Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Identifying galactic binary systems of neutron stars and black holes with LISA

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read For a realistic isolated-binary-evolution population, LISA can measure the individual masses of about 10% of detected black-hole and black-hole-neutron-star binaries and about 50% of neutron-star binaries, enabling component classification.

desk verdict A careful end-to-end Bayesian forecast that LISA can measure individual masses for ~10% of detected BHBHs/BHNSs and ~50% of NSNSs, but the headline fractions rest on analysis priors scaled to the true injections and should be read as optimistic upper bounds until tested with search-realistic priors. read the letter →

arxiv 2507.11442 v1 pith:72TYPWGI submitted 2025-07-15 astro-ph.HE

Hannah Middleton , Panagiota Kolitsidou , Antoine Klein , Connor Adam , Rowan Chalmers , Alberto Vecchio This is my paper
classification astro-ph.HE
keywords gravitationalwavesLISAdoublecompactobjectsneutronstarsblackholesBayesianinferenceorbitaleccentricitymassmeasurement
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

LISA, the space-based gravitational-wave observatory, will see roughly a hundred galactic binaries made of black holes and neutron stars amid about ten thousand double-white-dwarf binaries. This paper asks whether LISA can tell the black holes and neutron stars apart by measuring the masses of the two components. Using synthetic LISA data built from an astrophysically realistic isolated-binary-evolution population and a full Bayesian analysis, the paper finds that individual masses can be measured for about 10% of detected black-hole binaries and black-hole-neutron-star binaries, and for about 50% of detected neutron-star binaries, with typical fractional errors around 10% (ranging from about 1% to 100%). For those systems LISA also determines the binary's three-dimensional position in the Milky Way, enabling follow-up and population studies. For a significant fraction of the detected binaries, however, the nature of the constituents may remain unclear.

What carries the argument

The load-bearing mechanism is the trio of phase-evolution parameters—$\dot f_{\rm GW}$, $f_{\rm PP}$, and $e$—extracted from the harmonic decomposition of an eccentric quasi-monochromatic waveform. In the leading post-Newtonian approximation, $\dot f_{\rm GW} \propto \mathcal{M}^{5/3} f_{\rm GW}^{11/3} F(e)$, where $\mathcal{M}$ is the chirp mass and $F(e)$ encodes the eccentricity enhancement of gravitational-wave emission, while $f_{\rm PP} \propto M^{2/3} f_{\rm GW}^{5/3}/(1-e^2)$, where $M$ is the total mass; together they separate $\mathcal{M}$ and $M$ and hence the two component masses. A Bayesian nested-sampling analysis over the 11 signal parameters produces the posterior distributions, and a parameter is counted as measured when its median is more than three times its posterior width, $S_k = \mathrm{med}(\theta_k)/(3\sigma_{\theta_k}) > 1$.

What would settle it

Re-run the same end-to-end analysis on the same or similar synthetic data with priors that are not centered on the injected parameters (for example, physical ranges spanning plausible masses, periods, and eccentricities), and with a harmonic count larger than 10; the claim is contradicted if the fraction of sources with all three of $\dot f_{\rm GW}$, $f_{\rm PP}$, and $e$ measured, and mass errors at the stated level, drops significantly below 10% for BHBH/BHNS binaries and below 50% for NSNS binaries.

Watch

Extended reading notes

Core claim

The central discovery is that the quasi-monochromatic LISA signal of a galactic double compact object still carries enough structure to yield both component masses in a substantial minority of cases. When the gravitational-wave frequency derivative $\dot f_{\rm GW}$, the periastron precession frequency $f_{\rm PP}$, and the orbital eccentricity $e$ can all be measured, the chirp mass and total mass follow directly, and from them the individual masses. For a population consistent with current isolated-binary-evolution predictions, that happens for about 10% of detected BHBH and BHNS binaries and about 50% of NSNS binaries, with fractional mass errors from roughly 1% to 100% (typically about 10%). The same measurements break the mass-distance degeneracy, giving distances with 1%–100% errors and sky-localization areas of $10^{-2}$–$10$ deg$^2$, so these binaries can be placed within the Milky Way and targeted for follow-up.

Load-bearing premise

The analysis assumes the search already knows each source's parameters well enough to place its priors within a small factor of the true values—frequency-derivative and precession priors span zero to five times the injected value, and eccentricity spans a factor of 3–10—so the 10% and 50% fractions could shrink if real LISA searches use wider or biased priors.

Editorial extensions

If this is right

  • For the mass-measurable systems, LISA will also fix the binary's three-dimensional Milky Way location, with sky areas from $10^{-2}$ to $10$ deg$^2$ and distances accurate to about 1%–100%, making targeted electromagnetic follow-up possible.
  • The measured masses will classify most primaries in BHBH and BHNS binaries as black holes and will confidently exclude a black hole in nearly all NSNS binaries, though some secondaries remain ambiguous against massive white dwarfs.
  • Eccentricity will be measured down to about $10^{-3}$, and since double white dwarfs are expected to be nearly circular, a measured nonzero eccentricity helps separate neutron-star binaries from the dominant double-white-dwarf population.
  • Because NSNSs are detected at higher frequencies and signal-to-noise ratios, mass measurement succeeds for about half of them, even though their total number is five to ten times smaller than BHBHs and BHNSs.
  • The resulting sample of roughly ten gravitational-wave-selected galactic double compact objects with known masses and locations will inform the formation and evolution channels of black holes and neutron stars and the star formation history of the Milky Way.

Reading between the lines

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

  • If the true galactic population has fewer detected systems than the fiducial model (population variations bracket BHBH detections from roughly 6 to 154), the number of binaries with measured masses could drop to a handful, so the demographic payoff depends strongly on formation-channel assumptions.
  • The same $\dot f_{\rm GW}$–$f_{\rm PP}$–$e$ machinery should apply to neutron-star–white-dwarf binaries, whose residual eccentricity also survives into the LISA band; a dedicated population study could test how often mass measurements separate them from NSNSs.
  • The measurable subset is selected by frequency, eccentricity, and signal-to-noise ratio rather than chosen randomly, so population-level inferences drawn from it will need an explicit selection function to avoid biased conclusions about the Milky Way's compact-object demographics.
  • A direct testable extension would be to run the same end-to-end pipeline on mock LISA data with realistic broad priors and a larger harmonic count, checking whether the 10% and 50% measurement fractions survive outside the idealized injection-centered setup.
Share X Bluesky LinkedIn Reddit HN

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. The paper investigates whether LISA will be able to measure the individual component masses of Galactic double compact objects (DCOs: BHBHs, BHNSs, NSNSs) that are expected to be detected in large numbers. Using the Wagg et al. (2022) fiducial population, the authors draw 100 sources of each type, generate synthetic LISA TDI data with the SciRD noise model, and perform a full Bayesian inference with an 11-dimensional parameter space. They define a measurability criterion S_k based on the median and width of the marginalized posterior, and find that ~10% of detected BHBHs and BHNSs and ~50% of detected NSNSs have measurable frequency derivative, periastron precession, and eccentricity, enabling individual mass measurements with fractional errors from ~1% to 100%. For these systems they also report 3D sky localization and distance estimates. The paper concludes that a small but useful sample of ~10 DCOs will have identified constituent masses, enabling astrophysical follow-up and population studies, while acknowledging several idealized assumptions in the analysis.

Significance. If the quantitative fractions are robust, this is a valuable forecast for LISA science operations: it is the first end-to-end simulation of mass measurement for Galactic DCOs using an astrophysically motivated population, and it identifies the key observables (frequency derivative, periastron precession, eccentricity) that drive the measurement. Credit is due for the transparent presentation, the public release of posterior chains and notebooks, and the open discussion of many limitations, including noise assumptions and global-fit challenges. The qualitative conclusion that a subset of DCOs will have measurable masses is likely correct. However, the headline 10%/50% numbers are derived under idealized analysis priors that are scaled to the injected true parameters, and the same truncated waveform is used for injection and recovery, so the quantitative fractions must be considered provisional until the sensitivity to these choices is quantified. The paper is within the standard scope of a forecasting study, and the issues identified below are addressable within the manuscript's framework.

major comments (3)
  1. [Table 1; Sec. 5.2; Sec. 5.3] The analysis priors for fdot, fPP, and e are set relative to the injected values (Table 1: U[0,5 fdot_inj], U[0,5 fPP_inj], and U[0,10 e_inj] or U[0,3 e_inj]). In a real LISA global fit, these parameters are not known within factors of 3–10: fdot can be consistent with zero, and e can span orders of magnitude across the DCO population. The 10%/50% fractions quoted in Sec. 5.3 and Table 2 therefore assume the search has already localized each source to within a factor of a few of the truth. This is load-bearing for the central claim. I ask the authors to rerun the analysis with search-realistic priors (e.g., wide priors, or priors whose central values are not matched to the injections) and report how the fractions in Table 2 change, or at minimum to quantify what fraction of the 'measured' systems retain S_k>1 when the prior boundary is moved by a factor of 2–3.
  2. [Sec. 5.2; Eq. (3a)-(3b)] The same truncated n=10 harmonic waveform is used for both signal generation and the likelihood (Sec. 5.2). The analysis therefore tests parameter recovery conditional on the model being exactly the one used to create the data; it does not test the effect of missing harmonics, which the paper itself notes will be needed for e ≳ 0.1 in realistic data. Since the mass measurements rely on fdot, fPP, and e, a systematic error from harmonic truncation could shift the recovered masses for the very systems being counted. Please perform a check on a representative subset (for example, all systems in Tables 3–6 with e > 0.1) using a larger harmonic count or the full Peters–Mathews sum, and report whether the S_k>1 classifications and the mass posteriors change.
  3. [Eq. (20); Tables 3–6] The criterion S_k = med(θ_k)/(3σ_θk) is lenient. Because σ_θk is the 16–50 percentile width, for a Gaussian posterior S_k>1 corresponds to a fractional uncertainty of up to roughly 75–100%. Tables 3–6 indeed include systems with fractional mass errors of order 50–100% (e.g., BHBH-65, NSNS-5, NSNS-11 in Tables 3 and 5). The abstract and conclusions state that masses are measured 'typically at the 10% level,' which is not representative of the full 'measured' sample. I recommend reporting the fractions for a few threshold values of S_k (e.g., S_k>2 and S_k>3) and, more importantly, the distribution of fractional mass errors for the 'measured' set, so that the reader can evaluate how many systems have genuinely informative mass measurements.
minor comments (5)
  1. [Abstract] The phrase 'short-period galactic binaries ∼ 10^7 − 10^3 yr from coalescence' is confusing; a decreasing range (10^7 to 10^3) should be clarified, for example by writing '10^7 to 10^3 yr before coalescence' with the ordering reversed.
  2. [Sec. 6] There is a typo in the Acknowledgments-adjacent sentence: 'It will will play an important role' should read 'It will play an important role'.
  3. [Appendix A, Table 3 caption] The table caption says 'for BHBSs' but should say 'for BHBHs' to match the naming convention used elsewhere.
  4. [Fig. 3 caption] The listing of measured parameter combinations in the caption uses shorthand that is sometimes ambiguous; for example 'measured ˙fGW, e' could be read as 'measured fdot and e' or as a list, and it is inconsistent with the fuller notation in the text and Table 2.
  5. [Sec. 5.4] The sentence 'The 90% probability region of the projected location of the source in the sky is ∆Ω ≈ 10^-2 − 10 deg^2' would be clearer as 'ranges from about 10^-2 to 10 deg^2'.

Circularity Check

0 steps flagged · score 0.0 of 10

Simulation-based forecast; no Eq-to-Eq circularity. Injection-scaled priors in Table 1 are a realism caveat, not a logical reduction; the central mass-measurement results rest on independent Bayesian posteriors and an external population.

full rationale

The paper is a forward-modeling simulation study: it draws binaries from the external Wagg et al. (2022) population, injects signals into synthetic LISA TDI data, and runs a Bayesian inference pipeline to compute posteriors on the signal parameters. The mass parameters are then derived from standard gravitational-wave relations, Eqs. (9), (11), and (12), combined with Eq. (1); no equation defining the target quantity is itself defined in terms of that target. The measurability criterion in Eq. (20), S_k = med(theta_k)/(3 sigma_theta_k), is an independent statistical condition on the posterior width, not an identity that forces a parameter to be counted. The principal caveat is that the analysis priors in Table 1 are scaled to the true injected values (e.g., fdot prior U[0,5 fdot_inj], fpp prior U[0,5 fpp_inj], e prior within a factor of 3-10 of e_inj), so the headline fractions in Table 2 and Sec. 5.3 should be read as idealized upper estimates conditional on the global fit already having localized the source to within these factors. This is a limitation on realism, but it is not a circular reduction: the posteriors are still computed from the likelihood, and the S_k>1 criterion requires the posterior to be narrow relative to its own median rather than merely equal to the prior. Similarly, the choices of zero-noise realisations, known noise PSD, and the same n=10 harmonic model for injection and recovery (Sec. 5.2) are model-consistency simplifications; the paper explicitly acknowledges these and the search-related challenge of identifying the eccentricity harmonic forest as a global-fit problem (Sec. 6). Self-citations to the authors' own pipeline (Moore et al. 2024, Klein et al. 2022, etc.) are tool references, and the underlying waveform expressions trace to standard results by Peters & Mathews and Moreno-Garrido et al., not to a self-cited uniqueness theorem. No load-bearing self-citation chain or definitional equivalence is present, so no significant circularity is found.

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

The central forecast depends on the injected population model (W22), the simplified GR-only n=10 waveform, the injection-scaled priors, and the lenient S_k criterion. None of these is a fitted parameter in the usual sense, but the hand-chosen prior scales and measurability threshold directly set the reported 10-50% fractions. No new physical entities are introduced.

free parameters (6)
  • Analysis prior scale for frequency derivative = 5 (U[0, 5 fdot_inj])
    Table 1: the fdot prior is set to five times the true injected value; this assumes prior knowledge of fdot to within a factor of five and affects which weak signals pass the measurability criterion.
  • Analysis prior scale for periastron precession frequency = 5 (U[0, 5 fpp_inj])
    Table 1: same structure as fdot; assumes fpp known to within a factor of five.
  • Analysis prior scale for eccentricity = 10 or 3 times e_inj (capped at 0.9)
    Table 1: e prior is U[0,10 e_inj] if e_inj<0.01, else U[0,min(3 e_inj,0.9)]; the choice of these factors affects the posterior width and measurability.
  • Measurability threshold S_k = 1
    Eq. (20): a parameter is deemed measured if median/(3 sigma) > 1; this is an arbitrary, lenient threshold that admits systems with ~100% mass errors.
  • Harmonic truncation order = n = 10
    Sec. 5.2: signals and likelihood are truncated at 10 harmonics, which is exact for generation and analysis but may miss SNR for e > ~0.1 in real data.
  • Eccentricity inclusion cut = e < 0.9
    Sec. 5.1: binaries with e >= 0.9 are excluded for computational reasons; this removes the most harmonic-rich sources from the sample.
assumptions (6)
  • standard math Peters-Mathews gravitational radiation reaction and eccentricity enhancement F(e) describe the orbital decay of DCOs in the LISA band.
    Equations (2), (8)-(10) use the Peters (1964) and Peters & Mathews (1963) results as the basis of fdot and time-to-coalescence; the analysis later assumes GR-only radiation reaction to convert measured parameters into masses (Sec. 5.3).
  • standard math The Moreno-Garrido et al. (1995) harmonic amplitudes An(e) are a sufficient description of eccentric GW emission.
    Eqs. (3)-(4) adopt these amplitudes and neglect the subdominant [Sn-Cn] component; this is a standard PN result.
  • domain assumption The W22 fiducial isolated binary evolution model is representative of the true galactic DCO population detectable by LISA.
    Sec. 4 draws all injection parameters from Wagg et al. (2022); the authors note other formation channels could change the numbers.
  • domain assumption The LISA noise is Gaussian, stationary, known a priori, and the A/E/T TDI channels are noise-orthogonal.
    Sec. 3 states the likelihood assumes noise-orthogonal channels; Sec. 6 acknowledges the noise is assumed known and stationary, with gaps and transients ignored.
  • ad hoc to paper The analysis priors reflect the information available from a real LISA global fit.
    Table 1 centers priors on the true injected values (e.g., fdot to within a factor of 5), which is a strong assumption about the accuracy of the search or global fit stage.
  • ad hoc to paper The S_k>1 measurability criterion identifies parameters that are genuinely measured.
    Eq. (20) defines 'measured' as median > 3 sigma from zero; this does not require a tightly constrained posterior and therefore inflates the count of systems with usable mass measurements.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Identifying galactic binary systems of neutron stars and black holes with LISA." pith.science (2026). https://pith.science/paper/72TYPWGI

@misc{pith2026250711442,
  author       = {Pith},
  title        = {Pith review of: Identifying galactic binary systems of neutron stars and black holes with LISA},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/72TYPWGI}},
  note         = {Machine review of arXiv:2507.11442}
}
abstract

The Laser Interferometer Space Antenna (LISA) will detect ~ 100 galactic binary systems comprised of black holes (BHs) and neutron stars (NSs). Identifying the nature of the constituents of these binaries as BHs or NSs, and distinguishing them from $\sim 10^4$ detected double white dwarfs will be challenging. In the absence of any other information, the inferred values of the component masses can be used to classify the nature of these objects. However, short-period galactic binaries $\sim 10^7 - 10^3$ yr from coalescence produce a quasi-monochromatic signal which carries little information about their masses. We generate synthetic LISA data sets containing gravitational waves (GWs) from galactic binary BHs, binary NSs and BHNSs drawn from an astrophysically realistic population produced through the isolated binary evolution channel. We process the data with an end-to-end Bayesian inference pipeline to explore the accuracy with which the individual component masses can be measured. We find that for $\approx 10\% - 50\%$ of the detected systems LISA will be able to measure the individual component masses by measuring the orbital eccentricity, periapse precession frequency, and GW induced frequency derivative. Typical fractional mass errors are $\approx 1\% - 100\%$ (depending on the specific value of the source parameters), which will enable in many circumstances the classification of the objects as BHs or NSs. For these binaries, LISA will also be able to determine their 3-dimensional position in the Milky Way. The LISA-detected sample of these double compact objects will provide new information about the galactic population of BHs and NSs, the star formation history of the Milky Way and the astrophysical processes leading to the formation of these systems. However, for a significant fraction of the LISA-detected binaries the nature of their constituent objects may remain unclear.

Figures

Figures reproduced from arXiv: 2507.11442 by the authors.

Figure 1
Figure 1. The frequency, fGW, see Eq. (7), SNR, and eccentricity e of the 300 simulated systems used in this in￾vestigation (100 simulations of each source type). The three source types BHBH, BHNS, and NSBH are shown in the top, middle, and bottom panel, respectively. Each point rep￾resents a simulated signal’s SNR and frequency. The colour scale indicates the eccentricity. The circled systems are those whose analysis meet th… view at source ↗
Figure 2
Figure 2. The component masses (m1, m2) and frequency (fGW) of the 300 simulated systems used in this investigation (100 simulations of each source type). The three source types of BHBH, BHNS, and NSNS are shown in purple, green, and orange, respectively. Each point represents a simulated system. The colour scales indicate the frequency. We use the convention m1 > m2 and the grey shaded area indicates the region outside of th… view at source ↗
Figure 3
Figure 3. The cumulative number of binaries for which different combinations of ˙fGW, fPP, and e are measured, according to the criteria defined in Eq. (20). The top, middle and bottom rows show the three source types of BHBH, BHNS, and NSNS, respectively. The left and right panels show sources ordered by frequency and SNR, respectively. Within each panel, the shaded colouring shows the total cumulative count of injections fo… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Box plots showing the marginalised posterior distribution functions of selected parameters – total mass M, chirp mass M, primary (m1) and secondary (m2) mass – of the 13 BHBHs (top row) and 10 BHNSs (bottom row) for which mass parameters can be measured, see Tables 3 a…
Figure 5
Figure 5. Figure 5: Same as [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Sky map of injected BHBH, BHNS, and NSNS binaries. Each black cross marker represented an injected binary. The circles show the binaries for which we measure ˙f, fpp and e. The BHBH, BHNS, and NSNS systems are shown in purple, green and orange, respectively. The color …
Figure 7
Figure 7. Figure 7: Box plots showing the marginalised posterior distribution functions of the eccentricity e and distance D of the 13 BHBHs (left) and 10 BHNS (right) for which mass parameters can be measured, see [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: Same as [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]
Figure 9
Figure 9. Figure 9: An example posterior distribution for BHBH ID-19. This signal has SNR = 386 and fGW = 1.7 mHz Each panel shows a two-dimensional posterior distribution. Top row (from left to right): amplitude and cosine of inclination; periastron precession frequency and frequency der…
Figure 10
Figure 10. Figure 10: An example posterior for BHNS-88. This signal has SNR = 48 and fGW = 0.37 mHz. The panels are identical to [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]
Figure 11
Figure 11. Figure 11: An example posterior for NSNS-35. This signal has SNR = 88 and fGW = 1.9 mHz. The panels are identical to [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]

Discussion (0). Sign in to comment.

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. Pulsars in Globular Clusters With the SKAO

    astro-ph.HE 2026-07 conditional novelty 3.0 of 10

    SKA-MID and SKA-LOW are predicted to discover 150–1700 new pulsars in Galactic globular clusters, more than doubling the current population of 345.

Reference graph

Works this paper leans on

70 extracted references · 16 canonical work pages · cited by 1 Pith paper

  1. [1]

    2025, arXiv e-prints, arXiv:2503.12263, doi: 10.48550/arXiv.2503.12263

    Abac, A., Abramo, R., Albanesi, S., et al. 2025, arXiv e-prints, arXiv:2503.12263, doi: 10.48550/arXiv.2503.12263

    arXiv doi:10.48550/arxiv.2503.12263 2025

  2. [2]

    G., Abbott, R., Abouelfettouh, I., et al

    Abac, A. G., Abbott, R., Abouelfettouh, I., et al. 2024, ApJL, 970, L34, doi: 10.3847/2041-8213/ad5beb

    doi:10.3847/2041-8213/ad5beb 2024

  3. [3]

    P., Abbott, R., Abbott, T

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

    doi:10.1103/physrevx.9.031040 2019

  4. [4]

    D., Abraham, S., et al

    Abbott, R., Abbott, T. D., Abraham, S., et al. 2021, Physical Review X, 11, 021053, doi: 10.1103/PhysRevX.11.021053

    doi:10.1103/physrevx.11.021053 2021

  5. [5]

    D., Acernese, F., et al

    Abbott, R., Abbott, T. D., Acernese, F., et al. 2023, Physical Review X, 13, 041039, doi: 10.1103/PhysRevX.13.041039

    doi:10.1103/physrevx.13.041039 2023

  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]

    2020, ApJL, 892, L9, doi: 10.3847/2041-8213/ab5b9a Astropy Collaboration, Price-Whelan, A

    Safarzadeh, M. 2020, ApJL, 892, L9, doi: 10.3847/2041-8213/ab5b9a Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f

    doi:10.3847/2041-8213/ab5b9a 2020

  8. [8]

    2021, arXiv e-prints, arXiv:2108.01167, doi: 10.48550/arXiv.2108.01167

    Babak, S., Hewitson, M., & Petiteau, A. 2021, arXiv e-prints, arXiv:2108.01167, doi: 10.48550/arXiv.2108.01167

    arXiv doi:10.48550/arxiv.2108.01167 2021

Show all 70 references
  1. [9]

    2024, Living Reviews in Relativity, 27, 4, doi: 10.1007/s41114-024-00050-z

    Blanchet, L. 2024, Living Reviews in Relativity, 27, 4, doi: 10.1007/s41114-024-00050-z

    2024 doi

  2. [10]

    2020, ApJ, 898, 71, doi: 10.3847/1538-4357/ab9d85

    Breivik, K., Coughlin, S., Zevin, M., et al. 2020, ApJ, 898, 71, doi: 10.3847/1538-4357/ab9d85

    2020 doi

  3. [11]

    S., Berger, E., Neijssel, C

    Broekgaarden, F. S., Berger, E., Neijssel, C. J., et al. 2021, MNRAS, 508, 5028, doi: 10.1093/mnras/stab2716

    2021 doi

  4. [12]

    S., Berger, E., Stevenson, S., et al

    Broekgaarden, F. S., Berger, E., Stevenson, S., et al. 2022, MNRAS, 516, 5737, doi: 10.1093/mnras/stac1677

    2022 doi

  5. [13]

    R., & Katz, M

    Burke, O., Marsat, S., Gair, J. R., & Katz, M. L. 2025, arXiv e-prints, arXiv:2502.17426, doi: 10.48550/arXiv.2502.17426

    2025 doi arXiv

  6. [14]

    2021, PhRvD, 104, 044065, doi: 10.1103/PhysRevD.104.044065

    Buscicchio, R., Klein, A., Roebber, E., et al. 2021, PhRvD, 104, 044065, doi: 10.1103/PhysRevD.104.044065

    2021 doi

  7. [15]

    B., Fuller, J., et al

    Caiazzo, I., Burdge, K. B., Fuller, J., et al. 2021, Nature, 595, 39, doi: 10.1038/s41586-021-03615-y

    2021 doi

  8. [16]

    Casares, J., & Jonker, P. G. 2014, SSRv, 183, 223, doi: 10.1007/s11214-013-0030-6

    2014 doi

  9. [17]

    T., Gandolfi, S., et al

    Chatziioannou, K., Cromartie, H. T., Gandolfi, S., et al. 2024, arXiv e-prints, arXiv:2407.11153, doi: 10.48550/arXiv.2407.11153

    2024 doi

  10. [18]

    2024, arXiv e-prints, arXiv:2402.07571, doi: 10.48550/arXiv.2402.07571

    Colpi, M., Danzmann, K., Hewitson, M., et al. 2024, arXiv e-prints, arXiv:2402.07571, doi: 10.48550/arXiv.2402.07571

    2024 doi arXiv

  11. [19]

    1998, PhRvD, 57, 7089, doi: 10.1103/PhysRevD.57.7089 14

    Cutler, C. 1998, PhRvD, 57, 7089, doi: 10.1103/PhysRevD.57.7089 14

    1998 doi

  12. [20]

    Baker, J. G. 2025, ApJ, 981, 66, doi: 10.3847/1538-4357/ada9e2

    2025 doi

  13. [21]

    2025, PhRvD, 111, 103014, doi: 10.1103/PhysRevD.111.103014 D’Orazio, D

    Deng, S., Babak, S., Le Jeune, M., et al. 2025, PhRvD, 111, 103014, doi: 10.1103/PhysRevD.111.103014 D’Orazio, D. J., & Samsing, J. 2018, MNRAS, 481, 4775, doi: 10.1093/mnras/sty2568

    2025 doi

  14. [22]

    2023a, MNRAS, 518, 1057, doi: 10.1093/mnras/stac3140

    El-Badry, K., Rix, H.-W., Quataert, E., et al. 2023a, MNRAS, 518, 1057, doi: 10.1093/mnras/stac3140

    doi

  15. [23]

    2023b, MNRAS, 521, 4323, doi: 10.1093/mnras/stad799

    El-Badry, K., Rix, H.-W., Cendes, Y., et al. 2023b, MNRAS, 521, 4323, doi: 10.1093/mnras/stad799

    doi

  16. [24]

    D., Reggiani, H., et al

    El-Badry, K., Simon, J. D., Reggiani, H., et al. 2024a, The Open Journal of Astrophysics, 7, 27, doi: 10.33232/001c.116675

    doi

  17. [25]

    W., et al

    El-Badry, K., Rix, H.-W., Latham, D. W., et al. 2024b, The Open Journal of Astrophysics, 7, 58, doi: 10.33232/001c.121261

    doi

  18. [26]

    2023, MNRAS, 522, 5358, doi: 10.1093/mnras/stad1288 Gaia Collaboration, Panuzzo, P., Mazeh, T., et al

    Finch, E., Bartolucci, G., Chucherko, D., et al. 2023, MNRAS, 522, 5358, doi: 10.1093/mnras/stad1288 Gaia Collaboration, Panuzzo, P., Mazeh, T., et al. 2024, A&A, 686, L2, doi: 10.1051/0004-6361/202449763

    2023 doi

  19. [27]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    2020 doi

  20. [28]

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

    2007 doi

  21. [29]

    1996, ApJL, 470, L61, doi: 10.1086/310296

    Kalogera, V., & Baym, G. 1996, ApJL, 470, L61, doi: 10.1086/310296

    1996 doi

  22. [30]

    2021, PhRvD, 104, 043019, doi: 10.1103/PhysRevD.104.043019

    Littenberg, T. 2021, PhRvD, 104, 043019, doi: 10.1103/PhysRevD.104.043019

    2021 doi

  23. [31]

    2025, PhRvD, 111, 024060, doi: 10.1103/PhysRevD.111.024060

    Stergioulas, N. 2025, PhRvD, 111, 024060, doi: 10.1103/PhysRevD.111.024060

    2025 doi

  24. [32]

    2022, arXiv e-prints, arXiv:2204.03423, doi: 10.48550/arXiv.2204.03423

    Klein, A., Pratten, G., Buscicchio, R., et al. 2022, arXiv e-prints, arXiv:2204.03423, doi: 10.48550/arXiv.2204.03423

    2022 doi arXiv

  25. [33]

    P., Toonen, S., et al

    Korol, V., Igoshev, A. P., Toonen, S., et al. 2024, MNRAS, 530, 844, doi: 10.1093/mnras/stae889

    2024 doi

  26. [34]

    2020, A&A, 638, A153, doi: 10.1051/0004-6361/202037764

    Korol, V., Toonen, S., Klein, A., et al. 2020, A&A, 638, A153, doi: 10.1051/0004-6361/202037764

    2020 doi

  27. [35]

    2018, PhRvL, 120, 191103, doi: 10.1103/PhysRevLett.120.191103

    Kremer, K., Chatterjee, S., Breivik, K., et al. 2018, PhRvL, 120, 191103, doi: 10.1103/PhysRevLett.120.191103

    2018 doi

  28. [36]

    F., et al

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

    2018 doi

  29. [37]

    W., & Pati, D

    Lim, Y., Bhattacharya, A., Holt, J. W., & Pati, D. 2021, PhRvC, 104, L032802, doi: 10.1103/PhysRevC.104.L032802

    2021 doi

  30. [38]

    B., & Cornish, N

    Littenberg, T. B., & Cornish, N. J. 2023, PhRvD, 107, 063004, doi: 10.1103/PhysRevD.107.063004

    2023 doi

  31. [39]

    2016, ApJ, 830, 41, doi: 10.3847/0004-637X/830/1/41

    Kozlowski, S. 2016, ApJ, 830, 41, doi: 10.3847/0004-637X/830/1/41

    2016 doi

  32. [40]

    N., Hobbs, G

    Manchester, R. N., Hobbs, G. B., Teoh, A., & Hobbs, M. 2005, AJ, 129, 1993, doi: 10.1086/428488

    2005 doi

  33. [41]

    C., Lamb, F

    Miller, M. C., Lamb, F. K., Dittmann, A. J., et al. 2021, ApJL, 918, L28, doi: 10.3847/2041-8213/ac089b

    2021 doi

  34. [42]

    J., Finch, E., Klein, A., et al

    Moore, C. J., Finch, E., Klein, A., et al. 2024, MNRAS, 531, 2817, doi: 10.1093/mnras/stae1288

    2024 doi

  35. [43]

    1995, MNRAS, 274, 115, doi: 10.1093/mnras/274.1.115

    Moreno-Garrido, C., Mediavilla, E., & Buitrago, J. 1995, MNRAS, 274, 115, doi: 10.1093/mnras/274.1.115

    1995 doi

  36. [44]

    E., Rodriguez, J., Jourdain, E., et al

    Motta, S. E., Rodriguez, J., Jourdain, E., et al. 2021, NewAR, 93, 101618, doi: 10.1016/j.newar.2021.101618

    2021

  37. [45]

    2025, arXiv e-prints, arXiv:2505.19870, doi: 10.48550/arXiv.2505.19870

    Toubiana, A. 2025, arXiv e-prints, arXiv:2505.19870, doi: 10.48550/arXiv.2505.19870

    2025 doi arXiv

  38. [46]

    R., & Portegies Zwart, S

    Nelemans, G., Yungelson, L. R., & Portegies Zwart, S. F. 2001, A&A, 375, 890, doi: 10.1051/0004-6361:20010683

    2001 doi

  39. [47]

    Ng, K. K. Y., Vitale, S., Farr, W. M., & Rodriguez, C. L. 2021, ApJL, 913, L5, doi: 10.3847/2041-8213/abf8be ¨Ozel, F., & Freire, P. 2016, ARA&A, 54, 401, doi: 10.1146/annurev-astro-081915-023322

    2021 doi

  40. [48]

    Peters, P. C. 1964, Physical Review, 136, 1224, doi: 10.1103/PhysRev.136.B1224

    1964 doi

  41. [49]

    C., & Mathews, J

    Peters, P. C., & Mathews, J. 1963, Physical Review, 131, 435, doi: 10.1103/PhysRev.131.435

    1963 doi

  42. [50]

    J., et al

    Pratten, G., Klein, A., Moore, C. J., et al. 2023a, PhRvD, 107, 123026, doi: 10.1103/PhysRevD.107.123026

    doi

  43. [51]

    2023b, PhRvD, 108, 124045, doi: 10.1103/PhysRevD.108.124045

    Pratten, G., Schmidt, P., Middleton, H., & Vecchio, A. 2023b, PhRvD, 108, 124045, doi: 10.1103/PhysRevD.108.124045

    doi

  44. [52]

    K., Hebeler, K., et al

    Raaijmakers, G., Greif, S. K., Hebeler, K., et al. 2021, ApJL, 918, L29, doi: 10.3847/2041-8213/ac089a

    2021 doi

  45. [53]

    A., & McClintock, J

    Remillard, R. A., & McClintock, J. E. 2006, ARA&A, 44, 49, doi: 10.1146/annurev.astro.44.051905.092532

    2006 arXiv

  46. [54]

    E., & Ruffini, R

    Rhoades, C. E., & Ruffini, R. 1974, PhRvL, 32, 324, doi: 10.1103/PhysRevLett.32.324

    1974 doi

  47. [55]

    2020, ApJL, 894, L15, doi: 10.3847/2041-8213/ab8ac9

    Roebber, E., Buscicchio, R., Vecchio, A., et al. 2020, ApJL, 894, L15, doi: 10.3847/2041-8213/ab8ac9

    2020 doi

  48. [56]

    S., & Tyles, J

    Samsing, J., Hamers, A. S., & Tyles, J. G. 2019, PhRvD, 100, 043010, doi: 10.1103/PhysRevD.100.043010

    2019 doi

  49. [57]

    2020, MNRAS, 494, L75, doi: 10.1093/mnrasl/slaa039

    Sesana, A., Lamberts, A., & Petiteau, A. 2020, MNRAS, 494, L75, doi: 10.1093/mnrasl/slaa039

    2020 doi

  50. [58]

    2001, PhRvL, 87, 251101, doi: 10.1103/PhysRevLett.87.251101

    Seto, N. 2001, PhRvL, 87, 251101, doi: 10.1103/PhysRevLett.87.251101

    2001 doi

  51. [59]

    2020, PhRvD, 102, 063006, doi: 10.1103/PhysRevD.102.063006 15

    Shao, D.-S., Tang, S.-P., Jiang, J.-L., & Fan, Y.-Z. 2020, PhRvD, 102, 063006, doi: 10.1103/PhysRevD.102.063006 15

    2020 doi

  52. [60]

    2004, in American Institute of Physics Conference Series, Vol

    Skilling, J. 2004, in American Institute of Physics Conference Series, Vol. 735, Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. R. Fischer,...

    2004 doi

  53. [61]

    H., Ferraioli, L., Schmelzbach, C., St¨ ahler, S

    Strub, S. H., Ferraioli, L., Schmelzbach, C., St¨ ahler, S. C., & Giardini, D. 2024, PhRvD, 110, 024005, doi: 10.1103/PhysRevD.110.024005

    2024 doi

  54. [62]

    J., Meyer, R., et al

    Tang, P., Eldridge, J. J., Meyer, R., et al. 2024, MNRAS, 534, 1707, doi: 10.1093/mnras/stae2154

    2024 doi

  55. [63]

    Tinto, M., & Dhurandhar, S. V. 2021, Living Reviews in Relativity, 24, 1, doi: 10.1007/s41114-020-00029-6

    2021 doi

  56. [64]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

    2020 doi

  57. [65]

    S., de Mink, S

    Wagg, T., Broekgaarden, F. S., de Mink, S. E., et al. 2021, Dataset from: Gravitational wave sources in our Galactic backyard - Predictions for BHBH, BHNS and NSNS binaries in LISA, doi: 10.5281/zenodo.4699713 —. 2022, ApJ, 937, 118, doi: 10.3847/1538-4357/ac8675

    2021 doi

  58. [66]

    Waskom, M. L. 2021, Journal of Open Source Software, 6, 3021, doi: 10.21105/joss.03021

    2021 doi

  59. [67]

    Williams, M. J. 2021, nessai: Nested Sampling with Artificial Intelligence, latest, Zenodo, doi: 10.5281/zenodo.4550693

    2021 doi

  60. [68]

    J., Veitch, J., & Messenger, C

    Williams, M. J., Veitch, J., & Messenger, C. 2021, Phys. Rev. D, 103, 103006, doi: 10.1103/PhysRevD.103.103006 —. 2023, Mach. Learn. Sci. Tech., 4, 035011, doi: 10.1088/2632-2153/acd5aa

    2021 doi

  61. [69]

    2020, A&A, 636, A20, doi: 10.1051/0004-6361/201935842

    Wyrzykowski, L., & Mandel, I. 2020, A&A, 636, A20, doi: 10.1051/0004-6361/201935842

    2020 doi

  62. [70]

    2025, arXiv e-prints, arXiv:2501.18682, doi: 10.48550/arXiv.2501.18682 16 APPENDIX A

    Xuan, Z., Kremer, K., & Naoz, S. 2025, arXiv e-prints, arXiv:2501.18682, doi: 10.48550/arXiv.2501.18682 16 APPENDIX A. ADDITIONAL TABLES AND PLOTS In this Appendix, we provide Tables of injected and recovered (selected) parameters for the BHBHs (Table 3), BHNSs (Table 4) and N...

    2025 doi arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.