Pith. sign in

REVIEW 3 major objections 5 minor 82 references

Double Compact Binary Merger Rate Density in Open Star Clusters: Black Holes, Neutron Stars, and White Dwarfs

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read Open clusters, though short-lived, contribute compact-object merger rates that rival or exceed globular clusters, especially for white-dwarf-white-dwarf mergers that may power fast radio bursts.

desk verdict First systematic N-body look at WD-WD and WD-NS mergers in low-mass open clusters, with interesting FRB implications, but headline rates are not robust to binary fraction and cluster formation efficiency choices. read the letter →

arxiv 2506.22673 v2 pith:MWZZSO7W submitted 2025-06-27 astro-ph.SR astro-ph.GAastro-ph.HE

Savannah Cary , Michiko Fujii , Long Wang , Ataru Tanikawa This is my paper · ORCID
classification astro-ph.SRastro-ph.GAastro-ph.HE
keywords openstarclusterscompactbinarymergerswhitedwarfmergerratedensityfastradioburstsTypeIasupernovaeN-bodysimulationsblackhole-neutron
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 argues that previous merger-rate censuses missed a major channel: open clusters, which are far more numerous than globular clusters and form throughout cosmic time. From N-body simulations of $10^2$, $10^3$, and $10^4$ solar-mass open clusters with full stellar evolution, the authors derive merger efficiencies per solar mass and convert them into local-universe rate densities. They find white-dwarf-white-dwarf mergers at 46-1400 Gpc$^{-3}$ yr$^{-1}$, higher than globular-cluster estimates, with super-Chandrasekhar WD-WD mergers at 70-780 Gpc$^{-3}$ yr$^{-1}$ as candidate fast-radio-burst progenitors. They also find BH-NS mergers only in intermediate-mass $10^3$ solar-mass clusters and dynamically formed, sometimes eccentric BH-BH mergers only in $10^4$ solar-mass clusters. A sympathetic reader would care because the results reposition open clusters as nonnegligible contributors to the transient sky.

What carries the argument

The carrying object is the merger efficiency $\eta$, the number of mergers per solar mass of cluster, extracted from N-body simulations and converted into a cosmic rate density $R = \eta \, \dot{\rho}_*(z) \, f_{\rm cluster}$ using a cosmic star formation rate density and a cluster formation efficiency $f_{\rm cluster}$ bracketed between 0.1 and 1. The simulations evolve hundreds of open clusters of $10^2$, $10^3$, and $10^4$ solar masses with a tree-based N-body integrator, single and binary stellar evolution, natal kicks, and a Milky Way tidal field; matched isolated-binary runs provide the control that isolates the dynamical effect of the cluster environment.

What would settle it

Measure the primordial binary fraction in young open clusters with masses near $10^2$ to $10^4$ solar masses, for example through spectroscopic binary surveys of embedded clusters; if the fraction is substantially below 100%, the reported WD-WD, WD-NS, and BH-NS rates should be scaled down proportionally, and a fraction near 50% would pull the super-Chandrasekhar WD-WD rate toward the globular-cluster comparison level.

Watch

Extended reading notes

Core claim

The central claim is that the compact-binary merger rate density of the local universe receives a substantial contribution from open clusters, with the WD-WD channel dominating. Using a large suite of N-body cluster simulations plus matched isolated-binary control runs, the authors report WD-WD merger efficiencies of $2.5\times10^{-4}$ to $7.4\times10^{-4}$ per solar mass, which translate to rate densities of 46-460, 130-1300, and 140-1400 Gpc$^{-3}$ yr$^{-1}$ for $10^2$, $10^3$, and $10^4$ solar-mass clusters, respectively. These exceed previous globular-cluster estimates. Super-Chandrasekhar WD-WD mergers, whose total mass exceeds the Chandrasekhar limit, occur at 70-780 Gpc$^{-3}$ yr$^{-1}$, above the roughly $10$ Gpc$^{-3}$ yr$^{-1}$ estimated for globular clusters, and are proposed as magnetar and fast-radio-burst progenitors; carbon-oxygen WD-WD mergers that may produce Type Ia supernovae account for only 0.14%-2.6% of the observed local Type Ia rate. The paper also establishes mass-dependent behavior: BH-BH mergers are dynamically formed, some with high eccentricity, only in $10^4$ solar-mass clusters, while BH-NS mergers appear only in $10^3$ solar-mass clusters, with a local rate of 2.3-23 Gpc$^{-3}$ yr$^{-1}$ compatible with gravitational-wave detector limits.

Load-bearing premise

The rate numbers scale linearly with the assumed primordial binary fraction, and the paper sets that fraction to 100% for every cluster; if real open clusters have lower binary fractions, all quoted merger rates are correspondingly too high.

Editorial extensions

If this is right

  • Open clusters with masses near $10^3$ solar masses produce WD-WD merger rate densities of 130-1300 Gpc$^{-3}$ yr$^{-1}$, larger than globular-cluster estimates, so any census of white-dwarf mergers must include open clusters.
  • Super-Chandrasekhar WD-WD mergers in open clusters occur at 70-780 Gpc$^{-3}$ yr$^{-1}$, making old open clusters and their dissolved remnants candidate birth sites for magnetars and fast radio bursts.
  • Carbon-oxygen WD-WD mergers from open clusters supply only 0.14%-2.6% of the observed Type Ia supernova rate, so open clusters are a minor but not negligible Type Ia channel.
  • Dynamically formed and eccentric BH-BH mergers appear only in the most massive $10^4$ solar-mass open clusters, placing the active-dynamics boundary between $10^3$ and $10^4$ solar masses.
  • BH-NS mergers occur only in $10^3$ solar-mass clusters at 2.3-23 Gpc$^{-3}$ yr$^{-1}$, a rate within the range measured by gravitational-wave observatories.
  • Most WD-WD mergers from low- and intermediate-mass clusters happen after the host cluster is tidally disrupted, so the cluster's dynamical influence outlives the cluster itself.

Reading between the lines

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

  • Inference: because roughly 70% of WD-WD mergers in the $10^3$ solar-mass models occur outside the cluster, searches for repeating fast radio burst counterparts should also look in old tidal streams and field populations descended from dissolved open clusters, not only inside intact clusters.
  • Inference: if real open clusters have a primordial binary fraction near 50% rather than 100%, the reported rate ranges would shrink by about a factor of two but would likely remain within an order of magnitude of globular-cluster estimates, so the qualitative conclusion would survive.
  • Inference: the mass-dependence map found here suggests each merger channel has a preferred host-cluster mass, which could be tested by correlating gravitational-wave event host environments or fast radio burst environments with estimated cluster masses.
  • Inference: a direct check of the fast radio burst hypothesis would compare the predicted super-Chandrasekhar WD-WD rate of 70-780 Gpc$^{-3}$ yr$^{-1}$ with the volumetric rate of repeating fast radio bursts in old stellar environments; order-of-magnitude agreement would support the magnetar-from-merger channel.
Share X Bluesky LinkedIn Reddit HN

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. Using the PETAR N-body code with SSE/BSE stellar and binary evolution, the authors simulate open clusters of initial masses 10^2, 10^3, and 10^4 M_sun (596, 729, and 17 realizations, respectively), assuming a 100% primordial binary fraction, solar metallicity, and a Milky Way potential. They compare these cluster runs with isolated binary evolution ("BSE") and report merger efficiencies per solar mass for WD-WD, WD-NS, BH-BH, BH-NS, and NS-NS systems. These efficiencies are converted into local volumetric merger rates using the Madau-Fragos star formation rate density and f_cluster = 0.1-1, yielding WD-WD rates of 46-460, 130-1300, and 140-1400 Gpc^-3 yr^-1 for the three cluster masses, super-Chandrasekhar WD-WD rates of 70-780 Gpc^-3 yr^-1 (claimed to exceed globular-cluster estimates), CO WD-WD rates of 0.14%-2.6% of the observed Type Ia supernova rate, and BH-NS mergers only in the 10^3 M_sun clusters.

Significance. The paper fills a genuine gap by simulating WD-WD and WD-NS mergers in low-mass open clusters and by providing many realizations of stochastic low-mass clusters. The use of well-tested public codes, the explicit comparison with isolated binary evolution, and the authors' candid caveats about small samples are strengths. If the normalization assumptions were secure, the claimed open-cluster contribution to super-Chandrasekhar WD-WD mergers would be an important result for FRB progenitor studies. However, the absolute rate claims, and in particular the "larger than globular clusters" conclusion, are not robust to jointly realistic choices of the primordial binary fraction and the cluster formation efficiency, as detailed below.

major comments (3)
  1. [Section 2 / Table 2 / Section 3.2.1] The load-bearing assumption of a 100% primordial binary fraction is not tested or propagated into the quoted rates. Since only 2%-6% of WD-WD mergers are dynamically formed (Section 3.2.1), the WD-WD, WD-NS, and BH-NS efficiencies in Table 2 scale almost linearly with the assumed binary fraction. Observed binary fractions for the intermediate-mass stars that dominate WD-WD progenitors are typically 0.3-0.7, implying an overestimate by factors of roughly 1.4-3. The manuscript itself uses this assumption to explain why its BH-BH efficiency is about ten times larger than that of Kumamoto et al. (2019) (Section 3.2.3). A sensitivity study or a rate quoted for a fiducial binary fraction is needed before the headline "larger than globular clusters" statement can be considered robust.
  2. [Section 3.3, Eq. (2), and Table 2] The rate ranges use f_cluster = 0.1 and f_cluster = 1 as lower and upper bounds, applied separately to each cluster mass. f_cluster = 1 is not a physically meaningful upper bound: it would require that all star formation occur in clusters of a single mass, and the paper's own BH-BH rate at this end (20-200 Gpc^-3 yr^-1) violates the LIGO-Virgo constraint of 17.9-44 Gpc^-3 yr^-1 (Section 3.3). The mass-dependent normalization behind R_Local is likewise not an observationally grounded cluster formation efficiency: setting f_cluster,100 + f_cluster,1000 + f_cluster,10000 = 1 with f_cluster,M proportional to M^-1 assigns about 90% of all star formation to 10^2 M_sun clusters. The authors should derive rates from a continuous, observationally motivated cluster mass function and state explicitly what fraction of star formation passes through the simulated mass range.
  3. [Section 3.3 and Section 3.4.1] The headline super-Chandrasekhar WD-WD rate range (70-780 Gpc^-3 yr^-1) combines the two uncalibrated normalizations above with an efficiency that only counts mergers occurring within 500 Myr or 1 Gyr, while the merger-time distributions in Figure 3 are still rising at the end of the simulations. The reported rates are therefore truncated estimates for the adopted model, not full-lifetime yields. With a more realistic binary fraction of about 0.5 and a mass-function-weighted f_cluster of order 0.03 for the relevant cluster mass range, the lower end of the quoted range drops to roughly the globular-cluster value, so the qualitative conclusion that open clusters dominate the super-Chandrasekhar WD-WD channel is not yet established.
minor comments (5)
  1. [Eq. (2)] The displayed formula for the rate density is garbled: the text reads "R = ... z f_cluster" and omits both the efficiency eta and the star formation rate density psi. Please restate the equation with all symbols defined.
  2. [Table 2 and Section 3.3] Several entries in Table 2 are based on fewer than ten events (bracketed values), including the BH-NS and NS-NS channels. The text acknowledges limited sample sizes, but the abstract and conclusions quote these rates without uncertainties; Poisson confidence intervals should be reported and propagated into the quoted ranges.
  3. [Section 3.2.4] The conclusion that "BH-NS mergers only occur in 10^3 M_sun clusters" is based on nine events in that model and zero events in the 10^4 M_sun model; given the smaller total sample mass of the latter, the Poisson upper limit is not strongly inconsistent with the 10^3 M_sun efficiency. This claim should be softened or accompanied by a statistical test.
  4. [Section 3.3, R_Local] The text should clarify that the mass-dependent normalization f_cluster,M proportional to M^-1 is normalized to unity only over the three discrete cluster masses, which is not equivalent to integrating a continuous cluster mass function with dN/dM proportional to M^-2.
  5. [Figure 3 / Figure 4] The figures use vertical lines to indicate single events, but the event counts per channel and per cluster mass are never listed explicitly; adding the raw counts to Table 2 would make the statistical weight of each efficiency transparent.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the quoted merger rates are simulation efficiencies multiplied by an externally adopted star-formation-rate density and cluster-formation efficiency, with no calibration to FRB, Type Ia, or gravitational-wave event rates.

full rationale

The paper's central quantities are merger efficiencies measured directly from N-body simulations (PETAR with SSE/BSE stellar evolution), and the volumetric rates are then obtained by multiplying those efficiencies by the cosmic star-formation-rate density of Madau & Fragos (2017) and by an adopted cluster-formation efficiency f_cluster (Section 3.3, Equation 2). No FRB rate, Type Ia supernova rate, or LIGO-Virgo merger rate is used to fit, calibrate, or define any simulation input; the comparisons to observed rates are made after the fact. The 100% initial binary fraction and the choice of f_cluster = 0.1-1 are clearly stated assumptions that scale the final rates, but they are not derived from the quantities the paper claims to predict, so this is an assumption-sensitivity issue rather than a circular one. Self-citations to PETAR (Wang et al. 2020a), MCLUSTER (Kupper et al. 2011; Wang et al. 2019), and Tanikawa et al. (2024) are tool and code-validation references, not uniqueness theorems or ansatz-smuggling devices, and the central simulation results stand on the performed N-body integrations. There is no step in which a prediction reduces by construction to an input or to a self-citation chain.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central rate estimates depend on a chain of external stellar evolution and binary evolution recipes plus a handful of hand-chosen initial conditions. The largest systematic uncertainties come from the 100 percent binary fraction and the f_cluster normalization. No new physical entities are introduced.

free parameters (5)
  • Initial binary fraction = 100% (1.0)
    Set for all clusters in Section 2. Real open clusters have lower binary fractions; all merger efficiencies scale with this choice and the authors cite it to explain a 10x higher BH-BH efficiency than Kumamoto et al. 2019.
  • Cluster formation efficiency f_cluster = 0.1 and 1.0 as lower and upper bounds; mass-dependent f_cluster,M proportional to M^-1 for the Local rate
    Adopted in Equation 2 to convert per-solar-mass efficiencies to volumetric rates. The 10x spread from f_cluster=0.1 to 1 dominates the quoted rate ranges.
  • Initial half-mass radius = 0.1 pc, 0.4 pc, 1.0 pc
    Chosen to represent embedded, open, and young massive clusters respectively; directly affects dynamical encounter rates and dissolution timescales.
  • Fractal dimension = 1.6 for 10^2 and 10^3 M_sun clusters; 3.0 (spherical) for 10^4 M_sun clusters
    Adopted to mimic observed substructure in low-mass clusters; drives early dynamical interactions and dissolution timescales.
  • Simulation time t_end = 500 Myr for 10^2 M_sun; 1 Gyr for 10^3 and 10^4 M_sun
    Stopping time differs by mass because low-mass clusters dissolve quickly; the paper notes this biases the 10^2 M_sun WD-WD efficiency.
assumptions (5)
  • domain assumption The rapid core-collapse supernova model (Fryer et al. 2012) with pair-instability modifications (Belczynski et al. 2016) determines compact object masses and natal kicks.
    Invoked in Section 2 for BH and NS formation; the BH/NS mass spectrum and merger rates depend on this recipe.
  • domain assumption Binary evolutionary processes, including tides, wind accretion, stable mass transfer, and common envelope evolution, follow the SSE/BSE prescriptions of Hurley et al. 2002 and Banerjee et al. 2020.
    Invoked in Section 2; these prescriptions determine which binaries survive to become compact object mergers.
  • domain assumption Primordial binary properties follow Sana et al. 2012 for primary masses above 5 M_sun and Kroupa 1995a,b below 5 M_sun.
    Stated in Section 2 and shown in Figure 1; the orbital period, mass ratio, and eccentricity distributions directly set the merger population.
  • domain assumption The cosmic star formation rate density of Madau and Fragos 2017 with a Kroupa IMF applies to the local Universe rate calculation.
    Used in Equation 1 of Section 3.3; the absolute rate densities scale linearly with this externally adopted function.
  • domain assumption The WD-WD merger outcome mapping of Shen 2015 and Kremer et al. 2023 determines which mergers form neutron stars versus Type Ia supernovae.
    Used in Section 3.4 and Figure 4 to classify super-Chandrasekhar mergers and to derive FRB and Type Ia rates.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Double Compact Binary Merger Rate Density in Open Star Clusters: Black Holes, Neutron Stars, and White Dwarfs." pith.science (2026). https://pith.science/paper/MWZZSO7W

@misc{pith2026250622673,
  author       = {Pith},
  title        = {Pith review of: Double Compact Binary Merger Rate Density in Open Star Clusters: Black Holes, Neutron Stars, and White Dwarfs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MWZZSO7W}},
  note         = {Machine review of arXiv:2506.22673}
}
abstract

Studying compact-object binary mergers in star clusters is crucial for understanding stellar evolution and dynamical interactions in galaxies. Open clusters in particular are more abundant over cosmic time than globular clusters, however, previous research on low-mass clusters with $\lesssim 10^3~\textrm{M}_{\odot}$ has focused on binary black holes (BBHs) or black hole-neutron star (BH-NS) binaries. Binary mergers of other compact objects, such as white dwarfs (WDs), are also crucial as progenitors of transient phenomena such as Type Ia supernovae and Fast Radio Bursts. We present simulations of three types of open clusters with masses of $10^2$, $10^3$, and $10^4~\mathrm{M}_{\odot}$. In massive clusters with $\gtrsim 10^4~\textrm{M}_{\odot}$, BBHs are dynamically formed, however, less massive compact binaries such as WD-WD and WD-NS are perturbed inside the star clusters, causing them to evolve into other objects. We further find BH-NS mergers only in $10^3~\textrm{M}_{\odot}$ clusters. Considering star clusters with a typical open cluster mass, we observe that WD-WD merger rates slightly increase for $10^3~\textrm{M}_{\odot}$ clusters but decrease for $10^2~\textrm{M}_{\odot}$ clusters. Since the host clusters are tidally disrupted, most of them merge outside of the clusters. Our WD-WD merger results have further implications for two classes of transients. Super-Chandrasekhar WD-WD mergers are present in our simulations, demonstrating potential sources of Fast Radio Bursts at a rate of 70-780 Gpc$^{-3}$yr$^{-1}$, higher than the rate estimated for globular clusters. Additionally, we find that Carbon-Oxygen WD-WD mergers in our open clusters (34-640 $\textrm{Gpc}^{-3}$yr$^{-1}$) only account for 0.14-2.6% of the observed Type Ia supernova rate in our local Universe.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

82 extracted references · 58 canonical work pages

  1. [1]

    2023, LRR, 26, 2 Arca Sedda, M., Kamlah, A

    Amaro-Seoane, P., Andrews, J., Arca Sedda, M., et al. 2023, LRR, 26, 2 Arca Sedda, M., Kamlah, A. W. H., Spurzem, R., et al. 2024, MNRAS, 528, 5140

    2023

  2. [2]

    2016, MNRAS, 464, L36

    Askar, A., Szkudlarek, M., Gondek-Rosińska, D., Giersz, M., & Bulik, T. 2016, MNRAS, 464, L36

    2016

  3. [3]

    2017, MNRAS, 467, 524

    Banerjee, S. 2017, MNRAS, 467, 524

    work page 2017

  4. [4]

    L., et al

    Banerjee, S., Belczynski, K., Fryer, C. L., et al. 2020, A&A, 639, A41

    2020

  5. [5]

    2008, MNRAS, 390, 759

    Bastian, N. 2008, MNRAS, 390, 759

    2008

  6. [6]

    L., et al

    Belczynski, K., Bulik, T., Fryer, C. L., et al. 2010, ApJ, 714, 1217

    2010

  7. [7]

    2016, A&A, 594, A97

    Belczynski, K., Heger, A., Gladysz, W., et al. 2016, A&A, 594, A97

    2016

  8. [8]

    2014, ARA&A, 52, 43

    Berger, E. 2014, ARA&A, 52, 43

    2014

Show all 82 references
  1. [9]

    M., Kaspi, V

    Bhardwaj, M., Gaensler, B. M., Kaspi, V. M., et al. 2021, ApJL, 910, L18

    2021

  2. [10]

    D., Ravi, V., Belov, K

    Bochenek, C. D., Ravi, V., Belov, K. V., et al. 2020, Natur, 587, 59

    2020

  3. [11]

    T., Pignata, G., et al

    Cappellaro, E., Botticella, M. T., Pignata, G., et al. 2015, A&A, 584, A62

    2015

  4. [12]

    1985, ApJ, 298, 80

    Casertano, S., & Hut, P. 1985, ApJ, 298, 80

    1985

  5. [13]

    D., Ménard, B., & Toonen, S

    Cheng, S., Cummings, J. D., Ménard, B., & Toonen, S. 2020, ApJ, 891, 160 CHIME/FRB Collaboration, Andersen, B. C., Bandura, K. M., et al. 2020, Natur, 587, 54 Dall’Amico, M., Mapelli, M., Torniamenti, S., & Arca Sedda, M. 2024, A&A, 683, A186 Di Carlo, U. N., Giacobbo, N., Map...

    2020

  6. [14]

    2020, ApJL, 901, L16

    Fragione, G., & Banerjee, S. 2020, ApJL, 901, L16

    2020

  7. [15]

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

    Fryer, C. L., Belczynski, K., Wiktorowicz, G., et al. 2012, ApJ, 749, 91

    2012

  8. [16]

    P., & Whitworth, A

    Goodwin, S. P., & Whitworth, A. P. 2004, A&A, 413, 929

    2004

  9. [17]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Natur, 585, 357

    2020

  10. [18]

    R., Lyne, A

    Hobbs, G., Lorimer, D. R., Lyne, A. G., & Kramer, M. 2005, MNRAS, 360, 974

    2005

  11. [19]

    Hunter, J. D. 2007, CSE, 9, 90

    2007

  12. [20]

    R., Pols, O

    Hurley, J. R., Pols, O. R., & Tout, C. A. 2000, MNRAS, 315, 543

    2000

  13. [21]

    R., Tout, C

    Hurley, J. R., Tout, C. A., & Pols, O. R. 2002, MNRAS, 329, 897

    2002

  14. [22]

    J., & Tutukov, A

    Iben, I. J., & Tutukov, A. V. 1984, ApJS, 54, 335

    1984

  15. [23]

    2020, PASJ, 72, 13

    Iwasawa, M., Namekata, D., Nitadori, K., et al. 2020, PASJ, 72, 13

    2020

  16. [24]

    S., & Hori, Y

    Iwasawa, M., Oshino, S., Fujii, M. S., & Hori, Y. 2017, PASJ, 69, 81

    2017

  17. [25]

    2016, PASJ, 68, 54

    Iwasawa, M., Tanikawa, A., Hosono, N., et al. 2016, PASJ, 68, 54

    2016

  18. [26]

    2013, ApJL, 776, L39

    Kashiyama, K., Ioka, K., & Mészáros, P. 2013, ApJL, 776, L39

    2013

  19. [27]

    2012, arXiv:1211.4584

    Katz, B., & Dong, S. 2012, arXiv:1211.4584

    2012 arXiv

  20. [28]

    King, A., Olsson, E., & Davies, M. B. 2007, MNRAS, 374, L34

    2007

  21. [29]

    R., Pringle, J

    King, A. R., Pringle, J. E., & Wickramasinghe, D. T. 2001, MNRAS, 320, L45

    2001

  22. [30]

    2022, Natur, 602, 585

    Kirsten, F., Marcote, B., Nimmo, K., et al. 2022, Natur, 602, 585

    2022

  23. [31]

    L., & Ransom, S

    Kremer, K., Fuller, J., Piro, A. L., & Ransom, S. M. 2023, MNRAS, 525, L22

    2023

  24. [32]

    L., & Zhang, B

    Kremer, K., Li, D., Lu, W., Piro, A. L., & Zhang, B. 2023, ApJ, 944, 6

    2023

  25. [33]

    S., Heinke, C

    Kremer, K., Ye, C. S., Heinke, C. O., et al. 2024, ApJL, 977, L42

    2024

  26. [34]

    2001, MNRAS, 322, 231

    Kroupa, P. 2001, MNRAS, 322, 231

    2001

  27. [35]

    S., & Tanikawa, A

    Kumamoto, J., Fujii, M. S., & Tanikawa, A. 2019, MNRAS, 486, 3942

    2019

  28. [36]

    S., & Tanikawa, A

    Kumamoto, J., Fujii, M. S., & Tanikawa, A. 2020, MNRAS, 495, 4268 Küpper, A. H. W., Maschberger, T., Kroupa, P., & Baumgardt, H. 2011, MNRAS, 417, 2300

    2020

  29. [37]

    2013, ApJL, 778, L37

    Kushnir, D., Katz, B., Dong, S., Livne, E., & Fernández, R. 2013, ApJL, 778, L37

    2013

  30. [38]

    J., & Lada, E

    Lada, C. J., & Lada, E. A. 2003, ARA&A, 41, 57

    2003

  31. [39]

    F., et al

    Lamberts, A., Garrison-Kimmel, S., Hopkins, P. F., et al. 2018, MNRAS, 480, 2704

    2018

  32. [40]

    2011, MNRAS, 412, 1473

    Li, W., Chornock, R., Leaman, J., et al. 2011, MNRAS, 412, 1473

    2011

  33. [41]

    2020, MNRAS, 494, 3422

    Liu, D., & Wang, B. 2020, MNRAS, 494, 3422

    2020

  34. [42]

    2025, ApJL, 981, L29

    Liu, X.-J., Sengar, R., Bailes, M., et al. 2025, ApJL, 981, L29

    2025

  35. [43]

    2021, MNRAS, 510, 1867

    Lu, W., Beniamini, P., & Kumar, P. 2021, MNRAS, 510, 1867

    2021

  36. [44]

    2017, ApJ, 840, 39

    Madau, P., & Fragos, T. 2017, ApJ, 840, 39

    2017

  37. [45]

    Metzger, B. D. 2012, MNRAS, 419, 827

    2012

  38. [46]

    Goudis, C. D. 2006, A&A, 459, 113

    2006

  39. [47]

    1992, ApJL, 395, L83

    Narayan, R., Paczynski, B., & Piran, T. 1992, ApJL, 395, L83

    1992

  40. [48]

    1982, ApJ, 257, 780

    Nomoto, K. 1982, ApJ, 257, 780

    1982

  41. [49]

    1985, ApJ, 297, 531

    Nomoto, K., & Iben, I., Jr. 1985, ApJ, 297, 531

    1985

  42. [50]

    2011, PASJ, 63, 881

    Oshino, S., Funato, Y., & Makino, J. 2011, PASJ, 63, 881

    2011

  43. [51]

    2013, ApJL, 770, L8

    Pakmor, R., Kromer, M., Taubenberger, S., & Springel, V. 2013, ApJL, 770, L8

    2013

  44. [52]

    E., Schilbach, E., Kharchenko, N

    Piskunov, A. E., Schilbach, E., Kharchenko, N. V., Röser, S., & Scholz, R. D. 2007, A&A, 468, 151

    2007

  45. [53]

    Plummer, H. C. 1911, MNRAS, 71, 460

    1911

  46. [54]

    Podsiadlowski, P., Langer, N., Poelarends, A. J. T., et al. 2004, ApJ, 612, 1044 Portegies Zwart, S. F., & McMillan, S. L. W. 2000, ApJL, 528, L17 Portegies Zwart, S. F., McMillan, S. L. W., & Gieles, M. 2010, ARA&A, 48, 431

    2004

  47. [55]

    2019, MNRAS, 483, 1233

    Rastello, S., Amaro-Seoane, P., Arca-Sedda, M., et al. 2019, MNRAS, 483, 1233

    2019

  48. [56]

    Rastello, S., Mapelli, M., Carlo, U. N. D., et al. 2020, MNRAS, 497, 1563

    2020

  49. [57]

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

    Rodriguez, C. L., Chatterjee, S., & Rasio, F. A. 2016, PhRvD, 93, 084029

    2016

  50. [58]

    L., & Loeb, A

    Rodriguez, C. L., & Loeb, A. 2018, ApJL, 866, L5

    2018

  51. [59]

    2009, ApJL, 705, L128

    Rosswog, S., Kasen, D., Guillochon, J., & Ramirez-Ruiz, E. 2009, ApJL, 705, L128

    2009

  52. [60]

    J., Belczynski, K., Benacquista, M., Larson, S

    Ruiter, A. J., Belczynski, K., Benacquista, M., Larson, S. L., & Williams, G. 2010, ApJ, 717, 1006

    2010

  53. [61]

    2018, ApJ, 855, 124

    Samsing, J., Askar, A., & Giersz, M. 2018, ApJ, 855, 124

    2018

  54. [62]

    2017, ApJL, 840, L14

    Samsing, J., & Ramirez-Ruiz, E. 2017, ApJL, 840, L14

    2017

  55. [63]

    E., de Koter, A., et al

    Sana, H., de Mink, S. E., de Koter, A., et al. 2012, Sci, 337, 444

    2012

  56. [64]

    2020, ApJ, 898, 152

    Santoliquido, F., Mapelli, M., Bouffanais, Y., et al. 2020, ApJ, 898, 152

    2020

  57. [65]

    M., & Hurley, J

    Shara, M. M., & Hurley, J. R. 2002, ApJ, 571, 830

    2002

  58. [66]

    Shen, K. J. 2015, ApJL, 805, L6

    2015

  59. [67]

    J., & Bildsten, L

    Shen, K. J., & Bildsten, L. 2014, ApJ, 785, 61

    2014

  60. [68]

    2021, ApJL, 907, L20

    Tagawa, H., Kocsis, B., Haiman, Z., et al. 2021, ApJL, 907, L20

    2021

  61. [69]

    Tanikawa, A., Cary, S., Shikauchi, M., Wang, L., & Fujii, M. S. 2024, MNRAS, 527, 4031

    2024

  62. [70]

    B., Igoshev, A

    Toonen, S., Perets, H. B., Igoshev, A. P., Michaely, E., & Zenati, Y. 2018, A&A, 619, A53

    2018

  63. [71]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, NatMe, 17, 261

    2020

  64. [72]

    2021, MNRAS, 510, 2242

    Wang, J., Hammer, F., & Yang, Y. 2021, MNRAS, 510, 2242

    2021

  65. [73]

    2019, MNRAS, 484, 1843

    Wang, L., Kroupa, P., & Jerabkova, T. 2019, MNRAS, 484, 1843

    2019

  66. [74]

    2020b, SDAR: Slow-Down Algorithmic Regularization Code for Solving Few-body Problems, Astrophysics Source Code Library, ascl:2002.001

    Wang, L., Nitadori, K., & Makino, J. 2020b, SDAR: Slow-Down Algorithmic Regularization Code for Solving Few-body Problems, Astrophysics Source Code Library, ascl:2002.001

    2002

  67. [75]

    2022, MNRAS, 515, 5106

    Wang, L., Tanikawa, A., & Fujii, M. 2022, MNRAS, 515, 5106

    2022

  68. [76]

    Webbink, R. F. 1984, ApJ, 277, 355

    1984

  69. [77]

    2007, ApJL, 665, L59

    Willems, B., Kalogera, V., Vecchio, A., et al. 2007, ApJL, 665, L59

    2007

  70. [78]

    2022, Natur, 612, 232

    Yang, J., Ai, S., Zhang, B.-B., et al. 2022, Natur, 612, 232

    2022

  71. [79]

    2018, ApJ, 868, 31

    Yang, Y.-P., & Zhang, B. 2018, ApJ, 868, 31

    2018

  72. [80]

    S., Fong, W.-f., Kremer, K., et al

    Ye, C. S., Fong, W.-f., Kremer, K., et al. 2019, ApJL, 888, L10

    2019

  73. [81]

    2020, Natur, 587, 45

    Zhang, B. 2020, Natur, 587, 45

    2020

  74. [82]

    2020, ApJ, 893, 9 12 The Astrophysical Journal, 989:105 (12pp), 2025 August 10 Cary et al

    Zhong, S.-Q., & Dai, Z.-G. 2020, ApJ, 893, 9 12 The Astrophysical Journal, 989:105 (12pp), 2025 August 10 Cary et al

    2020

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.