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REVIEW 4 major objections 4 minor 68 references

Constraining the Galactic Structure using Time Domain Gravitational Wave Signal from Double White Dwarfs Detected by Space Gravitational Wave Detectors

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that four years of time-domain gravitational-wave data from LISA or Taiji on double white dwarfs can size the Galactic thin disk and bulge to tens of percent: ~30%, ~30%, ~40% from the low-frequency foreground and ~20%…

desk verdict The analytic noise derivation is solid, but the headline constraints are Fisher-like forecasts because the MCMC fits the noiseless mean YMP; it still deserves peer review. read the letter →

arxiv 2411.09298 v1 pith:6IDCPHOK submitted 2024-11-14 astro-ph.GA

Siqi Zhang , Furen Deng , Youjun Lu , Shenghua Yu This is my paper · ORCID
classification astro-ph.GA
keywords gravitationalwavesdoublewhitedwarfsGalacticstructureLISATaijiwaveforegroundtime-domainGWsignalMarkovChainMonteCarlo
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that the anisotropic shape of the Milky Way's thin disk and bulge can be read directly from the time-domain gravitational-wave signal of double white dwarfs, without resolving every source individually. As a space detector such as LISA or Taiji orbits the Sun, its antenna pattern sweeps across the sky once per year, imprinting the spatial distribution of the double-white-dwarf population onto the total signal's annual modulation. Using mock signals from a binary population synthesis model, the authors show that a Markov Chain Monte Carlo fit to this yearly modulated profile can recover the thin-disk scale height, thin-disk scale length, and bulge scale radius to tens of percent with four years of data. The central quantitative claim is fractional accuracies of about 30%, 30%, and 40% from the low-frequency unresolved foreground, and 20%, 10%, and 40% from the high-frequency resolved-source band, for either LISA or Taiji. If correct, this gives a computationally cheap Galactic-structure tracer that uses the same data these missions will already collect.

What carries the argument

The load-bearing object is the yearly modulated profile (YMP), defined as $F_k = (1/\Delta T)\int_{k\Delta T}^{(k+1)\Delta T} h^2(t)\,dt$ for one-day chunks of the combined time-domain signal $h(t)$ from all double white dwarfs. Because each double white dwarf radiates nearly monochromatically and the detector's orientation changes annually, the squared signal in each chunk is modulated by the detector beam-pattern functions; averaging over a day suppresses the gravitational-wave-frequency oscillations while preserving the annual anisotropy pattern. The paper derives an analytic expression for the variance of the YMP arising from the random phases of the sources (phase noise) plus detector noise, $\sigma_k^2 = (1/\Delta T)\int_0^\infty [S_s^2(\nu)+S_n^2(\nu)]\,d\nu$, and verifies numerically that covariance between one-day chunks is negligible. To isolate the signal, it uses a top-hat frequency filter and an enhanced filter $w_{\rm ehc}(\nu)=S_s(\nu)/[S_s^2(\nu)+S_n^2(\nu)]$ that minimizes the noise-to-signal ratio of the profile. The YMP is then the observable fed to a Gaussian likelihood and MCMC sampler, with the population-synthesis density models providing the model prediction $\langle F_k(\theta)\rangle$.

What would settle it

Re-run the same MCMC analysis on mock data that include a full four-year noise realization with random double-white-dwarf phases and detector noise, rather than the noise-free mean yearly modulated profile, and check whether the recovered parameters stay within the quoted uncertainties; if they do not, the error bars are underestimated. Alternatively, generate mock signals with an independent population synthesis model and test whether the fit returns that model's parameters or shows a bias.

Watch

Extended reading notes

Core claim

The central claim is that the yearly modulated profile of the squared time-domain gravitational-wave signal from Galactic double white dwarfs is sufficient to constrain the thin-disk scale height $h_z$, thin-disk scale length $h_R$, and bulge scale radius $h_r$. The anisotropy is encoded because the detector's beam-pattern functions change as the constellation orbits the Sun, so the one-day-averaged square of the signal varies over the year in a way that depends on where the double white dwarfs sit. Using mock double-white-dwarf populations and four-year LISA or Taiji observations, the authors fit a five-parameter Gaussian likelihood (the three structure parameters plus two amplitude normalizations for disk and bulge) and report that the input values are recovered within their quoted uncertainties, with $h_z\approx 0.35$ kpc, $h_R\approx 2.50$ kpc, and $h_r\approx 0.5$ kpc. The high-frequency band, dominated by resolvable sources, gives the tighter constraints, while the low-frequency unresolved foreground still yields useful ones. The paper further shows that using the bulk modulation alone, without localizing individual sources, gives constraints comparable to earlier approaches that used the sky distribution of resolved double white dwarfs.

Load-bearing premise

The quoted accuracies are obtained by fitting mock signals with the same population-synthesis model and the same Galactic density profiles that generated the mock signals, so the real-world accuracy depends entirely on that model being a faithful description of the actual double-white-dwarf population.

Editorial extensions

If this is right

  • Under the paper's model, four years of LISA or Taiji data alone could constrain the thin-disk scale height to 0.03–0.11 kpc and scale length to 0.10–0.56 kpc depending on band and filter, with the bulge scale radius constrained to 0.14–0.35 kpc.
  • The unresolved low-frequency foreground is a usable Galactic-structure probe in its own right, giving roughly 30%, 30%, and 40% fractional constraints on disk height, disk length, and bulge radius rather than acting only as noise.
  • The high-frequency band, dominated by resolvable double white dwarfs, carries most of the constraining power: about 20%, 10%, and 40% fractional accuracies for the same three parameters.
  • Source-by-source sky localization may not be necessary for structural constraints, because the bulk annual modulation of the resolved-source band already achieves accuracies similar to those obtained from the full spatial distribution of individually resolved sources.
  • LISA and Taiji perform comparably in the quoted uncertainties, so either mission could carry out the measurement.

Reading between the lines

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

  • Editorial inference: because the method uses only the bulk annual modulation, it could be combined with source-count or frequency-spectrum information to break degeneracies between the disk scale length and the bulge scale radius, which the paper's posteriors show are less tightly constrained than the scale height.
  • Editorial inference: if the analytic phase-noise variance is as accurate over four years as the 15-day validation suggests, the same YMP variance could be used to estimate the total double-white-dwarf chirp-mass distribution from a single detector, not just the three structural parameters.
  • Editorial inference: the real test of the method will be cross-validation against an independent population synthesis model or against double-white-dwarf spatial distributions informed by opt/high-precision astrometry; the current mock-to-mock setup is self-consistent by construction.
  • Editorial inference: because the annual modulation comes from the detector's motion, an analogous time-domain approach could be applied to the unresolved extragalactic stochastic gravitational-wave background to probe its anisotropy, though the signal-to-noise ratio would be considerably weaker.
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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

4 major / 4 minor

Summary. This paper proposes using the time-domain annual modulation of the gravitational-wave signal from Galactic double white dwarfs (the 'yearly modulated profile', YMP) to constrain the scale height and length of the thin disk and the scale radius of the bulge. The authors generate mock DWD populations with a population synthesis model, simulate LISA and Taiji signals, derive an analytic expression for the phase noise (validated numerically in Fig. 4), introduce top-hat and enhanced filters, and run MCMC fits. They report fractional accuracies of roughly 30%/30%/40% using the low-frequency band and 20%/10%/40% using the high-frequency band.

Significance. If the quoted accuracies hold on realistic noisy data, this would be a computationally simple complementary probe of Galactic structure that uses the bulk modulation of resolved and unresolved DWD signals, without needing individual source localization. Strengths of the paper include the analytic phase-noise derivation with a numerical check (Fig. 4), the explicit treatment of two frequency bands, and the comparison of LISA and Taiji. The main weakness is that the current MCMC demonstration fits the noiseless smoothed profile, and the enhanced filter is matched to the true spectrum, so the numbers in Table 2 are forecasts rather than end-to-end validations. The paper is a useful contribution if these issues are addressed.

major comments (4)
  1. [Section 3.6, Eq. (22) and Table 2] The likelihood compares the model profile ⟨F_k(θ)⟩ to the noiseless smooth profile ⟨F_k(θ0)⟩, not to a YMP extracted from a realization that includes phase noise and detector noise. Noise enters only through σ_k in the denominator, so the posterior widths in Table 2 are essentially a Fisher-matrix-type forecast and do not demonstrate that the estimator works on a single noisy data realization. Please rerun the MCMC on at least one simulated noisy YMP (e.g., a realization of Eq. (12) with h(t)+n(t)), or explicitly state in the abstract and Section 4 that all quoted accuracies are forecasts.
  2. [Section 3.5, Eq. (19)] The enhanced filter is constructed from S_s(ν), the spectrum of the same mock population that is being fitted, so it is matched to the true parameters. Since the enhanced filter produces the tightest constraints in Table 2, the quoted accuracies are optimistic if in practice the filter is built from a fiducial model. Please quantify the degradation from filter mismatch, e.g., by constructing the filter from a model with parameters offset by the quoted 1σ uncertainties and re-running the analysis.
  3. [Sections 2 and 3.6] The mock 'observation' and the likelihood model are generated with the same population synthesis code (Yu & Jeffery 2010) and the same density profiles (Eqs. 1-2), so the recovery of the input parameters is a self-consistency test rather than an independent validation. The fractional accuracies quoted in the abstract and Table 2 are therefore conditional on the adopted population model; the paper should state this explicitly and, ideally, inject a population generated with an independent synthesis code or alternative assumptions to assess systematic sensitivity.
  4. [Section 3.6, Eq. (22)] As written, the second term has a plus sign, so the expression is the negative of a Gaussian log-likelihood rather than the log-likelihood; if implemented literally, maximizing this quantity would push the parameters away from the data. Please correct the sign (the quadratic term should be negative) and confirm that the MCMC code used the correct sign.
minor comments (4)
  1. [Section 3.4, Eq. (17)] The use of δ_{k'k} inside the definition of σ_k^2 is confusing; please define the covariance σ^2_{kk'} in the equation and state that σ_k^2 is the diagonal element.
  2. [Figure 4] The caption says 'covariance of the ratio of the standard deviation', but the text discusses the diagonal terms being close to unity; please clarify that the plotted quantity is the covariance matrix of the ratios of analytic to numerical standard deviations.
  3. [Abstract and title] 'Gravitation Wave' should be 'Gravitational Wave'.
  4. [Table 2] The entry 'enhance' in the Detector/Filter column should be 'enhanced' for consistency.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: mock-injection recovery is a self-consistency test, not a circular derivation.

full rationale

The paper is an end-to-end injection study rather than a measurement claim. Mock DWD populations are generated from an updated version of Yu & Jeffery (2010) with the density profiles in Eqs. (1)-(2); the same population-synthesis machinery is then used to compute the model profiles <F_k(theta)> in Eq. (13) that enter the likelihood in Eq. (22). This means the MCMC recovery of the input parameters is a self-consistency check: if the generative model is wrong, the quoted 30%/30%/40% accuracies do not transfer to the real Galaxy. That is a genuine limitation, but it is not circularity under the strict definition. The likelihood compares two separately evaluated smooth profiles (data profile at theta_0 and model profile at theta), and the posterior widths are controlled by the phase-noise and detector-noise variances from Eq. (17), so the constraints are not equal to the inputs by construction and the fit can fail if the YMP is insensitive. The enhanced filter in Eq. (19) and the noise variance use the true signal spectrum S_s(nu) of the mock population, which makes the forecasts optimistic (a matched-filter/fiducial-spectrum assumption), but this is a known limitation of Fisher-like forecasts, not a derivation that reduces the output to the input. The Yu & Jeffery (2010) citation is by a co-author and supplies the input population model, but the YMP formalism, analytic phase-noise derivation, filter construction, and MCMC parameter estimation are new and do not depend on that citation for their logical content. No enumerated circular step is present.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The method rests on a chain of assumptions inherited from the population synthesis model and the Galactic density profiles. The only genuinely new analytic content is the phase-noise formula (Appendix A) and the enhanced filter (Appendix B), which are derived from stated assumptions. No new physical entities are introduced.

free parameters (5)
  • h_z (thin disk scale height) = 0.34+0.09/-0.09 (LFB enhanced), 0.35+0.04/-0.03 (HFB enhanced)
    Target parameter of the method; fitted by MCMC to the mock YMP. true value 0.352 kpc.
  • h_R (thin disk scale length) = 2.65+0.56/-0.38 (LFB), 2.50+0.13/-0.11 (HFB)
    Target parameter; recovered by MCMC. true value 2.5 kpc.
  • h_r (bulge scale radius) = 0.46+0.15/-0.17 (LFB), 0.47+0.22/-0.19 (HFB)
    Target parameter; recovered by MCMC. true value 0.5 kpc.
  • chi_d (thin disk amplitude normalization) = 0.96+0.18/-0.15 (LFB), 1.00+0.06/-0.06 (HFB)
    Introduced in Eq. 23 to absorb YMP amplitude uncertainty; true value 1.
  • chi_b (bulge amplitude normalization) = 1.17+0.48/-0.50 (LFB), 0.99+0.15/-0.15 (HFB)
    Introduced in Eq. 23 to absorb bulge amplitude uncertainty; true value 1.
assumptions (6)
  • domain assumption The binary population synthesis model of Yu & Jeffery (2010) correctly produces the DWD template bank (masses, separations, frequencies).
    Section 2: the mock DWD population is generated from this model; all downstream constraints inherit its accuracy.
  • domain assumption The Galactic DWD spatial distribution follows Eqs. (1)-(2) with the stated bulge and thin disk parameters.
    Section 2: the density profiles and their parameters (M_b, M_td, cutoff 3.5 kpc) are inputs to the mock population.
  • domain assumption The star formation history in Eq. (3) (from Yu & Jeffery 2010; Smith et al. 1978) determines the DWD age and property distributions.
    Section 2: SFR parameters (t0=4 Gyr, tau=9 Gyr) set the current DWD population.
  • domain assumption The detector noise is Gaussian and stationary, and its 4th moment factorizes as in Eq. (A24).
    Appendix A: used to derive the detector noise term in sigma_k^2.
  • ad hoc to paper Source properties are location-independent within each Galactic component, so the GW spectrum shape S_0(nu) is constant across time chunks.
    Section 3.3: this assumption allows the spectrum to be computed from any single day chunk, but it ignores position-dependent variations of the DWD population.
  • domain assumption The low-frequency approximation for the LISA/Taiji antenna pattern functions is valid for the DWD frequency band.
    Section 3.1 and the low-frequency approximation caveat in Section 4; the authors check that removing >10 mHz sources does not change results.

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Cite this review

Pith. "Pith review of Constraining the Galactic Structure using Time Domain Gravitational Wave Signal from Double White Dwarfs Detected by Space Gravitational Wave Detectors." pith.science (2026). https://pith.science/paper/6IDCPHOK

@misc{pith2026241109298,
  author       = {Pith},
  title        = {Pith review of: Constraining the Galactic Structure using Time Domain Gravitational Wave Signal from Double White Dwarfs Detected by Space Gravitational Wave Detectors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6IDCPHOK}},
  note         = {Machine review of arXiv:2411.09298}
}
read the original abstract

The Gravitation Wave (GW) signals from a large number of double white dwarfs (DWDs) in the Galaxy are expected to be detected by space GW detectors, e.g., the Laser Interferometer Space Antenna (LISA), Taiji, and Tianqin in the millihertz band. In this paper, we present an alternative method by directly using the time-domain GW signal detected by space GW detectors to constrain the anisotropic structure of the Galaxy. The information of anisotropic distribution of DWDs is naturally encoded in the time-domain GW signal because of the variation of the detectors' directions and consequently the pattern functions due to their annual motion around the sun. The direct use of the time-domain GW signal enables simple calculations, such as utilizing an analytical method to assess the noise arising from the superposition of random phases of DWDs and using appropriate weights to improve the constraints. We investigate the possible constraints on the scale of the Galactic thin disk and bulge that may be obtained from LISA and Taiji by using this method with mock signals obtained from population synthesis models. We further show the different constraining capabilities of the low-frequency signal (foreground) and the high-frequency signal (resolvable-sources) via the Markov Chain Monte Carlo method, and find that the scale height and length of the Galactic thin disk and the scale radius of bulge can be constrained to a fractional accuracy of ~ 30%, 30%, 40% (or 20%, 10%, 40%) by using the low-frequency (or high-frequency) signal detected by LISA or Taiji.

Figures

Figures reproduced from arXiv: 2411.09298 by the authors.

Figure 1
Figure 1. The chirp mass (top left panel), orbital separation (top right panel), GW frequency (bottom left panel), and heliocentric distance (bottom right panel) distributions of the mock Galactic DWDs. The solid blue and the dashed blue curves show the distribution of DWDs in the thin disk and the bulge, respectively. The mock Galactic DWD samples shown here are selected according to the frequencies of their GW radiation, i.… view at source ↗
Figure 2
Figure 2. The 2-D projection of the spatial distribution of those resolvable DWDs detected by LISA (the total num￾ber is 43682). The x- and y-axes indicate the location of DWDs in the Galactocentric rest frame, i.e., the distance of the DWD projected onto the Galactic plane to the Galac￾tic center and the height of the DWD above the Galactic plane, respectively. Different colors represent different num￾ber density of those re… view at source ↗
Figure 3
Figure 3. The power spectral density of the GW signal from the Galactic DWDs. For the left panel, the grey curve shows the total spectrum of the GW signal from all DWD sources and the two light blue curves which peaks at higher/lower frequency show the spectrum of the GW signals from all the resolvable DWDs/the unresolvable DWDs by LISA 4-year (left panel) or Taiji 4-year (right panel) observations, respectively. These curves… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The covariance of the ratio of the standard de￾viation calculated from the analytic method to the standard deviation calculated form numerical method in 15 consecu￾tive time chunks. The duration of each time chunk is 1 day. tion and the spectrum of Ss can be seen in Se…
Figure 6
Figure 6. Figure 6: shows the standard deviation of the phase noise obtained from our Monte Carlo calculations, de￾tector noise, signal+noise and the smoothly profile of mock time-domain average signals of LISA for different 2 0 2 4 6 sig n al a n d n ois e s ×10 41 top hat filter ("LFB")…
Figure 7
Figure 7. Figure 7: The marginalized probability distributions of the Galactic structure parameters obtained from the GW signal in different frequency band with different filters. Left to right panels show the results for hz, hR, and hr, respectively. In each panel, the solid line and the…
Figure 8
Figure 8. Figure 8: The probability distributions of the Galactic structure model parameters constrained by the YMP resulting from a realization of the Galactic DWDs using the enhanced filter to the “LFB” (blue) or the “HFB” (orange). The input values of these parameters are Θ0 = {0.352 k…

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Works this paper leans on

68 extracted references · 18 canonical work pages

  1. [1]

    R., & Cornish, N

    Adams, M. R., & Cornish, N. J. 2014, PhRvD, 89, 022001, doi: 10.1103/PhysRevD.89.022001

    doi:10.1103/physrevd.89.022001 2014

  2. [2]

    R., Cornish, N

    Adams, M. R., Cornish, N. J., & Littenberg, T. B. 2012, PhRvD, 86, 124032, doi: 10.1103/PhysRevD.86.124032 Constraining Galactic Structure Using GWs from DWDs 19

    work page doi:10.1103/physrevd.86.124032 2012

  3. [3]

    2022, in Fifteenth Marcel Grossmann Meeting on General Relativity, ed

    Amaro-Seoane, P. 2022, in Fifteenth Marcel Grossmann Meeting on General Relativity, ed. E. S. Battistelli, R. T. Jantzen, & R. Ruffini, 811–817, doi: 10.1142/9789811258251 0108

    doi:10.1142/9789811258251 2022

  4. [4]

    R., Freitag, M., et al

    Amaro-Seoane, P., Gair, J. R., Freitag, M., et al. 2007, Classical and Quantum Gravity, 24, R113, doi: 10.1088/0264-9381/24/17/R01

    doi:10.1088/0264-9381/24/17/r01 2007

  5. [5]

    2013, GW Notes, 6, 4, doi: 10.48550/arXiv.1201.3621

    Amaro-Seoane, P., Aoudia, S., Babak, S., et al. 2013, GW Notes, 6, 4, doi: 10.48550/arXiv.1201.3621

    arXiv doi:10.48550/arxiv.1201.3621 2013

  6. [6]

    2017, arXiv e-prints, arXiv:1702.00786, doi: 10.48550/arXiv.1702.00786

    Amaro-Seoane, P., Audley, H., Babak, S., et al. 2017, arXiv e-prints, arXiv:1702.00786, doi: 10.48550/arXiv.1702.00786

    arXiv doi:10.48550/arxiv.1702.00786 2017

  7. [7]

    2023, Living Reviews in Relativity, 26, 2, doi: 10.1007/s41114-022-00041-y Astropy Collaboration, Robitaille, T

    Amaro-Seoane, P., Andrews, J., Arca Sedda, M., et al. 2023, Living Reviews in Relativity, 26, 2, doi: 10.1007/s41114-022-00041-y Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-38...

    doi:10.1007/s41114-022-00041-y 2023

  8. [8]

    2017, PhRvD, 95, 103012, doi: 10.1103/PhysRevD.95.103012

    Babak, S., Gair, J., Sesana, A., et al. 2017, PhRvD, 95, 103012, doi: 10.1103/PhysRevD.95.103012

    doi:10.1103/physrevd.95.103012 2017

Show all 68 references
  1. [9]

    G., et al

    Babak, S., Caprini, C., Figueroa, D. G., et al. 2023, arXiv e-prints, arXiv:2304.06368, doi: 10.48550/arXiv.2304.06368

    2023 doi arXiv

  2. [10]

    2006, ApJ, 645, 589, doi: 10.1086/504024

    Benacquista, M., & Holley-Bockelmann, K. 2006, ApJ, 645, 589, doi: 10.1086/504024

    2006 doi

  3. [11]

    L., & Hils, D

    Bender, P. L., & Hils, D. 1997, Classical and Quantum Gravity, 14, 1439, doi: 10.1088/0264-9381/14/6/008

    1997 doi

  4. [12]

    2020, PhRvD, 102, 103023, doi: 10.1103/PhysRevD.102.103023

    Bonetti, M., & Sesana, A. 2020, PhRvD, 102, 103023, doi: 10.1103/PhysRevD.102.103023

    2020 doi

  5. [13]

    Breivik, K., Mingarelli, C. M. F., & Larson, S. L. 2020, ApJ, 901, 4, doi: 10.3847/1538-4357/abab99

    2020 doi

  6. [14]

    2024, arXiv e-prints, arXiv:2410.08263, doi: 10.48550/arXiv.2410.08263

    Buscicchio, R., Klein, A., Korol, V., et al. 2024, arXiv e-prints, arXiv:2410.08263, doi: 10.48550/arXiv.2410.08263

    2024 doi arXiv

  7. [15]

    Cornish, N. J. 2001, Classical and Quantum Gravity, 18, 4277, doi: 10.1088/0264-9381/18/20/307

    2001 doi

  8. [16]

    J., & Porter, E

    Cornish, N. J., & Porter, E. K. 2007, Classical and Quantum Gravity, 24, 5729, doi: 10.1088/0264-9381/24/23/001

    2007 doi

  9. [17]

    J., & Rubbo, L

    Cornish, N. J., & Rubbo, L. J. 2003, PhRvD, 67, 022001, doi: 10.1103/PhysRevD.67.022001

    2003 doi

  10. [18]

    W., Rieck, S., & Mandic, V

    Criswell, A. W., Rieck, S., & Mandic, V. 2024, arXiv e-prints, arXiv:2410.23260, doi: 10.48550/arXiv.2410.23260

    2024 doi arXiv

  11. [19]

    1998, PhRvD, 57, 7089, doi: 10.1103/PhysRevD.57.7089

    Cutler, C. 1998, PhRvD, 57, 7089, doi: 10.1103/PhysRevD.57.7089

    1998 doi

  12. [20]

    Cutler, C., & Flanagan, ´E. E. 1994, PhRvD, 49, 2658, doi: 10.1103/PhysRevD.49.2658

    1994 doi

  13. [21]

    2006, Nature, 439, 45, doi: 10.1038/nature04364

    Diehl, R., Halloin, H., Kretschmer, K., et al. 2006, Nature, 439, 45, doi: 10.1038/nature04364

    2006 doi

  14. [22]

    C., & Cornish, N

    Digman, M. C., & Cornish, N. J. 2022, ApJ, 940, 10, doi: 10.3847/1538-4357/ac9139

    2022 doi

  15. [23]

    A., Tinto, M., Kr´ olak, A., & Nelemans, G

    Edlund, J. A., Tinto, M., Kr´ olak, A., & Nelemans, G. 2005, PhRvD, 71, 122003, doi: 10.1103/PhysRevD.71.122003

    2005 doi

  16. [24]

    Finn, L. S. 1996, PhRvD, 53, 2878, doi: 10.1103/PhysRevD.53.2878

    1996 doi

  17. [25]

    2023, PhRvD, 107, 123029, doi: 10.1103/PhysRevD.107.123029

    Gao, P., Fan, X.-L., Cao, Z.-J., & Zhang, X.-H. 2023, PhRvD, 107, 123029, doi: 10.1103/PhysRevD.107.123029

    2023 doi

  18. [26]

    2023, MNRAS, 519, 2552, doi: 10.1093/mnras/stac3686 GRA VITY Collaboration, Abuter, R., Amorim, A., et al

    Stergioulas, N. 2023, MNRAS, 519, 2552, doi: 10.1093/mnras/stac3686 GRA VITY Collaboration, Abuter, R., Amorim, A., et al. 2019, A&A, 625, L10, doi: 10.1051/0004-6361/201935656

    2023 doi

  19. [27]

    F., et al

    Grover, K., Fairhurst, S., Farr, B. F., et al. 2014, PhRvD, 89, 042004, doi: 10.1103/PhysRevD.89.042004

    2014 doi

  20. [28]

    1998, MNRAS, 296, 1019, doi: 10.1046/j.1365-8711.1998.01475.x

    Han, Z. 1998, MNRAS, 296, 1019, doi: 10.1046/j.1365-8711.1998.01475.x

    1998

  21. [29]

    2020, PhRvD, 102, 063021, doi: 10.1103/PhysRevD.102.063021

    Huang, S.-J., Hu, Y.-M., Korol, V., et al. 2020, PhRvD, 102, 063021, doi: 10.1103/PhysRevD.102.063021

    2020 doi

  22. [30]

    R., Pols, O

    Hurley, J. R., Pols, O. R., & Tout, C. A. 2000, MNRAS, 315, 543, doi: 10.1046/j.1365-8711.2000.03426.x

    2000

  23. [31]

    R., Tout, C

    Hurley, J. R., Tout, C. A., & Pols, O. R. 2002, MNRAS, 329, 897, doi: 10.1046/j.1365-8711.2002.05038.x Juri´ c, M., Ivezi´ c,ˇZ., Brooks, A., et al. 2008, ApJ, 673, 864, doi: 10.1086/523619

    2002

  24. [32]

    2021, AJ, 162, 247, doi: 10.3847/1538-3881/ac23d8

    Kang, Y., Liu, C., & Shao, L. 2021, AJ, 162, 247, doi: 10.3847/1538-3881/ac23d8

    2021 doi

  25. [33]

    2016, PhRvD, 93, 024003, doi: 10.1103/PhysRevD.93.024003

    Klein, A., Barausse, E., Sesana, A., et al. 2016, PhRvD, 93, 024003, doi: 10.1103/PhysRevD.93.024003

    2016 doi

  26. [34]

    2022, MNRAS, 511, 5936, doi: 10.1093/mnras/stac415

    Korol, V., Hallakoun, N., Toonen, S., & Karnesis, N. 2022, MNRAS, 511, 5936, doi: 10.1093/mnras/stac415

    2022 doi

  27. [35]

    M., & Barausse, E

    Korol, V., Rossi, E. M., & Barausse, E. 2019, MNRAS, 483, 5518, doi: 10.1093/mnras/sty3440

    2019 doi

  28. [36]

    M., Groot, P

    Korol, V., Rossi, E. M., Groot, P. J., et al. 2017, MNRAS, 470, 1894, doi: 10.1093/mnras/stx1285

    2017 doi

  29. [37]

    2005, PhRvD, 71, 024025, doi: 10.1103/PhysRevD.71.024025

    Kudoh, H., & Taruya, A. 2005, PhRvD, 71, 024025, doi: 10.1103/PhysRevD.71.024025

    2005 doi

  30. [38]

    B., et al

    Lamberts, A., Blunt, S., Littenberg, T. B., et al. 2019, MNRAS, 490, 5888, doi: 10.1093/mnras/stz2834

    2019 doi

  31. [39]

    F., et al

    Lamberts, A., Garrison-Kimmel, S., Hopkins, P. F., et al. 2018, MNRAS, 480, 2704, doi: 10.1093/mnras/sty2035

    2018 doi

  32. [40]

    Lau, M. Y. M., Mandel, I., Vigna-G´ omez, A., et al. 2020, MNRAS, 492, 3061, doi: 10.1093/mnras/staa002

    2020 doi

  33. [41]

    2020, ApJ, 893, 2, doi: 10.3847/1538-4357/ab7dc2

    Li, Z., Chen, X., Chen, H.-L., et al. 2020, ApJ, 893, 2, doi: 10.3847/1538-4357/ab7dc2

    2020 doi

  34. [42]

    2023, PhRvD, 107, 083033, doi: 10.1103/PhysRevD.107.083033 20 Zhang, Deng, Lu, & Yu

    Liang, Z.-C., Li, Z.-Y., Cheng, J., et al. 2023, PhRvD, 107, 083033, doi: 10.1103/PhysRevD.107.083033 20 Zhang, Deng, Lu, & Yu

    2023 doi

  35. [43]

    2023, PhRvD, 107, 064021, doi: 10.1103/PhysRevD.107.064021

    Liu, C., Ruan, W.-H., & Guo, Z.-K. 2023, PhRvD, 107, 064021, doi: 10.1103/PhysRevD.107.064021

    2023 doi

  36. [44]

    2016, Classical and Quantum Gravity, 33, 035010, doi: 10.1088/0264-9381/33/3/035010

    Luo, J., Chen, L.-S., Duan, H.-Z., et al. 2016, Classical and Quantum Gravity, 33, 035010, doi: 10.1088/0264-9381/33/3/035010

    2016 doi

  37. [45]

    T., Bovy, J., Schiavon, R

    Mackereth, J. T., Bovy, J., Schiavon, R. P., et al. 2017, MNRAS, 471, 3057, doi: 10.1093/mnras/stx1774

    2017 doi

  38. [46]

    Marsh, T. R. 2011, Classical and Quantum Gravity, 28, 094019, doi: 10.1088/0264-9381/28/9/094019

    2011 doi

  39. [47]

    J., Chua, A

    Moore, C. J., Chua, A. J. K., & Gair, J. R. 2017, Classical and Quantum Gravity, 34, 195009, doi: 10.1088/1361-6382/aa85fa

    2017 doi

  40. [48]

    R., & Portegies Zwart, S

    Nelemans, G., Yungelson, L. R., & Portegies Zwart, S. F. 2004, MNRAS, 349, 181, doi: 10.1111/j.1365-2966.2004.07479.x

    2004

  41. [49]

    2020, Classical and Quantum Gravity, 37, 195020, doi: 10.1088/1361-6382/abb074

    Pan, Z., & Yang, H. 2020, Classical and Quantum Gravity, 37, 195020, doi: 10.1088/1361-6382/abb074

    2020 doi

  42. [50]

    2024, arXiv e-prints, arXiv:2410.08274, doi: 10.48550/arXiv.2410.08274

    Pozzoli, F., Buscicchio, R., Klein, A., et al. 2024, arXiv e-prints, arXiv:2410.08274, doi: 10.48550/arXiv.2410.08274

    2024 doi arXiv

  43. [51]

    A., Tinto, M., Larson, S

    Prince, T. A., Tinto, M., Larson, S. L., & Armstrong, J. W. 2002, PhRvD, 66, 122002, doi: 10.1103/PhysRevD.66.122002

    2002 doi

  44. [52]

    2017, Classical and Quantum Gravity, 34, 244002, doi: 10.1088/1361-6382/aa9601

    Robson, T., & Cornish, N. 2017, Classical and Quantum Gravity, 34, 244002, doi: 10.1088/1361-6382/aa9601

    2017 doi

  45. [53]

    J., & Liu, C

    Robson, T., Cornish, N. J., & Liu, C. 2019, Classical and Quantum Gravity, 36, 105011, doi: 10.1088/1361-6382/ab1101

    2019 doi

  46. [54]

    2018, arXiv e-prints, arXiv:1807.09495, doi: 10.48550/arXiv.1807.09495 —

    Ruan, W.-H., Guo, Z.-K., Cai, R.-G., & Zhang, Y.-Z. 2018, arXiv e-prints, arXiv:1807.09495, doi: 10.48550/arXiv.1807.09495 —. 2020, International Journal of Modern Physics A, 35, 2050075, doi: 10.1142/S0217751X2050075X

    2018 doi arXiv

  47. [55]

    2009, ApJ, 693, 383, doi: 10.1088/0004-637X/693/1/383

    Holley-Bockelmann, K. 2009, ApJ, 693, 383, doi: 10.1088/0004-637X/693/1/383

    2009 doi

  48. [56]

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

    Ruiter, A. J., Belczynski, K., Benacquista, M., Larson, S. L., & Williams, G. 2010, ApJ, 717, 1006, doi: 10.1088/0004-637X/717/2/1006

    2010 doi

  49. [57]

    F., Biermann, P., & Mezger, P

    Smith, L. F., Biermann, P., & Mezger, P. G. 1978, A&A, 66, 65

    1978

  50. [58]

    2006, PhRvD, 74, 104022, doi: 10.1103/PhysRevD.74.104022

    Taruya, A. 2006, PhRvD, 74, 104022, doi: 10.1103/PhysRevD.74.104022

    2006 doi

  51. [59]

    2005, PhRvD, 72, 104015, doi: 10.1103/PhysRevD.72.104015

    Taruya, A., & Kudoh, H. 2005, PhRvD, 72, 104015, doi: 10.1103/PhysRevD.72.104015

    2005 doi

  52. [60]

    I., Ziemer, J., Thorpe, I., et al

    Thorpe, J. I., Ziemer, J., Thorpe, I., et al. 2019, in Bulletin of the American Astronomical Society, Vol. 51, 77, doi: 10.48550/arXiv.1907.06482

    2019 doi arXiv

  53. [61]

    X., Diehl, R., & Hartmann, D

    Timmes, F. X., Diehl, R., & Hartmann, D. H. 1997, ApJ, 479, 760, doi: 10.1086/303913

    1997 doi

  54. [62]

    2015, PhRvD, 91, 042003, doi: 10.1103/PhysRevD.91.042003

    Veitch, J., Raymond, V., Farr, B., et al. 2015, PhRvD, 91, 042003, doi: 10.1103/PhysRevD.91.042003

    2015 doi

  55. [63]

    Wilhelm, M. J. C., Korol, V., Rossi, E. M., & D’Onghia, E. 2021, MNRAS, 500, 4958, doi: 10.1093/mnras/staa3457

    2021 doi

  56. [64]

    2023, arXiv e-prints, arXiv:2307.05568, doi: 10.48550/arXiv.2307.05568

    Wu, J., & Li, J. 2023, arXiv e-prints, arXiv:2307.05568, doi: 10.48550/arXiv.2307.05568

    2023 doi arXiv

  57. [65]

    2024, arXiv e-prints, arXiv:2403.04832, doi: 10.48550/arXiv.2403.04832

    Xuan, Z., Naoz, S., Kocsis, B., & Michaely, E. 2024, arXiv e-prints, arXiv:2403.04832, doi: 10.48550/arXiv.2403.04832

    2024 doi arXiv

  58. [66]

    Yu, S., & Jeffery, C. S. 2010, A&A, 521, A85, doi: 10.1051/0004-6361/201014827

    2010 doi

  59. [67]

    D., & Liu, Y.-X

    Zhang, X.-H., Zhao, S.-D., Mohanty, S. D., & Liu, Y.-X. 2022, PhRvD, 106, 102004, doi: 10.1103/PhysRevD.106.102004

    2022 doi

  60. [68]

    2021, MNRAS, 500, 1421, doi: 10.1093/mnras/staa2707

    Zhao, Y., & Lu, Y. 2021, MNRAS, 500, 1421, doi: 10.1093/mnras/staa2707

    2021 doi

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