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Probing Binary Neutron Star Merger Ejecta and Remnants with Gravitational Wave and Radio Observations

T0 review · 4 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Next-generation gravitational-wave and radio observations can turn binary neutron star mergers into a population that yields thousands of well-localized events per year and radio afterglows for a large fraction of them.

desk verdict Solid, transparent BNS-radio forecast with robust qualitative conclusions; treat the jet-afterglow rates as conditional on f=1 and add error bars before citing as planning-grade. read the letter →

arxiv 2506.22835 v1 pith:7GGGGE2B submitted 2025-06-28 astro-ph.HE astro-ph.COgr-qc

Kara Merfeld (JHU) , Alessandra Corsi (JHU) This is my paper
classification astro-ph.HEastro-ph.COgr-qc
keywords gravitationalwavesradiocontinuum:generalinstrumentation:interferometersgammarays:burstsstars:neutronbinarystarmergersmulti-messengerastronomykilonovaafterglows
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

Binary neutron star mergers are known from a single event, GW170817; this paper asks what happens when the same event becomes routine. The authors simulate a year's worth of mergers with total masses below about 3 solar masses, keep only those a gravitational-wave network can localize to a 10-square-degree patch, and then predict radio emission from four possible sources: a relativistic jet, dynamical ejecta, a disk wind, and a disk wind powered by a magnetar remnant. The central projection is that well-localized mergers will be found at rates of about 50 per year with a current-generation three-site network and about 2,000 per year with a next-generation three-site network, with high-frequency radio arrays detecting the jet afterglow for a large fraction of them. This matters because such a sample would answer questions that a single event cannot: whether every merger launches a jet, how jet energy is distributed in angle, and whether the remnant is a neutron star or a black hole. The forecast also extends the reach of radio-only counterpart discovery to redshifts around 0.8, past where kilonovae can be spotted in optical light.

What carries the argument

The argument is carried by a single simulation pipeline applied to a synthetic one-year population of $\approx 1.3\times10^6$ BNS mergers. A Fisher-matrix gravitational-wave code assigns each merger a network signal-to-noise ratio, a 90% sky localization, and a viewing angle; a synchrotron afterglow code (afterglowpy) produces light curves of Gaussian-structured jets; analytic ejecta-mass fits supply the dynamical and disk-wind masses and speeds; and a transient-modeling package (Redback) turns those ejecta into kilonova radio light curves, with a magnetar-injection option for bright disk-wind flares. Detection is decided by comparing each predicted flux to a 5$\times$ rms threshold for the VLA, ngVLA, and DSA-2000 at frequencies from about 1 to 93 GHz, with an approximate synchrotron self-absorption cutoff applied to low-frequency jet emission. The pivotal requirements are the two selection thresholds, SNR $\geq 10$ and localization $\leq 10$ square degrees, because they define the sample in which optical follow-up needs a single pointing and radio discovery can proceed independently of any other wavelength.

What would settle it

Observe the first 50-100 well-localized BNS mergers found by next-generation gravitational-wave networks in the tens-of-GHz band at microjansky sensitivity; if high-frequency radio jet afterglows appear in far fewer than the predicted 30-70% of cases, the universal-jet assumption is falsified and all jet-afterglow rates must be rescaled by the measured efficiency.

Watch

Extended reading notes

Core claim

On the paper's own terms, the claim is that a well-chosen pair of next-generation instruments will make radio afterglows of binary neutron star mergers a routine discovery rather than a rare one. For mergers with total mass $M_{\rm tot}\leq 3\,M_\odot$, network signal-to-noise ratio $\geq 10$, and 90% localization area $\Omega\leq 10\,\mathrm{deg}^2$, the yearly well-localized sample grows from roughly $5\times10^1$ with the current-generation H-L-A network to roughly $2.1\times10^3$ with the CE40-ET-A network, and the redshift reach grows from $z\lesssim 0.2$ to $z\lesssim 0.8$, encompassing the median redshift of short gamma-ray bursts. For jet afterglows, the model yields detection fractions of $\gtrsim 30\%$ with survey-mode observations at 27 GHz and $\gtrsim 70\%$ with pointed observations at 93 GHz, with synchrotron self-absorption suppressing the lower radio frequencies. Kilonova afterglows are harder: disk-wind emission is accessible to the VLA in the nearby universe and to the ngVLA or DSA-2000 more broadly, while dynamical-ejecta afterglows need next-generation sensitivity, and magnetar-powered disk winds are bright enough that roughly half of well-localized mergers should be detected even by the VLA. The jet numbers assume every merger launches a successful relativistic jet, and the paper notes they can be rescaled by a jet-efficiency factor once observations measure it.

Load-bearing premise

The forecast assumes every BNS merger with $M_{\rm tot}\leq 3\,M_\odot$ launches a successful relativistic jet whose parameters are drawn from GW170817-like distributions, and it adopts a local merger rate that could be about ten times smaller.

Editorial extensions

If this is right

  • Radio follow-up of well-localized events would measure the fraction of BNS mergers that produce successful jets, independently of gamma-ray detections, because the radio selection reaches viewing angles outside the jet core.
  • Multi-frequency, multi-epoch light curves plus sub-milliarcsecond astrometry can map jet angular energy structure and identify superluminal jet cores, as done once for GW170817.
  • The brightness of magnetar-powered disk-wind afterglows means the presence or absence of late-time radio flares directly reports whether a long-lived magnetized neutron star formed.
  • Well-localized samples at $z\lesssim 0.8$ would allow radio-only counterpart discovery beyond the optical kilonova horizon, enlarging the multi-messenger sample and supporting standard-siren cosmology.
  • Yearly well-localized rates of tens with current networks and thousands with next-generation networks would turn BNS multi-messenger astronomy from single-object case studies into population statistics.

Reading between the lines

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

  • If the universal-jet assumption is wrong, the same survey becomes a measurement rather than a failure: the observed fraction of well-localized mergers with high-frequency radio counterparts is the jet-formation efficiency, so the first sample of tens of events can discriminate universal-jet from rare-jet models.
  • Because the paper finds that the mass and redshift distributions of jet-afterglow detections track gravitational-wave detectability more than radio sensitivity, further improvements in radio sensitivity mainly buy SSA-free low-frequency coverage and kilonova-afterglow reach, which suggests the scientific case for future arrays depends on which ejecta component carries the priority.
  • The magnetar result implies that even existing radio telescopes could begin constraining magnetar-remnant fractions once current-generation networks improve localization, before any next-generation detector is built.
  • The same well-localized selection could be carried over to heavier mergers or neutron star-black hole binaries, where jet success rates and ejecta masses may differ, so the pipeline offers a template for comparing remnant channels.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 3 minor

Summary. The paper presents a simulation-based forecast of the multi-messenger (gravitational-wave plus radio) detectability of binary neutron star (BNS) mergers with total mass Mtot ≤ 3 M☉ and good gravitational-wave localization (network SNR ≥ 10, 90% localization area Ω ≤ 10 deg²). The authors construct a synthetic BNS population using a two-component Gaussian mass distribution, Madau-Dickinson redshifts, and a local merger rate of 320 Gpc⁻³ yr⁻¹; they then estimate GW detectability and localization with Gwbench for six detector networks ranging from H-L-A to CE40-CE20-ET. Radio light curves are generated with afterglowpy for Gaussian-structured jets, with Redback and the Coughlin et al. fitting formulas for dynamical ejecta and disk winds, and with a magnetar-injection model for disk winds; detectability is judged against 5×rms sensitivity thresholds for the VLA, ngVLA, and DSA-2000, with an approximate synchrotron self-absorption (SSA) correction. The headline results are yearly rates and detection fractions: for example, H-L-A yields roughly 52 well-localized events per year and CE40-ET-A roughly 2.1×10³ per year, of which a large fraction (30–70% or more depending on frequency and SSA treatment) would show jet afterglows; kilonova afterglow rates are lower, and magnetar-powered disk winds are claimed to be detectable at about 50% efficiency.

Significance. If the forecasts hold, the paper will be a useful planning tool for next-generation GW-radio follow-up programs, for advocating widely separated GW networks, and for motivating high-frequency ngVLA observations. Its strengths are its transparent, end-to-end methodology built on public tools (Gwbench, afterglowpy, Redback), its explicit treatment of SSA uncertainties, and the wide parameter coverage with many tabulated rates. The qualitative ranking of network/radio-array combinations is likely robust to several of the caveats below, even though the absolute rates are not. The paper also makes a useful scientific case that radio follow-up of well-localized GW events can probe off-axis jet populations and remnant diversity.

major comments (4)
  1. [Section 2.4, Tables 7–8, abstract] The central projection that ≥30–70% of well-localized BNS mergers yield detectable jet afterglows is computed under the assumption that every BNS merger launches a successful relativistic jet with a GW170817-like Gaussian structure. The only empirical anchor is GW170817, and the cited compatibility of BNS and short-GRB rates (Mandel & Broekgaarden 2022) does not pin down the jet-forming fraction f, because the short-GRB rate is degenerate with beaming, prompt-emission efficiency, and jet structure. The text notes that results can be rescaled by a jet efficiency factor, but the abstract and Tables 8–9 present f=1 numbers without a prior or a sensitivity table in f. For example, if f≈0.2 the CE40-ET-A 93 GHz rate drops from ≈1.6×10³ yr⁻¹ to ≈3×10² yr⁻¹, and the H-L-A rate drops from ≈5×10¹ yr⁻¹ to ≈1×10¹ yr⁻¹; the abstract's 'large fraction' becomes 'one in five.' Because the abstract and conclusions are phrased about all considered BNS mergers, the manuscript should either present f explicitly as a parameter with a plausible range or rephrase the headline as a conditional rate per jet-forming merger.
  2. [Section 2.1 and Table 8 caption] The scaling of the forecast rates to the minimum local merger rate is not carried through correctly. The assumed rate is R0=320 Gpc⁻³ yr⁻¹, and the stated allowed range is 10–1700 Gpc⁻³ yr⁻¹, so the minimum is a factor of 32 lower than the tabulated numbers, not 'about an order of magnitude' lower as stated in the Table 8 caption. Scaling Table 7 column 5 by 10/320 gives H-L-A ≈1.6 yr⁻¹ (consistent with 'a few') but CE40-ET-A ≈66 yr⁻¹, which is 'tens,' not 'hundreds' as claimed in the abstract. The authors should quote the minimum-rate numbers explicitly or state the scaling factor, and adjust the abstract and conclusions accordingly.
  3. [Section 3.3.1 and Tables 7–8] The sample used for the magnetar-powered disk wind rates is inconsistent. The text says the relevant set is column 7 of Table 7, which includes Mtot≤2.17 M☉ and has an H-L-A rate of only 1.4×10⁻³ yr⁻¹, but the Table 8 #Magn. rates (e.g., 2.8×10¹ yr⁻¹ for H-L-A at all frequencies) and the ≈50% detection fractions correspond instead to column 6 (Mtot≤3 M☉, 0.7≤q≤1; 5.1×10¹ yr⁻¹). If magnetar injection is restricted to the column 7 sample, the H-L-A rate is ~10⁻³ yr⁻¹ and the conclusion that current-generation GW detectors plus the VLA will 'systematically' probe these systems is not supported; if it is not restricted, the text and table caption should be made consistent by using column 6 throughout.
  4. [Section 3.1 and Tables 7–8] The yearly rates are quoted as point estimates without statistical uncertainties, and some entries are derived from very small simulated samples. The H-L-A entry in the last column of Table 7 (1.4×10⁻³ yr⁻¹) corresponds to about one event in the 700-yr population (Table 3), so its Poisson uncertainty is of order 100%; the CE40-CE20 entry is 0. Reporting these as tabulated rates without error bars or event counts can mislead comparisons between networks and the conclusions about probing stable-NS remnants. For rates ≲0.1 yr⁻¹, the authors should provide Poisson intervals or the number of simulated events passing the cuts, and treat zero entries as upper limits.
minor comments (3)
  1. [Title] The title contains a typo: 'Gravitational W ave' should read 'Gravitational Wave.'
  2. [Table 4] The layout of Table 4 is difficult to parse, especially the 2.4/2.2 GHz rows, where the reader cannot reliably tell which sensitivity refers to which array; please reformat the table so each column corresponds unambiguously to one instrument.
  3. [Figure 13 caption] The caption states '2,147 GW detections in total' for CE40-ET-A, but Table 7 lists 3.1×10⁵ SNR≥10 detections and 2.1×10³ after the Mtot and Dec cuts; please specify the exact selection cuts that give 2,147, or adjust the number.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the detection-rate forecasts are new outputs computed from external empirical inputs, not re-statements of those inputs.

full rationale

The paper's central outputs (Tables 7-8) are yearly rates and detection fractions obtained by a forward chain: generate a BNS population with externally motivated mass, spin, and redshift distributions (Farr & Chatziioannou 2020; Madau & Dickinson 2014; Gupta et al. 2024), compute GW SNR and localization with Gwbench, simulate radio light curves with afterglowpy and Redback using microphysical and jet parameters drawn from GW170817 and short-GRB samples (Makhathini et al. 2021; Fong et al. 2015), and compare against telescope sensitivities. The jet parameters are adopted from independent observations, not fitted to the predicted detection rates, so the detection fractions are not self-referential. The Section 2.4 assumption that all BNSs launch successful jets is an explicitly stated model input with a stated rescaling caveat, not a hidden circular derivation; it could be wrong (correctness risk) but it does not make the output equal to the input. The consistency check in Appendix A reproduces Sarin et al. (2022), and comparisons with Pandey et al. (2025) and Kalogera et al. (2024) are external benchmarks, not uniqueness imports. Self-citations (e.g., Corsi et al. 2024a,b; Balasubramanian et al. 2021, 2022) appear in contextual or data-reference roles and are not load-bearing for the forecast. No equation in the paper defines a predicted quantity in terms of the target quantity, and no fitted parameter is relabeled as a prediction. Verdict: no significant circularity.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central claims rest on a chain of empirical inputs and model assumptions. The free parameters listed are the load-bearing choices: the local merger rate scales all rates; the remnant-mass and ejecta prescriptions set which systems are selected and how bright the radio emission is; the jet efficiency sets the entire jet-afterglow normalization. The axioms are the standard background assumptions of the simulation codes and the empirical fitting formulas.

free parameters (7)
  • Local BNS merger rate R0 = 320 Gpc^-3 yr^-1
    Input from Beniamini & Lu (2021); scales all yearly rates linearly. The paper notes plausible range 10-1700 Gpc^-3 yr^-1, so rates can be up to ~10x lower.
  • Prompt-collapse threshold Mthr = 3 M_sun
    Fixed as the selection threshold for NS-remnant candidates (Section 2.1), although Eq. 6 with MTOV=2.01-2.17 and R1.6=10.3-13.5 km yields 2.69-3.31 M_sun. Affects sample size and the ejecta mass fits.
  • MTOV (maximum non-rotating NS mass) = 2.1 M_sun
    Set in Section 2.5 following Margalit & Metzger (2019) for computing disk masses via the Coughlin fit; affects disk-wind mass and hence radio flux.
  • Disk wind mass fraction = 0.5 x Mdisk
    Equation 16, assumed half the disk mass becomes wind; directly scales all disk-wind and magnetar-powered afterglow fluxes.
  • Jet formation efficiency = 1 (all BNSs)
    Section 2.4 assumes every BNS launches a successful jet; the headline jet-afterglow rates would scale linearly with this factor, as the authors note.
  • Jet and kilonova afterglow parameter distributions (E0, theta_c, nISM, p, eps_e, eps_B) = Ranges in Table 5 and Table 6
    Borrowed from GW170817 fits and Fong et al. (2015); these priors control predicted radio flux and detection fractions. Different distributions would directly change the reported efficiencies.
  • SSA normalization constant = Range spanning Granot et al. (1999) to Panaitescu & Kumar (2000)
    Equation 13; the authors bracket the jet detection efficiencies by this order-of-magnitude uncertainty rather than adopting a single value.
assumptions (5)
  • domain assumption Fisher-matrix approximation (as implemented in Gwbench) gives reliable SNR and 90% sky localization areas for BNSs with SNR>=10.
    Section 2.2; justified by reference to Pandey et al. (2025), but the approximation may be less accurate for marginal events near the SNR threshold.
  • domain assumption The bimodal Gaussian NS mass distribution (Eq. 4, parameters from Farr & Chatziioannou 2020) describes the true BNS population, and component masses are drawn independently.
    Section 2.1; this modifies the Gupta et al. (2024) assumption. If the mass distribution differs, the fraction of systems eligible for NS remnants changes.
  • domain assumption The Coughlin et al. (2019) fitting formulas (Eqs. 14, 17, 19) accurately predict disk and dynamical ejecta masses and velocities for BNSs with 0.7<=q<=1.
    Section 2.5; these fits are calibrated on numerical relativity simulations for a limited mass-ratio range, which motivates the q>=0.7 cut.
  • domain assumption Afterglowpy and Redback correctly implement synchrotron afterglow and kilonova afterglow emission for the adopted parameter ranges.
    Sections 2.4-2.6; the paper performs a consistency check against Sarin et al. (2022) in Appendix A, but the codes themselves are treated as faithful models.
  • domain assumption The prompt-collapse condition of Eq. 6 (Bauswein et al. 2013, 2017) is valid, and a fixed Mthr=3 Msun captures the selection defined by that condition.
    Section 2.1; the paper uses the full range of Mthr (2.69-3.31) to motivate the cutoff but then adopts a single value for the population selection.

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Cite this review

Pith. "Pith review of Probing Binary Neutron Star Merger Ejecta and Remnants with Gravitational Wave and Radio Observations." pith.science (2026). https://pith.science/paper/7GGGGE2B

@misc{pith2026250622835,
  author       = {Pith},
  title        = {Pith review of: Probing Binary Neutron Star Merger Ejecta and Remnants with Gravitational Wave and Radio Observations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7GGGGE2B}},
  note         = {Machine review of arXiv:2506.22835}
}
abstract

We present a study aimed at quantifying the detectability of radio counterparts of binary neutron star (BNS) mergers with total masses $\lesssim 3$\,M$_{\odot}$, which may form neutron star (NS) remnants. We focus on mergers localized by gravitational-wave (GW) observations to sky areas $\lesssim 10$\,deg$^2$, a precision that greatly facilitates optical counterpart identification and enables radio discovery even without detections at other wavelengths. Widely separated GW detectors are essential for building samples of well-localized BNS mergers accessible to US-based radio telescopes, with minimum yearly detection rates (assuming the smallest values of the BNS local merger rate) ranging from a few with current GW detectors to hundreds with next-generation GW instruments. Current GW networks limit well-localized detections to $z\lesssim 0.2$, while next-generation GW detectors extend the reach to $z\lesssim 0.8$, encompassing the median redshift of short gamma-ray bursts (GRBs). With next-generation radio arrays operating at a several tens of GHz and providing an order of magnitude improvement in sensitivity compared to the most sensitive ones available today, short GRB-like jet afterglows can be detected for a large fraction of the considered BNS mergers. At lower radio frequencies, detections with current radio interferometric arrays are feasible, though subject to synchrotron self-absorption effects. The enhanced sensitivity and survey speed of future radio interferometers operating at a few GHz, combined with the higher detection rate of well-localized BNSs enabled by next-generation GW observatories, are key to probing disk-wind and dynamical ejecta afterglows, as well as remnant diversity.

Figures

Figures reproduced from arXiv: 2506.22835 by the authors.

Figure 1
Figure 1. GW170817 observations at 3 GHz (brown dots) and their phenomenological best fit from Makhathini et al. (2021) (black solid line). The shaded areas enclose 90% of the light curves simulated in this work for various ejecta compo￾nents: the relativistic jet (cyan), the dynamical ejecta (red), the disk wind (blue), and the disk wind with magnetar in￾jection (yellow). The horizontal lines show the sensitivity reached by … view at source ↗
Figure 2
Figure 2. Bi-modal Gaussian distribution of the NS com￾ponent masses (see Equation 4). of three next-generation detectors considered in Evans et al. (2023) (hereafter, CE40-CE20-ET). The last rep￾resents one of the most ambitious networks (at least from the standpoint of US-based investments). For con￾sistency with Gupta et al. (2024) and Kalogera et al. (2024), we consider LIGO-India at A# sensitivity ev￾ery time that it is … view at source ↗
Figure 3
Figure 3. A comparison of the (source-frame) BNS chirp￾mass distributions in this work and that in Gupta et al. (2024). Here we focus on the BNS mergers which have the potential to form NS remnants (those with total masses ≤ 3M⊙, to the left of the dashed blue line). Hence, our BNS population is very similar to that of Gupta et al. (2024). We start from the population of BNS mergers used in Gupta et al. (2024). The two NSs in… view at source ↗
Figures from the paper (19 more)
Figure 4
Figure 4. Figure 4: A scatter plot of the chirp mass versus the total mass for all of the BNSs in our in 1-yr simulation (blue). The minimum and maximum values of the threshold total mass Mthr for prompt collapse is indicated in red. The mass region corresponding to prompt collapse, possi…
Figure 5
Figure 5. Figure 5: Sensitivity curves for the detectors used in our study, taken from Gwbench Borhanian (2021) and consistent with Kalogera et al. (2024) for next-generation detectors, compared with the sensitivity of the LIGO detectors dur￾ing their second observing run O2 (the run that…
Figure 6
Figure 6. Figure 6: An example light curve generated using afterglowpy at ngVLA frequencies (see [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Top: The dynamical ejecta mass, calculated from Equation 17. Bottom: The dynamical ejecta velocity as calculated from Equation 19. We only show the region of parameter space in which 0.7 ≤ q ≤ 1 and Mtot ≤ 3M⊙ [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Total ejecta mass (dynamical ejecta plus disk wind; purple) as a function of the total mass Mtot for BNSs with 0.7 ≤ q ≤ 1 (Margalit & Metzger 2019), and Mtot ≤ 3M⊙. The top horizontal axis shows the chirp mass for q = 1. In black, we plot the analytical fit given by E…
Figure 9
Figure 9. Figure 9: Maximum (dynamical or disk wind) ejecta velocities reached for a representative BNS in our simulation with energy injection from the least magnetized (Bext = Bint = 1014 G; left panel) and most magnetized (Bint = √1 3 × 1017 G, Bext = 1 × 1016 G; right panel) magnetar …
Figure 10
Figure 10. Figure 10: GW detections made by networks in our study, for different sky localization uncertainties and as a function redshift z. The dashed lines represent the yearly detection rate for our 50-year simulation, and the solid lines represent the number of detections made in our …
Figure 11
Figure 11. Figure 11: Jet afterglow detection efficiency as a function of time for the BNSs in our 1-yr population that pass the criteria summarized in [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: Histograms of the observing angles of all GW detections (blue), those with the sky area less than 10 deg2 (orange), and those for which jet afterglows are detected with the ngVLA at 93 GHz (see [PITH_FULL_IMAGE:figures/full_fig_p015_12.png]
Figure 13
Figure 13. Figure 13: Luminosity distances versus observing angles of BNSs for which GWs are detected by CE40-ET-A (blue; 2,147 GW detections in total). In orange we plot the sub￾sample of BNSs whose jet afterglows are also detected by the ngVLA at 93 GHz with sub-milli-arcsec resolution (…
Figure 14
Figure 14. Figure 14: Predictions for the GW plus radio average detection rates of BNS mergers by the H-L-A network and various radio arrays (as indicated at the top of each panel). When counting detections, we only consider observations made at νobs > νSSA (Panaitescu & Kumar 2000). For t…
Figure 15
Figure 15. Figure 15: Same as in [PITH_FULL_IMAGE:figures/full_fig_p019_15.png]
Figure 16
Figure 16. Figure 16: Left: The dynamical ejecta mass of each of the BNS mergers in our 1-year simulation (red). Right: The initial ejecta velocity of the dynamical ejecta (red). We overlay the dynamical ejecta velocity with the maximum and minimum fits given by Coughlin et al. (2019) (bla…
Figure 17
Figure 17. Figure 17: Same as in [PITH_FULL_IMAGE:figures/full_fig_p020_17.png]
Figure 18
Figure 18. Figure 18: Dynamical ejecta afterglow detection efficiency as a function of time for the BNSs in our 1-yr population that pass the criteria summarized in [PITH_FULL_IMAGE:figures/full_fig_p021_18.png]
Figure 19
Figure 19. Figure 19: Disk wind afterglow detection efficiency as a function of time for the BNSs in our 1-yr population that pass the criteria summarized in [PITH_FULL_IMAGE:figures/full_fig_p021_19.png]
Figure 20
Figure 20. Figure 20: Same as in [PITH_FULL_IMAGE:figures/full_fig_p022_20.png]
Figure 21
Figure 21. Figure 21: Same as [PITH_FULL_IMAGE:figures/full_fig_p022_21.png]
Figure 22
Figure 22. Figure 22: We replicate the kilonova afterglow radio light curves shown in [PITH_FULL_IMAGE:figures/full_fig_p024_22.png]

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

  1. Submillimeter Detectability of Gravitational-Wave Counterparts from Neutron-Star Mergers with the Xue-shan-mu-chang 15-meter Telescope

    astro-ph.HE 2026-08 conditional novelty 5.0 of 10

    A magnetar-fed neutron-star merger afterglow would stay above XSMT's 230 GHz limit for months at 40 Mpc, with an all-sky rate of 0.05 to 1.7 events per year as an upper limit.

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