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By fitting the afterglow and kilonova of six gamma-ray bursts in a single Bayesian model, this paper claims that electromagnetic data alone can identify whether the progenitor was a binary neutron star or a neutron star–black hole and recov

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Simultaneous afterglow+kilonova modeling of six GRBs favors BNS progenitors for four events, allows NSBH for two, and yields log M_wind = -20.23 + 0.38 log E0,J.

T0 review reviewed 2026-08-03 challenge →

load-bearing objection Solid joint afterglow+KN inference for six GRBs, but the binary-property 'confirmation' is largely inherited from the EOS-based mapping, not an independent EM measurement. the 5 major comments →

arxiv 2512.23354 v2 pith:AWTL5KY2 submitted 2025-12-29 astro-ph.HE

Kilonova and progenitor properties of merger-driven gamma-ray bursts

P. Singh , G. Stratta , A. Rossi , P.T.H. Pang , M. Bulla , F. Ragosta , A. De Rosa , D.A. Kann
show 1 more author
F. Cogato
This is my paper
classification astro-ph.HE
keywords gamma-ray burstskilonovaneutron star mergersNSBH mergersafterglowBayesian inferencetidal deformabilitychirp mass
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The paper aims to show that electromagnetic observations alone—without gravitational waves—can reveal what kind of compact-object merger produced a gamma-ray burst. It models the non-thermal afterglow and the thermal kilonova emission together in a single Bayesian fit for six nearby GRBs with claimed kilonovae, using GW170817/AT2017gfo as a calibrator. It finds that a kilonova is clearly present in five of the six events, that four events favor a binary neutron star progenitor, and that two slightly favor a neutron star–black hole merger. It then infers, for the first time from EM data alone, sample-level binary properties—chirp mass, tidal deformability, and mass ratio—and reports that the disk wind ejecta typically outweigh the dynamical ejecta by about a factor of two. If right, this turns existing GRB data into a population-level probe of neutron star mergers and their equation of state.

Core claim

The paper's central claim is that a single Bayesian fit to both the non-thermal afterglow and the thermal kilonova can simultaneously identify the merger type and recover binary properties from electromagnetic data alone. Applied to six nearby GRBs with claimed kilonovae—using GW170817/AT2017gfo as a calibrator—the joint fit clearly identifies a kilonova in five events (with GRB 150101B ambiguous), favors a binary neutron star progenitor for GRB 160821B, GRB 170817A, GRB 211211A, and GRB 230307A, and leans slightly toward a neutron star–black hole progenitor for GRB 150101B and GRB 191019A. The fit also yields, for the first time from EM data alone, sample-level binary parameters: wind eject

What carries the argument

The load-bearing tool is a joint Bayesian model that couples a forward-shock afterglow light-curve model (with Gaussian or top-hat jet geometries) to a multi-component kilonova model built from two ejecta components—fast dynamical ejecta and a slower disk wind—with a free half-opening angle for the lanthanide-rich region. The same viewing angle is shared by both emission components, so the fit can separate thermal kilonova light from non-thermal afterglow without subtracting one from the other. The inferred ejecta masses are then mapped to binary properties (chirp mass, tidal deformability, mass ratio) through equation-of-state-dependent phenomenological relations.

Load-bearing premise

The NSBH classifications for GRB 150101B and GRB 191019A rest on the grid assumption that NSBH ejecta have no lanthanide-poor polar component; if real NSBH ejecta can be lanthanide-poor there, the slight NSBH preferences could be artifacts of the model grid.

What would settle it

Re-run the analysis for GRB 150101B and GRB 191019A with an NSBH kilonova grid that permits lanthanide-poor ejecta at high latitudes; if the Bayes factor no longer prefers NSBH, the classification is a grid artifact. A future gravitational-wave-detected NSBH merger that shows a blue, lanthanide-poor kilonova would likewise contradict the fixed-geometry assumption.

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

If this is right

  • Kilonova emission is present in essentially all well-observed nearby merger-driven GRBs; the one ambiguous case is GRB 150101B, where the data cannot confirm or exclude the kilonova.
  • Four of the six events (GRB 160821B, GRB 170817A/AT2017gfo, GRB 211211A, GRB 230307A) favor a binary neutron star progenitor, while GRB 150101B and GRB 191019A slightly favor a neutron star–black hole system with BNS still viable.
  • Across the sample, the disk wind ejecta (median 0.027 solar masses) dominate over the dynamical ejecta (median 0.012 solar masses), so post-merger winds carry most of the mass that powers the kilonova.
  • The wind mass scales with beaming-corrected jet kinetic energy as log M_wind = -20.23 + 0.38 log E0,J, a statistically significant correlation reported here for the first time.
  • The inferred chirp mass and tidal deformability follow the expected inverse trend—lower chirp mass means higher tidal deformability—and for GW170817/AT2017gfo the EM-only values match the gravitational-wave measurements, validating the approach.

Where Pith is reading between the lines

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

  • If the M_wind–E0,J correlation survives a larger sample, kilonova brightness could become a proxy for jet energy in GRBs lacking X-ray afterglows, and the slope could be compared with disk-mass prescriptions to measure accretion-to-jet efficiency.
  • The paper's separation of short and long merger-driven GRBs in the mass-ratio versus dynamical-mass plane hints that burst duration may trace the amount of fallback accretion; this could be tested by measuring q and M_dyn for more long-duration merger GRBs.
  • A straightforward robustness check would be to rerun the NSBH fits with a grid that includes lanthanide-poor polar ejecta; the paper's classifications of GRB 150101B and GRB 191019A currently rest on the fixed 30-degree lanthanide-rich geometry.
  • The method's calibrated performance on GW170817 suggests it could one day be used to rank electromagnetic candidates for follow-up even before a gravitational-wave alert is available, though the paper does not discuss this.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper presents a uniform Bayesian analysis of six merger-driven GRBs with kilonova (KN) claims, simultaneously modeling the non-thermal afterglow (afterglowpy) and thermal KN emission (POSSIS) within the NMMA framework. For each event, the authors compare afterglow-only, BNS+KN, and NSBH+KN models via Bayes factors, infer ejecta masses (M_dyn, M_wind), and then map the inferred ejecta masses to binary properties (chirp mass, tidal deformability, mass ratio) using phenomenological relations built on a fixed EOS set. The main claims are: robust KN identification in all cases except GRB 150101B; BNS progenitors favored for 160821B, 170817A, 211211A, and 230307A; a marginal preference for NSBH in 150101B and 191019A; wind mass exceeding dynamical mass; a correlation between wind mass and beaming-corrected jet energy; and a confirmation of the anti-correlation between tidal deformability and chirp mass. The paper also reports this as the first sample-level determination of merger-driven GRB progenitor properties from EM data alone.

Significance. If the central claims hold, the paper provides a valuable methodological demonstration: simultaneous afterglow+KN fitting on a small but homogeneous sample, including a successful blind validation on GW170817/AT2017gfo, could become the standard for EM-only progenitor classification. The use of public tools (NMMA, POSSIS, afterglowpy) is a strength, as is the explicit model comparison with Bayes factors and the inclusion of systematic uncertainties via sigma_sys. However, the binary-property results are not independent measurements: they are derived through phenomenological mass–EOS relations, so the claimed 'confirmation' of theoretical Lambda–M_chirp relations and the 'first time' narrative are weaker than presented. The main value is the ejecta-parameter and progenitor-classification exercise, with the caveats discussed below.

major comments (5)
  1. [Sec. 3.4 and Sec. 5.5] The claim that the inferred anti-correlation between Lambda_tilde and M_Chirp is "not prescribed during inference" (Sec. 5.5) is misleading. Sec. 3.4 explicitly maps M_dyn/M_wind to binary properties via the phenomenological relations of Krüger & Foucart (2020) and Dietrich et al. (2020) built on the Huth et al. (2022) EOS set. Those relations already encode a near-universal Lambda_tilde–M_Chirp dependence, so the posterior will show the anti-correlation even if the light curves contain no independent information about it. The authors should quantify the difference between the posterior and the prior predictive distribution implied by their mapping, and temper the 'confirmation' statement. Section 6.1's suggestion to prescribe this dependence 'directly in the inference framework' further indicates the dependence is not currently absent; it is mediated by the phenomenological prior.
  2. [Sec. 3.1, Table 1, Table A.5, Fig. 1] For GRB 191019A, the model comparison shown in Figure 1 and Table A.5 uses log(eps_e)=-0.3 and log(eps_B)=-2.0 fixed, while the parameters reported in Table 1 and the main text use freely varying eps_e/eps_B. Section 3.1 states that these parameters were freed only after inspecting the n0 posterior. This is a post-hoc model choice and introduces potential selection bias. The paper should present the full model comparison (evidence and Bayes factors) for the free-eps version, and justify that the fixed-eps model is the appropriate reference for the scientific conclusions. If the free-eps model changes the ranking, the inference is not robust.
  3. [Sec. 5.4, Fig. 9] The claimed correlation between log M_wind and log E0,J is based on six events, with Pearson p=0.034 and Spearman p=0.019. With n=6, these p-values are fragile: they are driven by one or two points (notably GW170817), the sample is not independent of the fitting procedure (M_wind and E0 are jointly inferred with shared parameters), and no multiple-testing correction is applied to the many possible correlations examined. The authors should provide a jackknife or bootstrap test excluding each event in turn, and state the prior probability of testing this particular correlation. The abstract's 'statistically significant' wording should be softened unless these tests confirm the result.
  4. [Sec. 3.2, Sec. 5.5, Fig. 10] The NSBH preference for GRB 150101B and GRB 191019A rests on the NSBH grid geometry in POSSIS, in which the lanthanide-rich ejecta component is fixed within Phi=30 deg and no lanthanide-poor high-latitude component exists. If true NSBH ejecta have a larger lanthanide-poor fraction, the Bayes-factor preference for the NSBH model could be an artifact of the grid, not of the data. The manuscript should discuss how the evidence changes under plausible variations of the NSBH Phi parameter (e.g., a grid with Phi=15 or 45 deg, or a lanthanide-poor outer component), and how the quoted 'slight preference' depends on this modeling assumption.
  5. [Sec. 2.3] All early-time data (t<0.9 d) for GRB 160821B are converted to upper limits on the grounds that a reverse/refreshed shock is present. This is a conservative treatment for a forward-shock-only model, but it discards significant information and may bias the inferred KN parameters. The impact of this choice should be tested, for example by fitting with an early reverse-shock component or at least by varying the time threshold. Given that GRB 160821B is one of the four 'BNS favored' events, the robustness of its classification to this data-handling choice should be demonstrated.
minor comments (6)
  1. [Fig. 3 caption] 'GRB 191019A (left)' should read '(right)' — the figure shows 170817A on the left and 191019A on the right.
  2. [Sec. 4.2] The text says NSBH cases have 'higher <M_Chirp>=1.3±2 M_sun'; the quoted uncertainty seems numerically wrong (Table 3 gives 2.05+0.43/-0.34 and 1.86+0.45/-0.36). Please correct the value and error.
  3. [Sec. 2.7] 'because of its error≥0.3%' should refer to 0.3 magnitudes, not 0.3%.
  4. [Fig. 1 caption] Typo: 'NSBN-TH' should be 'NSBH-TH'.
  5. [Table A.3] The caption says 'BNS-GS & BNS-TH' but the table header uses 'BNS-TH' etc. In the header row, 'NSBN-TH' appears instead of 'NSBH-TH' — please proofread the table headers.
  6. [Sec. 4.1, GRB 170817A] The inclination angle is quoted as i=32.73 deg ±0.57 deg, which is consistent with the prior range Sine(0.20,0.60) rad; make clear that the posterior is very narrow, likely due to the informative prior and the high-quality data, and that the result may be prior-dominated.

Circularity Check

2 steps flagged

Λ̃–M_Chirp 'confirmation' and q–M_dyn trend are inherited from the EOS/phenomenological mapping used to infer binary properties, not independently measured from the EM light curves.

specific steps
  1. fitted input called prediction [Sec 3.4 (binary-property inference) and Sec 5.5 / Fig. 12]
    "These are incorporated in NMMA via phenomenological relations (Krüger & Foucart 2020; Dietrich et al. 2020) and built on an EOS set which takes into account astrophysical constraints from neutron star observations combined with constraints from nuclear theory and heavy-ion collision experiments (Huth et al. 2022). ... Since in our analysis, such a dependence is not prescribed during inference, which means that we consider Λ~ and M Chirp independently, finding a similar trend strengthens the result from Altiparmak et al. (2022); Magnall et al. (2025)."

    Λ̃ and M_Chirp are not measured directly from the EM light curves. They are posterior quantities obtained by mapping the fitted M_dyn/M_wind through EOS-based phenomenological relations. For the EOS set used, Λ̃ is a decreasing function of mass, so the joint (M_Chirp, Λ̃) posterior is correlated by construction. The claim that 'such a dependence is not prescribed during inference' is therefore misleading: the dependence enters through the phenomenological/EOS mapping itself. The agreement with the Altiparmak/Magnall relations in Fig. 12 is a property of the mapping inputs, not an independent EM-based confirmation.

  2. fitted input called prediction [Sec 5.5 / Fig. 11 (with Sec 3.4 mapping)]
    "We observe that as the mass ratio decreases as M_dyn increases. This implies that if the merging binaries have highly unequal masses, with the primary body being heavier than the secondary, a larger dynamical ejecta could be produced."

    The binary mass ratio q is not fitted to the light curves. Per Sec 3.4, q is inferred from the same EM-derived M_dyn via the Krüger & Foucart / Dietrich phenomenological relations, in which low-q mergers produce larger dynamical ejecta. Thus the q–M_dyn anti-correlation displayed in Fig. 11 is already present in the mapping used to produce q; presenting it as 'we observe' is a reduction-by-construction rather than an independent trend emerging from the data.

full rationale

The EM fitting framework itself is not circular: the simultaneous afterglow+KNe modeling is benchmarked against GW170817 and is able to reproduce its independent GW-inferred binary properties. The KN identification and the BNS/NSBH model comparison are based on actual light-curve fits and external radiative-transfer grids, so those steps have independent content. However, the paper's headline claims of 'confirming' the Λ̃–M_Chirp anti-correlation and of finding a q–M_dyn trend are partially circular. In Sec 3.4, binary properties are inferred by passing the fitted ejecta masses through phenomenological relations (Krüger & Foucart 2020; Dietrich et al. 2020) built on an EOS set (Huth et al. 2022). Those inputs already encode a near-universal relation between tidal deformability and mass, and a dynamical-ejecta dependence on mass ratio. Consequently, the posterior correlations in Figs. 11 and 12 are largely inherited from the model mapping, despite the paper stating that the dependence was 'not prescribed during inference.' This does not invalidate the ejecta-mass measurements or the GW170817 validation, but it means the 'confirmation' claims are not independent results from the EM data. Overall, the circularity is partial and localized to the binary-property trend claims, not to the central EM fitting and KN identification.

Axiom & Free-Parameter Ledger

5 free parameters · 6 axioms · 0 invented entities

No new particles, forces, or physical entities are introduced. The analysis relies on established modeling tools and on simulation-based mappings from ejecta mass to binary parameters; the main added ingredients are data choices, priors, and a free systematic-error term.

free parameters (5)
  • log M_dyn (per GRB) = -1.95 to -2.29 across sample (Table 1)
    Dynamical ejecta mass; free parameter in the BNS/NSBH KN grids and a primary output of the fit.
  • log M_wind (per GRB) = -1.01 to -2.20 across sample (Table 1)
    Wind ejecta mass; free parameter in the KN grids and central to the M_wind-E0,J correlation.
  • Phi (half-opening angle of lanthanide-rich ejecta) = 30 deg fixed for NSBH; 64-72 deg for BNS cases
    Free in BNS models, fixed in NSBH models; directly affects inferred ejecta composition and the BNS/NSBH model comparison.
  • Afterglow parameters (E0, n0, theta_c, theta_w, iota, p, eps_e, eps_B) = See Table 1 per GRB
    Free parameters of the afterglowpy forward-shock model; many are weakly constrained and covary with KN parameters.
  • sigma_sys (per GRB) = 0.13-0.70 mag in shown posteriors
    Additional free systematic-error term added to the likelihood; a broad prior can absorb model misspecification and inflate parameter uncertainties.
axioms (6)
  • standard math Bayesian inference and nested sampling (PyMultiNest) correctly estimate evidence and posteriors.
    Standard statistical machinery; no formal proof given but standard usage.
  • domain assumption The afterglow is adequately described by forward-shock synchrotron radiation from afterglowpy; reverse/refreshed shocks and flaring are absent or handled by truncation.
    Sec 3.1; early 160821B data at t<0.9 d are converted to upper limits because reverse/refreshed shocks are not modeled.
  • domain assumption POSSIS KN grids accurately represent the composition and geometry of BNS and NSBH ejecta.
    Sec 3.2; BNS and NSBH grids differ in lanthanide distribution, with Phi fixed to 30 deg for NSBH; the inferred BNS/NSBH classification depends on this grid choice.
  • domain assumption The ejecta masses inferred from KN light curves map to binary progenitor parameters through phenomenological relations from numerical simulations and a fixed EOS set.
    Sec 3.4; this is the bridge from EM-only fits to Mchirp, Lambda tilde, and q; if the relations or EOS set are wrong, the binary properties are wrong.
  • ad hoc to paper For GRB 191019A, eps_e and eps_B were first fixed and then freed after checking the n0 posterior.
    Sec 3.1: 'Once we consistently find that log(n0) does not attain the high values... we allow log(eps_e) and log(eps_B) to vary freely and report the model in Table 1'—a data-dependent prior adjustment.
  • domain assumption The selected GRBs have robust redshifts and host associations; z<0.5 ensures KNe are detectable.
    Sec 2.1 selection criteria, with the exception of GRB 230307A where pcc=0.09 is accepted.

reviewed 2026-08-03 · how reviews work

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Cite this review

Pith. "Pith review of Kilonova and progenitor properties of merger-driven gamma-ray bursts." pith.science (2026). https://pith.science/paper/AWTL5KY2

@misc{pith2026251223354,
  author       = {Pith},
  title        = {Pith review of: Kilonova and progenitor properties of merger-driven gamma-ray bursts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AWTL5KY2}},
  note         = {Machine review of arXiv:2512.23354}
}
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abstract

Gamma-Ray Burst (GRB) prompt and afterglow emission, as well as a kilonova (KN), are the expected electromagnetic (EM) counterparts of Binary Neutron Star (BNS) and Neutron Star -- Black Hole (NSBH) mergers. We aim to infer the KN ejecta parameters and the progenitor properties by modeling merger-driven GRBs with a claim of KN, good data and robust redshift measurement. We model the afterglow and KN, and perform a Bayesian analysis, within the Nuclear physics and Multi-Messenger Astrophysics (NMMA) framework. The KN emission is modeled with the radiative transfer code POSSIS and for afterglow we use the afterglowpy library. In contrast to previous approaches, our methodology simultaneously models both afterglow and KN. We find that all GRBs in our sample have a KN, but we were unable to confirm or exclude its presence in GRB 150101B. A BNS progenitor is favored for GRB 160821B, GRB 170817A/AT2017gfo, GRB 211211A, and GRB 230307A. For GRB 150101B and GRB 191019A, we obtain a slight preference for NSBH scenario, while a BNS is also viable. For KN emission, we find that the median wind mass $\langle M_{\rm wind}\rangle=0.027^{+0.046}_{-0.019}$ $M_{\odot}$ is larger than the dynamical $\langle M_{\rm dyn}\rangle = 0.012^{+0.007}_{-0.006}$ $M_{\odot}$. We find that $M_{\rm wind}$ and the beaming corrected kinetic energy of the jet can be attributed as $log(M_{\rm wind})=-20.23+0.38\,log(E_{0,J})$. We confirm the results of numerical simulation that $\tilde\Lambda$ increases with decrease in $\mathcal{M}_{\rm \,Chirp}$. Our work shows that EM modeling can be effective for probing the progenitors, and for the first time presents the progenitor properties of a sizable sample of merger-driven GRBs.

Figures

Figures reproduced from arXiv: 2512.23354 by A. De Rosa, A. Rossi, D.A. Kann, F. Cogato, F. Ragosta, G. Stratta, M. Bulla, P. Singh, P.T.H. Pang.

Figure 1
Figure 1. Figure 1: Bayes factor ln[B Test Ref ], quantifying the relative disfavor of test models compared the reference model. Afterglow-only models are charac￾terized by a Gaussian (GS) and Top-Hat (TH) jet structure. Combined afterglow and KN models correspond to a binary neutron star (BNS-GS & BNS-TH) and a neutron star-black hole (NSBH-GS & NSBN-TH). The star highlights the most-plausible (favored) model. days. Later, ≳… view at source ↗
Figure 2
Figure 2. Figure 2: The best-fitting light corresponding to the parameters in [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Same as [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Same as [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The dynamical mass MDyn (M⊙) and the wind mass MWind (M⊙) of KN ejecta. The shaded region highlights the 68% confidence region and the dotted lines show the median. and morphologies of the ejecta have been utilized, is challenging (see, e.g., Ascenzi et al. 2019a; Kawaguchi et al. 2020; Nicholl et al. 2021; Heinzel et al. 2021; King et al. 2025). Therefore, for completeness, we show the comparison of the t… view at source ↗
Figure 6
Figure 6. Figure 6: The distribution of the total ejected mass, MTotal M⊙ and comparison with pervious studies. The dotted line shows the median and the shaded part highlights the 68% confidence region. The measurements for each GRB have been slightly shifted along the x-axis for clarity. The comparison of AT2017gfo is shown in a separate plot (see [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of the MTotal M⊙, for AT2017gfo. The shaded region corresponds to 2σ region of MTotal from our analysis. 150101B 160821B 170817A 191019A 211211A 230307A 30 40 50 60 70 80 Half-opening angle Φ (deg) Φ (deg) Median = 70.56 (deg) 68% Confidence Region [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of the half-opening angle, Φ (deg) of lanthanide-rich ejecta. For GRB 150101B and GRB 191019A, highlighted with square, the Φ is fixed = 30 (deg) corresponding to the NSBH models. The me￾dian value is computed by excluding the former two GRBs. us from drawing any strong conclusion for GRB 150101B. In terms of the duration of the burst, a low q would result in a large ejected mass that could fa… view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of the binary mass ratio q and the dynamical ejected mass Mdyn (M⊙) for the short and long merger-driven GRBs. 0 200 400 600 800 1000 Tidal Deformability Λ˜ 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 Chirp Mass MChirp (M ) 150101B 160821B 170817A 191019A 211211A 230307A Altiparmak et al. 2022 (BNS) Magnall et al. 2025 (NSBH) 0.02 0.04 0.06 0.08 0.10 0.12 MTotal (M ) [PITH_FULL_IMAGE:figures/ful… view at source ↗
Figure 12
Figure 12. Figure 12: The chirp mass MChirp (M⊙) and mass-weighted tidal deforma￾bility Λ˜ from our analysis plotted over the analytical limits for BNS mergers shown in blue (Altiparmak et al. 2022) and for NSBH merger shown in green (Magnall et al. 2025). afterglow model from the opt/NIR observations. Another dif￾ference from our analysis is that in Rastinejad et al. (2025) the viewing angle for AT2017gfo ι = 22◦ , and for al… view at source ↗

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Works this paper leans on

2 extracted references · 1 linked inside Pith · cited by 1 Pith paper

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    Aasi, J. et al. 2015, Class. Quant. Grav., 32, 074001 Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017a, ApJ, 848, L12 Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017b, Phys. Rev. Lett., 119, 161101 Abdikamalov, E. & Beniamini, P. 2025, MNRAS, 539, 2707 Acernese, F., Agathos, M., Agatsuma, K., et al. 2015, Classical and Quantum Gravity, 32, 024...

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    In the case of the Top-Hat (TH) jet structure,θ w is not a model parameter andΦis fixed=30 (deg) for all NSBH models (see Section 3)

    Table A.1.Priors used for simultaneous inference for models summarized in Table 1, where the angle of inclinationUιis uniform on a sphere. In the case of the Top-Hat (TH) jet structure,θ w is not a model parameter andΦis fixed=30 (deg) for all NSBH models (see Section 3). Parameters GS TH BNS-GS BNS-TH NSBH-GS NSBH-TH log(Mdyn) (M⊙) - -−2.35 +0.42 −0.35 −...

    2025

This paper was first reviewed by deepseek-v4-flash on August 3, 2026.