Pith. sign in

REVIEW 3 major objections 4 minor 6 cited by

Revealing the $\chi_{\rm eff}$-$q$ Correlation among Coalescing Binary Black Holes and Tentative Evidence for AGN-driven Hierarchical Mergers

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read The spin–mass anti-correlation in LIGO's black holes is best explained by two distinct populations, not one.

desk verdict A careful decomposition of the χeff–q correlation into two subpopulations, but the conclusion depends on an untested shared-distribution assumption. read the letter →

arxiv 2501.09495 v4 pith:R6ZRUKDZ submitted 2025-01-16 astro-ph.HE astro-ph.GA

Yin-Jie Li , Yuan-Zhu Wang , Shao-Peng Tang , Tong Chen , Yi-Zhong Fan This is my paper · ORCID
classification astro-ph.HEastro-ph.GA
keywords gravitationalwavesbinaryblackholeseffectivespinmassratiopopulationinferencehierarchicalmergersactivegalacticnucleiGWTC-3
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 asks why the binary black holes detected by LIGO-Virgo-KAGRA show an anti-correlation between effective spin and mass ratio: does a single formation process naturally produce low spins at low mass ratios, or does the observed sample mix populations with different spin properties? It argues for the second explanation. Modelling the population as a mixture of a low-spin, lower-mass component and a broad, positive-spin, higher-mass component removes most of the anti-correlation, and the two-component model beats a single mass-ratio-dependent spin model with Bayes factor $\ln B > 4.2$. If correct, the overall spin–mass trend is a superposition artifact, and the high-spin tail points toward hierarchical mergers in AGN disks, with star clusters and other formation channels not fully excluded.

What carries the argument

The central machinery is a pair of hierarchical-Bayesian population models that add a second $\chi_{\rm eff}$ distribution for a high-mass subpopulation. The Mixture model writes $\pi_{\rm mix}(m_1,m_2,\chi_{\rm eff}|\Lambda)=P(m_2|m_1)[(1-r_2)\pi_1+r_2\pi_2]$, where $\pi_1$ is the original mass-ratio-dependent Base model with mean $\mu_{\chi,0}+a(q-0.5)$ and log-width $\log\sigma_{\chi,0}+b(q-0.5)$, and $\pi_2$ is a truncated Gaussian in $\chi_{\rm eff}$ independent of $q$. The Transition model instead switches between the same two $\chi_{\rm eff}$ laws through a logistic function of primary mass with transition mass $m_t$ and width $\delta_t$. Both are fit to 69 GWTC-3 events with false-alarm rate below $1\,{\rm yr}^{-1}$, using PowerLaw+Spline and PowerLaw+Peak mass functions and a Madau–Dickinson redshift evolution; end-to-end injection studies are used to show the two components are recoverable. This machinery carries the argument because the anti-correlation slope weakens or disappears once the second component absorbs the high-mass, high-spin events.

What would settle it

Run the same hierarchical analysis with a single-population model in which the mean and width of $\chi_{\rm eff}$ are free nonparametric functions of both mass ratio and primary mass; if it fits GWTC-3 as well as the Transition or Mixture model, or if the second component's recovered $\chi_{\rm eff}$ mean drops to zero when the shared $P(m_2|m_1)$ and redshift-evolution assumptions are relaxed, the superposition claim fails.

Watch

Extended reading notes

Core claim

The paper claims that the reported $\chi_{\rm eff}$–$q$ anti-correlation among LIGO-Virgo-KAGRA binary black holes is not primarily an intrinsic property of one formation channel. Instead, it is a mixture effect: the population contains a dominant low-spin component with $\chi_{\rm eff}$ narrowly peaking near $0.05$ and primary masses falling sharply above about $40\,M_\odot$, consistent with first-generation black holes, plus a secondary high-mass component with a broad $\chi_{\rm eff}$ distribution peaking near $\mu_{\chi,2}\sim0.4$, the signature expected from hierarchical mergers in gas-rich AGN disks. Introducing this second $\chi_{\rm eff}$ distribution makes the slope parameters $a$ and $b$ that encode $\chi_{\rm eff}$–$q$ correlation consistent with zero when a flexible mass model is used, and strongly favors two separate $\chi_{\rm eff}$ distributions over a single mass-ratio-dependent distribution. Negative $\chi_{\rm eff}$ values in the second component cannot be excluded, so star clusters and isolated-binary channels such as stable mass transfer or chemically homogeneous evolution remain possible contributors.

Load-bearing premise

The two subpopulations are assumed to share the same distribution of companion masses and the same merger-rate evolution with redshift; if the high-spin, high-mass group has its own mass-ratio law or cosmic-time dependence, the decomposition could be misattributed.

Editorial extensions

If this is right

  • The apparent $\chi_{\rm eff}$–$q$ anti-correlation in the overall BBH population does not require a single formation channel that intrinsically links spin to mass ratio.
  • A second, distinct $\chi_{\rm eff}$ component peaking near $\mu_{\chi,2}\sim0.4$ and dominating above roughly $50\,M_\odot$ is favoured, matching predictions for hierarchical mergers in AGN disks.
  • Star clusters alone cannot account for the second subpopulation: a $\chi_{\rm eff}$ distribution symmetric about zero is disfavoured, though only mildly, with $\ln B\sim1.4$–$1.5$.
  • The first subpopulation, with narrow $\chi_{\rm eff}$ peaking near $0.05$ and primary masses declining rapidly beyond $\sim40\,M_\odot$, is consistent with first-generation black holes.
  • Alternative formation channels, including stable mass transfer and chemically homogeneous evolution, could mimic the second $\chi_{\rm eff}$ distribution, so the AGN interpretation is tentative rather than unique.

Reading between the lines

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

  • If the superposition interpretation is right, population-level spin–mass correlations should be treated as mixture diagnostics; a single correlation parameter fit across all masses can mislead formation-channel inferences.
  • The inferred transition mass near $\sim49\,M_\odot$ may mark the boundary set by pair-instability supernova mass loss, and future data could test whether that transition sharpens as expected.
  • With the much larger O4 dataset, the mixture fraction between AGN-disk and star-cluster hierarchical mergers and the negative-$ ailing$ of the second $\chi_{\rm eff}$ distribution could be constrained enough to distinguish the channels.
  • A straightforward test of the paper's claim would be to fit a single-population model in which the mean and width of $\chi_{\rm eff}$ are free nonparametric functions of both mass ratio and primary mass; if that model fits GWTC-3 as well as the two-component models, the superposition claim weakens.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper reanalyzes 69 GWTC-3 BBH events to test whether the observed chi_eff-q anti-correlation is a superposition of two subpopulations: a low-mass, low-spin first-generation population and a high-mass, high-spin second subpopulation. Using hierarchical Bayesian inference, the authors introduce two models (Mixture and Transition) that add a second chi_eff distribution for high-mass BBHs, and compare them against the standard single-population mass-ratio-dependent Base model. They report Bayes factors ln B > 4.2 favoring the two-population models, and find that the chi_eff-q correlation weakens or disappears in the low-mass subpopulation when the second subpopulation is included. The inferred second chi_eff distribution peaks at ~0.4 and is broad, which the authors interpret as tentative evidence for hierarchical mergers in AGN disks, while acknowledging alternative formation channels. The analysis is supported by end-to-end mock injection studies in Appendix E.

Significance. If the superposition claim holds, it would resolve the long-standing debate about the origin of the chi_eff-q anti-correlation, showing that it is a mixture artifact rather than a single-channel evolutionary property. The paper is careful in its methodology: it uses both parametric (PP) and semi-parametric (PS) mass models, applies selection corrections, and validates its inferences with end-to-end mock studies. The evidence, however, is moderate (ln B between 4.2 and 6.7), and the identification of a second high-spin subpopulation is tentative, as the title itself concedes. The work builds on the authors' previous identification of two subpopulations via spin magnitude and component mass (Li et al. 2024b), and would be an important step toward understanding BBH formation channels if the model assumptions are validated.

major comments (3)
  1. [Eqs. (1)-(4), Appendix E] The two-subpopulation models in Eq. (1) and Eq. (4) factor out a common secondary-mass distribution P(m2|m1) (Eq. B4) and apply the same redshift evolution R(z) (Eq. B7) to both subpopulations. If the high-mass, high-chi_eff subpopulation actually has a different mass-ratio distribution or redshift evolution, the decomposition of the observed chi_eff-q anti-correlation into a superposition of two chi_eff distributions could be misattributed. The end-to-end mock studies in Appendix E only vary the chi_eff distribution while keeping a single P(m2|m1) (they use beta=1 for all components), so they cannot validate this assumption. The authors should extend the model to allow, for example, a different power-law index beta_2 for the secondary-mass distribution of the second subpopulation, or explicitly state and justify this as a limitation. This assumption is load-bearing for the central claim that the anti-correlation mainly results from the superposition of two subpopulations.
  2. [Section 4 and Appendix D.1] The claim that the second subpopulation is 'very likely' contributed by AGN-disk hierarchical mergers is not supported by the presented evidence. The asymmetry test in Appendix D.1 gives only ln B = 1.4-1.5 against a symmetric distribution, which is weak-to-moderate evidence on the Jeffreys scale, even though the posterior probability mu_chi,2 > 0 is quoted at 98% (95%) credibility. The paper should temper this conclusion and clearly state that the AGN interpretation is tentative, as the title acknowledges.
  3. [Table 1 and Section 3] The abstract describes the evidence as 'strongly favoring' two separate chi_eff distributions, but the reported Bayes factors ln B > 4.2 (with the largest being 6.7) are moderate-to-strong, not decisive. Additionally, in the PP case the chi_eff-q correlation remains at ~90% credibility even after introducing the second subpopulation, so the statement that the correlation 'significantly weakens or disappears' is model-dependent. The authors should be more precise about the strength and robustness of the evidence in the abstract and conclusions.
minor comments (4)
  1. [Abstract] The phrase 'whose primary-mass function showing a rapid decline' should be 'whose primary-mass function shows a rapid decline'.
  2. [Table 1 note] The note contains a typo: 'fucntion' should be 'function'.
  3. [Figure 4 and Section 3] The inferred mixture fraction r2 is very small (median ~0.01-0.02) for the Mixture models. The paper does not discuss this value or its implications for the contribution of the second subpopulation to the observed correlation; reporting the detection fraction of the second subpopulation would help assess its importance.
  4. [Appendix E] The mock studies state 'For each case, we adopt 69 events' but do not specify how the events are selected (e.g., random draw or loudest events). A brief clarification would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the two-subpopulation model is tested against GWTC-3 with Bayes factors and mock injections, and the AGN interpretation is a post-hoc comparison rather than a derived prediction.

full rationale

The paper's central claim, that the chi_eff-q anti-correlation can be explained by a superposition of two subpopulations, is established by hierarchical Bayesian model comparison on GWTC-3 data. The Base model (Callister et al. 2021) and the new Mixture and Transition models are explicitly written out in Eqs. (1)-(5) and Appendix B; the quoted Bayes factors (ln B > 4.2) come from the likelihood integral, not from any parameter that was pre-fitted to the target conclusion. The second subpopulation's chi_eff mean mu_chi,2 is a free hyperparameter, and its consistency with AGN disk simulations is an interpretive comparison, acknowledged by the authors to be non-unique ('might alternatively arise from other formation channels'). The shared P(m2|m1) and R(z) between subpopulations is a simplifying model assumption, not a circular step: it could have failed, and the mock studies in Appendix E explicitly test the recovery of injected two-population signals. Self-citations to Li et al. (2024b) motivate the two-component ansatz and support labeling the second subpopulation as hierarchical mergers, but the statistical evidence for superposition does not depend on those citations; removing them would not change the likelihood calculation. No equation in the paper reduces to another by construction, and no fitted parameter is renamed as a prediction. The analysis is therefore self-contained against external data and benchmarks, and no circular step is identified.

Assumptions & free parameters 7 free parameters · 4 assumptions · 1 invented entities

The central claim depends on several fitted parameters (µχ,2, mt, δt, r2, a, b) and on modeling assumptions. The most consequential assumption is that both subpopulations share the same mass-ratio and redshift distributions. The second subpopulation is an invented model component without independent evidence.

free parameters (7)
  • µχ,2 (mean of second χeff Gaussian) = ~0.40 for PS, ~0.39 for PP
    The second subpopulation's effective-spin distribution peak; fitted to GWTC-3 data, then compared to AGN predictions.
  • σχ,2 (width of second χeff Gaussian) = not explicitly quoted; posteriors shown in figures
    Width of the second χeff distribution, fitted.
  • mt (transition mass) = ~49 M_sun
    Primary mass separating the two subpopulations in the Transition model.
  • δt (transition scale) = ~6 M_sun
    Sharpness of the transition.
  • a (χeff-q slope for first subpopulation) = consistent with 0 for PS; <0 at ~90% for PP
    The correlation parameter whose vanishing is the central claim.
  • b (log σχ-q slope) = consistent with 0
    The correlation parameter for width, part of the Base model.
  • r2 (mixture fraction) = ~0.02 in Mixture model
    Fraction of the second subpopulation; small but affects the high-mass tail.
assumptions (4)
  • domain assumption The 69 selected GWTC-3 events with FAR < 1 yr^-1 are a fair sample of the underlying population after selection correction.
    Adopted from Abbott et al. (2023a), Section 2.
  • ad hoc to paper The two subpopulations share the same secondary-mass distribution P(m2|m1) and the same redshift evolution MD model.
    Eq. (1) and Eq. (B7); this is the weakest structural assumption.
  • ad hoc to paper The effective-spin distribution of each subpopulation is adequately described by a truncated Gaussian or spline.
    Appendix B.2 and D.1; model choice.
  • domain assumption Posterior samples from GWTC-3 'C01:Mixed' are reliable and the injection campaign models selection correctly.
    Section 2.
invented entities (1)
  • Second (high-mass) subpopulation of BBHs
    purpose: To explain the χeff-q correlation as a superposition rather than a within-population trend
    The subpopulation is identified through fitted mixture/transition models on the same GWTC-3 data; no external dataset confirms it.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Revealing the $\chi_{\rm eff}$-$q$ Correlation among Coalescing Binary Black Holes and Tentative Evidence for AGN-driven Hierarchical Mergers." pith.science (2026). https://pith.science/paper/R6ZRUKDZ

@misc{pith2026250109495,
  author       = {Pith},
  title        = {Pith review of: Revealing the $\chi_\rm eff$-$q$ Correlation among Coalescing Binary Black Holes and Tentative Evidence for AGN-driven Hierarchical Mergers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R6ZRUKDZ}},
  note         = {Machine review of arXiv:2501.09495}
}
abstract

The origin of the correlation between the effective spins ($\chi_{\rm eff}$) and mass ratios ($q$) of LIGO-Virgo-KAGRA's binary black holes (BBHs) is still an open question. Motivated by the recent identification of two subpopulations of the BBHs, in this work we investigate the potential $\chi_{\rm eff}-q$ correlation for each subpopulation. Surprisingly, the $\chi_{\rm eff}$-$q$ correlation {either significantly weakens or disappears} for the low-mass subpopulation if we introduce a second $\chi_{\rm eff}$ distribution for the high-mass subpopulation, which likely originates from hierarchical mergers. {This suggests that the $\chi_{\rm eff}$-$q$ correlation in the overall population can be explained by the superposition of two distinct subpopulations.} {We find Bayesian evidence strongly favoring two separate $\chi_{\rm eff}$ distributions over a single mass-ratio-dependent distribution, with Bayes factors $\ln\mathcal{B}>4.2$.} The first subpopulation has a narrow $\chi_{\rm eff}$ distribution peaking at $\sim0.05$, whose primary-mass function {showing a rapid decline beyond} $\sim 40M_{\odot}$, in agreement with first-generation BBHs. The second $\chi_{\rm eff}$ distribution is broad and peaks at $\mu_{\chi,2} \sim 0.4$, aligning with predictions for hierarchical mergers in active galactic nucleus (AGN) disks. {However, we cannot exclude negative $\chi_{\rm eff}$values in the second subpopulation, suggesting hierarchical mergers might occur both in AGN disks and stellar clusters. Furthermore, the inferred second $\chi_{\rm eff}$ distribution might alternatively arise from other formation channels, such as stable mass transfer or chemically homogeneous evolution, if not interpreted as hierarchical mergers.}

Figures

Figures reproduced from arXiv: 2501.09495 by the authors.

Figure 1
Figure 1. Left&Mid: Constraints on the mean µχ(q) and standard deviation σχ(q) of the χeff distribution, as a function of BBH mass ratio q. The solid curves are the medians and the colored bands are the 90% credible intervals. Right: Posteriors of the hyperparameters describing the χeff − q correlation. The contours mark the central 50% and 90% posterior credible regions, the values represent the median and 90% credible inter… view at source ↗
Figure 2
Figure 2. Effective-spin distributions of the two subpopu￾lation inferred by the Transition and Mixture models with a = 0,b = 0. The lines represent the mean values and 90% credible intervals. negative values for the second χeff distributions can not be ruled out. 4. CONCLUSIONS AND DISCUSSION In this work, we investigate the origins of χeff-q corre￾lation in the BBHs (Callister et al. 2021; Abbott et al. 2023a) with data of … view at source ↗
Figure 3
Figure 3. Posteriors of the special hyperparameters for the Transition model. The contours mark the central 50% and 90% posterior credible regions, the values represent the median and 90% credible intervals. (labeled PS+PS). Despite this modification, the results are nearly identical to those without low-mass tapering and perturbation functions, as demonstrated in [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Posteriors of the special hyperparameters for the Mixture model. The contours mark the central 50% and 90% posterior credible regions, the values represent the median and 90% credible intervals. restrict the 1-st and 6-th nodes to be -10 corresponding to PS (χeff = 1),…
Figure 5
Figure 5. Figure 5: Posteriors of the maximum mass, masses of 99.5th and 99.75th, and 99.9th percentiles of the first subpopulation for the Mixture models. The contours mark the central 50% and 90% posterior credible regions, the values represent the median and 90% credible intervals. 0 2…
Figure 6
Figure 6. Figure 6: Mass distributions inferred with various models. The thick and thin lines represent the mean values and 90% credible intervals. D.2. Is there χeff − q correlation in the second subpopulation? To test whether there is χeff − q correlation in the second subpopulation, we…
Figure 7
Figure 7. Figure 7: χeff distributions inferred using Transition (PS) with Truncated model and Spline model describing the second χeff distribution. The thick and thin lines represent the mean values and 90% credible intervals. PDF 2 0 2 a2 1.5 0.0 1.5 b2 1.5 0.0 1.5 b2 0.04 +2.19 2.17 Tr…
Figure 8
Figure 8. Figure 8: Hyperparameters describing the χeff − q correlation for the second subpopulation. The contours mark the central 50% and 90% posterior credible regions, the values represent the median and 90% credible intervals. E. MOCK DATA STUDIES To test the validity of our findings…
Figure 9
Figure 9. Figure 9: Top: χeff distributions recovered from mock population with Transition model and non-evolving model, the black lines are for the injections. Bottom: the χeff distributions of the second sub-population recovered from several datasets, the black lines are for the distrib…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

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

  1. A new group of low-spin $50-70M_\odot$ Black Holes and the high pair-instability mass cutoff

    astro-ph.HE 2025-10 conditional novelty 6.0 of 10

    GWTC-4.0 reveals low-spin black holes up to ~68.5 Msun, implying a higher pair-instability mass cutoff and a lower 12C(alpha,gamma)16O rate.

  2. Aligned Hierarchical Black Hole Mergers in Active-Galactic-Nuclei Disks Revealed by GWTC-4

    astro-ph.HE 2025-09 conditional novelty 6.0 of 10

    GWTC-4 reveals a high-spin, high-mass black hole subpopulation with spin-orbit alignment, interpreted as evidence for hierarchical mergers in AGN disks.

  3. Tracing the light: Identification for the optical counterpart candidates of binary black-holes during O3

    astro-ph.HE 2025-07 conditional novelty 6.0 of 10

    After adding three years of ZTF data, only two of seven proposed AGN flare and GW event pairs, GW190521 with J124942.30+344928.9 and GW190803 022701 with J120437.98+500024.0, show strong statistical association.

  4. A Possible Mass Ratio and Spin-Orbit Misalignment Correlation for Mergers of Binary Black Holes in Nuclear Star Clusters

    astro-ph.HE 2025-01 conditional novelty 6.0 of 10

    Stellar binaries that experience two stable mass-transfer phases and are driven to merger by a supermassive black hole produce an anti-correlation between black-hole mass ratio and spin-orbit misalignment.

  5. Inferring the pair-instability mass gap from gravitational wave data

    astro-ph.HE 2025-06 conditional novelty 5.0 of 10

    Non-parametric analysis of GWTC-3 finds a transition at roughly 46 solar masses above which the effective spin distribution broadens and becomes consistent with symmetry around zero, consistent with second-generation ...

  6. The first decade of gravitational-wave measurements of black hole spins

    gr-qc 2026-06 unverdicted novelty 1.0 of 10

    A review summarizing formation-channel predictions, waveform effects, and population-level constraints on stellar-mass black hole spins from the first decade of gravitational-wave observations.

Reference graph

Works this paper leans on

49 extracted references · 2 canonical work pages · cited by 6 Pith papers

  1. [1]

    P., Abbott, R., Abbott, T

    Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2019, Physical Review X, 9, 031040, doi: 10.1103/PhysRevX.9.031040

    doi:10.1103/physrevx.9.031040 2019

  2. [2]

    D., Abraham, S., et al

    Abbott, R., Abbott, T. D., Abraham, S., et al. 2021a, Physical Review X, 11, 021053, doi: 10.1103/PhysRevX.11.021053 —. 2021b, ApJL, 913, L7, doi: 10.3847/2041-8213/abe949

    doi:10.1103/physrevx.11.021053

  3. [3]

    D., Acernese, F., et al

    Abbott, R., Abbott, T. D., Acernese, F., et al. 2023a, Physical Review X, 13, 011048, doi: 10.1103/PhysRevX.13.011048 —. 2023b, Physical Review X, 13, 041039, doi: 10.1103/PhysRevX.13.041039 —. 2024, PhRvD, 109, 022001, doi: 10.1103/PhysRevD.109.022001

    doi:10.1103/physrevx.13.011048 2024

  4. [4]

    D., & Thrane, E

    Adamcewicz, C., Lasky, P. D., & Thrane, E. 2023, ApJ, 958, 13, doi: 10.3847/1538-4357/acf763

    doi:10.3847/1538-4357/acf763 2023

  5. [5]

    2023, MNRAS, 522, 466, doi: 10.1093/mnras/stad972

    Antonini, F., Gieles, M., Dosopoulou, F., & Chattopadhyay, D. 2023, MNRAS, 522, 466, doi: 10.1093/mnras/stad972

    doi:10.1093/mnras/stad972 2023

  6. [6]

    M., & Callister, T

    Antonini, F., Romero-Shaw, I. M., & Callister, T. 2025, PhRvL, 134, 011401, doi: 10.1103/PhysRevLett.134.011401

    doi:10.1103/physrevlett.134.011401 2025

  7. [7]

    D., et al

    Ashton, G., H¨ ubner, M., Lasky, P. D., et al. 2019, Bilby: Bayesian inference library, Astrophysics Source Code Library, record ascl:1901.011. http://ascl.net/1901.011 14 Li et al

    work page 2019

  8. [8]

    2024, arXiv e-prints, arXiv:2411.15112, doi: 10.48550/arXiv.2411.15112

    Banerjee, S., & Olejak, A. 2024, arXiv e-prints, arXiv:2411.15112, doi: 10.48550/arXiv.2411.15112

    arXiv doi:10.48550/arxiv.2411.15112 2024

Show all 49 references
  1. [9]

    S., Fragos, T., Qin, Y., et al

    Bavera, S. S., Fragos, T., Qin, Y., et al. 2020, A&A, 635, A97, doi: 10.1051/0004-6361/201936204

    2020 doi

  2. [10]

    S., Fragos, T., Zevin, M., et al

    Bavera, S. S., Fragos, T., Zevin, M., et al. 2021, A&A, 647, A153, doi: 10.1051/0004-6361/202039804

    2021 doi

  3. [11]

    A., Haster, C.-J., et al

    Biscoveanu, S., Callister, T. A., Haster, C.-J., et al. 2022, ApJL, 932, L19, doi: 10.3847/2041-8213/ac71a8

    2022 doi

  4. [12]

    2016, PyMultiNest: Python interface for

    Buchner, J. 2016, PyMultiNest: Python interface for

    2016

  5. [13]

    Callister, T. A. 2024, arXiv e-prints, arXiv:2410.19145, doi: 10.48550/arXiv.2410.19145

    2024 doi arXiv

  6. [14]

    Farr, W. M. 2021, ApJL, 922, L5, doi: 10.3847/2041-8213/ac2ccc

    2021 doi

  7. [15]

    E., McKernan, B., Ford, K

    Cook, H. E., McKernan, B., Ford, K. E. S., et al. 2024, arXiv e-prints, arXiv:2411.10590, doi: 10.48550/arXiv.2411.10590

    2024 doi arXiv

  8. [16]

    2022, ApJ, 924, 101, doi: 10.3847/1538-4357/ac3667

    Edelman, B., Doctor, Z., Godfrey, J., & Farr, B. 2022, ApJ, 924, 101, doi: 10.3847/1538-4357/ac3667

    2022 doi

  9. [17]

    2022, arXiv e-prints, arXiv:2204.00461, doi: 10.48550/arXiv.2204.00461

    Essick, R., & Farr, W. 2022, arXiv e-prints, arXiv:2204.00461, doi: 10.48550/arXiv.2204.00461

    2022 doi arXiv

  10. [18]

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

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

    2019 doi

  11. [19]

    Farr, W. M. 2019, Research Notes of the American Astronomical Society, 3, 66, doi: 10.3847/2515-5172/ab1d5f

    2019 doi

  12. [20]

    2022, ApJL, 935, L26, doi: 10.3847/2041-8213/ac86c4

    Fishbach, M., Kimball, C., & Kalogera, V. 2022, ApJL, 935, L26, doi: 10.3847/2041-8213/ac86c4

    2022 doi

  13. [21]

    2021, Nature Astronomy, 5, 749, doi: 10.1038/s41550-021-01398-w

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

    2021 doi

  14. [22]

    2023, arXiv e-prints, arXiv:2304.01288, doi: 10.48550/arXiv.2304.01288

    Godfrey, J., Edelman, B., & Farr, B. 2023, arXiv e-prints, arXiv:2304.01288, doi: 10.48550/arXiv.2304.01288

    2023 doi arXiv

  15. [23]

    2023, PhRvD, 108, 103009, doi: 10.1103/PhysRevD.108.103009

    Golomb, J., & Talbot, C. 2023, PhRvD, 108, 103009, doi: 10.1103/PhysRevD.108.103009

    2023 doi

  16. [24]

    2024, ApJ, 975, 54, doi: 10.3847/1538-4357/ad758a

    Guo, W.-H., Li, Y.-J., Wang, Y.-Z., et al. 2024, ApJ, 975, 54, doi: 10.3847/1538-4357/ad758a

    2024 doi

  17. [25]

    2024, PhRvD, 109, 103006, doi: 10.1103/PhysRevD.109.103006

    Heinzel, J., Vitale, S., & Biscoveanu, S. 2024, PhRvD, 109, 103006, doi: 10.1103/PhysRevD.109.103006

    2024 doi

  18. [26]

    2024, arXiv e-prints, arXiv:2411.09195, doi: 10.48550/arXiv.2411.09195

    Li, G.-P., & Fan, X.-L. 2024, arXiv e-prints, arXiv:2411.09195, doi: 10.48550/arXiv.2411.09195

    2024 doi arXiv

  19. [27]

    2024a, ApJ, 977, 67, doi: 10.3847/1538-4357/ad83b5

    Li, Y.-J., Tang, S.-P., Gao, S.-J., Wu, D.-C., & Wang, Y.-Z. 2024a, ApJ, 977, 67, doi: 10.3847/1538-4357/ad83b5

    doi

  20. [28]

    2024b, PhRvL, 133, 051401, doi: 10.1103/PhysRevLett.133.051401

    Li, Y.-J., Wang, Y.-Z., Tang, S.-P., & Fan, Y.-Z. 2024b, PhRvL, 133, 051401, doi: 10.1103/PhysRevLett.133.051401

    doi

  21. [29]

    2022, ApJL, 933, L14, doi: 10.3847/2041-8213/ac78dd

    Li, Y.-J., Wang, Y.-Z., Tang, S.-P., et al. 2022, ApJL, 933, L14, doi: 10.3847/2041-8213/ac78dd

    2022 doi

  22. [30]

    2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    2014 doi

  23. [31]

    Mandel, I., & de Mink, S. E. 2016, MNRAS, 458, 2634, doi: 10.1093/mnras/stw379

    2016 doi

  24. [32]

    2022, PhR, 955, 1, doi: 10.1016/j.physrep.2022.01.003

    Mandel, I., & Farmer, A. 2022, PhR, 955, 1, doi: 10.1016/j.physrep.2022.01.003

    2022 doi

  25. [33]

    M., & Gair, J

    Mandel, I., Farr, W. M., & Gair, J. R. 2019, MNRAS, 486, 1086, doi: 10.1093/mnras/stz896

    2019 doi

  26. [34]

    McKernan, B., Ford, K. E. S., Callister, T., et al. 2022, MNRAS, 514, 3886, doi: 10.1093/mnras/stac1570

    2022 doi

  27. [35]

    McKernan, B., Ford, K. E. S., Bellovary, J., et al. 2018, ApJ, 866, 66, doi: 10.3847/1538-4357/aadae5

    2018 doi

  28. [36]

    2024, A&A, 689, A305, doi: 10.1051/0004-6361/202450480

    Olejak, A., Klencki, J., Xu, X.-T., et al. 2024, A&A, 689, A305, doi: 10.1051/0004-6361/202450480

    2024 doi

  29. [37]

    2024, ApJL, 966, L16, doi: 10.3847/2041-8213/ad3e82

    Payne, E., Kremer, K., & Zevin, M. 2024, ApJL, 966, L16, doi: 10.3847/2041-8213/ad3e82

    2024 doi

  30. [38]

    2020, PhRvD, 102, 064001, doi: 10.1103/PhysRevD.102.064001

    Pratten, G., Husa, S., Garc ´ ıa-Quir´ os, C., et al. 2020, PhRvD, 102, 064001, doi: 10.1103/PhysRevD.102.064001

    2020 doi

  31. [39]

    2024, arXiv e-prints, arXiv:2404.03166, doi: 10.48550/arXiv.2404.03166

    Ray, A., Maga˜ na Hernandez, I., Breivik, K., & Creighton, J. 2024, arXiv e-prints, arXiv:2404.03166, doi: 10.48550/arXiv.2404.03166

    2024 doi arXiv

  32. [40]

    2023, PhRvD, 108, 083033, doi: 10.1103/PhysRevD.108.083033

    Santini, A., Gerosa, D., Cotesta, R., & Berti, E. 2023, PhRvD, 108, 083033, doi: 10.1103/PhysRevD.108.083033

    2023 doi

  33. [41]

    2023, MNRAS, 526, 3495, doi: 10.1093/mnras/stad2968

    Talbot, C., & Golomb, J. 2023, MNRAS, 526, 3495, doi: 10.1093/mnras/stad2968

    2023 doi

  34. [42]

    2018, ApJ, 856, 173, doi: 10.3847/1538-4357/aab34c

    Talbot, C., & Thrane, E. 2018, ApJ, 856, 173, doi: 10.3847/1538-4357/aab34c

    2018 doi

  35. [43]

    2022, ApJ, 928, 155, doi: 10.3847/1538-4357/ac589a

    Tiwari, V. 2022, ApJ, 928, 155, doi: 10.3847/1538-4357/ac589a

    2022 doi

  36. [44]

    2021, ApJL, 913, L19, doi: 10.3847/2041-8213/abfbe7

    Tiwari, V., & Fairhurst, S. 2021, ApJL, 913, L19, doi: 10.3847/2041-8213/abfbe7

    2021 doi

  37. [45]

    S., et al

    Wang, Y.-Z., Li, Y.-J., Vink, J. S., et al. 2022, ApJL, 941, L39, doi: 10.3847/2041-8213/aca89f

    2022 doi

  38. [46]

    J., Veitch, J., Chapman-Bird, C., & Tenorio, R

    Williams, M. J., Veitch, J., Chapman-Bird, C., & Tenorio, R. 2025, mj-will/nessai: v0.14.0.post0, v0.14.0.post0, Zenodo, doi: 10.5281/zenodo.14627250

    2025 doi

  39. [47]

    2019, PhRvL, 123, 181101, doi: 10.1103/PhysRevLett.123.181101

    Yang, Y., Bartos, I., Gayathri, V., et al. 2019, PhRvL, 123, 181101, doi: 10.1103/PhysRevLett.123.181101

    2019 doi

  40. [48]

    Zevin, M., & Holz, D. E. 2022, ApJL, 935, L20, doi: 10.3847/2041-8213/ac853d

    2022 doi

  41. [49]

    S., Berry, C

    Zevin, M., Bavera, S. S., Berry, C. P. L., et al. 2021, ApJ, 910, 152, doi: 10.3847/1538-4357/abe40e

    2021 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.