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Progress toward the detection of the gravitational-wave background from stellar-mass binary black holes: a mock data challenge

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A fully functional pipeline for the phase-coherent search of the gravitational-wave background from stellar-mass binary black holes is demonstrated on mock and time-reversed real data, after correcting noise-spectrum uncertainty…

desk verdict A real step toward a phase-coherent BBH background search, with an honest but unproven plug-in approximation at its core; deserves refereeing, not a desk reject. read the letter →

arxiv 2506.14179 v1 pith:3QHL2Z2N submitted 2025-06-17 gr-qc astro-ph.HE

Xiao-Xiao Kou , Muhammed Saleem , Vuk Mandic , Colm Talbot , Eric Thrane This is my paper
classification gr-qcastro-ph.HE
keywords gravitational-wavebackgroundstellar-massbinaryblackholesphase-coherentsearchstochasticdutycyclefinite-durationeffectsnoisePSDuncertaintyglitchmitigation
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

This paper aims to show that the phase-coherent search for the gravitational-wave background produced by unresolved stellar-mass binary black holes can be carried out on realistic detector data, not just idealized simulations. The search combines per-segment Bayesian evidences into a mixture-model likelihood for the duty cycle xi, the fraction of 4-second data segments that contain a sub-threshold binary black hole signal. The authors identify three systematic errors that the original proposal did not address, namely uncertainty in the noise power spectral density, finite-duration windowing correlations, and transient glitches, and show how to remove or model each one. On mock time-domain data and on time-reversed real data with injected signals, the duty-cycle posterior recovers the injected values, including xi = 0 with no false positive. If the pipeline holds up, the payoff is a search that could reach detection roughly a thousand times faster than the cross-correlation upper limits set to date.

What carries the argument

The central object is the duty cycle xi, the fraction of 4-second segments containing a sub-threshold binary black hole signal, inferred from the product of mixture-model segment likelihoods xi Z_s + (1-xi) Z_n. The argument is carried by three corrections: a noise-marginalized likelihood that integrates out power-spectral-density uncertainty; an effective-PSD approximation that lets that marginalization be combined with the finite-duration covariance correction at per-sample cost; and a time-domain Toeplitz covariance likelihood computed from the noise auto-correlation function, which makes the finite-duration correction fast enough to apply via importance-sampling reweighting. A glitch mixture model adds incoherent single-detector terms so that transient noise artifacts are absorbed as nuisance parameters rather than counted as astrophysical signal.

What would settle it

Run the same 3000-segment mock analyses with known injected xi through both the approximate effective-PSD plus reweighting pipeline and a direct, much more expensive computation that explicitly marginalizes over the noise PSD inside the full finite-duration likelihood; if the two duty-cycle posteriors differ by more than the statistical scatter expected from the number of segments, the approximation is biased. A cleaner version is to repeat the xi = 0 no-injection case on many independent time-reversed real-noise datasets and check whether the 90 percent credible interval contains zero in roughly 90 percent of trials.

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Extended reading notes

Core claim

The central claim is that the phase-coherent method is no longer just a theoretical proposal: a fully functional pipeline exists and works on data that include the main departures from the idealized Gaussian setting. For each segment, the pipeline computes signal and noise evidences with a noise-marginalized likelihood, then reweights those evidences with a finite-duration likelihood that accounts for the full covariance between frequency bins, evaluated efficiently in the time domain using the noise auto-correlation function. To make both corrections affordable, the authors define an effective noise PSD by equating the noise-marginalized likelihood with a Whittle likelihood and solving per posterior sample, then use importance-sampling reweighting to obtain corrected evidences. They show that this removes the duty-cycle bias for injected xi = 0, 0.05, and 0.1 on time-domain mock data, and that a glitch-aware mixture likelihood prevents non-Gaussian noise artifacts in time-reversed real data from producing a false-positive background. The authors state explicitly that the effective-PSD reweighting has no proven unbiasedness guarantee, leaving that as an open question.

Load-bearing premise

The pipeline's combined systematic correction replaces the true noise-marginalized finite-duration likelihood with an effective power-spectral-density approximation and then reweights posterior samples as if the approximate posterior were the target; the paper states there is no proof this estimator is unbiased, and if it is biased on real data the recovered duty-cycle values shown here would not carry over.

Editorial extensions

If this is right

  • A phase-coherent search on real 4-second segments is computationally feasible; the paper estimates that ten days of the most sensitive detector data would require about 5e5 core hours, under a month on a 1000-core cluster.
  • Excluding resolvable binaries with network signal-to-noise ratio above threshold only slightly broadens the duty-cycle posterior once selection effects are accounted for, so the search can be protected against double-counting loud events.
  • The pipeline recovers an injected duty cycle of zero on time-reversed real data, meaning a future positive detection would not be attributable to the modeled noise and glitch systematics.
  • Because the method uses waveform phase information, it can in principle measure the background at a level that cross-correlation searches cannot reach, giving access to binaries at cosmological distances.
  • The glitch-modeling analysis separates the astrophysical duty cycle from per-detector glitch duty cycles, so weak unclassified glitches can be handled without biasing the background estimate.

Reading between the lines

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

  • If the effective-PSD reweighting is eventually proven unbiased or replaced by an exact marginalization, the same pipeline could be applied directly to full observing-run data, and the first detection claim would become a measurement of the binary black hole merger rate at redshifts beyond individually resolvable events.
  • The paper's own admission that the reweighting lacks a guarantee suggests a targeted validation: compare duty-cycle posteriors from the approximation against exact noise-marginalized finite-duration likelihoods on small segment counts where the exact computation is feasible.
  • The glitch model is deliberately conservative, treating glitches as binary black hole waveforms; testing the pipeline against more realistic non-binary-black-hole glitch morphologies would show whether this conservatism is sufficient or excessive.
  • The prior-mismatch issue flagged in the discussion implies the inferred duty cycle is conditional on the assumed mass and spin population; extending the mixture to allow population hyper-parameters, as the paper suggests, would turn the search into a population measurement rather than a detection-only statistic.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper presents a mock data challenge for the phase-coherent search for a gravitational-wave background from stellar-mass binary black holes. The authors implement the Smith & Thrane mixture-model framework and extend it to account for two systematic effects: uncertainty in the noise power spectral density and finite-duration (off-diagonal covariance) effects. They propose a PSD-marginalized likelihood, a Toeplitz-based time-domain finite-duration likelihood, and an importance-sampling reweighting step in which an effective PSD is derived per posterior sample via Eq. (21). The pipeline is applied to frequency-domain and time-domain simulated data sets with injected duty cycles ξ=0, 0.05, 0.1, and to time-reversed LIGO O3b data with and without a glitch model. The paper reports that the systematic bias is removed and that glitch modeling prevents false positives.

Significance. If the effective-PSD reweighting is unbiased, this paper represents an important step toward a phase-coherent GWB search, potentially an order-of-magnitude more sensitive than cross-correlation. The work combines several previously separate techniques, demonstrates them on realistic simulated data, and goes as far as time-reversed O3b data. The authors are transparent about the main limitation: Eq. (21) has no proof of unbiasedness, and the posterior width is known to be underestimated. Because the central demonstration relies entirely on this approximation, the significance is conditional on additional validation. The manuscript is also commendable for explicitly stating its open problems and for providing detailed appendices with derivations.

major comments (3)
  1. [IV C, Eq. (21)] The effective-PSD step equates, per frequency bin, the PSD-marginalized likelihood with a Whittle form and then uses that effective PSD to build the full finite-duration covariance matrix. Matching one-dimensional marginals does not guarantee that the joint off-diagonal structure of the noise-marginalized finite-duration likelihood is reproduced, and the off-diagonal terms are exactly the finite-duration correction. The paper's own statement that 'we do not have any guarantees that we have an unbiased estimator of the target likelihood' is an explicit acknowledgment of this gap. I recommend a targeted validation: on a small number of segments, compute the fully marginalized finite-duration likelihood (for example by sampling over PSD realizations or by diagonalizing the covariance) and compare the resulting evidence values and parameter posteriors against the reweighted ones.
  2. [Footnote 4; Figs. 6 and 7] The posterior width is acknowledged to be underestimated, but the magnitude is not quantified and no coverage test is provided. The demonstration of 'no false positive' in the ξ=0 panels is a single realization; it does not establish that the pipeline controls the false-positive rate. I ask for a coverage study: repeat the ξ=0 analysis over many independent noise realizations and report the fraction of times the posterior excludes zero at, say, 90% credibility. Without this, the claim that the pipeline avoids false positives in real data is not quantitatively supported.
  3. [III C, footnote 3] The unexplained bias in Bilby's time-of-coalescence marginalization is a known systematic in the pipeline. Since real data require a principled treatment of the unknown coalescence time, the paper should either identify the origin of this bias or demonstrate that the non-marginalized sampling approach is unbiased in the mock data (for example by comparing parameter posteriors with and without the analytic marginalization on a subset of segments). As written, the workaround is ad hoc and could hide a problem that will reappear in a real-data analysis.
minor comments (5)
  1. [II B and Appendix F] The SNR threshold for excluding resolvable binaries is inconsistent: Section II B states network SNR ≥ 12, while Appendix F uses matched-filter SNR > 8 for the selection cut and for pdet. Please clarify which threshold is actually used and ensure the text, equations, and figure captions are consistent.
  2. [Abstract and Introduction] The notation 'O(105)' appears in the compiled text; it should be typeset as O(10^5). This occurs in the abstract and in the first sentence of the introduction.
  3. [Appendix D and Fig. 11] The comparison of the Whittle likelihood with the full time-domain likelihood in Appendix D uses a loud signal with SNR 17, which is not the regime of interest for this search. Figure 11 shows differences for noise-only segments, but the text could more explicitly state that the Whittle approximation is inadequate for the sub-threshold regime, which is why the reweighting is needed.
  4. [Fig. 12] The caption refers to 'blue dashed lines' as 'best-fit ξ values' from the individual detector analyses, but the figure shows posterior distributions. Please clarify whether these are posterior modes or maximum-likelihood estimates, and define the convention in the caption.
  5. [Throughout] There are several typographical and grammatical issues, e.g., 'each segments' (Sec. II B), 'annd' in reference [36], and 'reweighs' in the Fig. 4 caption. A careful proofreading pass would improve readability.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the pipeline is tested against independent injections, and the one heuristic step (effective-PSD reweighting) is an admitted plug-in with an open unbiasedness question — a correctness risk, not a circular reduction.

full rationale

The paper's central claims — that the effective-PSD reweighting of Sec. IV C removes the duty-cycle bias (Fig. 6) and that glitch modeling avoids false positives on time-reversed O3b data (Fig. 7) — are validated by injection-recovery tests in which the injected ξ values are known and are not used anywhere in the analysis; the recovered posteriors are genuine outputs, not inputs. The load-bearing formalism is independently supported: the PSD-marginalized likelihood (Eq. 18) is derived in Appendix A from the chi-square distribution of the PSD estimate, the finite-duration likelihood is implemented in the time domain and cross-checked against the frequency-domain version in Appendix D (Fig. 11), and the mixture-model evidence framework is re-presented in Section II. Citations to prior work by the same group ([23] Smith & Thrane; [34] Talbot & Thrane; [35] Talbot et al.) are therefore real evidence: each cited result is re-derived, re-implemented, or externally tested here, and the cited assumptions do not include the target result. The one heuristic step — Eq. 21, defining the effective PSD P-hat by equating the noise-marginalized likelihood with a Whittle form — is a plug-in/empirical-Bayes construction, and the manuscript itself flags the risk: 'we do not have any guarantees that we have an unbiased estimator of the target likelihood... and hence remains an open problem for future' (Sec. IV C). That is a correctness limitation on the readiness claim, not a circular reduction: the ξ = 0, 0.05, 0.1 recoveries are not forced by the definition of P-hat, and no equation equates the reported posteriors to fitted inputs by construction. Other flagged limitations (footnote 3, unexplained coalescence-time marginalization bias; footnote 4, underestimated posterior width; footnote 6, unexplained prior success of [23]; Appendix A, 'supporting studies carried out separately' not shown) are also completeness concerns rather than circularity. Overall: no significant circularity; the score of 1 reflects only the heavy reliance on the authors' own prior framework, which is re-validated here rather than assumed.

Assumptions & free parameters 4 free parameters · 7 assumptions · 0 invented entities

No new physical entities are introduced. The effective PSD P̂ is a derived numerical quantity, not a postulated entity. The analysis relies on a set of modeling choices and approximations, the most fragile being the effective-PSD reweighting whose unbiasedness is not proven and is explicitly flagged by the authors as an open problem.

free parameters (4)
  • BBH population prior (masses, spins, distances) = Uniform(30,50) M_sun, q in (0.5,1), chi in (0,0.8), comoving-volume distance (0.1,5) Gpc
    Used both for injected signals and as the parameter-estimation prior. If the real BBH population differs, evidence values and the recovered duty cycle are biased; the paper explicitly defers prior mismatch to future work (Sec. VI).
  • Segmentation duration T = 4 s
    Chosen so a high-mass BBH signal fits in the sensitive band while making overlaps unlikely; enters all evidence computations and the mixture model.
  • PSD estimation length = 128 s (32 sub-segments of 4 s)
    Sets N in the noise-marginalized likelihood (Eq. 18) and the width of the PSD prior; larger N would narrow the marginalization.
  • Selection cut (matched-filter SNR > 8) = SNR = 8
    Used in Appendix F to compute pdet for resolvable-binary exclusion; the threshold is chosen by hand and affects the selection correction.
assumptions (7)
  • domain assumption Whittle likelihood is a valid approximation for the noise distribution in each segment.
    Used in Eq. 5 for parameter estimation; the paper notes it is an approximation and shows in Appendix D that it differs from the full likelihood for weak signals. Relies on noise Gaussianity and stationarity.
  • domain assumption Each segment contains at most one BBH signal, and signal and glitch do not occur simultaneously.
    The mixture model (Eq. 12) and glitchy likelihood (Eq. 23) ignore multi-signal and signal-plus-glitch terms. With ~1% occupancy and 4 s segments this is plausible, but not derived.
  • domain assumption The IMRPhenomPv2 waveform model accurately describes all BBH signals in the analysis band.
    Used to compute signal evidences; waveform systematics are not modeled. The paper does not quantify waveform-model error.
  • domain assumption Time reversal of O3b data suppresses astrophysical signals while preserving the statistical properties of detector noise.
    The time-reversed-data validation (Sec. V) relies on this to treat the data as signal-free. GW chirps are not time-symmetric, so this is plausible, but residual mismodeled signals could remain.
  • ad hoc to paper The effective-PSD reweighting (Eq. 21) yields an unbiased estimate of the noise-marginalized, finite-duration likelihood.
    The paper explicitly states there is no guarantee of unbiasedness and calls it an open problem (Sec. IV C). This approximation is central to the claimed bias removal in Figs. 6 and 7.
  • domain assumption The population prior used for parameter estimation matches the true BBH population.
    Prior mismatch is acknowledged in Sec. VI as not addressed; if the prior is wrong, the evidence ratio Zs/Zn is miscalibrated.
  • ad hoc to paper Glitches are adequately modeled as BBH-like waveforms uncorrelated between detectors.
    Used in Eq. 23 to absorb glitch contamination; the paper argues this is the worst case, but real glitch morphology may not be BBH-like.

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Cite this review

Pith. "Pith review of Progress toward the detection of the gravitational-wave background from stellar-mass binary black holes: a mock data challenge." pith.science (2026). https://pith.science/paper/3QHL2Z2N

@misc{pith2026250614179,
  author       = {Pith},
  title        = {Pith review of: Progress toward the detection of the gravitational-wave background from stellar-mass binary black holes: a mock data challenge},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3QHL2Z2N}},
  note         = {Machine review of arXiv:2506.14179}
}
abstract

While the third LIGO--Virgo gravitational-wave transient catalog includes 90 signals, it is believed that ${\cal O}(10^5)$ binary black holes merge somewhere in the Universe every year. Although these signals are too weak to be detected individually with current observatories, they combine to create a stochastic background, which is potentially detectable in the near future. LIGO--Virgo searches for the gravitational-wave background using cross-correlation have so far yielded upper limits. However, Smith \& Thrane (2017) showed that a vastly more sensitive ``coherent'' search can be carried out by incorporating information about the phase evolution of binary black hole signals. This improved sensitivity comes at a cost; the coherent method is computationally expensive and requires a far more detailed understanding of systematic errors than is required for the cross-correlation search. In this work, we demonstrate the coherent approach with realistic data, paving the way for a gravitational-wave background search with unprecedented sensitivity.

Figures

Figures reproduced from arXiv: 2506.14179 by the authors.

Figure 1
Figure 1. FIG. 1. A flowchart depicting the analysis from Smith & Thrane [23], which is reviewed here in Section. II. The data are [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Characterizing LIGO noise. Top: the noise power [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Comparison of analyzing frequency-domain and time [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: FIG. 4. The flowchart illustrating our analysis—similar to the Smith & Thrane analysis shown in Fig. 1 except we take into [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Flowchart depicting the procedure for estimating [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 7
Figure 7. Figure 7: FIG. 7. Posterior distributions of the duty cycle parameter, [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: FIG. 8. Removing the PSD uncertainty effects. Each panel [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: FIG. 9. Removing the finite duration effects. Each panel [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 11
Figure 11. Figure 11: FIG. 11. The log-likelihoods evaluated on 600 randomly gen [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]
Figure 12
Figure 12. Figure 12: FIG. 12. Top: Posterior distributions of the duty cycle param [PITH_FULL_IMAGE:figures/full_fig_p015_12.png]
Figure 5
Figure 5. Figure 5: The blue dashed lines represent the best-fit [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 13
Figure 13. Figure 13: FIG. 13. Posterior distributions for duty cycle [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]

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Forward citations

Cited by 1 Pith paper

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

  1. Probing the peak of star formation with the stochastic background of binary black hole mergers

    astro-ph.HE 2025-06 conditional novelty 6.0 of 10

    A simulation study shows that unresolved binary black hole mergers, although too weak to detect the stochastic background on their own, sharpen constraints on the merger redshift distribution beyond the peak of star f...

Reference graph

Works this paper leans on

70 extracted references · 13 canonical work pages · cited by 1 Pith paper

  1. [1]

    Aasi et al

    J. Aasi et al. (LIGO Scientific), Class. Quant. Grav. 32, 074001 (2015), arXiv:1411.4547 [gr-qc]

    arXiv 2015

  2. [2]

    Acernese et al

    F. Acernese et al. (VIRGO), Class. Quant. Grav. 32, 024001 (2015), arXiv:1408.3978 [gr-qc]

    arXiv 2015

  3. [3]

    Abbott et al

    R. Abbott et al. (LIGO Scientific, Virgo), Phys. Rev. X 11, 021053 (2021), arXiv:2010.14527 [gr-qc]

    arXiv 2021

  4. [4]

    Abbott et al

    R. Abbott et al. (LIGO Scientific, VIRGO, KAGRA), (2021), arXiv:2111.03606 [gr-qc]

    arXiv 2021

  5. [5]

    For each segment, we compute the signal evidenceZ i s and the noise evidence Z i n using Bilby [50], which employs nested sampling with the dynesty sampler [51]

    that we used to generate the data. For each segment, we compute the signal evidenceZ i s and the noise evidence Z i n using Bilby [50], which employs nested sampling with the dynesty sampler [51]. We do not explicitly marginal- ize over the time of coalescence, as our studies indicate that Bilby’s [50] marginalization method introduced a bias in the duty ...

    work page 2019

  6. [6]

    Abbott et al

    R. Abbott et al. (LIGO Scientific, VIRGO), (2021), arXiv:2108.01045 [gr-qc]

    arXiv 2021

  7. [7]

    Abbott et al

    R. Abbott et al. (KAGRA, Virgo, LIGO Scientific), Phys. Rev. D 104, 022004 (2021), arXiv:2101.12130 [gr-qc]

    arXiv 2021

  8. [8]

    Kimball et al

    C. Kimball et al. , Astrophys. J. Lett. 915, L35 (2021), arXiv:2011.05332 [astro-ph.HE]

    arXiv 2021

Show all 70 references
  1. [9]

    Zevin, S

    M. Zevin, S. S. Bavera, C. P. L. Berry, V. Kalogera, T. Fragos, P. Marchant, C. L. Rodriguez, F. Antonini, D. E. Holz, and C. Pankow, Astrophys. J. 910, 152 (2021), arXiv:2011.10057 [astro-ph.HE]

    2021 arXiv

  2. [10]

    Mapelli, N

    M. Mapelli, N. Giacobbo, F. Santoliquido, and M. C. Artale, Mon. Not. Roy. Astron. Soc. 487, 2 (2019), arXiv:1902.01419 [astro-ph.HE]

    2019 arXiv

  3. [11]

    Baibhav, E

    V. Baibhav, E. Berti, D. Gerosa, M. Mapelli, N. Gia- cobbo, Y. Bouffanais, and U. N. Di Carlo, Phys. Rev. D 100, 064060 (2019), arXiv:1906.04197 [gr-qc]. 13 We have assumed here that no noise-only segments in the data will pass our selection cut

    2019 arXiv

  4. [12]

    F. S. Broekgaarden et al. , (2021), 10.1093/mn- ras/stac1677, arXiv:2112.05763 [astro-ph.HE]

    2021 arXiv

  5. [13]

    L. A. C. van Son, S. E. de Mink, T. Callister, S. Justham, M. Renzo, T. Wagg, F. S. Broekgaarden, F. Kummer, R. Pakmor, and I. Mandel, Astrophys. J. 931, 17 (2022), arXiv:2110.01634 [astro-ph.HE]

    2022 arXiv

  6. [14]

    Abbott et al

    R. Abbott et al. (KAGRA, VIRGO, LIGO Scientific), Phys. Rev. X 13, 011048 (2023), arXiv:2111.03634 [astro- ph.HE]

    2023 arXiv

  7. [15]

    Callister, M

    T. Callister, M. Fishbach, D. Holz, and W. Farr, Astro- phys. J. Lett. 896, L32 (2020), arXiv:2003.12152 [astro- ph.HE]

    2020 arXiv

  8. [16]

    R. J. E. Smith, C. Talbot, F. Hernandez Vivanco, and E. Thrane, Mon. Not. Roy. Astron. Soc. 496, 3281 (2020), arXiv:2004.09700 [astro-ph.HE]

    2020 arXiv

  9. [17]

    Turbang, M

    K. Turbang, M. Lalleman, T. A. Callister, and N. van Remortel, (2023), arXiv:2310.17625 [astro-ph.HE]

    2023 arXiv

  10. [18]

    Allen and J

    B. Allen and J. D. Romano, Phys. Rev. D 59, 102001 (1999), arXiv:gr-qc/9710117

    1999 arXiv

  11. [19]

    B. P. Abbott et al. (LIGO Scientific, Virgo), Phys. Rev. D 100, 061101 (2019), arXiv:1903.02886 [gr-qc]

    2019 arXiv

  12. [20]

    B. P. Abbott et al. (LIGO Scientific, Virgo), Phys. Rev. X 9, 031040 (2019), arXiv:1811.12907 [astro-ph.HE]

    2019 arXiv

  13. [21]

    B. P. Abbott et al. (LIGO Scientific, Virgo), Phys. Rev. Lett. 118, 121101 (2017), [Erratum: Phys.Rev.Lett. 119, 029901 (2017)], arXiv:1612.02029 [gr-qc]

    2017 arXiv

  14. [22]

    B. P. Abbott et al. (LIGO Scientific, Virgo), Phys. Rev. Lett. 120, 091101 (2018), arXiv:1710.05837 [gr-qc]. 17

    2018 arXiv

  15. [23]

    A. D. Johnson, K. Chatziioannou, and W. M. Farr, Phys. Rev. D 109, 084015 (2024), arXiv:2402.06836 [gr-qc]

    2024 arXiv

  16. [24]

    Smith and E

    R. Smith and E. Thrane, Phys. Rev. X 8, 021019 (2018), arXiv:1712.00688 [gr-qc]

    2018 arXiv

  17. [25]

    S. D. andEanna E. Flanagan, Phys. Rev. D 67, 082003 (2003)

    2003

  18. [26]

    Martellini and T

    L. Martellini and T. Regimbau, Phys. Rev. D 89, 124009 (2014), arXiv:1405.5775 [astro-ph.CO]

    2014 arXiv

  19. [27]

    Thrane, Phys

    E. Thrane, Phys. Rev. D 87, 043009 (2013)

    2013

  20. [28]

    Sharma and J

    A. Sharma and J. Harms, Phys. Rev. D 102, 063009 (2020), arXiv:2006.16116 [gr-qc]

    2020 arXiv

  21. [29]

    Buscicchio, A

    R. Buscicchio, A. Ain, M. Ballelli, G. Cella, and B. Patri- celli, Phys. Rev. D 107, 063027 (2023), arXiv:2209.01400 [gr-qc]

    2023 arXiv

  22. [30]

    Braglia, J

    M. Braglia, J. Garcia-Bellido, and S. Kuroyanagi, Mon. Not. Roy. Astron. Soc. 519, 6008 (2023), arXiv:2201.13414 [astro-ph.CO]

    2023 arXiv

  23. [31]

    T. S. Yamamoto, S. Kuroyanagi, and G.-C. Liu, Phys. Rev. D 107, 044032 (2023), arXiv:2208.13156 [gr-qc]

    2023 arXiv

  24. [32]

    Lawrence, K

    J. Lawrence, K. Turbang, A. Matas, A. I. Renzini, N. van Remortel, and J. D. Romano, Phys. Rev. D 107, 103026 (2023), arXiv:2301.07675 [gr-qc]

    2023 arXiv

  25. [33]

    Mukherjee and J

    S. Mukherjee and J. Silk, Mon. Not. Roy. Astron. Soc. 491, 4690 (2020), arXiv:1912.07657 [gr-qc]

    2020 arXiv

  26. [34]

    M. R. Sah and S. Mukherjee, (2023), arXiv:2307.06405 [gr-qc]

    2023 arXiv

  27. [35]

    Talbot and E

    C. Talbot and E. Thrane, Phys. Rev. Res. 2, 043298 (2020), arXiv:2006.05292 [astro-ph.IM]

    2020 arXiv

  28. [36]

    Talbot, E

    C. Talbot, E. Thrane, S. Biscoveanu, and R. Smith, Phys. Rev. Res. 3, 043049 (2021), arXiv:2106.13785 [astro-ph.IM]

    2021 arXiv

  29. [37]

    Biscoveanu, C.-J

    S. Biscoveanu, C.-J. H. annd Salvatore Vitale, and J. Davies, Phys. Rev. D 102, 023008 (202)

  30. [38]

    Thrane and C

    E. Thrane and C. Talbot, Pub. Astron. Soc. Aust. 36, E010 (2019)

    2019

  31. [39]

    Skilling, AIP Conf

    J. Skilling, AIP Conf. Proc. 735, 395 (2004)

    2004

  32. [40]

    Skilling, Bayesian Analysis 1, 833 (2006)

    J. Skilling, Bayesian Analysis 1, 833 (2006)

    2006

  33. [41]

    A. I. Renzini, T. A. Callister, K. Chatziioannou, and W. M. Farr, Phys. Rev. D 110, 023014 (2024)

    2024

  34. [42]

    Essick and M

    R. Essick and M. Fishbach, Astrophys. J. 962, 169 (2024), arXiv:2310.02017 [gr-qc]

    2024 arXiv

  35. [43]

    Banagiri, M

    S. Banagiri, M. W. Coughlin, J. Clark, P. D. Lasky, M. A. Bizouard, C. Talbot, E. Thrane, and V. Mandic, Mon. Not. Roy. Astron. Soc. 492, 4945 (2020), arXiv:1909.01934 [astro-ph.IM]

    2020 arXiv

  36. [44]

    Allen, J

    B. Allen, J. D. E. Creighton, E. E. Flanagan, and J. D. Romano, Phys. Rev. D 65, 122002 (2002), arXiv:gr- qc/0105100

    2002

  37. [45]

    Unser, Signal Processing 7, 231 (1984)

    M. Unser, Signal Processing 7, 231 (1984)

    1984

  38. [46]

    Isi and W

    M. Isi and W. M. Farr, (2021), arXiv:2107.05609 [gr-qc]

    2021 arXiv

  39. [47]

    Siegel, M

    H. Siegel, M. Isi, and W. M. Farr, (2024), arXiv:2410.02704 [gr-qc]

    2024 arXiv

  40. [48]

    Burke, S

    O. Burke, S. Marsat, J. R. Gair, and M. L. Katz, (2025), arXiv:2502.17426 [gr-qc]

    2025 arXiv

  41. [49]

    B. P. Abbott et al. (LIGO Scientific, Virgo), Phys. Rev. Lett. 119, 141101 (2017), arXiv:1709.09660 [gr-qc]

    2017 arXiv

  42. [50]

    Hannam, P

    M. Hannam, P. Schmidt, A. Boh´ e, L. Haegel, S. Husa, F. Ohme, G. Pratten, and M. P¨ urrer, Phys. Rev. Lett. 113, 151101 (2014), arXiv:1308.3271 [gr-qc]

    2014 arXiv

  43. [51]

    Ashton et al

    G. Ashton et al. , Astrophys. J. Suppl. 241, 27 (2019), arXiv:1811.02042 [astro-ph.IM]

    2019 arXiv

  44. [52]

    J. S. Speagle, Mon. Not. Roy. Astron. Soc. 493, 3132 (2020), arXiv:1904.02180 [astro-ph.IM]

    2020 arXiv

  45. [53]

    Rover, R

    C. Rover, R. Meyer, and N. Christensen, Class. Quant. Grav. 28, 015010 (2011), arXiv:0804.3853 [stat.ME]

    2011 arXiv

  46. [54]

    S. J. Miller, M. Isi, K. Chatziioannou, V. Varma, and I. Mandel, Phys. Rev. D 109, 024024 (2024), arXiv:2310.01544 [astro-ph.HE]

    2024 arXiv

  47. [55]

    Wang and L

    H.-T. Wang and L. Shao, Phys. Rev. D 109, 043027 (2024), arXiv:2401.13997 [gr-qc]

    2024 arXiv

  48. [56]

    Payne, C

    E. Payne, C. Talbot, and E. Thrane, Phys. Rev. D 100, 123017 (2019)

    2019

  49. [57]

    Zweizig and K

    J. Zweizig and K. Riles, Information on self-gating of h(t) used in O3 continuous-wave searches , Tech. Rep. LIGO- T2000384-v4

  50. [58]

    Smith, S

    R. Smith, S. E. Field, K. Blackburn, C.-J. Haster, M. P¨ urrer, V. Raymond, and P. Schmidt, Phys. Rev. D 94, 044031 (2016), arXiv:1604.08253 [gr-qc]

    2016 arXiv

  51. [59]

    Qi and V

    H. Qi and V. Raymond, Phys. Rev. D 104, 063031 (2021), arXiv:2009.13812 [gr-qc]

    2021 arXiv

  52. [60]

    Zackay, L

    B. Zackay, L. Dai, and T. Venumadhav, (2018), arXiv:1806.08792 [astro-ph.IM]

    2018 arXiv

  53. [61]

    N. J. Cornish, Phys. Rev. D 104, 104054 (2021), arXiv:2109.02728 [gr-qc]

    2021 arXiv

  54. [62]

    Krishna, A

    K. Krishna, A. Vijaykumar, A. Ganguly, C. Talbot, S. Biscoveanu, R. N. George, N. Williams, and A. Zim- merman, (2023), arXiv:2312.06009 [gr-qc]

    2023 arXiv

  55. [63]

    Lange, R

    J. Lange, R. O’Shaughnessy, and M. Rizzo, (2018), arXiv:1805.10457 [gr-qc]

    2018 arXiv

  56. [64]

    Roulet, J

    J. Roulet, J. Mushkin, D. Wadekar, T. Venumadhav, B. Zackay, and M. Zaldarriaga, Phys. Rev. D 110, 044010 (2024), arXiv:2404.02435 [gr-qc]

    2024 arXiv

  57. [65]

    M. Dax, S. R. Green, J. Gair, J. H. Macke, A. Buonanno, and B. Sch¨ olkopf, Phys. Rev. Lett.127, 241103 (2021), arXiv:2106.12594 [gr-qc]

    2021 arXiv

  58. [66]

    Chatterjee et al

    D. Chatterjee et al. , Mach. Learn. Sci. Tech. 5, 045030 (2024), arXiv:2407.19048 [gr-qc]

    2024 arXiv

  59. [67]

    M. J. Williams, J. Veitch, and C. Messenger, Mach. Learn. Sci. Tech. 4, 035011 (2023), arXiv:2302.08526 [astro-ph.IM]

    2023 arXiv

  60. [68]

    K. W. K. Wong, M. Isi, and T. D. P. Edwards, Astro- phys. J. 958, 129 (2023), arXiv:2302.05333 [astro-ph.IM]

    2023 arXiv

  61. [69]

    J. U. Lange, Mon. Not. Roy. Astron. Soc. 525, 3181 (2023), arXiv:2306.16923 [astro-ph.IM]

    2023 arXiv

  62. [70]

    Karamanis, D

    M. Karamanis, D. Nabergoj, F. Beutler, J. A. Peacock, and U. Seljak, J. Open Source Softw. 7, 4634 (2022), arXiv:2207.05660 [astro-ph.IM]

    2022 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.