Pith. sign in

REVIEW 3 major objections 5 minor 72 references

The Cosmological Population of Gamma-Ray Bursts from the Disks of Active Galactic Nuclei

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read Gamma-ray bursts from AGN disks are mostly invisible: at most a few percent are detectable if their light diffuses, and the survivors are low-redshift, outer-disk events around very massive black holes.

desk verdict First population synthesis of AGN-disk GRBs with useful predictions, but the undiffused 40–50% detection fraction is an on-axis conditional number that needs a beaming correction before the paper's central comparison holds. read the letter →

arxiv 2412.17714 v2 pith:6K25OPTP submitted 2024-12-23 astro-ph.HE

Hoyoung D. Kang , Rosalba Perna , Davide Lazzati , Yi-Han Wang This is my paper · ORCID
classification astro-ph.HE
keywords gamma-rayburstsactivegalacticnucleiaccretiondiskshigh-densityGRBscosmologicalpopulationgravitational-wavecounterpartsMonteCarlosimulationsynchrotronself-absorption
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper turns the idea that gamma-ray bursts can be born inside active galactic nucleus disks into a testable population prediction: how many such bursts would actually be detected, at what wavelengths, and from which host disks. The authors draw a cosmological population of AGN disks from a supermassive black hole mass function, place long and short GRBs within two accretion disk models, and pass the resulting prompt and afterglow emission through either free escape or Thomson diffusion in the disk medium. In the diffused case, at most a few percent of AGN-disk GRBs would be detectable, and those would preferentially come from low redshifts, the outer disk, and black holes above $10^{7}$.5 solar masses; in the undiffused case, where the progenitor's winds have cleared a funnel, the detection probability rises to roughly 40-50 percent. If correct, AGN-disk GRBs are a rare but informative population that could calibrate the stellar and compact-object content of accretion disks and the AGN contribution to gravitational-wave mergers.

What carries the argument

The machinery is a Monte Carlo population synthesis whose pivotal quantity is the Thomson optical depth $\tau(R_{\rm em},R_{\rm GRB})$ from the burst location to the disk surface. Host AGNs are drawn from a supermassive black hole mass function; disk density and scale height come from the two disk models; each GRB is placed at a radius weighted by disk surface density; the prompt emission is computed with the high-density GRB prescription, in which the external shock forms before internal shocks when $n>6\times10^{6}\,E_{52}\Gamma_{\infty,2}^{-8}\Delta t_{-3}\,\mathrm{cm^{-3}}$; and the afterglow is computed with a Monte Carlo synchrotron model including self-absorption. The emission radius is the external shock radius $R_{\rm ES}=\max(R_{\rm ES}^{\rm thin},R_{\rm ES}^{\rm thick})$, and the dichotomy between scenarios is set by $\tau$: for $\tau\lesssim1$ radiation escapes undiffused, while for $\tau\gtrsim1$ it emerges on the diffusion timescale $t_{\rm diff}\simeq[H(R)-R_{\rm em}]\tau/c$ with the reduced luminosity $L_{\rm diff}\sim L_0(t_0/t_{\rm diff})(\Omega/4\pi)$.

What would settle it

Find and localize AGN-disk GRB candidates in a large sample: the model predicts detectable events overwhelmingly at low redshift (z<1-ish), around SMBHs with M>$10^{7}$.5 solar masses, and from outer disk radii, so a well-localized burst at high redshift or in a low-mass AGN - or with a bright radio afterglow, which self-absorption should suppress - would contradict the central prediction.

Watch

Extended reading notes

Core claim

In the paper's own terms, the central result is a selection function: the AGN disk itself decides which GRBs are visible. When the burst radiation must diffuse through the disk (the fully diffused scenario), the expected detectable fraction is at most a few percent - below one percent in the SG disk model and 2-3 percent in the TQM model for prompt gamma-rays - and the survivors are concentrated at low redshift, at outer radii $R\sim [10^6,10^7]R_g$, and around supermassive black holes with $M\gtrsim 10^{7.5}M_\odot$. When radiation escapes through a low-opacity funnel (the undiffused scenario), the detection probability is 38-53 percent depending on burst type and disk model, with a noticeable additional contribution from intermediate disk radii around lower-mass black holes. In both scenarios radio afterglows are essentially invisible because synchrotron self-absorption suppresses low frequencies, while the most promising channels are prompt gamma-rays in the undiffused case and X-ray afterglows in the diffused case. The duration $T_{90}$ is stretched in the dense environment, so short GRBs can appear as long bursts and long bursts as very long ones.

Load-bearing premise

The load-bearing assumption is that the number of GRB progenitors in a disk is proportional to the disk mass and to the local surface density, so bursts are placed exactly where the disk is heaviest; if migration traps or other formation channels concentrate progenitors elsewhere, the predicted detectable fractions and the mass/radius distribution of detections change materially.

Editorial extensions

If this is right

  • A fully diffused AGN-disk GRB population would be a rare source class: prompt gamma-ray detection probabilities are below 1-3 percent, so detections should be uncommon even if AGN disks produce a sizable burst rate.
  • If the undiffused funnel scenario operates, roughly 40-50 percent of AGN-disk GRBs would be detectable in prompt gamma-rays, making the population accessible to current instruments.
  • Radio afterglows from AGN-disk GRBs are predicted to be effectively undetectable with VLA-class sensitivity, so a detected radio counterpart would be difficult to reconcile with the standard self-absorption picture.
  • Observed durations are stretched in dense disks: short GRBs would be misclassified as long and long GRBs as very long, so AGN-disk GRBs should be searched for among long and ultra-long bursts with AGN host associations.
  • The marked differences between the SG and TQM disk predictions mean that even a few detected AGN-disk GRBs could discriminate between competing AGN disk structures.

Reading between the lines

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

  • The same opacity filter implies that any electromagnetic counterpart to a gravitational-wave merger inside an AGN disk would be strongly biased toward low redshift, massive SMBHs, and outer disk radii; a systematic search along those lines may be more fruitful than an all-sky blind search.
  • If the undiffused case is the one realized in nature, current Fermi-era data should already contain a measurable AGN-disk GRB population, and the observed rate could be inverted to place upper limits on the star-formation and merger rates inside AGN disks.
  • The predicted near-invisibility of radio afterglows is a sharp testable corollary for wide-field radio facilities; detecting such an afterglow would force the disk models or the self-absorption treatment to be revised.
  • The duration-stretching effect suggests that some GRBs currently classified as long with no supernova and an AGN host may be AGN-disk bursts, a classification that follow-up X-ray and optical observations can test.
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 constructs a Monte Carlo population synthesis of long and short GRBs occurring in the disks of AGNs, combining a cosmological SMBH mass function, two disk models (Sirko-Goodman and Thompson-Quataert-Murray, implemented via pAGN), and high-density GRB emission models for the prompt and afterglow phases. It considers two extreme propagation scenarios: 'undiffused', in which the jet escapes through a low-opacity funnel, and 'diffused', in which radiation is Thomson-scattered and absorbed by the disk. The output is a set of detection probabilities in gamma-ray, X-ray, optical, and radio bands against representative instrument thresholds (Fermi, Chandra, HST, VLA), together with distributions of peak luminosity, afterglow flux, source location in the SMBH mass-radius plane, and T90 stretching. The central qualitative findings are that diffused bursts are observable in only a few percent of cases, preferentially from low redshifts, high SMBH masses, and outer disk radii, while undiffused bursts have much higher on-axis detection probabilities (~40-50%); the T90 distributions are stretched so that short GRBs appear long and long GRBs appear very long.

Significance. If the orientation issue identified below is addressed, the paper would provide a useful first end-to-end population model for AGN-disk GRBs, with falsifiable trends (T90 stretching, mass/radius/redshift selection, and band-dependent detectability) that can be tested with current and future facilities. The work is a forward model that does not fit to its own target predictions, and it makes helpful use of publicly available pAGN disk profiles and of previously published high-density emission calculations. The decision to present probability distributions rather than absolute rates is appropriate given the large uncertainty in the normalization of the AGN stellar population, and the explicit two-scenario treatment (undiffused vs. diffused) frames the problem usefully. The qualitative distinction between a rare, outer-disk, high-mass, low-redshift detectable population in the diffused case and a more accessible population in the undiffused case is physically plausible and worth communicating, pending the quantitative corrections discussed below.

major comments (3)
  1. [Sec. 3.1(v), Eqs. (9)-(11), Figs. 9, 14, 15] The undiffused detection fractions quoted in the abstract (∼40-50%) are on-axis conditional probabilities, not population-averaged detection probabilities. The Monte Carlo fixes the viewing angle to θobs = 0 for every realization, and Eq. (10), L_att = L0 exp(-τ), contains no solid-angle factor, so these events are only seen if the jet happens to point at the observer. Averaging over a random orientation of the disk/jet axis with the top-hat jet half-opening angle of 5° assumed in Sec. 2.2.2 reduces the undiffused per-event probability by (1 - cos 5°) ≈ 3.8×10^-3, i.e., from ≈40-50% to ≈0.2%. The diffused channel, by contrast, already contains the Ω/4π factor in Eq. (9). The paper therefore does not currently provide a consistent population-normalized comparison of the two scenarios; the undiffused numbers should be relabeled as conditional or, preferably, the simulation should be rerun with θobs drawn from an isotropic distribution.
  2. [Sec. 3.1(iii), Figs. 9-15] The assumed radial and mass distributions of progenitors, P(R) ∝ Σ(R) and P(M) ∝ MΦ(M,z), are load-bearing for the quantitative fractions in Figs. 9-15. The paper explicitly calls this a zeroth-order approximation and notes that migration traps could alter the radial distribution, but it does not explore the sensitivity of the reported percentages to plausible alternatives. A simple test (e.g., a uniform-in-log-radius prior or a migration-trap-concentrated prior) or an analytic scaling of the detection fraction with the prior would be needed to know whether the 'few percent' and '40-50%' numbers are robust. Without it, these numbers are predictions for one specific stellar-distribution model, not for the AGN-disk GRB population as a whole.
  3. [Secs. 2.2.1 and 3.2, Fig. 3, Figs. 9-15] The prompt-emission calculations fix Eiso = 10^53 erg for LGRBs and 10^51 erg for SGRBs, with Γ∞ = 100, for every realization. Observed GRB isotropic energies span several orders of magnitude, and the detection fraction is a strong function of the distance at which this fixed luminosity falls below the detector threshold. The quoted quantitative probabilities are therefore conditional on a single representative engine. Sampling Eiso from an observed distribution, or at least showing how the detection fractions vary with Eiso, would make the population predictions meaningful.
minor comments (5)
  1. [Fig. 7 caption] The caption reads 'LGBRs' but should read 'LGRBs'.
  2. [Sec. 3.1(v)] The phrase 'massive star collap' should be 'massive star collapse'.
  3. [Eq. (2)] The notation 'Δt−3' is ambiguous; it should be written as Δt_{-3} with a definition, e.g., Δt_{-3} = Δt / 10^{-3} s.
  4. [Sec. 3.2 and Fig. 16] The text refers to observed T90 distributions but does not state whether the cosmological (1+z) time dilation has been applied to the simulated durations; a clarification or correction is needed.
  5. [Fig. 1 caption] The caption says 'mass profile' but the quantity plotted is a mass function; the wording should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an explicit forward Monte Carlo model built on independently published inputs; no target quantity is a fitted parameter rewritten as a prediction.

full rationale

The paper's central outputs—detectable fractions and the mass/radius/redshift distributions of detectable AGN-disk GRBs—are generated by a Monte Carlo forward model (Sec. 3.1) from externally specified ingredients: the Merloni & Heinz (2008) SMBH mass function, the SG and TQM disk models implemented in pAGN, the HD-GRB prompt-emission code of Lazzati et al. (2022), and the afterglow code of Wang et al. (2022). These inputs are not fitted to, nor defined in terms of, the paper's target detection probabilities. The authors explicitly label P(R) ∝ Sigma(R) and P(M) ∝ M Phi(M,z) as zeroth-order assumptions, and they explicitly note that the high-SMBH-mass dominance of the detectable fraction is 'an immediate corollary of the assumption of the number of GRB transients being proportional to the disk mass'—an admission, not a disguised fit. The T90 duration distribution is taken from an observed catalog (Bhat et al. 2016) as an assumed intrinsic engine-duration distribution, with the stated subdominance rationale; the predicted observable T90 stretching is then computed, not fitted. Self-citations to Perna et al. (2021a), Lazzati et al. (2022), and Wang et al. (2022) provide the emission and photosphere machinery as prior, code-based calculations; they do not presuppose the present population conclusions. The theta_obs = 0 choice and the absence of an Omega/4pi beaming factor in Eq. (10) mean the quoted 40-50% undiffused detection fractions are on-axis conditional numbers rather than per-event, randomly oriented detection probabilities; that is a normalization/interpretation caveat (a correctness risk), but it is not a circular reduction of a prediction to an input. No step in the derivation chain is equivalent to its inputs by construction.

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

The central predictions rest on a chain of published empirical inputs and simplified physical approximations. The free parameters are mostly standard disk and engine parameters chosen from the literature rather than fitted to the target result. The most consequential ad hoc assumptions are the mass and surface-density proportional placement of progenitors and the face-on aligned geometry, both acknowledged by the authors as simplifications.

free parameters (5)
  • SG disk parameters = alpha=0.01, eps_s=0.1, l_E=0.5
    Chosen standard SG model values (Section 2.1.2). They determine the disk density and scale height profiles that set optical depths and detection fractions.
  • TQM disk parameters = eps_T=0.001, m=0.2, xi=1
    Chosen standard TQM model values (Section 2.1.2). They determine the disk density profile and thus all detectability outcomes.
  • GRB engine parameters = Eiso=1e53 erg (LGRB), 1e51 erg (SGRB), Gamma_inf=100
    Fixed explosion energies and Lorentz factor used in the light-curve simulations (Figure 3 caption and Section 2.2). These directly set the luminosities that enter the detectability calculation.
  • Fermi T90 log-normal fit parameters = Short: A=36.87, mu=-0.17, sigma=0.48; Long: A=151.71, mu=1.43, sigma=0.48
    Fitted to the observed GRB duration distribution from Bhat et al. (2016) and used as the intrinsic engine duration distribution (Table 1). This is an empirical input, not fitted to the target result.
  • Afterglow jet opening angle = 5 degrees
    Top-hat jet opening angle used for the afterglow light curves (Section 2.2.2). It sets the beaming and therefore the afterglow flux levels that feed detectability estimates.
assumptions (6)
  • domain assumption The SMBH mass function of Merloni and Heinz (2008) describes the cosmological population of AGN disks for redshifts 0 to 5.
    Invoked in Section 2.1.1 to sample host redshifts and SMBH masses; the paper bilinearly interpolates that mass function.
  • domain assumption SG and TQM disk models, as implemented in pAGN, give representative density and scale height profiles for AGN disks.
    Used throughout Section 2.1.2 and Section 3 to compute optical depths, diffusion timescales, and intrinsic emission properties. The paper itself notes these models are approximations.
  • ad hoc to paper The number of GRB progenitors in a disk is proportional to disk mass, and their radial distribution is proportional to local surface density.
    Section 3.1 steps (ii) and (iii) set P(M) proportional to M Phi(M,z) and P(R) proportional to Sigma(R). The authors explicitly label this a zeroth-order approximation that could be modified by migration traps or dynamical formation channels.
  • ad hoc to paper All GRB progenitors lie in the disk mid-plane and all jets are aligned with the disk angular momentum and observed face-on.
    Section 3.1 step (v) and the end of Section 3.1 assume mid-plane location and theta_obs=0. These choices respectively maximize absorption and maximize detectability, bounding the true situation.
  • domain assumption The intrinsic engine duration distribution of AGN-disk GRBs equals the observed low-density T90 distribution.
    Section 2.2.1 and the footnote there state this is a necessary approximation because AGN-disk bursts are expected to be subdominant and the central engine physics is assumed density-independent.
  • standard math The fireball external shock radius and the diffusion timescale formulas from Sari and Piran (1995), Sari et al. (1998), and related work apply in the dense AGN disk regime.
    Equations (4) through (8) use these published fireball and Thomson-scattering formulas without re-derivation; they are background physics inputs.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Cosmological Population of Gamma-Ray Bursts from the Disks of Active Galactic Nuclei." pith.science (2026). https://pith.science/paper/6K25OPTP

@misc{pith2026241217714,
  author       = {Pith},
  title        = {Pith review of: The Cosmological Population of Gamma-Ray Bursts from the Disks of Active Galactic Nuclei},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6K25OPTP}},
  note         = {Machine review of arXiv:2412.17714}
}
abstract

With the discovery of gravitational waves (GWs), Active Galactic Nuclei (AGN) disks have emerged as an interesting environment for hosting a fraction of their sources. AGN disks are conducive to forming both long and short Gamma-Ray Bursts (GRBs), and their anticipated cosmological occurrence within these disks has potential to serve as an independent tool for probing and calibrating the population of stars and compact objects within them, and their contribution to the GW-detected population. In this study, we employ Monte Carlo methods in conjunction with models for GRB electromagnetic emission in extremely dense media to simulate the cosmological occurrence of both long and short GRBs within AGN disks, while also estimating their detectability across a range of wavelengths, from gamma-rays to radio. We investigate two extreme scenarios: ``undiffused", in which the radiation escapes without significant scattering (i.e. if the progenitor has excavated a funnel within the disk), and ``diffused", in which the radiation is propagated through the high-density medium, potentially scattered and absorbed. In the diffused case, we find that the majority of detectable GRBs, which are at most a few percent of the total, are likely to originate from lower redshifts, and from the outermost regions of large supermassive black hole (SMBH) masses, $\gtrsim 10^{7.5} \rm M_{\odot}$. In the undiffused case, which has a GRB detection probability $\sim 40-50\%$, we expect a similar trend, but with a considerable contribution from the intermediate regions of lower SMBH masses. Detectable emission is generally expected to be dominant in prompt $\gamma$-rays if diffusion is not dominant, and X-ray afterglow if diffusion is important; however, the nature of the dominant observable signal highly depends on the specific AGN disk model, hence making GRBs in AGN disks also potential probes of the disk structures.

Figures

Figures reproduced from arXiv: 2412.17714 by the authors.

Figure 1
Figure 1. — Redshift evolution of the global SMBH mass func￾tion density. The mass profile has been reconstructed by in￾terpolating the mass functions from Merloni & Heinz (2008). medium leads to the loss of kinetic energy, further pro￾moting binary formation(Rowan et al. 2023; Li et al. 2023). Recently a candidate for a GRB in a disk has been proposed (Levan et al. 2023; Lazzati et al. 2023, but see Stratta et al. 2024), len… view at source ↗
Figure 2
Figure 2. — Properties of the SG (top panels) and TQM (bottom panels) disk models adopted from pAGN code. Left figures show the disk mid-plane number density, n, contour map as a function of radius and SMBH masses in the range of 106 - 109 M⊙. Similarly, right figures show the scale height, H. α ≃ 0.01 − 0.1. Here, to be consistent with the value assumed in the standard SG model, we adopt α = 0.01, ϵs = 0.1, and lE = 0.5. Thu… view at source ↗
Figure 3
Figure 3. — Simulated light curves for a short-engine (left panels) and a long-engine (right panels) HD-GRB. In both cases, the engine has 5 distinct radiation pulses. The top panels show the variation with engine duration at fixed ambient density, while the bottom panels show the variation with density for a duration of the engine. In all the cases the light curves are characterized by a single FRED pulse, resulting from the… view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: — Observed duration T90 (top panels) and peak frequency of the light curves (bottom panels) as a function of the engine duration (short in the left panels and long in the right ones), for GRBs in high-density media, for various values of the density. For both short and…
Figure 5
Figure 5. Figure 5: — Probability distributions of event durations of short- (left) and long- (right) duration engines, for GRB sources in high-density media. In both cases, the distribution of engine duration is taken from the observed ones. Note the shift to longer durations as the dens…
Figure 6
Figure 6. Figure 6: — Top: Face-on afterglow light curves (viewing angle of 5◦ ) in four representative bands: radio at 4.5 × 109 Hz, optical at 4.5×1014 Hz, X-ray at 4.5×1017 Hz, and gamma ray at 2 ×1020 Hz for a wide range of densities, from the one typical of the interstellar medium to…
Figure 7
Figure 7. Figure 7: — Probability distributions of peak prompt luminosity for SGRBs (left panels) and LGBRs (right panels), contrasting the intrinsic luminosity distribution (undiffused model) with the diffused one. The former distributions remain relatively narrow, despite some broadenin…
Figure 8
Figure 8. Figure 8: — Afterglow peak luminosity distributions for SGRBs (top panels) and LGRBs (bottom panels) at four representative wavelengths: radio, optical, X-ray, and gamma-ray bands. For both SGRBs and LGRBs, the peak luminosities in the diffused scenario are several orders of mag…
Figure 9
Figure 9. Figure 9: — Cumulative distribution function of prompt emission flux for SGRBs (left) and LGRBs (right), again contrasting the intrinsic distributions of the undiffused (solid line) scenario with the diffused ones (dashed line). Blue and orange lines represent the curves for the…
Figure 10
Figure 10. Figure 10: — Cumulative distribution functions for the peak afterglow flux densities of SGRBs (top row) and LGRB (bottom row) across multiple energy bands: radio (left), optical (second from left), X-ray (second from right), and gamma-rays (right). Similarly to [PITH_FULL_IMAGE…
Figure 11
Figure 11. Figure 11: — Scatter plots of mass versus radius for the SGRBs (left) and LGRBs (right) whose prompt emission is above the Fermi/Swift detection sensitivity threshold for SG model (top) and TQM model (bottom). The color bar represents the redshift of the events, where warmer col…
Figure 12
Figure 12. Figure 12: — Afterglow properties of SG model. Scatter plots of mass versus radius for the afterglows of SGRBs (top row) and LGRBs (bottom row) that are above the detection sensitivity threshold in different observational bands: Optical (left), X-ray (middle), and Gamma-ray (rig…
Figure 13
Figure 13. Figure 13: — Same as [PITH_FULL_IMAGE:figures/full_fig_p013_13.png]
Figure 14
Figure 14. Figure 14: — Fraction of observable events for the SG model as a function of the SMBH mass for Short (top panels) and Long (bottom panels) GRBs, comparing in each case the undiffused model (left panels) with the diffused one (right panels). In the undiffused case, the highest ob…
Figure 15
Figure 15. Figure 15: — Same as [PITH_FULL_IMAGE:figures/full_fig_p015_15.png]
Figure 16
Figure 16. Figure 16: — Probability distributions of the GRB prompt duration T90 for sources in an SG disk (top panels) and a TQM disk (bottom panels). Left panels show SGRBs and right ones LGRBs. In all the cases, the grey dotted line represents the assumed intrinsic engine duration, whil…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

72 extracted references · 10 canonical work pages

  1. [1]

    P., Abbott, R., Abbott, T

    Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2020, ApJ, 892, L3, doi: 10.3847/2041-8213/ab75f5

    doi:10.3847/2041-8213/ab75f5 2020

  2. [2]

    D., Abraham, S., et al

    Abbott, R., Abbott, T. D., Abraham, S., et al. 2020, Phys. Rev. Lett., 125, 101102, doi: 10.1103/PhysRevLett.125.101102

    doi:10.1103/physrevlett.125.101102 2020

  3. [3]

    Artymowicz, P., Lin, D. N. C., & Wampler, E. J. 1993, ApJ, 409, 592, doi: 10.1086/172690

    doi:10.1086/172690 1993

  4. [4]

    1993, ApJ, 413, 281, doi: 10.1086/172995

    Band, D., Matteson, J., Ford, L., et al. 1993, ApJ, 413, 281, doi: 10.1086/172995

    doi:10.1086/172995 1993

  5. [5]

    M., Mac Low, M.-M., McKernan, B., & Ford, K

    Bellovary, J. M., Mac Low, M.-M., McKernan, B., & Ford, K. E. S. 2016, ApJ, 819, L17, doi: 10.3847/2041-8205/819/2/L17

    doi:10.3847/2041-8205/819/2/l17 2016

  6. [6]

    N., Meegan, C

    Bhat, P. N., Meegan, C. A., von Kienlin, A., et al. 2016, The Astrophysical Journal Supplement Series, 223, 28, doi: 10.3847/0067-0049/223/2/28

    doi:10.3847/0067-0049/223/2/28 2016

  7. [7]

    A., Haster, C.-J., Ng, K

    Callister, T. A., Haster, C.-J., Ng, K. K. Y., Vitale, S., & Farr, W. M. 2021, ApJ, 922, L5, doi: 10.3847/2041-8213/ac2ccc

    doi:10.3847/2041-8213/ac2ccc 2021

  8. [8]

    S., & Lin, D

    Cantiello, M., Jermyn, A. S., & Lin, D. N. C. 2021, ApJ, 910, 94, doi: 10.3847/1538-4357/abdf4f

    doi:10.3847/1538-4357/abdf4f 2021

Show all 72 references
  1. [9]

    Chen, Y.-X., Jiang, Y.-F., Goodman, J., & Lin, D. N. C. 2024, ApJ, 974, 106, doi: 10.3847/1538-4357/ad6dd4

    2024 doi

  2. [10]

    Chen, Y.-X., Jiang, Y.-F., Goodman, J., & Ostriker, E. C. 2023, ApJ, 948, 120, doi: 10.3847/1538-4357/acc023

    2023 doi

  3. [11]

    Chen, Y.-X., & Lin, D. N. C. 2023, MNRAS, 522, 319, doi: 10.1093/mnras/stad992

    2023 doi

  4. [12]

    J., Jermyn, A

    Dittmann, A. J., Jermyn, A. S., & Cantiello, M. 2023, ApJ, 946, 56, doi: 10.3847/1538-4357/acacf2

    2023 doi

  5. [13]

    2024, arXiv e-prints, arXiv:2405.09380, doi: 10.48550/arXiv.2405.09380

    Epstein-Martin, M., Tagawa, H., Haiman, Z., & Perna, R. 2024, arXiv e-prints, arXiv:2405.09380, doi: 10.48550/arXiv.2405.09380

    2024 doi arXiv

  6. [14]

    J., Cantiello, M., Perna, R., & Samsing, J

    Fabj, G., Dittmann, A. J., Cantiello, M., Perna, R., & Samsing, J. 2024, arXiv e-prints, arXiv:2408.16050, doi: 10.48550/arXiv.2408.16050

    2024 doi arXiv

  7. [15]

    S., Caban, F., et al

    Fabj, G., Nasim, S. S., Caban, F., et al. 2020, MNRAS, 499, 2608, doi: 10.1093/mnras/staa3004

    2020 doi

  8. [16]

    E., Madras, C

    Fenimore, E. E., Madras, C. D., & Nayakshin, S. 1996, ApJ, 473, 998, doi: 10.1086/178210

    1996 doi

  9. [17]

    Fong, W., Berger, E., Margutti, R., & Zauderer, B. A. 2015, ApJ, 815, 102, doi: 10.1088/0004-637X/815/2/102

    2015 doi

  10. [18]

    A., Bonnerot, C., & Gerosa, D

    Gangardt, D., Trani, A. A., Bonnerot, C., & Gerosa, D. 2024, Monthly Notices of the Royal Astronomical Society, 530, 3689–3705, doi: 10.1093/mnras/stae1117

    2024 doi

  11. [19]

    L., et al

    Gendre, B., Stratta, G., Atteia, J. L., et al. 2013, The Astrophysical Journal, 766, 30, doi: 10.1088/0004-637X/766/1/30

    2013 doi

  12. [20]

    Gilbaum, S., & Stone, N. C. 2022, ApJ, 928, 191, doi: 10.3847/1538-4357/ac4ded

    2022 doi

  13. [22]

    2002, The Astrophysical Journal, 568, 820, doi: 10.1086/339985

    Granot, J., & Sari, R. 2002, The Astrophysical Journal, 568, 820, doi: 10.1086/339985

    2002 doi

  14. [23]

    Grishin, E., Bobrick, A., Hirai, R., Mandel, I., & Perets, H. B. 2021, arXiv e-prints, arXiv:2105.09953. https://arxiv.org/abs/2105.09953 18 H. D. Kang et al

    2021 arXiv

  15. [24]

    Grishin, E., Gilbaum, S., & Stone, N. C. 2024, MNRAS, 530, 2114, doi: 10.1093/mnras/stae828

    2024 doi

  16. [25]

    T., Soszy´ nski, I., Gladders, M

    Holland, S. T., Soszy´ nski, I., Gladders, M. D., et al. 2002, The Astronomical Journal, 124, 639–645, doi: 10.1086/341388

    2002 doi

  17. [26]

    S., Dittmann, A

    Jermyn, A. S., Dittmann, A. J., Cantiello, M., & Perna, R. 2021, ApJ, 914, 105, doi: 10.3847/1538-4357/abfb67

    2021 doi

  18. [27]

    C., & Merloni, A

    Kelly, B. C., & Merloni, A. 2012, Advances in Astronomy, 2012, 970858, doi: 10.1155/2012/970858

    2012 doi

  19. [28]

    2015, Annual Review of Astronomy and Astrophysics, 53, 115, doi: 10.1146/annurev-astro-082214-122316

    King, A., & Pounds, K. 2015, Annual Review of Astronomy and Astrophysics, 53, 115, doi: 10.1146/annurev-astro-082214-122316

    2015 doi

  20. [29]

    Krongold, Y., & Prochaska, J. X. 2013, The Astrophysical Journal, 774, 115, doi: 10.1088/0004-637x/774/2/115

    2013 doi

  21. [30]

    P., & Levan, A

    Lazzati, D., Perna, R., Gompertz, B. P., & Levan, A. J. 2023, ApJ, 950, L20, doi: 10.3847/2041-8213/acd18c

    2023 doi

  22. [31]

    2022, ApJ, 938, L18, doi: 10.3847/2041-8213/ac98ad

    Lazzati, D., Soares, G., & Perna, R. 2022, ApJ, 938, L18, doi: 10.3847/2041-8213/ac98ad

    2022 doi

  23. [32]

    J., Malesani, D

    Levan, A. J., Malesani, D. B., Gompertz, B. P., et al. 2023, Nature Astronomy, 7, 976, doi: 10.1038/s41550-023-01998-8

    2023 doi

  24. [33]

    M., Li, H., Lai, D., & Li, S

    Li, J., Dempsey, A. M., Li, H., Lai, D., & Li, S. 2023, ApJ, 944, L42, doi: 10.3847/2041-8213/acb934

    2023 doi

  25. [34]

    2024, ApJ, 969, 37, doi: 10.3847/1538-4357/ad463a

    Liu, J.-R., Wang, Y.-L., & Wang, J.-M. 2024, ApJ, 969, 37, doi: 10.3847/1538-4357/ad463a

    2024 doi

  26. [35]

    McKernan, B., Ford, K. E. S., Callister, T., et al. 2022, MNRAS, 514, 3886, doi: 10.1093/mnras/stac1570

    2022 doi

  27. [36]

    2008, MNRAS, 388, 1011, doi: 10.1111/j.1365-2966.2008.13472.x M´ esz´ aros, P., & Rees, M

    Merloni, A., & Heinz, S. 2008, MNRAS, 388, 1011, doi: 10.1111/j.1365-2966.2008.13472.x M´ esz´ aros, P., & Rees, M. J. 1997, ApJ, 476, 232, doi: 10.1086/303625

    2008

  28. [37]

    D., Giannios, D., & Mimica, P

    Metzger, B. D., Giannios, D., & Mimica, P. 2012, Monthly Notices of the Royal Astronomical Society, 420, 3528, doi: 10.1111/j.1365-2966.2011.20247.x

    2012

  29. [38]

    L., et al

    Muccino, M., Ruffini, R., Bianco, C. L., et al. 2013, The Astrophysical Journal, 772, 62, doi: 10.1088/0004-637x/772/1/62

    2013 doi

  30. [39]

    2021, ApJ, 923, 173, doi: 10.3847/1538-4357/ac249c

    Pan, Z., & Yang, H. 2021, ApJ, 923, 173, doi: 10.3847/1538-4357/ac249c

    2021 doi

  31. [40]

    2000, ApJ, 544, L17, doi: 10.1086/317301

    Panaitescu, A., & M´ esz´ aros, P. 2000, ApJ, 544, L17, doi: 10.1086/317301

    2000 doi

  32. [41]

    2002, ApJ, 580, 261, doi: 10.1086/343081

    Perna, R., & Lazzati, D. 2002, ApJ, 580, 261, doi: 10.1086/343081

    2002 doi

  33. [42]

    2021a, ApJ, 906, L7, doi: 10.3847/2041-8213/abd319

    Perna, R., Lazzati, D., & Cantiello, M. 2021a, ApJ, 906, L7, doi: 10.3847/2041-8213/abd319

    doi

  34. [43]

    2003, ApJ, 585, 775, doi: 10.1086/346109

    Perna, R., Lazzati, D., & Fiore, F. 2003, ApJ, 585, 775, doi: 10.1086/346109

    2003 doi

  35. [44]

    2021b, ApJ, 915, 10, doi: 10.3847/1538-4357/abfdb4

    Perna, R., Tagawa, H., Haiman, Z., & Bartos, I. 2021b, ApJ, 915, 10, doi: 10.3847/1538-4357/abfdb4

    doi

  36. [45]

    2004, Reviews of Modern Physics, 76, 1143, doi: 10.1103/RevModPhys.76.1143

    Piran, T. 2004, Reviews of Modern Physics, 76, 1143, doi: 10.1103/RevModPhys.76.1143

    2004 doi

  37. [46]

    Proga, D., & Kallman, T. R. 2004, The Astrophysical Journal, 616, 688, doi: 10.1086/424913

    2004 doi

  38. [47]

    2023, MNRAS, 521, 4233, doi: 10.1093/mnras/stad816

    Ray, M., Lazzati, D., & Perna, R. 2023, MNRAS, 521, 4233, doi: 10.1093/mnras/stad816

    2023 doi

  39. [48]

    J., & Meszaros, P

    Rees, M. J., & Meszaros, P. 1994, ApJ, 430, L93, doi: 10.1086/187446

    1994 doi

  40. [49]

    2022, ApJ, 940, L44, doi: 10.3847/2041-8213/aca025

    Ren, J., Chen, K., Wang, Y., & Dai, Z.-G. 2022, ApJ, 940, L44, doi: 10.3847/2041-8213/aca025

    2022 doi

  41. [50]

    2023, MNRAS, 524, 2770, doi: 10.1093/mnras/stad1926

    Rowan, C., Boekholt, T., Kocsis, B., & Haiman, Z. 2023, MNRAS, 524, 2770, doi: 10.1093/mnras/stad1926

    2023 doi

  42. [51]

    J., et al

    Samsing, J., Bartos, I., D’Orazio, D. J., et al. 2022, Nature, 603, 237, doi: 10.1038/s41586-021-04333-1

    2022 doi

  43. [52]

    1995, ApJ, 455, L143, doi: 10.1086/309835

    Sari, R., & Piran, T. 1995, ApJ, 455, L143, doi: 10.1086/309835

    1995 doi

  44. [53]

    Sari, R., Piran, T., & Halpern, J. P. 1999, ApJ, 519, L17, doi: 10.1086/312109

    1999 doi

  45. [54]

    1998, ApJ, 497, L17, doi: 10.1086/311269

    Sari, R., Piran, T., & Narayan, R. 1998, ApJ, 497, L17, doi: 10.1086/311269

    1998 doi

  46. [55]

    Scalo, J., & Wheeler, J. C. 2001, The Astrophysical Journal, 562, 664–669, doi: 10.1086/323858

    2001 doi

  47. [56]

    I., & Sunyaev, R

    Shakura, N. I., & Sunyaev, R. A. 1973, A&A, 500, 33

    1973

  48. [57]

    2003, MNRAS, 341, 501, doi: 10.1046/j.1365-8711.2003.06431.x

    Sirko, E., & Goodman, J. 2003, MNRAS, 341, 501, doi: 10.1046/j.1365-8711.2003.06431.x

    2003

  49. [58]

    M., Klose, S., et al

    Stratta, G., Nicuesa Guelbenzu, A. M., Klose, S., et al. 2024, arXiv e-prints, arXiv:2412.04059, doi: 10.48550/arXiv.2412.04059

    2024 doi arXiv

  50. [59]

    2020, ApJ, 898, 25, doi: 10.3847/1538-4357/ab9b8c

    Tagawa, H., Haiman, Z., & Kocsis, B. 2020, ApJ, 898, 25, doi: 10.3847/1538-4357/ab9b8c

    2020 doi

  51. [60]

    S., Haiman, Z., Perna, R., & Bartos, I

    Tagawa, H., Kimura, S. S., Haiman, Z., Perna, R., & Bartos, I. 2023a, ApJ, 950, 13, doi: 10.3847/1538-4357/acc4bb —. 2023b, ApJ, 946, L3, doi: 10.3847/2041-8213/acc103

    doi

  52. [61]

    2013, Publications of the Astronomical Society of Japan, 65, doi: 10.1093/pasj/65.4.88

    Takeuchi, S., Ohsuga, K., & Mineshige, S. 2013, Publications of the Astronomical Society of Japan, 65, doi: 10.1093/pasj/65.4.88

    2013 doi

  53. [62]

    A., Quataert, E., & Murray, N

    Thompson, T. A., Quataert, E., & Murray, N. 2005, ApJ, 630, 167, doi: 10.1086/431923

    2005 doi

  54. [63]

    Wang, Y., Zhu, Z., & Lin, D. N. C. 2024, MNRAS, 528, 4958, doi: 10.1093/mnras/stae321

    2024 doi

  55. [64]

    2022, MNRAS, 516, 5935, doi: 10.1093/mnras/stac1968

    Wang, Y.-H., Lazzati, D., & Perna, R. 2022, MNRAS, 516, 5935, doi: 10.1093/mnras/stac1968

    2022 doi

  56. [65]

    2021, ApJ, 923, L23, doi: 10.3847/2041-8213/ac400a

    Wang, Y.-H., McKernan, B., Ford, S., et al. 2021, ApJ, 923, L23, doi: 10.3847/2041-8213/ac400a

    2021 doi

  57. [66]

    Waxman, E., & Draine, B. T. 2000, ApJ, 537, 796, doi: 10.1086/309053

    2000 doi

  58. [67]

    2016, ApJ, 824, L17, doi: 10.3847/2041-8205/824/2/L17

    Xie, C., Fang, T., Wang, J., Liu, T., & Jiang, X. 2016, ApJ, 824, L17, doi: 10.3847/2041-8205/824/2/L17

    2016 doi

  59. [68]

    2022, ApJ, 933, L28, doi: 10.3847/2041-8213/ac7c0b

    Yang, Y., Bartos, I., Fragione, G., et al. 2022, ApJ, 933, L28, doi: 10.3847/2041-8213/ac7c0b

    2022 doi

  60. [69]

    2024a, ApJ, 976, 63, doi: 10.3847/1538-4357/ad8139

    Zhang, H.-H., Zhu, J.-P., & Yu, Y.-W. 2024a, ApJ, 976, 63, doi: 10.3847/1538-4357/ad8139

    doi

  61. [70]

    Zhang, S.-R., Yuan, Y.-F., Wang, J.-M., & Ho, L. C. 2024b, MNRAS, 532, 1330, doi: 10.1093/mnras/stae1546

    doi

  62. [71]

    E., & Heger, A

    Zhang, W., Woosley, S. E., & Heger, A. 2004, ApJ, 608, 365, doi: 10.1086/386300

    2004 doi

  63. [72]

    2021a, ApJ, 914, L19, doi: 10.3847/2041-8213/abff5a

    Zhu, J.-P., Yang, Y.-P., Zhang, B., et al. 2021a, ApJ, 914, L19, doi: 10.3847/2041-8213/abff5a

    doi

  64. [73]

    2021b, ApJ, 906, L11, doi: 10.3847/2041-8213/abd412 This paper was built using the Open Journal of As- trophysics LATEX template

    Zhu, J.-P., Zhang, B., Yu, Y.-W., & Gao, H. 2021b, ApJ, 906, L11, doi: 10.3847/2041-8213/abd412 This paper was built using the Open Journal of As- trophysics LATEX template. The OJA is a journal which provides fast and easy peer review for new papers in the astro-ph section of...

    doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.