Pith. sign in

REVIEW 4 major objections 4 minor 13 cited by

The chirp-mass distribution of binary black holes shows three peaks spaced by a factor of about 1.9, evidence the author interprets as hierarchical mergers shaping the population.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · deepseek-v4-flash

2026-08-04 07:25 UTC pith:6EC2VX24

load-bearing objection A careful but incremental GWTC-4 population analysis whose central claim of three factor-of-two chirp-mass peaks leans on an unpublished, self-cited significance method and a flexible mixture model. the 4 major comments →

arxiv 2510.25579 v3 pith:6EC2VX24 submitted 2025-10-29 astro-ph.HE

Population of Binary Black Holes Inferred from One Hundred and Fifty Gravitational Wave Signals

Vaibhav Tiwari This is my paper
classification astro-ph.HE
keywords gravitational wavesbinary black holespopulation inferencehierarchical mergerschirp mass distributionmass peaksVamanaGWTC-4
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 analyzes 153 gravitational-wave signals from the fourth LIGO-Virgo-KAGRA catalog using a flexible mixture-model framework. It finds that the chirp-mass distribution of binary black holes contains three statistically significant peaks near 8, 14, and 27 solar masses, spaced by roughly a factor of 1.9. This ladder matches the pattern expected if black holes merge repeatedly and their remnants merge again in dense environments. The author argues that hierarchical mergers, not just isolated stellar evolution, may substantially shape the observed mass distribution. If correct, the most precisely measured gravitational-wave parameter—chirp mass—would carry a direct record of black-hole merger history.

Core claim

The paper claims that the chirp-mass distribution of binary black holes, inferred with the Vamana mixture model, exhibits four distinct peaks, with the first three exceeding 99% confidence. The first peak spans a chirp mass of about 6–10 solar masses; the second and third are near 14 and 27 solar masses. The peaks are separated by approximately a factor of 1.9, consistent with the roughly 5% mass loss expected when a merger remnant is retained and merges again. The primary and secondary masses correlate uniquely to produce a strong chirp-mass peak at 14 solar masses without a comparably strong peak in the individual component masses. In addition, black holes in binaries with high effective s

What carries the argument

The central tool is Vamana, a mixture-model framework that fits the joint distribution of primary mass, secondary mass, aligned spins, and redshift evolution using ten multivariate Gaussian components. A key feature is a covariance term between the primary and secondary masses, which lets the model reproduce the chirp-mass distribution accurately—important because chirp mass is the best-measured mass parameter. The supporting identity is the hierarchical-merger ladder: a first-generation black hole of mass m leaves a remnant of roughly 1.9m (about 5% of mass is radiated as gravitational waves), so successive generations produce peaks spaced by a factor of about 1.9. A secondary analysis infe

Load-bearing premise

The load-bearing premise is that the three chirp-mass peaks at 8, 14, and 27 solar masses are real features of the population and not artifacts of the flexible 10-component Gaussian mixture model or of the chosen priors on peak locations.

What would settle it

A decisive test would be to re-run the analysis with a non-parametric method that does not assume Gaussian components, or to wait for the full O4 catalog: if the peaks at 14 and 27 solar masses disappear or shift so that their spacing is no longer consistent with a common factor of about 1.9, the hierarchical interpretation loses its foundation. Similarly, if high-spin binaries' component masses (measured with improved spin precision) no longer cluster at the chirp-mass peaks, the claimed alignment would fail.

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

If this is right

  • If the peaks are real, the binary black hole mass distribution is not a smooth continuum but carries discrete structure, implying distinct formation channels or repeated merger generations.
  • The factor-of-1.9 spacing gives a quantitative prediction: future detections should continue to populate peaks at multiples of the base mass, and the 10–12 solar-mass chirp-mass gap should be partially filled by intergenerational mergers, as already seen in events like GW241110.
  • The mass distribution alone can be used to test hierarchical merger scenarios, even where current spin measurements remain imprecise.
  • The correlation between primary and secondary masses near the 14 solar-mass peak implies specific pairing rules that formation models must reproduce.
  • Once the remaining O4 data roughly double the catalog, the statistical significance of the peaks can be tested decisively.

Where Pith is reading between the lines

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

  • If confirmed, the chirp-mass ladder could serve as a cosmic mass ruler, cross-checked against independent black-hole mass measurements from astrometric surveys to validate the generation interpretation.
  • A testable extension: if hierarchical mergers are responsible, the high-spin subpopulation's merger rate should rise with redshift faster than the low-spin population, since repeated mergers require dense environments that were more common earlier.
  • The fact that the clearest structure appears in chirp mass—the most precisely measured parameter—suggests that future generation studies should prioritize chirp-mass distributions, which may reveal structure that is smeared out in primary-mass analyses.

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

4 major / 4 minor

Summary. The paper applies the Vamana mixture-model framework—ten multivariate Gaussian components for primary/secondary masses with an m1–m2 covariance term, Gaussian aligned-spin components, and a redshift power law—to 153 LVK binary black hole observations from GWTC-4 and some later events. It reports a low-mass overdensity and three peaks in the chirp-mass distribution near 8, 14, and 27 Msun, spaced by approximately a factor 1.9. The paper claims over 99% confidence in the second and third peaks based on the author's earlier method papers, and interprets the peaks plus the component masses of high-spin binaries as evidence for hierarchical mergers. It also reports mass-ratio, aligned-spin, and redshift-evolution distributions.

Significance. If the three-peak chirp-mass structure and the factor-of-two spacing are robust, this is a significant result for BBH formation astrophysics and would add to the case that hierarchical mergers shape a large part of the observed mass distribution. The paper uses a larger catalog than previous analyses, explicitly models an m1–m2 correlation, and makes data products and plotting scripts publicly available. The independent mass selection of the high-spin subset provides a useful internal cross-check. However, the central statistical significance claim rests on a method described only by citation, the flexible mixture model is not tested against simpler alternatives, and the high-spin subpopulation analysis is not selection-corrected. The result is therefore promising but not yet established at the level claimed.

major comments (4)
  1. [§3.1, Fig. 1] The statement 'over 99% confidence in the two peaks around 14 Msun and 26 Msun' is the paper's principal quantitative claim, but it is supported only by 'methodologies from Tiwari (2024) and Tiwari (2025)' with no description of the peak-confidence procedure in this manuscript. In particular, there is no null-model comparison (e.g., a smooth power-law or reduced-component mixture), no posterior-predictive check, and no demonstration that the 10-component covariance mixture cannot produce spurious diagonal ridges. Because the 14-Msun chirp peak is claimed to arise from an m1–m2 correlation (§3.2.1) rather than from marginal peaks, a robustness test against a diagonal-covariance model or a lower-component model is essential. As written, the >99% confidence is not independently assessable.
  2. [§4.1, Appendix B] The inference of the BH mass distribution from high-spin binaries explicitly states 'The inferred distribution has not been corrected for the selection effect' and fixes the mass-ratio distribution and redshift evolution to the standard PE prior. This makes the comparison with the chirp-mass peaks in Fig. 12 a raw data-overdensity comparison, not a population-level measurement. Since the |χ_eff|>0.2 selection depends on spin and mass (selection is effective-spin dependent and mass-dependent through SNR), the absence of a selection correction can shift peak locations and heights. The claim in §5 that this offers 'direct evidence in support of hierarchical scenarios' therefore goes beyond what the current analysis can support. I request a selection-corrected version (using the LVK sensitivity estimate) or, at minimum, a quantitative test of how much selection changes the peak locations.
  3. [§3.1 / Fig. 1] The figure caption says 'four distinct peaks, with the first three exceeding a 99% confidence level,' while the abstract and conclusion describe 'three peaks.' This discrepancy must be resolved; either the fourth peak is being ignored or the claim is misstated. Additionally, the only prior-sensitivity statement concerns the first-peak rate fraction (66% vs. 78% in the footnote). The stability of the second and third peak significances under the alternative mass-location prior p(μ) ∝ 1/μ is not reported. Because the mixture model has 10 components with free weights and covariance, prior sensitivity is a central concern for the multi-peak claim, not a footnote.
  4. [§4] The 'factor of 1.9' ladder is asserted from the fitted peak positions, but no statistical test is given for why the spacing is better than arbitrary or why a hierarchical model is preferred over a smooth multi-component mass distribution. The peaks are outputs of the fit; the hierarchical interpretation is applied post hoc. A concrete assessment—e.g., comparing the evidence for a model with peaks constrained at 1.9 spacing versus free peak locations, or quantifying how well the 1.9 ladder matches the posterior peak distribution—is needed before the statement 'offer direct evidence' can be accepted.
minor comments (4)
  1. [§2 vs. Appendix A] The selection criteria differ between §2 ('mean secondary mass greater than 3 Msun') and Appendix A ('only observations with a mean chirp mass greater than 5 Msun'). Please state whether both criteria were applied and reconcile the text.
  2. [§3.1] The text says the first peak 'contributes approximately 66%' to the merger rate, but the footnote gives 78% under an alternative prior. Please quote the full range in the main text or otherwise prominently caveat the value.
  3. [§4.1] The claim that GW241110_124123 'fills the 10–12 Msun chirp mass gap' should be reconciled with the gap quoted in §3.1 (8.9–11.7 Msun). Also clarify whether this event (and GW241011_233834) are used in the Fig. 1 inference despite not being part of GWTC-4.0.
  4. [Appendix B, Eq. B1] The summands in terms (i)–(iv) have ambiguous indices: the subscript i is used for both the mixture component and an observation index. Use distinct labels for component index, observation index, and posterior-sample index, and define p_PE consistently.

Circularity Check

2 steps flagged

Peak-significance claim rests on undeveloped self-citation, and the hierarchical-merger 'predictions' at 16.2 and 30.7 M_sun are rescaled versions of the same fitted chirp-mass peaks, making the support for the central claim partially circular.

specific steps
  1. self citation load bearing [Sec. 3.1 (Marginalised Distributions in One Dimension)]
    "Using methodologies from Tiwari (2024) and Tiwari (2025), we estimate over 99% confidence in the two peaks around 14 M_sun and 26 M_sun (see Roy et al. (2025) for a detailed study of this peak)."

    The paper's central quantitative claim—over 99% confidence in the 14 and 26 M_sun chirp-mass peaks—is not derived or described in this paper; it is imported from two prior papers by the same author. The current Vamana model is updated (primary/secondary masses instead of chirp mass and mass ratio), but the confidence methodology is not reproduced, so the significance claim reduces to the author's own earlier assertion. This is load-bearing because the factor-of-two peak ladder and the hierarchical interpretation depend on these peaks being significant.

  2. fitted input called prediction [Sec. 4.1 (High-Spin Binary Black Holes)]
    "The first peak in chirp mass is located around 7.5 M_sun. The component mass for a comparable mass binary corresponding to this value is 7.5×2^0.2 = 8.5 M_sun. Consequently, the BHs involved in intra- and inter-generation mergers will have masses distributed around 8.5 M_sun×1.9=16.2 M_sun and 8.5 M_sun×1.9^2=30.7 M_sun."

    The 'predicted' hierarchical-merger masses (16.2 and 30.7 M_sun) are obtained by scaling the observed first chirp-mass peak by the same factor ~1.9 that already separates the fitted chirp-mass peaks (~7.5, ~14, ~27 M_sun). In equal-mass conversion, 16.2 M_sun corresponds exactly to the 14 M_sun chirp peak and 30.7 M_sun to the 27 M_sun peak. The high-spin binaries then compared against these values are drawn from the same 153-event catalog used to infer the chirp-mass peaks, so the subsequent 'alignment' is a within-sample consistency check, not an independent prediction; the expected positions were read off the same data.

full rationale

The underlying population inference with Vamana is a self-contained Bayesian analysis of LVK data, and the peak locations are posterior outputs rather than inputs. However, the paper's headline support for hierarchical mergers is weakened by two circular elements. First, the over-99% confidence in the 14 and 26 M_sun chirp-mass peaks is justified only by citing the author's own earlier works (Tiwari 2024, 2025), with no description or independent reproduction of the method; this is a load-bearing self-citation. Second, the expected high-spin component masses (16.2 and 30.7 M_sun) are not independent hierarchical-merger predictions: they are the observed first chirp peak rescaled by the same 1.9 factor that already separates the fitted chirp peaks, and the high-spin events are part of the same sample from which those peaks were inferred. The paper is candid about limitations—the high-spin selection is 'not the most robust criterion,' the high-spin mass distribution is not selection-corrected, and 'the confidence in the structure is weak'—and this honesty reduces the severity. Because the central claim still has independent content in the data-driven inference and the spin-selected subset is not completely redundant, this is partial circularity rather than a full reduction.

Axiom & Free-Parameter Ledger

6 free parameters · 5 axioms · 0 invented entities

No new astrophysical entities are introduced; hierarchical merger remnants are an existing proposed population. The model's free hyperparameters and stated modeling assumptions carry the analysis, and the central interpretive leap is the post-hoc hierarchical-merger explanation of fitted peaks.

free parameters (6)
  • Gaussian component means for m1 and m2 (10 components) = posterior; peaks near 8.5/16.2/30.7 Msun in high-spin subset; full-population chirp peaks near 8/14/27 Msun
    Uniform priors 5-75 Msun (Table A1); locations determine the reported peaks.
  • Gaussian component widths sigma_m1, sigma_m2 = posterior; prior range 0.05*mu/sqrt(N) to 0.185*mu/sqrt(N)
    Widths control peak sharpness and significance; the chosen prior range affects the significance claims.
  • m1-m2 covariance C per component = posterior; uniform +/-0.5*sigma1*sigma2
    Added to model chirp-mass features; the correlation drives the 14 Msun chirp peak without a comparable component-mass peak.
  • Spin hyperparameters mu_chi, sigma_chi = low-spin |chi|<0.4 and high-spin 0.4<|chi|<0.9 components
    Aligned-spin Gaussians; affect the high-spin subpopulation claims.
  • Redshift power-law index kappa_p and local rate R0 = kappa=0.4-4.7 at 90% credibility; R0=14.0+4.8/-5.9 Gpc^-3 yr^-1
    Power-law merger-rate evolution; uniform prior -5 to 5, per-component offset +/-2.
  • Mixture weights w_i = Dirichlet(1/N) prior
    Relative contributions of the 10 components; first-peak fraction 66-78% depending on the mass-location prior.
axioms (5)
  • domain assumption Hierarchical Bayesian likelihood with LVK sensitivity estimates correctly maps observed events to the underlying population.
    Used throughout Sec 2 and Appendix A; if sensitivity estimates are wrong, inferred peaks and rates shift.
  • domain assumption The 153 selected events (FAR<=1/yr, mean m2>3 Msun, excluding GW190814 and NS-BH candidates) are a representative sample after selection correction.
    Sec 2; selection cuts are choices that affect the inferred mass distribution.
  • domain assumption The BBH mass/spin distribution is well approximated by 10 multivariate Gaussians plus a power-law redshift evolution.
    Appendix A Eq. A1 and Table A1; model form is not derived from astrophysics.
  • ad hoc to paper Peak spacing near 1.9 indicates hierarchical mergers, assuming ~5% gravitational-wave mass loss and remnant retention/re-merger.
    Sec 4; alternative explanations (multiple formation channels, pair-instability pileup) are not modeled or compared.
  • ad hoc to paper For the high-spin subpopulation, fixing mass-ratio and redshift priors and ignoring selection effects does not bias the component-mass peak comparison.
    Appendix B and Sec 4.1; the author notes these limitations but proceeds with the comparison.

pith-pipeline@v1.3.0-alltime-deepseek · 15228 in / 14457 out tokens · 130704 ms · 2026-08-04T07:25:39.378459+00:00 · methodology

0 comments
read the original abstract

The LIGO-Virgo-KAGRA collaborations have reported gravitational wave signals from more than 150 binary black holes in the fourth catalog (GWTC-4). Here, we investigate the population properties of these binary black holes using the mixture-model framework Vamana. We present one-dimensional distributions of masses and spins, explore their correlations, and examine their evolution with redshift. These features may reflect astrophysical processes associated with binary black hole formation channels, although most remain poorly constrained. A notable feature is a peak near $10M_\odot$ in the primary mass and $8M_\odot$ in the chirp mass. Additionally, the primary and secondary masses correlate uniquely, producing pronounced peaks in the chirp mass around $14M_\odot$ and $27M_\odot$. The three peaks are roughly separated by a factor of two. A simple explanation for such well-placed peaks is a hierarchical merger scenario, in which the first peak arises from mergers of black holes of stellar origin, and higher-mass peaks arise from repeated mergers of black holes from lower-mass peaks. Although most binaries do not exhibit the high spins and characteristic mass ratios expected from hierarchical mergers, those that do are associated with the peaks observed in the chirp mass distribution.

Figures

Figures reproduced from arXiv: 2510.25579 by Vaibhav Tiwari.

Figure 1
Figure 1. Figure 1: The (a) chirp, (b) secondary, (c) primary, and (d) component mass distribution of BBHs. Solid curves indicate the median, and shaded bands show the 90% credible intervals for the differential merger rate. All mass parameters show an over-density at the lower-mass end. The chirp mass distribution exhibits four distinct peaks, with the first three exceeding a 99% confidence level. The region between the firs… view at source ↗
Figure 2
Figure 2. Figure 2: Inferred mass-ratio (left) and aligned spin (right) distributions. Solid lines indicate the median, shaded bands the 90% credible interval. The mass ratio is mostly uniform from 0.6 to 0.9. Aligned spins are typically small, ranging from -0.30 to 0.37 at 90% credibility [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Local merger rate (left) and redshift evolution (right). Solid lines indicate the mean, shaded bands the 90% credible interval. The local merger rate is 𝑅0 = 14.0 +4.8 −5.9 Gpc−3 , yr−1 . Redshift evolution is confidently positive. expected to bear a factor of around 1.9, which accounts for the dou￾bling of masses and approximately a 5% loss in mass due to the emission of GWs (Barausse et al. 2012). The lo… view at source ↗
Figure 4
Figure 4. Figure 4: Differential merger rate on the component mass plane (right plot is lower-left quadrant of the left). Three over-dense regions correspond to peaks in the chirp mass distribution. The dashed curve indicates a constant chirp mass track, M = 13.9𝑀⊙, which requires the component masses to correlate uniquely. The over-density around 60–40𝑀⊙ has been interpreted as due to the presence of a gap in the secondary m… view at source ↗
Figure 5
Figure 5. Figure 5: Variation of mass ratio with mass parameters. The primary/total mass combines uniquely with the mass ratio to produce the second chirp mass peak at 14𝑀⊙. The jagged structure arises from a change in the sign of the correlation around 𝑚1 = 19𝑀⊙. In contrast, the third peak mostly consists of binaries with comparable masses [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Variation of aligned spins with mass ratio. The distribution is mostly flat, showing no strong correlation. These observations have mass ratios clustered around 1.0 and 0.5. GW190412 has a mean mass ratio of around 0.25. The high-spin binaries constitute a population substantially different from the BBH population as shown in [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Aligned spin as a function of mass parameters. The solid line represents the median, and the blue band indicates the 90% credible interval. Heavier binaries tend to have larger aligned spins [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Merger rate increase at 𝑧 = 0.5 relative to the local universe. The distribution is mostly flat, with no strong evidence for mass-dependent evolution [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Population-averaged aligned spin (left) and mass ratio (right) as a function of redshift. The distributions are flat, indicating no significant evolution. MNRAS 000, 1–11 (2025) [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The mass ratio (left) and effective spin (right) distribution of BBHs with | ⟨𝜒eff ⟩ | > 0.2. Parameters are estimated using standard prior (Ashton et al. 2019). The prior is approximately uniform for the mass ratio. Combined with the priors on spins, which are assumed to have a uniform magnitude with isotropic orientation, the corresponding prior on 𝜒eff is shown on the figure using a dashed red line. We… view at source ↗
Figure 11
Figure 11. Figure 11: The KS p-values between possible values of mass ratio and effec￾tive spins between the high-spin BBHs used in estimating BH mass distribu￾tion shown in [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The BH mass (left) and aligned spin (right) distribution inferred from BBHs with | ⟨𝜒eff ⟩ | > 0.2. After accommodating the scaling factor of 2 0.2 required to scale the chirp mass to the component masses of a comparable mass binary, the peaks in this figure and [PITH_FULL_IMAGE:figures/full_fig_p009_12.png] view at source ↗

discussion (0)

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

Forward citations

Cited by 13 Pith papers

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

  1. Population Properties of Binary Black Holes with Eccentricity

    astro-ph.HE 2026-02 conditional novelty 8.0

    First joint population inference on binary black hole eccentricity from GWTC-4 bounds the eccentric branching ratio below 5% at 90% confidence, with results consistent with quasi-circular models but highly model-dependent.

  2. Secondary-Mass Features improve Spectral-Siren $H_0$ Constraints

    astro-ph.CO 2026-05 unverdicted novelty 7.0

    A new model emphasizing secondary mass features and pairing transitions improves spectral siren H0 constraints by ~30% using 142 GW events from GWTC-4.0.

  3. Mind the peak: improving cosmological constraints from GWTC-4.0 spectral sirens using semiparametric mass models

    astro-ph.CO 2026-01 unverdicted novelty 7.0

    A data-driven Bspline model for the binary black hole mass distribution from 137 GW events resolves three peaks and improves H0 precision by 12-21% over parametric alternatives.

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

    astro-ph.HE 2026-07 conditional novelty 6.0

    Eccentric waveform-model differences, small per event, accumulate across the GWTC-4 catalog and alter inferred redshift evolution and effective-spin population distributions.

  5. Uncovering Hierarchical Sub-Population of Binary Black Holes

    astro-ph.HE 2026-07 conditional novelty 6.0

    A flexible six-component fit to 259 LIGO/Virgo/KAGRA black-hole mergers finds a roughly geometric sequence of mass peaks but no aligned-spin signal except in the lowest-mass component.

  6. Second-Generation Mass Peak in the Gravitational-Wave Population as a Probe of Globular Clusters

    astro-ph.HE 2026-04 unverdicted novelty 6.0

    Dynamical formation in globular clusters produces a robust second black-hole mass peak at ~70 solar masses from second-generation mergers when the first-generation spectrum is truncated by pair-instability supernovae.

  7. Pushing spectral siren cosmology into the third-generation era: a blinded mock data challenge

    astro-ph.CO 2026-02 conditional novelty 6.0

    Three public spectral-siren pipelines agree on blinded Einstein Telescope mock catalogs and scale to ~10^5 events, forecasting ~2.4% precision on H(z) at z≈1.5.

  8. Signatures of a subpopulation of hierarchical mergers in the GWTC-4 gravitational-wave dataset

    gr-qc 2026-01 unverdicted novelty 6.0

    GWTC-4 data show a transition to nearly all hierarchical mergers above 46 solar masses, with the hierarchical rate peaking at 15.7 solar masses, indicating mass-dependent substructure in black hole spins.

  9. Signatures of a subpopulation of hierarchical mergers in the GWTC-4 gravitational-wave dataset

    gr-qc 2026-01 conditional novelty 6.0

    Using a joint effective-spin and precession-spin model on 155 gravitational-wave events, the authors infer that the hierarchical (second-generation) merger fraction rises sharply above ~46 M_sun and peaks again near 1...

  10. The Chirp-Mass Ladder: A New Rung Emerges

    astro-ph.HE 2026-06 conditional novelty 5.5

    GWTC-5 chirp-mass peaks form a ~1.9-spaced ladder with a new ~19 M⊙ rung matching predicted 2G+3G mergers, unifying prior 1G+2G spin-transition groups under one hierarchical scenario.

  11. Population-level correlations in Bayesian statistics: an illustrative model for gravitational-wave astronomy

    gr-qc 2026-06 unverdicted novelty 5.0

    An idealized Gaussian model demonstrates that single-event correlations inflate uncertainties in population correlations and that catalog-wide correlated biases can be misread as population correlations.

  12. The Chirp-Mass Ladder: A New Rung Emerges

    astro-ph.HE 2026-06 unverdicted novelty 5.0

    The chirp-mass distribution of GW-detected binary black holes shows a ladder of peaks doubling in mass, with a new intermediate peak at 19 solar masses confirming a prior prediction from the hierarchical merger model.

  13. Emergent structure in the binary black hole mass distribution and implications for population-based cosmology

    gr-qc 2026-04 unverdicted novelty 5.0

    B-spline agnostic reconstruction of binary black hole masses from GWTC-4.0 reveals multiple features and a logarithmic hierarchy that impacts Hubble constant measurements, with a low-mass subpopulation isolation metho...

Reference graph

Works this paper leans on

129 extracted references · 18 linked inside Pith · cited by 11 Pith papers

  1. [1]

    P., Abbott R., Abbott T., et al., 2015, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/32/7/074001 , https://ui.adsabs.harvard.edu/\\#abs/2015CQGra..32g4001L 32, 074001

    Aasi J., Abbott B. P., Abbott R., Abbott T., et al., 2015, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/32/7/074001 , https://ui.adsabs.harvard.edu/\\#abs/2015CQGra..32g4001L 32, 074001

    doi:10.1088/0264-9381/32/7/074001 2015

  2. [2]

    P., et al., 2016, @doi [ ] 10.1103/PhysRevLett.116.241103 , https://ui.adsabs.harvard.edu/abs/2016PhRvL.116x1103A 116, 241103

    Abbott B. P., et al., 2016, @doi [ ] 10.1103/PhysRevLett.116.241103 , https://ui.adsabs.harvard.edu/abs/2016PhRvL.116x1103A 116, 241103

    doi:10.1103/physrevlett.116.241103 2016

  3. [3]

    D., Abraham S., et al., 2019, @doi [Physical Review X] 10.1103/PhysRevX.9.031040 , https://ui.adsabs.harvard.edu/abs/2019PhRvX...9c1040A 9, 031040

    Abbott R., Abbott T. D., Abraham S., et al., 2019, @doi [Physical Review X] 10.1103/PhysRevX.9.031040 , https://ui.adsabs.harvard.edu/abs/2019PhRvX...9c1040A 9, 031040

    doi:10.1103/physrevx.9.031040 2019

  4. [4]

    Abbott R., et al., 2020, @doi [ ] 10.1103/PhysRevD.102.043015 , https://ui.adsabs.harvard.edu/abs/2020PhRvD.102d3015A 102, 043015

    doi:10.1103/physrevd.102.043015 2020

  5. [5]

    D., Abraham S., et al., 2021, @doi [Physical Review X] 10.1103/PhysRevX.11.021053 , https://ui.adsabs.harvard.edu/abs/2021PhRvX..11b1053A 11, 021053

    Abbott R., Abbott T. D., Abraham S., et al., 2021, @doi [Physical Review X] 10.1103/PhysRevX.11.021053 , https://ui.adsabs.harvard.edu/abs/2021PhRvX..11b1053A 11, 021053

    doi:10.1103/physrevx.11.021053 2021

  6. [6]

    D., Acernese F., et al., 2023a, @doi [Physical Review X] 10.1103/PhysRevX.13.011048 , https://ui.adsabs.harvard.edu/abs/2023PhRvX..13a1048A 13, 011048

    Abbott R., Abbott T. D., Acernese F., et al., 2023a, @doi [Physical Review X] 10.1103/PhysRevX.13.011048 , https://ui.adsabs.harvard.edu/abs/2023PhRvX..13a1048A 13, 011048

    doi:10.1103/physrevx.13.011048

  7. [7]

    D., Acernese F., et al., 2023b, @doi [Physical Review X] 10.1103/PhysRevX.13.041039 , https://ui.adsabs.harvard.edu/abs/2023PhRvX..13d1039A 13, 041039

    Abbott R., Abbott T. D., Acernese F., et al., 2023b, @doi [Physical Review X] 10.1103/PhysRevX.13.041039 , https://ui.adsabs.harvard.edu/abs/2023PhRvX..13d1039A 13, 041039

    doi:10.1103/physrevx.13.041039

  8. [8]

    D., Acernese F., et al., 2024, @doi [ ] 10.1103/PhysRevD.109.022001 , https://ui.adsabs.harvard.edu/abs/2024PhRvD.109b2001A 109, 022001

    Abbott R., Abbott T. D., Acernese F., et al., 2024, @doi [ ] 10.1103/PhysRevD.109.022001 , https://ui.adsabs.harvard.edu/abs/2024PhRvD.109b2001A 109, 022001

    doi:10.1103/physrevd.109.022001 2024

  9. [9]

    Acernese F., et al., 2015, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/32/2/024001 , https://ui.adsabs.harvard.edu/abs/2015CQGra..32b4001A 32, 024001

    doi:10.1088/0264-9381/32/2/024001 2015

  10. [10]

    D., Thrane E., 2023, @doi [ ] 10.3847/1538-4357/acf763 , https://ui.adsabs.harvard.edu/abs/2023ApJ...958...13A 958, 13

    Adamcewicz C., Lasky P. D., Thrane E., 2023, @doi [ ] 10.3847/1538-4357/acf763 , https://ui.adsabs.harvard.edu/abs/2023ApJ...958...13A 958, 13

    doi:10.3847/1538-4357/acf763 2023

  11. [11]

    D., Thrane E., 2024, @doi [ ] 10.3847/2041-8213/ad2df2 , https://ui.adsabs.harvard.edu/abs/2024ApJ...964L...6A 964, L6

    Adamcewicz C., Galaudage S., Lasky P. D., Thrane E., 2024, @doi [ ] 10.3847/2041-8213/ad2df2 , https://ui.adsabs.harvard.edu/abs/2024ApJ...964L...6A 964, L6

    doi:10.3847/2041-8213/ad2df2 2024

  12. [12]

    Ade P. A. R., Aghanim N., Arnaud M., et al., 2016, @doi [ ] 10.1051/0004-6361/201525830 , https://ui.adsabs.harvard.edu/abs/2016A&A...594A..13P 594, A13

    doi:10.1051/0004-6361/201525830 2016

  13. [13]

    arXiv:2505.22739

    Afroz S., Mukherjee S., 2025a, @doi [arXiv e-prints] 10.48550/arXiv.2505.22739 , https://ui.adsabs.harvard.edu/abs/2025arXiv250522739A p. arXiv:2505.22739

    doi:10.48550/arxiv.2505.22739

  14. [14]

    arXiv:2509.09123

    Afroz S., Mukherjee S., 2025b, @doi [arXiv e-prints] 10.48550/arXiv.2509.09123 , https://ui.adsabs.harvard.edu/abs/2025arXiv250909123A p. arXiv:2509.09123

    Pith/arXiv arXiv doi:10.48550/arxiv.2509.09123

  15. [15]

    Ajith P., et al., 2011, @doi [ ] 10.1103/PhysRevLett.106.241101 , https://ui.adsabs.harvard.edu/abs/2011PhRvL.106x1101A 106, 241101

    doi:10.1103/physrevlett.106.241101 2011

  16. [16]

    Akutsu T., et al., 2021, @doi [Progress of Theoretical and Experimental Physics] 10.1093/ptep/ptaa125 , https://ui.adsabs.harvard.edu/abs/2021PTEP.2021eA101A 2021, 05A101

    doi:10.1093/ptep/ptaa125 2021

  17. [17]

    Antonini F., Gieles M., Gualandris A., 2019, @doi [ ] 10.1093/mnras/stz1149 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.5008A 486, 5008

    doi:10.1093/mnras/stz1149 2019

  18. [18]

    M., Callister T., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2406.19044 , https://ui.adsabs.harvard.edu/abs/2024arXiv240619044A p

    Antonini F., Romero-Shaw I. M., Callister T., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2406.19044 , https://ui.adsabs.harvard.edu/abs/2024arXiv240619044A p. arXiv:2406.19044

    Pith/arXiv arXiv doi:10.48550/arxiv.2406.19044 2024

  19. [19]

    arXiv:2509.04637

    Antonini F., Romero-Shaw I., Callister T., Dosopoulou F., Chattopadhyay D., Gieles M., Mapelli M., 2025a, @doi [arXiv e-prints] 10.48550/arXiv.2509.04637 , https://ui.adsabs.harvard.edu/abs/2025arXiv250904637A p. arXiv:2509.04637

    Pith/arXiv arXiv doi:10.48550/arxiv.2509.04637

  20. [20]

    M., Chattopadhyay D., 2025b, @doi [ ] 10.1103/nxnr-pdyx , https://ui.adsabs.harvard.edu/abs/2025PhRvD.112f3040A 112, 063040

    Antonini F., Callister T., Dosopoulou F., Romero-Shaw I. M., Chattopadhyay D., 2025b, @doi [ ] 10.1103/nxnr-pdyx , https://ui.adsabs.harvard.edu/abs/2025PhRvD.112f3040A 112, 063040

    doi:10.1103/nxnr-pdyx

  21. [21]

    Ashton G., et al., 2019, @doi [The Astrophysical Journal Supplement Series] 10.3847/1538-4365/ab06fc , https://ui.adsabs.harvard.edu/abs/2019ApJS..241...27A 241, 27

    doi:10.3847/1538-4365/ab06fc 2019

  22. [22]

    Astropy Collaboration Astropy Project Contributors 2022, @doi [ ] 10.3847/1538-4357/ac7c74 , https://ui.adsabs.harvard.edu/abs/2022ApJ...935..167A 935, 167

    doi:10.3847/1538-4357/ac7c74 2022

  23. [23]

    arXiv:2412.03461

    Baibhav V., Kalogera V., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2412.03461 , https://ui.adsabs.harvard.edu/abs/2024arXiv241203461B p. arXiv:2412.03461

    Pith/arXiv arXiv doi:10.48550/arxiv.2412.03461 2024

  24. [24]

    Baibhav V., Doctor Z., Kalogera V., 2023, @doi [ ] 10.3847/1538-4357/acbf4c , https://ui.adsabs.harvard.edu/abs/2023ApJ...946...50B 946, 50

    doi:10.3847/1538-4357/acbf4c 2023

  25. [25]

    Baird E., Fairhurst S., Hannam M., 2013, Phys. Rev. D, 87, 024035

    2013

  26. [26]

    A., Doctor Z., Kalogera V., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.06712 , https://ui.adsabs.harvard.edu/abs/2025arXiv250106712B p

    Banagiri S., Callister T. A., Doctor Z., Kalogera V., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.06712 , https://ui.adsabs.harvard.edu/abs/2025arXiv250106712B p. arXiv:2501.06712

    Pith/arXiv arXiv doi:10.48550/arxiv.2501.06712 2025

  27. [27]

    Banerjee S., 2021, @doi [ ] 10.1093/mnras/staa2392 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.3002B 500, 3002

    doi:10.1093/mnras/staa2392 2021

  28. [28]

    Barausse E., Morozova V., Rezzolla L., 2012, @doi [ ] 10.1088/0004-637X/758/1/63 , https://ui.adsabs.harvard.edu/abs/2012ApJ...758...63B 758, 63

    doi:10.1088/0004-637x/758/1/63 2012

  29. [29]

    S., et al., 2020, @doi [ ] 10.1051/0004-6361/201936204 , https://ui.adsabs.harvard.edu/abs/2020A&A...635A..97B 635, A97

    Bavera S. S., et al., 2020, @doi [ ] 10.1051/0004-6361/201936204 , https://ui.adsabs.harvard.edu/abs/2020A&A...635A..97B 635, A97

    doi:10.1051/0004-6361/201936204 2020

  30. [30]

    Belczynski K., et al., 2020, @doi [ ] 10.1051/0004-6361/201936528 , https://ui.adsabs.harvard.edu/abs/2020A&A...636A.104B 636, A104

    doi:10.1051/0004-6361/201936528 2020

  31. [31]

    arXiv:2502.04278

    Biscoveanu S., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2502.04278 , https://ui.adsabs.harvard.edu/abs/2025arXiv250204278B p. arXiv:2502.04278

    Pith/arXiv arXiv doi:10.48550/arxiv.2502.04278 2025

  32. [32]

    A., Haster C.-J., Ng K

    Biscoveanu S., Callister T. A., Haster C.-J., Ng K. K. Y., Vitale S., Farr W. M., 2022, @doi [ ] 10.3847/2041-8213/ac71a8 , https://ui.adsabs.harvard.edu/abs/2022ApJ...932L..19B 932, L19

    doi:10.3847/2041-8213/ac71a8 2022

  33. [33]

    A., Farr W

    Callister T. A., Farr W. M., 2024, @doi [Physical Review X] 10.1103/PhysRevX.14.021005 , https://ui.adsabs.harvard.edu/abs/2024PhRvX..14b1005C 14, 021005

    doi:10.1103/physrevx.14.021005 2024

  34. [34]

    A., Haster C.-J., Ng K

    Callister T. A., Haster C.-J., Ng K. K. Y., Vitale S., Farr W. M., 2021, @doi [ ] 10.3847/2041-8213/ac2ccc , https://ui.adsabs.harvard.edu/abs/2021ApJ...922L...5C 922, L5

    doi:10.3847/2041-8213/ac2ccc 2021

  35. [35]

    M., 2023, @doi [ ] 10.1093/mnras/stad3048 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.4908C 526, 4908

    Chattopadhyay D., Stegmann J., Antonini F., Barber J., Romero-Shaw I. M., 2023, @doi [ ] 10.1093/mnras/stad3048 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.4908C 526, 4908

    doi:10.1093/mnras/stad3048 2023

  36. [36]

    E., 1994, @doi [Phys

    Cutler C., Flanagan E. E., 1994, @doi [Phys. Rev. D] 10.1103/PhysRevD.49.2658 , 49, 2658

    doi:10.1103/physrevd.49.2658 1994

  37. [37]

    Damour T., 2001, @doi [ ] 10.1103/PhysRevD.64.124013 , https://ui.adsabs.harvard.edu/abs/2001PhRvD..64l4013D 64, 124013

    doi:10.1103/physrevd.64.124013 2001

  38. [38]

    E., Farr B., 2020, @doi [ ] 10.3847/1538-4357/ab7fac , https://ui.adsabs.harvard.edu/abs/2020ApJ...893...35D 893, 35

    Doctor Z., Wysocki D., O'Shaughnessy R., Holz D. E., Farr B., 2020, @doi [ ] 10.3847/1538-4357/ab7fac , https://ui.adsabs.harvard.edu/abs/2020ApJ...893...35D 893, 35

    doi:10.3847/1538-4357/ab7fac 2020

  39. [39]

    Edelman B., Farr B., Doctor Z., 2023, @doi [ ] 10.3847/1538-4357/acb5ed , https://ui.adsabs.harvard.edu/abs/2023ApJ...946...16E 946, 16

    doi:10.3847/1538-4357/acb5ed 2023

  40. [40]

    El-Badry K., et al., 2023a, @doi [ ] 10.1093/mnras/stac3140 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.518.1057E 518, 1057

    doi:10.1093/mnras/stac3140

  41. [41]

    El-Badry K., et al., 2023b, @doi [ ] 10.1093/mnras/stad799 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.4323E 521, 4323

    doi:10.1093/mnras/stad799

  42. [42]

    E., Galaudage S., 2022, @doi [ ] 10.3847/1538-4357/ac5f03 , https://ui.adsabs.harvard.edu/abs/2022ApJ...931..108F 931, 108

    Farah A., Fishbach M., Essick R., Holz D. E., Galaudage S., 2022, @doi [ ] 10.3847/1538-4357/ac5f03 , https://ui.adsabs.harvard.edu/abs/2022ApJ...931..108F 931, 108

    doi:10.3847/1538-4357/ac5f03 2022

  43. [43]

    M., Edelman B., Zevin M., Fishbach M., Mar \' a Ezquiaga J., Farr B., Holz D

    Farah A. M., Edelman B., Zevin M., Fishbach M., Mar \' a Ezquiaga J., Farr B., Holz D. E., 2023, @doi [ ] 10.3847/1538-4357/aced02 , https://ui.adsabs.harvard.edu/abs/2023ApJ...955..107F 955, 107

    doi:10.3847/1538-4357/aced02 2023

  44. [44]

    M., Stevenson S., Miller M

    Farr W. M., Stevenson S., Miller M. C., Mand el I., Farr B., Vecchio A., 2017, @doi [ ] 10.1038/nature23453 , https://ui.adsabs.harvard.edu/abs/2017Natur.548..426F 548, 426

    doi:10.1038/nature23453 2017

  45. [45]

    E., Farr W

    Fishbach M., Holz D. E., Farr W. M., 2018, @doi [Astrophys. J. Lett.] 10.3847/2041-8213/aad800 , https://ui.adsabs.harvard.edu/abs/2018ApJ...863L..41F 863, L41

    doi:10.3847/2041-8213/aad800 2018

  46. [46]

    E., 2020, @doi [ ] 10.3847/2041-8213/aba7b6 , https://ui.adsabs.harvard.edu/abs/2020ApJ...899L...8F 899, L8

    Fishbach M., Essick R., Holz D. E., 2020, @doi [ ] 10.3847/2041-8213/aba7b6 , https://ui.adsabs.harvard.edu/abs/2020ApJ...899L...8F 899, L8

    doi:10.3847/2041-8213/aba7b6 2020

  47. [47]

    A., Hoyle F., 1964, @doi [ ] 10.1086/190103 , https://ui.adsabs.harvard.edu/abs/1964ApJS....9..201F 9, 201

    Fowler W. A., Hoyle F., 1964, @doi [ ] 10.1086/190103 , https://ui.adsabs.harvard.edu/abs/1964ApJS....9..201F 9, 201

    doi:10.1086/190103 1964

  48. [48]

    A., 2023, @doi [ ] 10.3847/1538-4357/acd9c9 , https://ui.adsabs.harvard.edu/abs/2023ApJ...951..129F 951, 129

    Fragione G., Rasio F. A., 2023, @doi [ ] 10.3847/1538-4357/acd9c9 , https://ui.adsabs.harvard.edu/abs/2023ApJ...951..129F 951, 129

    doi:10.3847/1538-4357/acd9c9 2023

  49. [49]

    Gaia Collaboration et al., 2024, @doi [ ] 10.1051/0004-6361/202449763 , https://ui.adsabs.harvard.edu/abs/2024A&A...686L...2G 686, L2

    doi:10.1051/0004-6361/202449763 2024

  50. [50]

    Galaudage S., Talbot C., Nagar T., Jain D., Thrane E., Mandel I., 2021, @doi [ ] 10.3847/2041-8213/ac2f3c , https://ui.adsabs.harvard.edu/abs/2021ApJ...921L..15G 921, L15

    doi:10.3847/2041-8213/ac2f3c 2021

  51. [51]

    Gennari V., Mastrogiovanni S., Tamanini N., Marsat S., Pierra G., 2025, @doi [ ] 10.1103/ftw9-7xd5 , https://ui.adsabs.harvard.edu/abs/2025PhRvD.111l3046G 111, 123046

    doi:10.1103/ftw9-7xd5 2025

  52. [52]

    Gerosa D., Fishbach M., 2021, @doi [Nature Astronomy] 10.1038/s41550-021-01398-w , https://ui.adsabs.harvard.edu/abs/2021NatAs...5..749G 5, 749

    doi:10.1038/s41550-021-01398-w 2021

  53. [53]

    J., 2024, @doi [ ] 10.1093/mnras/stae2198 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.534.1868G 534, 1868

    Ghodla S., Eldridge J. J., 2024, @doi [ ] 10.1093/mnras/stae2198 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.534.1868G 534, 1868

    doi:10.1093/mnras/stae2198 2024

  54. [54]

    arXiv:2304.01288

    Godfrey J., Edelman B., Farr B., 2023, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2023arXiv230401288G p. arXiv:2304.01288

    Pith/arXiv arXiv 2023

  55. [55]

    Golomb J., Talbot C., 2023, @doi [ ] 10.1103/PhysRevD.108.103009 , https://ui.adsabs.harvard.edu/abs/2023PhRvD.108j3009G 108, 103009

    doi:10.1103/physrevd.108.103009 2023

  56. [56]

    arXiv:2312.03973

    Golomb J., Isi M., Farr W., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2312.03973 , https://ui.adsabs.harvard.edu/abs/2023arXiv231203973G p. arXiv:2312.03973

    Pith/arXiv arXiv doi:10.48550/arxiv.2312.03973 2023

  57. [57]

    D., Thrane E., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2509.09876 , https://ui.adsabs.harvard.edu/abs/2025arXiv250909876G p

    Guttman N., Payne E., Lasky P. D., Thrane E., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2509.09876 , https://ui.adsabs.harvard.edu/abs/2025arXiv250909876G p. arXiv:2509.09876

    Pith/arXiv arXiv doi:10.48550/arxiv.2509.09876 2025

  58. [58]

    R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

    Harris C. R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

    doi:10.1038/s41586-020-2649-2 2020

  59. [59]

    arXiv:2406.16813

    Heinzel J., Mould M., \'A lvarez-L \'o pez S., Vitale S., 2024a, @doi [arXiv e-prints] 10.48550/arXiv.2406.16813 , https://ui.adsabs.harvard.edu/abs/2024arXiv240616813H p. arXiv:2406.16813

    Pith/arXiv arXiv doi:10.48550/arxiv.2406.16813

  60. [60]

    arXiv:2406.16844

    Heinzel J., Mould M., Vitale S., 2024b, @doi [arXiv e-prints] 10.48550/arXiv.2406.16844 , https://ui.adsabs.harvard.edu/abs/2024arXiv240616844H p. arXiv:2406.16844

    Pith/arXiv arXiv doi:10.48550/arxiv.2406.16844

  61. [61]

    Heinzel J., Vitale S., Biscoveanu S., 2024c, @doi [ ] 10.1103/PhysRevD.109.103006 , https://ui.adsabs.harvard.edu/abs/2024PhRvD.109j3006H 109, 103006

    doi:10.1103/physrevd.109.103006

  62. [62]

    E., Bird S., Dawson W

    Ho M.-F., Perkins S. E., Bird S., Dawson W. A., Golovich N., Lu J. R., McGill P., 2024, @doi [ ] 10.1103/PhysRevD.110.063031 , https://ui.adsabs.harvard.edu/abs/2024PhRvD.110f3031H 110, 063031

    doi:10.1103/physrevd.110.063031 2024

  63. [63]

    Hoy C., Fairhurst S., Mandel I., 2025, @doi [ ] 10.1103/PhysRevD.111.023037 , https://ui.adsabs.harvard.edu/abs/2025PhRvD.111b3037H 111, 023037

    doi:10.1103/physrevd.111.023037 2025

  64. [64]

    D., 2007, @doi [Computing in Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

    Hunter J. D., 2007, @doi [Computing in Science & Engineering] 10.1109/MCSE.2007.55 , 9, 90

    doi:10.1109/mcse.2007.55 2007

  65. [65]

    arXiv:2411.02252

    Hussain A., Isi M., Zimmerman A., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2411.02252 , https://ui.adsabs.harvard.edu/abs/2024arXiv241102252H p. arXiv:2411.02252

    Pith/arXiv arXiv doi:10.48550/arxiv.2411.02252 2024

  66. [66]

    Kinugawa T., Nakamura T., Nakano H., 2024, @doi [ ] 10.1093/mnras/stae1460 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.531.4725K 531, 4725

    doi:10.1093/mnras/stae1460 2024

  67. [67]

    E., Koursoumpa E

    Koloniari A. E., Koursoumpa E. C., Nousi P., Lampropoulos P., Passalis N., Tefas A., Stergioulas N., 2025, @doi [Mach. Learn. Sci. Tech.] 10.1088/2632-2153/adb5ed , 6, 015054

    doi:10.1088/2632-2153/adb5ed 2025

  68. [68]

    Kumar P., Dent T., 2024, @doi [Phys. Rev. D] 10.1103/PhysRevD.110.043036 , 110, 043036

    doi:10.1103/physrevd.110.043036 2024

  69. [69]

    LIGO Scientific Collaboration Virgo Collaboration 2019, @doi [Astrophys. J. Lett.] 10.3847/2041-8213/ab3800 , https://ui.adsabs.harvard.edu/abs/2019ApJ...882L..24A 882, L24

    doi:10.3847/2041-8213/ab3800 2019

  70. [70]

    LIGO Scientific Collaboration VIRGO Collaboration Kagra Collaboration 2023, @doi [ ] 10.3847/1538-4365/acdc9f , https://ui.adsabs.harvard.edu/abs/2023ApJS..267...29A 267, 29

    doi:10.3847/1538-4365/acdc9f 2023

  71. [71]

    LIGO Scientific Collaboration VIRGO Collaboration Kagra Collaboration 2025, @doi [ ] 10.3847/2041-8213/ae0d54 , 993, L21

    doi:10.3847/2041-8213/ae0d54 2025

  72. [72]

    Li Y.-J., Wang Y.-Z., Han M.-Z., Tang S.-P., Yuan Q., Fan Y.-Z., Wei D.-M., 2021, @doi [ ] 10.3847/1538-4357/ac0971 , https://ui.adsabs.harvard.edu/abs/2021ApJ...917...33L 917, 33

    doi:10.3847/1538-4357/ac0971 2021

  73. [73]

    Li Y.-J., Wang Y.-Z., Tang S.-P., Fan Y.-Z., 2024, @doi [ ] 10.1103/PhysRevLett.133.051401 , https://ui.adsabs.harvard.edu/abs/2024PhRvL.133e1401L 133, 051401

    doi:10.1103/physrevlett.133.051401 2024

  74. [74]

    J., 2004, AIP Conf

    Loredo T. J., 2004, AIP Conf. Proc., 735

    2004

  75. [75]

    Lousto C. O., Campanelli M., Zlochower Y., Nakano H., 2010, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/27/11/114006 , https://ui.adsabs.harvard.edu/abs/2010CQGra..27k4006L 27, 114006

    doi:10.1088/0264-9381/27/11/114006 2010

  76. [76]

    S., Arun K

    Mahapatra P., Chattopadhyay D., Gupta A., Favata M., Sathyaprakash B. S., Arun K. G., 2025, @doi [ ] 10.1103/PhysRevD.111.023013 , https://ui.adsabs.harvard.edu/abs/2025PhRvD.111b3013M 111, 023013

    doi:10.1103/physrevd.111.023013 2025

  77. [77]

    S., 2022, @doi [Living Reviews in Relativity] 10.1007/s41114-021-00034-3 , https://ui.adsabs.harvard.edu/abs/2022LRR....25....1M 25, 1

    Mandel I., Broekgaarden F. S., 2022, @doi [Living Reviews in Relativity] 10.1007/s41114-021-00034-3 , https://ui.adsabs.harvard.edu/abs/2022LRR....25....1M 25, 1

    doi:10.1007/s41114-021-00034-3 2022

  78. [78]

    M., Gair J

    Mandel I., Farr W. M., Gair J. R., 2019, @doi [Mon. Not. R. Astron. Soc.] 10.1093/mnras/stz896 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.1086M 486, 1086

    doi:10.1093/mnras/stz896 2019

  79. [79]

    Mapelli M., et al., 2021, @doi [ ] 10.1093/mnras/stab1334 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505..339M 505, 339

    doi:10.1093/mnras/stab1334 2021

  80. [80]

    C., 2022, @doi [ ] 10.1093/mnras/stac422 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.5797M 511, 5797

    Mapelli M., Bouffanais Y., Santoliquido F., Arca Sedda M., Artale M. C., 2022, @doi [ ] 10.1093/mnras/stac422 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.5797M 511, 5797

    doi:10.1093/mnras/stac422 2022

Showing first 80 references.