A case study of GW190425 for classifying binary neutron star versus binary black hole mergers and constraining asymmetric dark matter with gravitational wave detectors

Divya Singh; Sanika Khadkikar

REVIEW 4 major objections 5 minor 1 cited by

A case study of GW190425 for classifying binary neutron star versus binary black hole mergers and constraining asymmetric dark matter with gravitational wave detectors

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper argues that GW190425, often classified as a neutron-star merger, may instead be a black-hole merger from dark-matter-induced collapse, and that tidal deformability can settle the classification and constrain dark matter.

desk verdict The case study is new and worth examining, but the Savage-Dickey metric is broken at Lambda=0 and the A# classification claims don't follow from the paper's own numbers. read the letter →

arxiv 2507.07895 v1 pith:PQKPIXOD submitted 2025-07-10 astro-ph.HE gr-qchep-ph

Sanika Khadkikar , Divya Singh This is my paper

classification astro-ph.HEgr-qchep-ph

keywords gravitationalwavesGW190425binaryneutronstarblackholetidaldeformabilityasymmetricdarkmattermatter-inducedcollapsenext-generationwavedetectors

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

GW190425 is one of only two known neutron-star merger candidates, but it had no electromagnetic counterpart and its low signal-to-noise ratio leaves its nature unresolved. This paper takes seriously the possibility that it was instead a binary black hole merger, formed when asymmetric bosonic dark matter accumulated in two neutron stars and collapsed each into a black hole. Under that assumption, the event's nearly zero effective tidal deformability becomes a measurement that constrains the dark matter particle mass and its interaction cross-section with ordinary matter. The paper then simulates GW190425-like signals for future detector generations and shows that near-term upgrades cannot confidently classify such events, while a next-generation network can distinguish neutron-star from black-hole binaries using tidal information alone. If the assumption is right, a single low-mass merger could probe dark matter physics that direct-detection experiments struggle to reach.

What carries the argument

The load-bearing observable is the effective tidal deformability \(\tilde{\Lambda}\), the mass-ratio-weighted combination of the two stars' dimensionless tidal deformabilities \(\Lambda_1\) and \(\Lambda_2\), which is zero for black holes and strictly positive for neutron stars and enters the gravitational-wave phase at fifth post-Newtonian order. Because a black-hole signal analyzed with a neutron-star waveform returns a posterior for \(\tilde{\Lambda}\) concentrated at zero, the sharpness of that posterior, quantified by the Savage-Dickey ratio and the 90% width \(\sigma_{\tilde{\Lambda}}\), serves as the classification metric. On the dark matter side, the central mechanism is the collapse-time inference: a merger-rate model built from the star-formation rate, a delay-time distribution, and the local binary merger rate gives the expected number of observable black-hole mergers as a function of the neutron-star-to-black-hole collapse time \(t_c\), and that time is then mapped to dark matter mass and cross-section through the Bose-Einstein condensation and direct-collapse formation timescales.

What would settle it

A future high-signal-to-noise observation of a GW190425-like event whose effective tidal deformability \(\tilde{\Lambda}\) is measured with a 90% credible interval excluding zero would show the source was a neutron-star binary rather than a black-hole binary, breaking the dark-matter constraint derived under the black-hole assumption. A population study finding low-mass black-hole mergers in the 1-3 \(M_\odot\) range at a rate inconsistent with the predicted number from the collapse-time model would likewise indicate the formation channel or its rate normalization is wrong.

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Extended reading notes

Core claim

On its own terms, the paper claims that a compact-binary gravitational-wave event with no electromagnetic counterpart and a posterior for the effective tidal deformability \(\tilde{\Lambda}\) that peaks at zero can be interpreted as a binary black hole produced by dark-matter-induced collapse of neutron stars, and that this interpretation converts the event into a dark matter constraint. The empirical hook is GW190425's \(\tilde{\Lambda}\) posterior from the GWTC-2.1 catalog, which favors zero but is broad; the authors proceed under the assumption that the source was a black-hole merger. From that assumption they derive limits on the dark matter particle mass \(m_\chi\) and interaction cross-section \(\sigma_\chi\) by way of the inferred collapse time. They then forecast, using simulated injections analyzed with Bayesian inference and a Fisher-matrix population study, that a network with A+ sensitivity cannot confidently classify a GW190425-like event, that an A# network can classify it only for stiff equations of state, and that a next-generation network combining the Einstein Telescope and Cosmic Explorer separates even soft-equation-of-state neutron-star signals from black-hole signals, enabling confident classification and tighter dark matter bounds.

Load-bearing premise

The load-bearing premise is that GW190425 really was a binary black hole merger formed when dark matter collapsed two neutron stars; the paper's evidence is a tidal-deformability posterior that peaks at zero but is too broad at the event's low signal-to-noise ratio to exclude a neutron-star binary with a soft equation of state, a limitation the authors acknowledge.

Editorial extensions

If this is right

If GW190425 was a dark-matter-induced black-hole merger, its observed tidal posteriors already place limits on the asymmetric dark matter mass and cross-section, complementing direct-detection bounds.
An A+-sensitivity network cannot distinguish a GW190425-like neutron-star binary from a black-hole binary, leaving classification ambiguous in the near term.
An A# network resolves the classification only if neutron stars follow a relatively stiff equation of state; for soft equations of state, the neutron-star and black-hole tidal posteriors overlap.
A next-generation Einstein Telescope plus Cosmic Explorer network distinguishes even soft-equation-of-state neutron-star mergers from black-hole mergers, because the neutron-star \(\tilde{\Lambda}\) posterior moves clearly away from zero.
A single confidently classified low-mass black-hole merger in a next-generation network would infer a much tighter collapse time than currently possible, with the remaining uncertainty dominated by the local merger-rate measurement.

Reading between the lines

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

If the dark-matter-implosion channel is real, the apparent mass gap between neutron stars and stellar black holes should be filled with black holes near 1-3 \(M_\odot\), and future population studies could look for this excess rather than automatically classifying every such event as a neutron-star binary.
The forecasting results imply that the neutron-star equation of state is now the key unknown for classification strategy: a stiff equation of state would let A# networks settle these cases, while a soft one pushes the decision to next-generation observatories.
The same Savage-Dickey-based classification metric could be applied to any low-mass merger without an electromagnetic counterpart, avoiding the Occam-penalty that makes standard Bayes factors favor the black-hole model even when tides are present.
A future high-signal-to-noise event with decisively positive \(\tilde{\Lambda}\) would falsify the dark-matter-implosion interpretation for that event, while the derived dark-matter bounds would still stand as conservative limits for events whose tidal posteriors remain consistent with zero.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The paper's real contribution is a concrete case study: taking the dark-matter-implosion channel from Singh et al. and asking what GW190425 would look like if it were a BBH from that channel, then forecasting how future networks would separate that from a BNS. That is genuinely new, and the authors are honest about the speculative starting point. They also do the heavy lifting of simulating injections across A+, A#, and ET/CE networks and working through the rate and dark-matter constraints. The limitations section is unusually candid.

But the classification metric they lean on is not sound as used. The Savage-Dickey ratio comparing Lambda=0 against a continuous prior is undefined here: with uniform priors on the component Lambdas, the induced prior density of Lambda_tilde is zero at Lambda_tilde=0, so the denominator is zero. Replacing the point density with an interval probability over Lambda<1 changes the quantity being reported. The table also self-contradicts: the HLI# DD2 row gives a 90% credible interval for Lambda_tilde that excludes zero (roughly 113 to 505) yet reports a Savage-Dickey ratio of 1.04, which would require the posterior and prior densities at zero to be nearly equal. Those two statements can't both be true. And the translation of ratios to probabilities is off elsewhere: the text says the CE4020ET APR4 BNS has about 19% BBH probability, but a ratio of 5.2 would give roughly 84% under the stated formula.

This is a load-bearing problem because the abstract and conclusions sell the A# stiff-EoS result on the strength of this metric. The Fisher-based forecasts and the dark-matter constraints are less affected, since they use the width of the tidal posterior rather than the ratio, but the central classification claims are not established as presented. A referee should ask for a re-analysis with a well-defined model comparison—say, a prior with a point mass at Lambda=0, or a Bayes factor that doesn't require a density at a point where the prior vanishes.

I'd send this to review, not desk reject. The question is interesting, the work is substantial, and the flaw is fixable. But as it stands, the headline classification results don't hold up. Readers working on GW classification or dark-matter searches with future detectors may still find the forecast framework worth a look, but they should not take the A# conclusion at face value.

Referee Report

4 major / 5 minor

Summary. The paper reanalyzes GW190425 under the hypothesis that it was a binary black hole (BBH) formed by dark-matter-induced collapse of neutron stars, using the LVK posterior for the effective tidal deformability. It then simulates GW190425-like BNS and BBH injections for future detector networks (HLVK+, HLI#, CE4020ET) and uses Savage–Dickey ratios and posterior widths to forecast source classification. Under the BBH assumption, the paper uses Fisher-matrix forecasts and collapse-time formulas from Singh et al. to derive constraints on the dark-matter mass and cross-section, both for O3 and for future networks. The abstract's main claims are that an A#-sensitivity network can classify a stiff-EoS BNS event, that next-generation observatories can classify even soft-EoS events, and that a single low-mass BBH detection can constrain asymmetric dark matter.

Significance. If the analysis were correct, the paper would offer a novel way to constrain asymmetric dark matter from a single low-mass merger and a useful forecast for third-generation detectors. Strengths include the use of public GWTC-2.1 posterior samples, a transparent simulation setup with bilby/dynesty, and explicit statements of caveats (low SNR, model dependence of the collapse timescale, and the conditional nature of the BBH assumption). However, the classification metric is not valid as implemented, and the numerical results in Table IV are internally inconsistent. These problems affect the paper's central classification claims and, indirectly, the DM forecasts, so the significance of the results as presented is not established.

major comments (4)

[Appendix A / Table IV] The quantity called the Savage–Dickey ratio is not actually a Savage–Dickey ratio. With uniform priors on the component tidal deformabilities Λ1 and Λ2 in Eq. (4), the induced prior density of Λ̃ vanishes at Λ̃=0, so the denominator π(Λ̃=0) in the quoted formula is zero and the ratio is undefined. The Appendix's prescription of evaluating the ratio on a grid of points with Λ̃<1 replaces a point density with an interval probability, which is not a Bayes factor for the null hypothesis Λ̃=0. Consequently, the conversion of the reported values to 'probability of a BBH origin' via B/(1+B) is not justified, and every classification probability derived from Table IV is unsupported.
[Table IV, HLI#/DD2 row] The reported Savage–Dickey ratio of 1.04 (≈51% probability of BBH origin) is inconsistent with the same row's 90% credible interval for Λ̃, [112.7, 504.9], which excludes Λ̃=0. A posterior whose 90% interval excludes zero has negligible density at zero, so a density ratio near unity is impossible; at best the interval-based quantity is not the point-null Bayes factor it is claimed to be. This directly undercuts the abstract and Sec. V statement that an A# network can confidently classify a stiff-EoS BNS event, because the table indicates a coin-flip classification for the DD2 injection.
[Table IV, CE4020ET/APR4 row] The text in Sec. IVA states that for the CE4020ET APR4 injection the probability of a BBH origin is about 19%, which would require a Savage–Dickey ratio of approximately 0.23. Table IV lists a ratio of 5.2, which under the same conversion corresponds to about 84%. This is a direct numerical contradiction in the central case used to support the claim that CE4020ET provides robust classification even for the softest equation of state. The authors should either report the actual posterior probability or correct the table; as it stands, the claim is not established.
[Sec. IVC / Fig. 7] The inference of the collapse time uses the 95th percentile of the Λ̃ posterior of the same event to set the threshold σ_Λ̃T and then assumes NBBH=1 for that event. This makes the inferred collapse time depend on the event's own noise realization and posterior width, rather than on an independently calibrated population criterion. The resulting DM constraints in Figs. 8 and 9 inherit this dependence without a demonstrated calibration. A concrete test would be to repeat the inference with σ_Λ̃T fixed by an independent population threshold or by an ensemble of simulated noise realizations.

minor comments (5)

[Table IV / Fig. 4] The axis labels in Figs. 1 and 4 contain a placeholder symbol '×10□3'; the superscripts should be restored so the figures are readable.
[Sec. IVA] The text says that in the HLVK+ network the SNR is similar to that of GW190425, but Table IV lists SNR ≈ 23 for HLVK+ injections versus ≈ 13 for the original event; these values should be reconciled.
[Table V] Table V reports Bayes factors ln BF_BBH/BNS but does not state which injected signal type (BBH or BNS) or equation of state was used; without this information the table is not reproducible.
[Sec. II / Sec. IIA] There are sentence fragments and missing words in Sec. II, for example 'the recovery of tidal information. Due to their increased sensitivity'; the text should be revised for clarity.
[Eqs. (9)-(10)] Equations (9) and (10) are quoted from Ref. [28] without derivation. Because these formulas are load-bearing for the DM constraints and Ref. [28] is closely connected to the present work, a brief derivation sketch or independent check would strengthen the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the DM constraints are a conditional model application and the classification forecasts are independent injection-recovery studies; the Savage-Dickey issue is a statistical-validity flaw, not a circular reduction.

full rationale

The paper's derivation chain is conditional rather than self-referential. The central assumption (Sec. IIB) is that GW190425 was a BBH formed via dark-matter-induced collapse; on that assumption, the DM constraints in Sec. IVD are obtained by solving t_collapse from N_BBH=1 using the collapse-timescale formulas imported from Singh et al. [28]. Those formulas are external model inputs with stated assumptions (NS temperature, DM density, velocity dispersion), not quantities fitted to the GW data in this paper, so invoking them is not a circular step. The classification forecasts are self-contained injection-recovery simulations: BNS and BBH signals are generated with IMRPhenomXAS/NRTidalv3 and analyzed with the same waveform families across detector networks, and the claimed separability is read off the recovered Lambda-tilde posteriors. The threshold-setting in Sec. IVC uses the event's own tidal posterior to define sigma_Lambda_tilde_T, but the inferred t_collapse is not a fit to DM parameters; it is an inverse-population calculation conditioned on N_BBH=1, so it does not reduce by construction to the input tidal measurement. The self-citations to [28] and [78] are load-bearing only in the sense of importing an external model and a side remark; they are not unverified uniqueness claims, and the cited model is independently published and conditional on assumptions outside the present data. The one serious flaw is the Savage-Dickey metric in Sec. IVA and Appendix A: with uniform priors on the component Lambda_i, the induced prior density of Lambda_tilde vanishes at Lambda_tilde=0 (Eq. 4), so the point-null ratio as written is undefined, and the grid prescription with Lambda_tilde<1 defines a different interval probability rather than the claimed density ratio. This undermines the quantitative HLI# stiff-EoS classification support and should be treated as a statistical-validity/correctness problem, but it is not an instance of a prediction being equivalent to its input by construction.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a speculative dark-matter model from prior work, an unproven assumption about the nature of GW190425, and several astrophysical inputs chosen by hand. The free parameters are not fitted to the target result but are assumed values that affect the dark matter constraints.

free parameters (5)

Local dark matter density rho_chi = 1 GeV/cm^3 and 0.1 GeV/cm^3
Fiducial values assumed for the ambient DM density; the paper computes limits for these two values rather than fitting. These choices affect the collapse timescales and resulting constraints.
Neutron star core temperature T = 1e5 K
Assumed temperature entering the BEC formation timescale (Eq. 9). The paper lists this as an assumed quantity in the caveats.
Threshold cross-section sigma_th = ≈ 2e-45 cm^2
Geometric threshold cross-section for a 1.4 Msun, 12 km NS, used in the collapse time formulas. Not fitted to data.
Delay time distribution exponent = -1 (P(td) ∝ td^{-1})
Assumed delay time distribution for merging binaries, taken from prior literature and not varied.
Local merger rate normalization A = Set to GWTC-3 BNS merger rate 105.5^{+190.2}_{-83.9} Gpc^{-3} yr^{-1}
The normalization of the DM-induced BBH merger rate is chosen so that the local rate of ≤3 Msun mergers equals the inferred BNS rate. This assumes all such mergers come from the DM channel.

assumptions (4)

domain assumption The dark-matter-induced neutron star collapse scenario of Singh et al. is correct.
The collapse timescale formulas (Eqs. 9-10) are taken from a prior paper co-authored by one of the present authors and are not independently verified in this work.
domain assumption GW190425 is a binary black hole merger.
The paper proceeds under this assumption based on the tidal posterior peaking at zero, despite the broad uncertainty noted in Sec. IIB.
domain assumption All compact binary mergers with component masses ≤3 Msun are drawn from the same population as the DM-induced BBH channel.
The merger rate normalization sets A so that the local rate of all ≤3 Msun binaries equals the observed BNS rate, thereby attributing the entire rate to this channel.
standard math Standard waveform models (IMRPhenomXAS, NRTidalv3) and Fisher formalism are accurate for these signals.
The analysis relies on these models and the Fisher approximation (Secs. IIIB, IIC).

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Cite this review

Pith. "Pith review of A case study of GW190425 for classifying binary neutron star versus binary black hole mergers and constraining asymmetric dark matter with gravitational wave detectors." pith.science (2026). https://pith.science/paper/PQKPIXOD

@misc{pith2026250707895,
  author       = {Pith},
  title        = {Pith review of: A case study of GW190425 for classifying binary neutron star versus binary black hole mergers and constraining asymmetric dark matter with gravitational wave detectors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PQKPIXOD}},
  note         = {Machine review of arXiv:2507.07895}
}

read the original abstract

The LIGO Scientific, Virgo, and KAGRA collaboration has identified two binary neutron star merger candidates, GW170817 and GW190425, along with several binary black hole candidates. While GW170817 was confirmed as a BNS merger through its electromagnetic counterparts, GW190425 lacked such observations, leaving its classification uncertain. We examine the possibility that GW190425 originated from black holes that merged after dark matter accretion caused their progenitor neutron stars to implode. Using this event, we place constraints on dark matter parameters, such as its mass and interaction cross section. We simulate GW190425-like events and analyze them using future gravitational wave detector networks, including upcoming upgrades to current detector networks and next-generation observatories. We show that a network with A+ sensitivity can not classify a GW190425-like event with sufficient confidence. Detector networks with A# sensitivity can classify such events only if the neutron stars follow a relatively stiff equation of state, whose stronger tidal imprint differs measurably from a binary black hole waveform. Next-generation observatories like the Einstein Telescope and Cosmic Explorer recover the tidal signature even for soft, compact stars, enabling confident classification. Finally, we forecast the dark matter constraints that future gravitational wave networks could achieve for similar events.

Figures

Figures reproduced from arXiv: 2507.07895 by the authors.

**Figure 2.** Amplitude spectral density (ASD) of detector noise [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Mass–radius curves for the APR4, MPA1, and DD2 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Forecasted posterior distributions of the tidal deformability parameter [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Cumulative distribution function of σ 90% Λ˜ in the detector networks considered in this work. Note that the fraction of events detected with an error σ 90% Λ˜ ≃ 100 is 0.07% for O3, 0.15% for HLVK+, 0.65% for HLI# and 20% for CE4020ET respectively. through an interp…

**Figure 6.** Figure 6: Detectable merger rate of BBH mergers as a function of collapse time and the threshold on [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

**Figure 7.** Figure 7: Inferred collapse time for NBBH = 1 per yr for SNRT = 8(blue) and NBBH = 1 for SNRT set to event SNR in respective detector networks given in table IV(yellow). For both cases, σ 90% Λ˜ is equal to the 95th percentile value for the GW190425-like BBH signal from table IV…

**Figure 8.** Figure 8: Limits on the scattering cross-section, σχ as a function of mass of DM particle, mχ using the collapse time, tc inferred from the detection of GW190425 in O3. We use SNRT = 8 and σΛ˜T = 1327 to determine NBBH(tc) for the O3 observing period, T = 0.9 yr [3]. We present…

**Figure 9.** Figure 9: Limits on the scattering cross section σχ as a function of the dark-matter particle mass mχ, using the collapse time tc inferred from GW190425. Panel (a) sets SNRT = 8 for all networks. Panel (b) uses SNRT equal to the SNR of the GW190425-like BBH signal in each detec…

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Reference graph

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Additionally, to infer the collapse time from the observa- tion of a GW190425-like signal, we also perform exten- sive simulations using the Fisher formalism (Sec

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Simulation Setup We generate both BNS and BBH signals like the event GW190425, to assess their distinguishability using future upgrades and generations of GW detectors. To simulate BNS signals, we use theIMRPhenomXAS_NRTidalv3 [40, 41] waveform model, which augments the closed- form, phenomenological, quadrupolarIMRPhenomXAS [40] model by incorporating ti...
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Pith tools

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

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Additionally, to infer the collapse time from the observa- tion of a GW190425-like signal, we also perform exten- sive simulations using the Fisher formalism (Sec

Detector Networks In this study, we consider three different detector net- works to analyze simulated signals following the inferred source properties of GW190425 using Bayesian inference. Additionally, to infer the collapse time from the observa- tion of a GW190425-like signal, we also perform exten- sive simulations using the Fisher formalism (Sec. IIIB...

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Simulation Setup We generate both BNS and BBH signals like the event GW190425, to assess their distinguishability using future upgrades and generations of GW detectors. To simulate BNS signals, we use theIMRPhenomXAS_NRTidalv3 [40, 41] waveform model, which augments the closed- form, phenomenological, quadrupolarIMRPhenomXAS [40] model by incorporating ti...

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the uncertainty in the measurement of the effective tidal deformability signified by (σ90% ˜Λ ), and

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the collapse time for the NS to become a BH (tc) The measured 90% confidence interval of the effective tidal deformability, σ90% ˜Λ , is used as the discriminator between a BNS and BBH signal. Therefore, it is used in conjunction with the SNR to build the detector network efficiency, ϵ(z) = 1 N NX i=1 Π SNR SNRT − 1 z Π σ ˜ΛT σ ˜Λ − 1 z (5) which encodes ...

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As the sensitivity of the GW detectors in the network increases, more sources are detectable with higher SNRs and lowerσ ˜Λ

is applied. As the sensitivity of the GW detectors in the network increases, more sources are detectable with higher SNRs and lowerσ ˜Λ. Therefore, the overall observable rates are highest for the CE4020ET network at every σ ˜ΛT for a common SNR threshold(SNRT = 8) across all networks. The bands in Fig. 6a correspond to the uncertainty in the local merger...