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REVIEW 3 major objections 5 minor 105 references

Tiny, consistent differences in the distances two eccentric waveform models recover from each gravitational-wave event accumulate across the GWTC-4 catalog and change the inferred redshift evolution of merging black holes.

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2026-08-01 23:18 UTC pith:IUHDEPSR

load-bearing objection End-to-end comparison of two eccentric waveform models on GWTC-4 shows plausible but not fully quantified population-level redshift bias; useful paper that needs a sharper null test. the 3 major comments →

arxiv 2607.15453 v1 pith:IUHDEPSR submitted 2026-07-16 astro-ph.HE

Assessing the waveform systematics from parameter estimation to population inference with eccentricity

Muhammad Zeeshan , Richard O'Shaughnessy , Natalie Malagon , Katelyn J. Wagner This is my paper
classification astro-ph.HE
keywords gravitational waveswaveform systematicseccentricityhierarchical Bayesian population inferenceredshift evolutioneffective spinGWTC-4compact binary mergers
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper argues that the redshift distribution of merging black holes inferred from gravitational-wave catalogs is not yet a robust observable: two state-of-the-art eccentric waveform models, SEOBNRv5EHM and TEOBResumS-DALI, produce per-event distance estimates that differ by less than a single-event uncertainty but in a consistent direction. When those small offsets are combined hierarchically across 153 binary black holes, the models yield measurably different merger-rate evolution with redshift and different effective-spin distributions. The authors trace the culprit to coherent, catalog-wide bias that grows roughly as the square root of the number of events, meaning larger future catalogs will make the problem more visible rather than averaging it away. They also show that mass, mass-ratio, and eccentricity population distributions are largely robust, and they introduce a synthetic-data pipeline for testing how well eccentric populations can be recovered.

Core claim

The central discovery, stated on the paper's own terms, is that the recovered redshift distribution is not a robust observable at the level of accuracy the two eccentric waveform models allow: SEOBNRv5EHM and TEOBResumS-DALI give broadly consistent per-event parameters, but a small consistent bias in recovered luminosity distance accumulates across the catalog and produces discrepant inferred redshift evolution, with a secondary secular bias in effective spin. The discrepancy is not driven by a few outliers such as GW231123135430, since removing it leaves the redshift difference effectively unchanged; the spin difference, by contrast, is sensitive to that event. Other population properties —

What carries the argument

The load-bearing machinery is a hierarchical Bayesian population-inference pipeline driven by RIFT per-event posterior samples: an inhomogeneous Poisson process likelihood reweights each event's posterior by the population model and a selection function, yielding posteriors on hyperparameters for mass, spin, eccentricity, and redshift evolution. The two waveform models being compared, SEOBNRv5EHM and TEOBResumS-DALI, are the source of the systematic; a sqrt(N)-weighted z-score-like diagnostic B_x exposes catalog-level shifts in mean redshift, spin, eccentricity, and mass ratio that are invisible event by event. The eccentricity power-law extension is what lets the same framework probe whethe

Load-bearing premise

The burden of the argument rests on the inherited per-event posterior catalogs from the earlier RIFT study being directly comparable — same priors, reference prior, and sampling quality for both waveform models — so that the population-level offsets reflect waveform physics rather than analysis choices.

What would settle it

Regenerate per-event posterior samples for both waveform models from identical priors and sampling settings on the same set of GWTC-4 events, or on synthetic injections; if the median recovered luminosity-distance difference between SEOBNRv5EHM and TEOBResumS-DALI does not reproduce the sign and size of the offset that drives the redshift discrepancy, then the claimed accumulated bias is not a waveform systematic.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If the claim holds, any single-waveform measurement of the black-hole merger-rate evolution with redshift should be treated as model-dependent at current precision.
  • Population constraints on effective spin are likewise model-sensitive, although the paper finds this sensitivity is tied to specific high-mass events like GW231123135430.
  • Primary mass and mass-ratio distributions are more robust; the 33 solar-mass peak is present in both eccentric models but less sharp than in quasi-circular analyses.
  • The eccentricity distribution at population level is robust between models despite event-level eccentricity differences.
  • As catalogs grow, coherent waveform systematics grow roughly as the square root of the number of events, so multi-waveform, self-consistent population inference becomes necessary rather than optional.

Where Pith is reading between the lines

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

  • A natural next test the paper leaves implicit: repeat the same two-model population comparison on identical synthetic injections generated with each model in turn; whichever model's injections are recovered with the cross-model bias in the same direction would isolate whether the redshift offset is astrophysical or purely a modeling artifact.
  • The result implies a systematic-error floor for standard-siren cosmology and other redshift-sensitive analyses built on current catalogs: distance systematics at the few-percent level do not cancel in population averages.
  • Because the paper uses nonprecessing models, adding precessing eccentric waveforms could either sharpen or dilute the redshift discrepancy; the paper sets this up as future work.
  • The synthetic framework, with only 39 of 155 injected binaries passing detection, could be upgraded to a fully self-consistent selection function, turning a proof of concept into a calibration tool for eccentric population recovery.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper compares source-level parameter estimation and population-level hierarchical inference for GWTC-4 compact binaries using two eccentric waveform models, SEOBNRv5EHM and TEOBResumS-DALI, alongside quasi-circular results. The authors report that, although per-event differences are small, coherent systematic offsets in recovered distances accumulate across the catalog, leading to discrepant inferred redshift evolution and effective-spin population distributions. They also present a joint BNS/NSBH population analysis and a synthetic-data validation framework. The central claim is that the recovered redshift distribution is not robust to waveform model choice at the accuracy these models currently allow, with biases growing roughly as sqrt(N).

Significance. If substantiated, the paper addresses an important and timely issue: whether small per-event waveform systematics can bias population-level conclusions in the current and future LVK catalogs. The comparison is non-circular, using two independently developed waveform models on external GWTC-4 data, and the end-to-end computational framework (RIFT + GWKokab) is a useful contribution. The paper is also commendably transparent about several limitations, including the SNR mismatch in the synthetic catalog and the reliance on inherited PE settings. However, the central claim currently rests on qualitative comparisons and a diagnostic (Eq. 12) that the authors themselves describe as consistent with unity for most events. Without a formal significance test or a demonstration that the inherited PE catalogs are prior-compatible, the paper's headline conclusion is under-supported rather than definitively established.

major comments (3)
  1. [§III.A, Eq. (12)] The B_x diagnostic is described as 'qualitatively consistent with unity' for most events, and Fig. 6 shows per-event shifts much smaller than one standard deviation. The paper argues this understates the redshift effect, but no formal statistical test is supplied. As written, Eq. (12) is a z-score-like quantity, not a significance test, and the claim that coherent biases grow as sqrt(N) is not accompanied by confidence intervals, bootstrap/permutation tests, or posterior predictive p-values. This is load-bearing for the central claim that the redshift distribution is not a robust observable. A quantitative test comparing the two population posteriors (e.g., overlap coefficient, divergence measure, or a permutation test over events) should be added.
  2. [§II.E and Eqs. (4)–(5)] The population inference inherits all RIFT parameter-estimation settings from Ref. [98], including priors and sampling parameters, but no evidence is provided that the SEOBNRv5EHM and TEOBResumS-DALI runs used identical detector-frame priors and comparable convergence quality. The reference-prior formalism switches between Euclidean and comoving forms (Eqs. 4–5) depending on observing run, so any mismatch between RIFT's actual distance prior and the reference prior could masquerade as a coherent redshift offset. The authors should either verify prior compatibility, or rerun a subset of events with controlled settings, or explicitly quantify the sensitivity of the population result to plausible prior differences.
  3. [§III.B, Fig. 7] The population-level redshift discrepancy is shown as overlapping posterior predictive distributions, but the paper provides no quantitative measure of the difference between the two waveform-model results or between each model and the quasi-circular baseline. The text asserts that the distributions 'differ' and that the effect accumulates, but visual inspection of Fig. 7 is not a substitute for a null-hypothesis test or a Bayesian model-comparison statistic. Given that the B_x diagnostic is weak for most events, the statistical evidence for the population-level discrepancy must be made explicit, including whether the difference is significant after accounting for sampling noise in both the PE and population stages.
minor comments (5)
  1. [§III.B] Typo: 'TEOBReumS' should be 'TEOBResumS' in the sentence about omitting GW231123135430.
  2. [§III.A] 'frequentest' should be 'frequentist' in the first sentence.
  3. [References] Refs. [68] and [98] are identical (Malagon and O'Shaughnessy, arXiv:2605.12818). One should be cited, not both.
  4. [Fig. 5 caption] The caption says 'the red dashed line shows the equal mean of all the events' — this is ambiguous. Presumably it marks the average of the B_x values or zero; please clarify.
  5. [§II.D and §III.D] The synthetic-data section is explicitly described as a 'preliminary proof of concept' because only 39 of 155 events pass detection. This limitation is acceptable, but the text should more clearly separate the synthetic validation from the real-data conclusions, since the real-data population analysis does not use the same detection-consistency framework.

Circularity Check

0 steps flagged

No significant circularity; the central claim is an empirical cross-waveform comparison against external GWTC-4 data.

full rationale

The paper's central claim is that SEOBNRv5EHM and TEOBResumS-DALI produce small, coherent distance differences that accumulate in hierarchical population inference, making the recovered redshift distribution non-robust. This is an empirical comparison of two independently constructed waveform models applied to external GWTC-4 events, not a case where an input is fitted and then renamed as a prediction. The per-event posterior samples are inherited from Ref. [98] and used in Eq. (3) within the standard hierarchical likelihood of Eq. (2); this reliance on a prior self-produced PE catalog is a data-input choice, not a circular derivation, because the conclusion is not built into those samples. The B_x diagnostic (Eq. 12) is acknowledged by the authors as understating the redshift effect, and the population-level difference is read directly from Fig. 7; the absence of a formal null-hypothesis test is a statistical rigor concern, not circularity. The synthetic-data validation is explicitly labeled a 'preliminary proof of concept' and uses the same parametric family for generation and recovery, which makes it a self-consistency/pipeline test rather than load-bearing evidence for the main waveform-systematics claim. Self-citations to GWKokab and Ref. [98] supply tools and inputs, not the argumentative conclusion, so they do not constitute circularity under the review rules.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 0 invented entities

No new particles, forces, or entities are introduced; eccentricity is an established binary parameter. The main assumptions are standard hierarchical-Bayes machinery, the sufficiency of nonprecessing eccentric waveforms, the omission of eccentricity from real-data selection effects, and an ad hoc power-law eccentricity population prior.

free parameters (5)
  • Eccentricity power-law index α_e = Posterior; prior U(-5,5)
    p(e) ∝ e^α_e over [1e-6, 0.5] (Eq. 7); a free population hyperparameter fitted to the GWTC-4 data.
  • Redshift-evolution index κ = Posterior; prior U(-5,5)
    Power-law merger-rate evolution used for BNS/NSBH and synthetic analyses; BBH redshift uses the GWTC-4 Power Law model parameters.
  • Broken Power Law + 2 Peaks mass-model hyperparameters = Posteriors; priors from Table 6 of Ref. [37]
    Default GWTC-4 mass distribution parameters fitted to the catalog.
  • Skew-normal effective-spin hyperparameters = Posteriors; priors from Table 9 of Ref. [37]
    Parameters of the χ_eff population model fitted to the data.
  • BNS/NSBH truncated-Gaussian mass and spin means/widths = Posteriors; priors in Table I
    Hyperparameters in the joint BNS/NSBH population model fitted to only 9 events.
axioms (4)
  • standard math Hierarchical Bayesian Poisson-process likelihood is the correct framework for population inference.
    Eqs. 1–2 follow the standard inhomogeneous Poisson process used in GW population inference (Refs. [87–89]); this is unproved background but standard.
  • domain assumption GWTC-4 sensitivity injections, with eccentricity ignored in Pdet, correctly represent detection selection for real data.
    Sec. II.A states 'ignoring the eccentricity from selection effects' for real data; if selection depends strongly on eccentricity, the recovered eccentricity population is biased.
  • domain assumption Nonprecessing aligned-spin eccentric waveform models are sufficiently accurate for the source and population inferences.
    The Conclusion explicitly notes 'all of our analyses in this study employ nonprecessing waveform models'; precession systematics are not assessed and could shift the results.
  • ad hoc to paper The single power-law eccentricity distribution p(e) ∝ e^α_e is a valid population prior.
    Eq. 7: the power law is introduced as a 'compact way to test' eccentricity support, replacing a mixture model from previous work; it is not derived from formation physics.

pith-pipeline@v1.3.0-alltime-deepseek · 15256 in / 10469 out tokens · 103172 ms · 2026-08-01T23:18:19.853066+00:00 · methodology

0 comments
read the original abstract

While masses and spins are routinely used to constrain compact binary formation channels, eccentricity provides an additional and potentially powerful diagnostic of binary origin, particularly for dynamically assembled systems. Recent advances in eccentric waveform modeling now make it possible to search for eccentric signatures in gravitational wave data; however, differences between waveform models can introduce systematic effects that may propagate into astrophysical population inference. In this work, we analyze 153 binary black holes, 2 binary neutron stars and 7 neutron star black hole binaries from the GWTC-4 catalog. We compare the source and population level inferences obtained with two eccentric waveform models, SEOBNRv5EHM and TEOBResumS-DALI, as well as with quasi circular waveform analyses. We find that the two eccentric models give broadly consistent source parameter estimates for most events, but some events exhibit subtle and coherent differences. These small, systematic offsets can accumulate in hierarchical population inference, leading to differences in inferred population properties, most notably in the redshift evolution and effective spin distribution. Because coherent event level biases can grow approximately as $\sqrt{N}$ for a catalog of N events, waveform systematics become increasingly important as gravitational wave catalogs expand. We also introduce a synthetic data framework that generates eccentric populations and corresponding RIFT posterior samples, enabling injection studies that test the recoverability of eccentric population properties.

Figures

Figures reproduced from arXiv: 2607.15453 by Katelyn J. Wagner, Muhammad Zeeshan, Natalie Malagon, Richard O'Shaughnessy.

Figure 1
Figure 1. Figure 1: FIG. 1 [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2: This figure shows the parameter inference for a [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3: This shows the comparison of GWTC-4 studies assuming quasi-circular binaries vs RIFT PE using [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4: This figure shows the median comparison for each event, and red dashed diagonal lines shows where both models infer [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5: This figure shows the systematic shift of mean scale with [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6: This figure shows the individual event systematic shift of mean value for each event. The red dashed line shows the [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7: This shows the comparison of GWTC-4 studies assuming quasi-circular binaries vs RIFT PE using [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8: This shows the comparison of eccentricity distribu [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9: The top left figure shows the mass distribution of BHs in NSBH systems, and the top right figure shows the mass [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: FIG. 10: The left figure shows the joint distribution of redshift for BNS and NSBH and right figure shows the joint distribution [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11: This figure shows the recovery of hyper-parameter of population model used for synthetic dataset. We were able to [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗

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Reference graph

Works this paper leans on

105 extracted references · 79 linked inside Pith

  1. [1]

    B. P. Abbott, R. Abbott, T. D. Abbott, S. Abraham, F. Acernese, K. Ackley, C. Adams, R. X. Adhikari, V. B. Adya, C. Affeldt, et al., Physical Review X9, 031040 (2019), 1811.12907

    Pith/arXiv arXiv 2019

  2. [3]

    Abbott, T

    R. Abbott, T. D. Abbott, F. Acernese, K. Ackley, C. Adams, N. Adhikari, R. X. Adhikari, V. B. Adya, C. Affeldt, D. Agarwal, et al., Phys. Rev. D109, 022001 (2024), 2108.01045

    Pith/arXiv arXiv 2024

  3. [5]

    The LIGO Scientific Collaboration, the Virgo Collabo- ration, the KAGRA Collaboration, A. G. Abac, R. Ab- bott, H. Abe, F. Acernese, K. Ackley, C. Adamcewicz, S. Adhicary, et al., Submitted to ApJ, available as 13 arXiv:2508.18082 arXiv:2508.18082 (2025), 2508.18082

    Pith/arXiv arXiv 2025

  4. [7]

    Acernese et al

    F. Acernese et al. (VIRGO), Class. Quant. Grav.32, 024001 (2015), 1408.3978

    Pith/arXiv arXiv 2015

  5. [8]

    Akutsu, M

    T. Akutsu, M. Ando, K. Arai, Y. Arai, S. Araki, A. Araya, N. Aritomi, Y. Aso, S. Bae, Y. Bae, et al., Progress of Theoretical and Experimen- tal Physics2021, 05A101 (2020), ISSN 2050- 3911, https://academic.oup.com/ptep/article- pdf/2021/5/05A101/37974994/ptaa125.pdf, URL https://doi.org/10.1093/ptep/ptaa125

    doi:10.1093/ptep/ptaa125 2020

  6. [9]

    LIGO Scientific Collaboration, J. Aasi, B. P. Abbott, R. Abbott, T. Abbott, M. R. Abernathy, K. Ackley, C. Adams, T. Adams, P. Addesso, et al., Classical and Quantum Gravity32, 074001 (2015), 1411.4547

    Pith/arXiv arXiv 2015

  7. [10]

    Akutsu, M

    Kagra Collaboration, T. Akutsu, M. Ando, K. Arai, Y. Arai, S. Araki, A. Araya, N. Aritomi, H. Asada, Y. Aso, et al., Nature Astronomy3, 35 (2019), 1811.08079

    arXiv 2019

  8. [11]

    Akutsu, M

    T. Akutsu, M. Ando, K. Arai, Y. Arai, S. Araki, A. Araya, N. Aritomi, Y. Aso, S. Bae, Y. Bae, et al., Progress of Theoretical and Experimental Physics2021, 05A101 (2021), 2005.05574

    arXiv 2021

  9. [13]

    B. P. Abbott, R. Abbott, T. D. Abbott, S. Abraham, F. Acernese, K. Ackley, C. Adams, V. B. Adya, C. Af- feldt, M. Agathos, et al., Living Reviews in Relativity 23, 3 (2020)

    2020

  10. [14]

    Abbott et al

    B. Abbott et al. (The LIGO Scientific Collaboration and the Virgo Collaboration), Phys. Rev. Lett116, 241103 (2016)

    2016

  11. [15]

    The LIGO Scientific Collaboration, the Virgo Collabo- ration, B. P. Abbott, R. Abbott, T. D. Abbott, F. Ac- ernese, K. Ackley, C. Adams, T. Adams, P. Addesso, et al., Phys. Rev. Lett119, 161101 (2017)

    2017

  12. [16]

    The LIGO Scientific Collaboration, the Virgo Collabo- ration, B. P. Abbott, R. Abbott, T. D. Abbott, S. Abra- ham, F. Acernese, K. Ackley, C. Adams, V. B. Adya, et al., Phys. Rev. D102, 043015 (2020)

    2020

  13. [17]

    The LIGO Scientific Collaboration, the Virgo Collabo- ration, B. P. Abbott, R. Abbott, T. D. Abbott, S. Abra- ham, F. Acernese, K. Ackley, C. Adams, V. B. Adya, et al., Astrophysical Journal896, L44 (2020)

    2020

  14. [18]

    The LIGO Scientific Collaboration, the Virgo Collab- oration, B. P. Abbott, R. Abbott, T. D. Abbott, S. Abraham, F. Acernese, K. Ackley, C. Adams, V. B. Adya, et al., Astrophysical Journal900, L13 (2020), 2009.01190

    arXiv 2020

  15. [19]

    The LIGO Scientific Collaboration, the Virgo Collabo- ration, the KAGRA Collaboration, A. G. Abac, R. Ab- bott, H. Abe, F. Acernese, K. Ackley, C. Adamcewicz, S. Adhicary, et al., Submitted to ApJL (2025), URL https://dcc.ligo.org/P2500402

    2025

  16. [20]

    A. G. Abac, I. Abouelfettouh, F. Acernese, K. Ackley, C. Adamcewicz, S. Adhicary, D. Adhikari, N. Adhikari, R. X. Adhikari, V. K. Adkins, et al., Astrophysical Jour- nal993, L25 (2025), 2507.08219

    Pith/arXiv arXiv 2025

  17. [21]

    B. S. Sathyaprakash and B. F. Schutz, Living Reviews in Relativity12, 2 (2009), 0903.0338

    Pith/arXiv arXiv 2009

  18. [22]

    K. S. Thorne,The Generation of Gravitational Waves: A Review of Computational Tecniques(Springer US, Boston, MA, 1977), pp. 1–61, ISBN 978-1-4684-0853- 9, URLhttps://doi.org/10.1007/978-1-4684-085 3-9_1

    doi:10.1007/978-1-4684-085 1977

  19. [23]

    Mandel and R

    I. Mandel and R. O’Shaughnessy, Classical and Quan- tum Gravity27, 114007 (2010), 0912.1074

    Pith/arXiv arXiv 2010

  20. [24]

    O’Shaughnessy, Phys

    R. O’Shaughnessy, Phys. Rev. D88, 084061 (2013), URLhttp://xxx.lanl.gov/abs/1204.3117

    Pith/arXiv arXiv 2013

  21. [25]

    Belczynski, D

    K. Belczynski, D. E. Holz, T. Bulik, and R. O’Shaughnessy, Nature534, 512 (2016), 1602.04531

    Pith/arXiv arXiv 2016

  22. [26]

    B. P. Abbott, R. Abbott, T. D. Abbott, M. R. Aber- nathy, F. Acernese, K. Ackley, and et al., Astrophysi- cal Journal818, L22 (2016), 1602.03846, URLhttps: //dcc.ligo.org/LIGO-P1500262/public/main

    Pith/arXiv arXiv 2016

  23. [27]

    Zevin, C

    M. Zevin, C. Pankow, C. L. Rodriguez, L. Sampson, E. Chase, V. Kalogera, and F. A. Rasio, Astrophysical Journal846, 82 (2017), 1704.07379

    Pith/arXiv arXiv 2017

  24. [28]

    Vitale, D

    S. Vitale, D. Gerosa, C.-J. Haster, K. Chatziioan- nou, and A. Zimmerman, Phys. Rev. Lett119, 251103 (2017), 1707.04637

    Pith/arXiv arXiv 2017

  25. [29]

    Mandel and A

    I. Mandel and A. Farmer, Physics Reports955, 1 (2022), 1806.05820

    Pith/arXiv arXiv 2022

  26. [30]

    Romero-Shaw, P

    I. Romero-Shaw, P. D. Lasky, and E. Thrane, Astro- physical Journal940, 171 (2022), 2206.14695

    Pith/arXiv arXiv 2022

  27. [31]

    Romero-Shaw, P

    I. Romero-Shaw, P. D. Lasky, E. Thrane, and J. Calder´ on Bustillo, Astrophysical Journal903, L5 (2020), 2009.04771

    Pith/arXiv arXiv 2020

  28. [32]

    Zevin, I

    M. Zevin, I. M. Romero-Shaw, K. Kremer, E. Thrane, and P. D. Lasky, Astrophysical Journal921, L43 (2021), 2106.09042

    Pith/arXiv arXiv 2021

  29. [33]

    Mapelli, Frontiers in Astronomy and Space Sciences 7, 38 (2020), 2105.12455

    M. Mapelli, Frontiers in Astronomy and Space Sciences 7, 38 (2020), 2105.12455

    Pith/arXiv arXiv 2020

  30. [34]

    Chandra, J

    K. Chandra, J. Fernandes, A. Mittal, and G. Carullo, arXiv e-prints arXiv:2606.15150 (2026), 2606.15150

    arXiv 2026

  31. [35]

    Li, Y.-Z

    Y.-J. Li, Y.-Z. Wang, S.-P. Tang, and Y.-Z. Fan, arXiv e-prints arXiv:2509.23897 (2025), 2509.23897

    Pith/arXiv arXiv 2025

  32. [36]

    The LIGO Scientific Collaboration, the Virgo Collabo- ration, the KAGRA Collaboration, A. G. Abac, R. Ab- bott, H. Abe, F. Acernese, K. Ackley, C. Adamcewicz, S. Adhicary, et al., Submitted to ApJ, available as arXiv:2508.18081 arXiv:2508.18081 (2025), 2508.18081

    Pith/arXiv arXiv 2025

  33. [37]

    The LIGO Scientific Collaboration, the Virgo Collabo- ration, and the KAGRA Collaboration, arXiv e-prints arXiv:2508.18083 (2025), 2508.18083

    Pith/arXiv arXiv 2025

  34. [38]

    Abbott, T

    The LIGO Scientific Collaboration, the Virgo Col- laboration, R. Abbott, T. D. Abbott, S. Abraham, F. Acernese, K. Ackley, A. Adams, C. Adams, V. B. Adya, et al., Astrophysical Journal913, L7 (2021), 2010.14533

    Pith/arXiv arXiv 2021

  35. [39]

    Ab- bott, T

    The LIGO Scientific Collaboration, The Virgo Collab- oration, The KAGRA Scientific Collaboration, R. Ab- bott, T. D. Abbott, F. Acernese, K. Ackley, C. Adams, N. Adhikari, R. X. Adhikari, et al. (LIGO Scientific Col- laboration, Virgo Collaboration, and KAGRA Collabo- ration), Phys. Rev. X13, 011048 (2023), URLhttps: //link.aps.org/doi/10.1103/PhysRevX.13.011048

    doi:10.1103/physrevx.13.011048 2023

  36. [40]

    Wysocki, J

    D. Wysocki, J. Lange, and R. O’Shaughnessy, Phys. Rev. D100, 3012 (2019), URLhttps://arxi v.org/abs/1805.06442

    Pith/arXiv arXiv 2019

  37. [41]

    Wysocki, D

    D. Wysocki, D. Gerosa, R. O’Shaughnessy, K. Bel- czynski, W. Gladysz, E. Berti, M. Kesden, and D. E. Holz, Phys. Rev. D97, 043014 (2017), 1709.01943, URL http://xxx.lanl.gov/abs/arXiv:1709.01943. 14

    Pith/arXiv arXiv 2017

  38. [42]

    Gerosa, E

    D. Gerosa, E. Berti, R. O’Shaughnessy, K. Belczynski, M. Kesden, D. Wysocki, and W. Gladysz, Phys. Rev. D 98, 084036 (2018), 1808.02491

    Pith/arXiv arXiv 2018

  39. [43]

    Doctor, D

    Z. Doctor, D. Wysocki, R. O’Shaughnessy, D. E. Holz, and B. Farr, Astrophysical Journal893, 35 (2020), 1911.04424

    Pith/arXiv arXiv 2020

  40. [44]

    Delfavero, R

    V. Delfavero, R. O’Shaughnessy, K. Belczynski, P. Drozda, and D. Wysocki, Phys. Rev. D108, 043023 (2023), 2303.05436, URLhttps://link.aps.org/doi /10.1103/PhysRevD.108.043023

    Pith/arXiv arXiv 2023

  41. [45]

    Delfavero, K

    V. Delfavero, K. Breivik, R. O’Shaughnessy, and J. Baker, Astrophysical Journal981, 66 (2025), 2409.15230

    Pith/arXiv arXiv 2025

  42. [46]

    Gayathri, D

    V. Gayathri, D. Wysocki, Y. Yang, V. Delfavero, R. O’Shaughnessy, Z. Haiman, H. Tagawa, and I. Bar- tos, Astrophysical Journal945, L29 (2023), 2301.04187

    Pith/arXiv arXiv 2023

  43. [47]

    Gayathri, V

    G. Gayathri, V. Iorio, H. Tagawa, D. Wysocki, J. An- glin, I. Bartos, S. Bhaumik, Z. Haiman, M. Mapelli, R. O’Shaughnessy, and L. Xue, Submitted to Astron- omy and Astrophysics, available as arXiv:2509.09647 arXiv:2509.09647 (2025), 2509.09647, URLhttps:// dcc.ligo.org/LIGO-P2500484

    Pith/arXiv arXiv 2025

  44. [48]

    Tiwari, Astrophysical Journal928, 155 (2022), 2111.13991

    V. Tiwari, Astrophysical Journal928, 155 (2022), 2111.13991

    Pith/arXiv arXiv 2022

  45. [49]

    Gennari, T

    V. Gennari, T. Bertheas, and N. Tamanini, arXiv e- prints arXiv:2604.14290 (2026), 2604.14290

    Pith/arXiv arXiv 2026

  46. [50]

    Galaudage, arXiv e-prints arXiv:2605.25994 (2026), 2605.25994

    S. Galaudage, arXiv e-prints arXiv:2605.25994 (2026), 2605.25994

    Pith/arXiv arXiv 2026

  47. [51]

    Li, S.-P

    Y.-J. Li, S.-P. Tang, L.-Q. Xue, and Y.-Z. Fan, arXiv e-prints arXiv:2507.17551 (2025), 2507.17551

    arXiv 2025

  48. [52]

    Banagiri, E

    S. Banagiri, E. Thrane, and P. D. Lasky, arXiv e-prints arXiv:2509.15646 (2025), 2509.15646

    Pith/arXiv arXiv 2025

  49. [53]

    H. Tong, T. A. Callister, M. Fishbach, E. Thrane, F. Antonini, S. Stevenson, I. M. Romero-Shaw, and F. Dosopoulou, Available as arXiv:2511.05316 arXiv:2511.05316 (2025), 2511.05316

    arXiv 2025

  50. [54]

    Tiwari, arXiv e-prints arXiv:2510.25579 (2025), 2510.25579

    V. Tiwari, arXiv e-prints arXiv:2510.25579 (2025), 2510.25579

    Pith/arXiv arXiv 2025

  51. [55]

    Antonini, I

    F. Antonini, I. M. Romero-Shaw, and T. Callister, Phys. Rev. Lett134, 011401 (2025), 2406.19044

    Pith/arXiv arXiv 2025

  52. [56]

    Li, Y.-Z

    Y.-J. Li, Y.-Z. Wang, S.-P. Tang, and Y.-Z. Fan, Phys. Rev. Lett133, 051401 (2024), 2303.02973

    Pith/arXiv arXiv 2024

  53. [57]

    Franciolini and P

    G. Franciolini and P. Pani, Phys. Rev. D105, 123024 (2022), 2201.13098

    Pith/arXiv arXiv 2022

  54. [58]

    Pierra, S

    G. Pierra, S. Mastrogiovanni, and S. Perri` es, arXiv e- prints arXiv:2406.01679 (2024), 2406.01679

    Pith/arXiv arXiv 2024

  55. [59]

    S. R. Taylor, J. R. Gair, and I. Mandel, Phys. Rev. D 85, 023535 (2012), 1108.5161

    Pith/arXiv arXiv 2012

  56. [60]

    J. M. Ezquiaga and D. E. Holz, Phys. Rev. Lett129, 061102 (2022), 2202.08240

    Pith/arXiv arXiv 2022

  57. [61]

    Maga˜ na Hernandez and A

    I. Maga˜ na Hernandez and A. Palmese, arXiv e-prints arXiv:2509.03607 (2025), 2509.03607

    Pith/arXiv arXiv 2025

  58. [62]

    H. Tong, M. Fishbach, and E. Thrane, Astrophysical Journal985, 220 (2025), 2502.10780

    Pith/arXiv arXiv 2025

  59. [63]

    The LIGO Scientific Collaboration, the Virgo Collabo- ration, and the KAGRA Collaboration, arXiv e-prints arXiv:2605.27227 (2026), 2605.27227

    Pith/arXiv arXiv 2026

  60. [64]

    A. Z. Jan, A. B. Yelikar, J. Lange, and R. O’Shaughnessy, Phys. Rev. D102, 124069 (2020), 2011.03571

    Pith/arXiv arXiv 2020

  61. [65]

    Islam, A

    T. Islam, A. Vajpeyi, F. H. Shaik, C.-J. Haster, V. Varma, S. E. Field, J. Lange, R. O’Shaughnessy, and R. Smith, Phys. Rev. D112, 044001 (2025), 2309.14473

    arXiv 2025

  62. [66]

    A. B. Yelikar, R. O’Shaughnessy, J. Lange, and A. Z. Jan, Phys. Rev. D110, 064024 (2024), 2404.16599

    Pith/arXiv arXiv 2024

  63. [67]

    Fernando, R

    D. Fernando, R. O’Shaughnessy, and D. Williams, Phys. Rev. D112, 084004 (2025), 2412.02999

    Pith/arXiv arXiv 2025

  64. [69]

    Kumar, M

    S. Kumar, M. Melching, F. Ohme, H. Narola, T. Dooney, and C. Van Den Broeck, arXiv e-prints arXiv:2604.21859 (2026), 2604.21859

    Pith/arXiv arXiv 2026

  65. [70]

    A. H. Nitz and C. D. Capano, Astrophysical Journal 907, L9 (2021), 2010.12558

    Pith/arXiv arXiv 2021

  66. [71]

    Gayathri, J

    V. Gayathri, J. Healy, J. Lange, B. O’Brien, M. Szczepa´ nczyk, I. Bartos, M. Campanelli, S. Kli- menko, C. O. Lousto, and R. O’Shaughnessy, Nature Astronomy arXiv:2009.05461 (2022), 2009.05461, URL https://www.nature.com/articles/s41550-021-015 68-w

    Pith/arXiv arXiv 2009

  67. [72]

    K. J. Wagner and R. O’Shaughnessy, Phys. Rev. D110, 124024 (2024), 2402.08039

    Pith/arXiv arXiv 2024

  68. [73]

    McMillin, K

    P. McMillin, K. J. Wagner, G. Ficarra, C. O. Lousto, and R. O’Shaughnessy, arXiv e-prints arXiv:2507.22862 (2025), 2507.22862

    Pith/arXiv arXiv 2025

  69. [74]

    A. Jan, S. Nicolella, D. Shoemaker, and R. O’Shaughnessy, Phys. Rev. D113, 084052 (2026)

    2026

  70. [75]

    Lange, D

    J. Lange, D. Chiaramello, P. Lott, C. Henshaw, A. Na- gar, R. O’Shaughnessy, and L. Cadonati, Submitted to PRD; available as arxiv:2605.21640 (2026), 2605.21640, URLhttps://arxiv.org/abs/2605.21640

    Pith/arXiv arXiv 2026

  71. [76]

    Dhani, S

    A. Dhani, S. H. V¨ olkel, A. Buonanno, H. Estelles, J. Gair, H. P. Pfeiffer, L. Pompili, and A. Toubiana, Physical Review X15, 031036 (2025), 2404.05811

    arXiv 2025

  72. [77]

    Kunert, J

    N. Kunert, J. Gair, P. T. H. Pang, and T. Dietrich, Phys. Rev. D110, 043520 (2024), 2405.18158

    Pith/arXiv arXiv 2024

  73. [78]

    Pompili, A

    L. Pompili, A. Gamboa, and A. Buonanno, arXiv e- prints arXiv:2605.28716 (2026), 2605.28716

    Pith/arXiv arXiv 2026

  74. [79]

    Das and S

    U. Das and S. Mukherjee, arXiv e-prints arXiv:2605.16510 (2026), 2605.16510

    Pith/arXiv arXiv 2026

  75. [80]

    Padhyegurjar and S

    S. Padhyegurjar and S. Mukherjee, arXiv e-prints arXiv:2606.02318 (2026), 2606.02318

    Pith/arXiv arXiv 2026

  76. [81]

    Padhyegurjar and S

    S. Padhyegurjar and S. Mukherjee, arXiv e-prints arXiv:2606.00234 (2026), 2606.00234

    Pith/arXiv arXiv 2026

  77. [82]

    Afroz and S

    S. Afroz and S. Mukherjee, arXiv e-prints arXiv:2509.09123 (2025), 2509.09123

    Pith/arXiv arXiv 2025

  78. [83]

    Afroz and S

    S. Afroz and S. Mukherjee, Phys. Rev. D112, 023531 (2025), 2411.07304

    Pith/arXiv arXiv 2025

  79. [84]

    Karathanasis, S

    C. Karathanasis, S. Mukherjee, and S. Mastrogiovanni, MNRAS523, 4539 (2023), 2204.13495

    Pith/arXiv arXiv 2023

  80. [85]

    Qazalbash, M

    M. Qazalbash, M. Zeeshan, and R. O’Shaughnessy, GWKokab: A jax-based gravitational-wave population inference toolkit for parametric models(2024), URL https://github.com/kokabsc/gwkokab

    2024

Showing first 80 references.