., & Mapelli, M. 2017, MNRAS, 470, 4739, doi: 10.1093/mnras/stx1576

    work page doi:10.1093/mnras/stx1576 2017

  75. [75]

    2015, MNRAS, 451, 4086, doi: 10.1093/mnras/stv1161

    Spera, M., Mapelli, M., & Bressan, A. 2015, MNRAS, 451, 4086, doi: 10.1093/mnras/stv1161

    work page doi:10.1093/mnras/stv1161 2015

  76. [76]

    2019, MNRAS, 485, 889, doi: 10.1093/mnras/stz359

    Spera, M., Mapelli, M., Giacobbo, N., et al. 2019, MNRAS, 485, 889, doi: 10.1093/mnras/stz359

    work page doi:10.1093/mnras/stz359 2019

  77. [77]

    J., Miller-Jones, J

    Strader, J., Chomiuk, L., Maccarone, T. J., Miller-Jones, J. C. A., & Seth, A. C. 2012, Nature, 490, 71, doi: 10.1038/nature11490

    work page doi:10.1038/nature11490 2012

  78. [78]

    J., Brodie, J

    Strader, J., Romanowsky, A. J., Brodie, J. P., et al. 2011, ApJS, 197, 33, doi: 10.1088/0067-0049/197/2/33

    work page doi:10.1088/0067-0049/197/2/33 2011

  79. [79]

    GWTC-4.0: Updating the Gravitational-Wave Transient Catalog with Observations from the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run

    Janka, H.-T. 2016, ApJ, 821, 38, doi: 10.3847/0004-637X/821/1/38 The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, et al. 2025, arXiv e-prints, arXiv:2508.18082, doi: 10.48550/arXiv.2508.18082

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.3847/0004-637x/821/1/38 2016

  80. [80]

    , keywords =

    Usher, C., Forbes, D. A., Brodie, J. P., et al. 2012, MNRAS, 426, 1475, doi: 10.1111/j.1365-2966.2012.21801.x

    work page doi:10.1111/j.1365-2966.2012.21801.x 2012

Showing first 80 references.