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REVIEW 2 major objections 7 minor 94 references

GRB 221009A and the Apparently Most Energetic Gamma-Ray Bursts

T0 review · 2 major / 7 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Even the brightest gamma-ray burst ever seen does not break the energy ceiling of the long-burst population.

desk verdict A solid, incremental confirmation of the Eiso cutoff with a doubled sample; the BOAT doesn't break it, but the 'not an outlier' claim leans on the CPL model. read the letter →

arxiv 2501.13505 v1 pith:5QTLH5OF submitted 2025-01-23 astro-ph.HE

J-L. Atteia , L. Bouchet , J-P. Dezalay , F. Fortin , O. Godet , S. Guillot , A. Klotz , F. Daigne
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R. Mochkovitch D. Turpin
This is my paper · ORCID
classification astro-ph.HE
keywords gamma-rayburstsGRB221009AEisodistributionisotropicequivalentenergyjetbeamingpromptemissioncutoffpowerlawlongGRBs
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 asks whether the brightest gamma-ray burst ever detected, GRB 221009A, breaks the apparent-energy ceiling inferred for long bursts. Using a sample of 185 long gamma-ray bursts with $E_{\rm iso} \geq 10^{53}$ erg, the authors fit the bright end of the isotropic equivalent energy distribution with a power law, a cutoff power law, and a broken power law. They find that models with a cutoff near $4\times10^{54}$ erg describe the data far better than a plain power law, and that including GRB 221009A shifts the cutoff only slightly. Their conclusion is that the burst is an extreme draw from the same population, not a new class, with its record apparent energy explained by an unusually narrow jet. If correct, this turns a one-off record explosion into a measurable constraint on how much energy GRB jets can carry and how narrow they can be.

What carries the argument

The central object is $E_{\rm iso}$, the isotropic equivalent energy of the prompt phase, computed from the measured redshift and gamma-ray spectrum as though the burst radiated equally in all directions. The statistical argument is carried by comparing three models of the bright-end differential distribution, a simple power law, a cutoff power law, and a broken power law, through maximum likelihood, a Kolmogorov-Smirnov test, and the Bayesian Information Criterion. The physical interpretation rests on the identity $E_{\rm iso} = E_\gamma \times f_b$, where $E_\gamma$ is the true gamma-ray energy and $f_b$ is the beaming factor, which allows the observed cutoff to be translated into an upper limit on jet power and a lower limit on jet opening angle of about one degree.

What would settle it

Compute how many GRBs with $E_{\rm iso} > 10^{55}$ erg should exist within the surveyed volume out to redshift 6.3 under the best-fit cutoff power law, then compare with an unbiased all-sky survey; finding several high-redshift bursts above $10^{55}$ erg would falsify the cutoff, as would a reanalysis showing that requiring a measured redshift biases the sample against faint, distant bursts.

Watch

Extended reading notes

Core claim

The central claim is that the isotropic equivalent energy distribution of long gamma-ray bursts has a genuine cutoff near $E_{\rm iso} = 4\times10^{54}$ erg, and that GRB 221009A does not erase it. In a sample of 185 long bursts with $E_{\rm iso} \geq 10^{53}$ erg, maximum-likelihood fits of the unbinned differential distribution show that cutoff power-law and broken power-law models raise Kolmogorov-Smirnov probabilities from about 1% to about 50% and reduce the Bayesian Information Criterion by 16 to 17 relative to a simple power law. With the BOAT included, the cutoff power-law fit gives a break at $4.2^{+3.9}_{-1.5}\times10^{54}$ erg. The paper interprets the cutoff as a physical limit on jets: a maximum jet power near $10^{52}$ erg in the collapsar picture, and a scarcity of jets narrower than about one degree, which may fail to form or be destroyed while crossing the stellar envelope.

Load-bearing premise

The sample of 185 bursts is a nearly complete and unbiased census of long GRBs with $E_{\rm iso} \geq 10^{53}$ erg, a claim the paper supports from the presence of moderate-energy bursts at high redshift but does not quantify with a redshift-dependent selection function.

Editorial extensions

If this is right

  • GRB 221009A does not require a new emission mechanism or a new class of burst; it is consistent with being the extreme tail of the long-GRB energy distribution.
  • The probability of drawing a burst as energetically extreme as the BOAT from the best-fit distribution is about 0.1%, so one such event in nearly 200 energetic bursts is expected occasionally rather than forbidden.
  • A physical cutoff near $4\times10^{54}$ erg implies that the most apparently energetic long GRBs have beaming factors near $10^3$ and true jet energies around $10^{52}$ erg.
  • If the cutoff comes from a scarcity of ultra-narrow jets, future observations should find very few long GRBs with $E_{\rm iso}$ above $10^{55}$ erg at any redshift.
  • The upcoming SVOM mission, with fast positions and broad-band spectra, should grow the sample and test whether the cutoff sharpens or fills in.

Reading between the lines

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

  • Beyond the paper, an unbiased all-sky sample built purely on gamma-ray detection, with redshifts obtained for every burst, could settle whether the cutoff is physical or an artifact of requiring a measured redshift.
  • The BOAT's combination of the lowest redshift and the highest energy in the sample is the most fragile part of the story; if an energy-redshift correlation exists, the apparent cutoff in $E_{\rm iso}$ could partly reflect evolution rather than a jet limit.
  • The same beaming argument could be applied to the bright end of the $E_{\rm peak}$-$E_{\rm iso}$ relation, where the scatter of the most energetic bursts would carry direct information about jet opening-angle distributions.
  • If jet simulations confirm that jets narrower than one degree are unable to pierce the stellar envelope, then the cutoff becomes a probe of jet-launch physics that can be tested independently of the GRB sample.
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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

2 major / 7 minor

Summary. This paper revisits the bright end of the long-GRB isotropic-equivalent energy (Eiso) distribution using a newly assembled sample of 185 bursts with Eiso >= 10^53 erg, now including the BOAT GRB 221009A. The authors fit the differential Eiso distribution above 10^53 erg with power-law, cutoff power-law, and broken power-law models using unbinned maximum likelihood, and they use KS p-values and BIC differences to argue that a cutoff/break near 4e54 erg is statistically preferred over a pure power law even when GRB 221009A is included. They further compute the tail probability of a burst as energetic as the BOAT under the best-fit models and conclude that, from the shape of the distribution alone, GRB 221009A is an extreme but not a clear outlier; the final sections discuss jet-opening-angle and central-engine explanations for the apparent cutoff.

Significance. If the central claim holds, the paper provides a useful, updated census of the most energetic long GRBs and strengthens earlier evidence that the Eiso distribution breaks near a few times 10^54 erg. It also gives a reasoned (if speculative) interpretation of GRB 221009A as an extreme member of the known population. The statistical machinery is standard and clearly described, and the construction of the 185-burst table is a service to the community. The significance of the result is, however, conditional on two issues: the unquantified completeness of the sample and the model dependence of the "not a clear outlier" conclusion. These need to be addressed before the paper's headline claims can be regarded as robust.

major comments (2)
  1. [Section 2, Figure 1] The assertion that the sample is nearly volume complete is supported only by the qualitative statement that bursts with Eiso ~ 10^53 erg are seen at high redshift; no quantitative selection function is provided. Because inclusion requires both a prompt gamma-ray detection and a measured redshift, and because the completeness of redshift follow-up is known to decline with redshift and with afterglow brightness or dust extinction, the sample could be missing an Eiso-dependent fraction of bursts at intermediate and high redshift. A preferential loss of high-Eiso bursts at z ~ 1-3 would artificially create or steepen the apparent cutoff at 4e54 erg, and the KS/BIC comparisons in Section 3 cannot distinguish a physical cutoff from such incompleteness. The authors should quantify the selection function (e.g., via detection-threshold and redshift-follow-up models) or demonstrate robustness by restricting to a subsample with well-defined completeness, and they should state the resulting caveat explicitly in the abstract and conclusions.
  2. [Section 3, Table 2 and following paragraph] The conclusion that GRB 221009A is not a clear outlier is drawn exclusively from the CPL fit, which gives P(Eiso > 1.2e55) = 1.7e-3 for the full 185-burst sample. The BPL fit, however, yields P = 3.5e-6 for the 184-burst sample (and 1.2e-3 when the BOAT is included), and the BIC does not strongly prefer CPL over BPL (reductions of 19.5 vs 23.7 for the 184-burst sample). Under the BPL, GRB 221009A would be a highly significant outlier. Since the paper's headline conclusion depends on this choice, the authors must report the outlier probability under both models and either justify a preference for CPL or explicitly state that the outlier status is model-dependent. Moreover, using the full-sample fit (which includes the BOAT) to evaluate the probability of the BOAT is conservative in that it inevitably raises the tail probability; a cleaner outlier test uses the 184-burst sample, under which the BPL would make the BOAT a significant outlier.
minor comments (7)
  1. [Abstract] The typeset abstract mentions only "two fits" (PL and CPL) and omits the broken power law that is analyzed in the body; the abstract should be consistent with Section 3.
  2. [Section 3, first paragraph] The text "Bayesian Information Criterion Criterion" contains a duplicated word; please correct it and define the KS statistic in a self-contained way.
  3. [Section 4.1] The phrase "the probability of GRB 221009A in our sample" should read "the probability of a GRB with the properties of GRB 221009A in our sample" to avoid implying that a specific observed burst has a probability.
  4. [Section 5 (Conclusions)] There are typographical errors in the conclusions ("resdhifts", "currrent", "a a highly unprobable, unique detection") that should be corrected.
  5. [Table 2] The BPL high-energy slope for the 184-burst sample is quoted as a one-sided limit (<= -3.6); the caption should explain that this is a limit and how it was estimated.
  6. [Section 3] The KS test "probability" is a p-value, not the probability that the model is correct; the wording "increases the probability of the fits" is imprecise and should be revised to "increases the p-value of the KS test".
  7. [Appendix Table 1] Given the length and utility of the 185-burst table, providing a machine-readable version in a supplementary file would improve reproducibility and ease of use for the community.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the claimed Eiso cutoff is derived from maximum-likelihood model comparison on the new 185-GRB sample, not from a self-referential definition or a load-bearing self-citation.

full rationale

The paper's central claim is that the bright end of the long-GRB Eiso distribution has a cutoff near 4e54 erg even when GRB 221009A is included. This is established by fitting power-law, cutoff-power-law, and broken-power-law models to the unbinned differential Eiso distribution and comparing them via a Kolmogorov-Smirnov test and the Bayesian Information Criterion. The cutoff is not an input that is then reported as a discovery: the PL model is explicitly rejected (KS probability about 1%, BIC reduction 16-24 for CPL/BPL), and the cutoff parameter is estimated from the data, giving E_break values around 3-5e54 erg for both the 184-burst sample and the 184-burst-plus-BOAT sample. The probability that a burst is as energetic as GRB 221009A is the survival probability of the fitted CPL, and the paper reports it for both the sample excluding the BOAT (p = 7.0e-4) and the sample including it (p = 1.7e-3), so the qualitative conclusion does not depend on an in-sample circular evaluation. Self-citations to Atteia et al. (2017) are used for context, for the beaming-factor discussion in Section 4.2, and to note a typographical error, but the main cutoff claim is re-derived from the new sample and cross-checked against the independent analysis of Lan et al. (2023). The unquantified sample-completeness assumption noted in Section 2 is a potential selection-effect concern that could affect the astrophysical interpretation, but it is not a circularity: the statistical fits and model comparisons are performed on the sample as defined, and the paper does not define the cutoff in terms of the fitted parameter. No load-bearing step reduces, by the paper's own equations or by self-citation, to its own inputs.

Assumptions & free parameters 3 free parameters · 7 assumptions · 0 invented entities

The analysis rests mostly on the input Eiso catalog values and on the assumption that the sample is unbiased. The fitted cutoff energy is the key free parameter; the slopes are shape parameters. No new entities are introduced. The theoretical discussion in Section 4.2 is explicitly speculative and is not used in the fitting.

free parameters (3)
  • Cutoff energy E_break (CPL/BPL) = ≈ 4 × 10^54 erg (CPL with BOAT: 4.2+3.9-1.5; BPL: 4.0+2.3-2.0)
    The central fitted parameter; its existence and location are the main claim.
  • Low-energy power-law slope of differential distribution = ≈ -1.3 to -1.5
    Fitted slope below the break; determines the overall normalization and the inferred number of high-energy events.
  • High-energy slope (BPL slope 2) = ≤ -3.6 (upper limit)
    Very steep and poorly constrained; the uncertainty in this slope drives the large range in the inferred probability of a BOAT-like event.
assumptions (7)
  • domain assumption Flat ΛCDM cosmology with Planck 2014 parameters (H0 = 67.3 km/s/Mpc, Ωm = 0.315) is used to compute volumes and Eiso.
    Section 1; used for Eiso and volume calculations. Adopted for consistency with source catalogs.
  • domain assumption Eiso values from Tsvetkova et al. (2017, 2021) and Poolakkil et al. (2021) are reliable and directly comparable without recalibration.
    Section 2; the authors do not recompute Eiso and assume the three catalogs are mutually consistent.
  • domain assumption The 185-burst sample is representative of all long GRBs with Eiso ≥ 10^53 erg, with no strong redshift-dependent selection bias.
    Section 2 and Figure 1; the sample is asserted to be nearly volume-complete above 10^53 erg, but no quantitative selection function is given.
  • domain assumption No luminosity or energy evolution with redshift for the probability calculation in Section 4.1.
    Used to compute p = 1 - (1 - p1*p2)^184; the authors acknowledge their ignorance of the joint Eiso-redshift distribution.
  • domain assumption GRB jets are uniform cones with beaming factor fb = 4π/Ωj, so that Eiso = Eγ × fb.
    Eq. 1 in Section 4.2; a standard but simplified jet geometry. Structured jets would change the interpretation.
  • domain assumption The theoretical upper limits on jet power from Metzger et al. (2011) and Jacquemin-Ide et al. (2024) apply to GRB central engines.
    Section 4.2; used to argue that the cutoff could result from a maximum jet energy. These are model-dependent predictions, not established facts.
  • domain assumption The GRB formation rate density evolution of Palmerio et al. (2022) is correct.
    Section 4.1; used to compute the fraction of bursts at z < 0.151 (p2 = 4.3e-4).

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Cite this review

Pith. "Pith review of GRB 221009A and the Apparently Most Energetic Gamma-Ray Bursts." pith.science (2026). https://pith.science/paper/5QTLH5OF

@misc{pith2026250113505,
  author       = {Pith},
  title        = {Pith review of: GRB 221009A and the Apparently Most Energetic Gamma-Ray Bursts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5QTLH5OF}},
  note         = {Machine review of arXiv:2501.13505}
}
abstract

Gamma-Ray Bursts (GRBs) are often referred to as the most luminous explosions in the Universe, due to their short and highly luminous prompt emission. This apparent luminosity, however, does not reflect the true energy budget of the prompt emission, which is strongly beamed. Accurate estimations of the energy radiated during the prompt phase require taking into account the geometry of GRB jets, which remains poorly known. Nevertheless, one may establish the distribution of well measured quantities, like Eiso, the GRB isotropic equivalent energy, which encrypts crucial information about GRB jets, with the aim of providing constraints on the jets radiated energy. In this work, we study the bright end of the GRB isotropic equivalent energy distribution (hereafter called "apparent energy"), using an updated sample of 185 apparently energetic GRBs with Eiso $\geq 10^{53}$ erg. This new sample includes GRB 221009A, allowing to discuss this apparently super-energetic GRB in the context of the general Eiso distribution of long GRBs. We describe the construction of the sample and compare fits of the Eiso distribution with a simple power law, a cutoff power law and a broken power law. Our study confirms the existence of a cutoff around Eiso = $4\times10^{54}$ erg, even when GRB 221009A is included in the sample. Based on this finding, we discuss the possible reasons behind the rapid decrease of the number of apparently energetic gamma-ray bursts beyond Eiso = $4\times10^{54}$ erg and the interpretation of GRB 221009A, the most apparently energetic GRB detected to date, in this context.

Figures

Figures reproduced from arXiv: 2501.13505 by the authors.

Figure 1
Figure 1. Distribution of 185 apparently energetic GRBs showing their isotropic equivalent energy Eiso and its uncertainty as a function of the volume enclosed within their distance. The colored points show the position of GRB 221009A, based on the isotropic equivalent energy measured by GRBAlpha (R´ıpa et al. ˇ 2023, green), SIRI-2 (Mitchell et al. 2024, blue),Fermi (Lesage et al. 2022, red), Konus (Frederiks et al. 2023b, b… view at source ↗
Figure 2
Figure 2. Complementary cumulative energy distribution (CCDF = 1 - CDF) of 185 GRBs, including GRB 221009A. The green, blue, red, black and magenta points show the isotropic equivalent energy of GRB 221009A respectively measured by GRBAlpha (R´ıpa et al. ˇ 2023), SIRI-2 (Mitchell et al. 2024), Fermi (Lesage et al. 2022), Konus (Frederiks et al. 2023b) and GECAM (An et al. 2023). The green dashed line, the blue and brown solid… view at source ↗
Figure 3
Figure 3. Best fit parameters for the cutoff power law (CPL) in a plane showing the slope vs cutoff energy. The black star shows the best fit parameters for the sample in this study (with GRB 221009A included in the sample), and the solid contours show the 68 and 90% confidence regions on these parameters. The blue star and dotted blue contour are the best fit and 90% contour for the sample of Lan et al. (2023) restricted to … view at source ↗

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Works this paper leans on

94 extracted references · 34 canonical work pages

  1. [1]

    2019, ApJ, 878, 52, doi: 10.3847/1538-4357/ab1d4e

    Ajello, M., Arimoto, M., Axelsson, M., et al. 2019, ApJ, 878, 52, doi: 10.3847/1538-4357/ab1d4e

    doi:10.3847/1538-4357/ab1d4e 2019

  2. [2]

    2009, A&A, 508, 173, doi: 10.1051/0004-6361/200912788

    Amati, L., Frontera, F., & Guidorzi, C. 2009, A&A, 508, 173, doi: 10.1051/0004-6361/200912788

    doi:10.1051/0004-6361/200912788 2009

  3. [3]

    2023, arXiv e-prints, arXiv:2303.01203, doi: 10.48550/arXiv.2303.01203

    An, Z.-H., Antier, S., Bi, X.-Z., et al. 2023, arXiv e-prints, arXiv:2303.01203, doi: 10.48550/arXiv.2303.01203

    work page doi:10.48550/arxiv.2303.01203 2023

  4. [4]

    L., Frederiks, D

    Aptekar, R. L., Frederiks, D. D., Golenetskii, S. V., et al. 1995, SSRv, 71, 265, doi: 10.1007/BF00751332 14 Atteia et al. Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f

    doi:10.1007/bf00751332 1995

  5. [5]

    L., Cordier, B., & Wei, J

    Atteia, J. L., Cordier, B., & Wei, J. 2022, International Journal of Modern Physics D, 31, 2230008, doi: 10.1142/S0218271822300087

    doi:10.1142/s0218271822300087 2022

  6. [6]

    L., Kawai, N., Vanderspek, R., et al

    Atteia, J. L., Kawai, N., Vanderspek, R., et al. 2005, ApJ, 626, 292, doi: 10.1086/430057

    work page doi:10.1086/430057 2005

  7. [7]

    L., Heussaff, V., Dezalay, J

    Atteia, J. L., Heussaff, V., Dezalay, J. P., et al. 2017, ApJ, 837, 119, doi: 10.3847/1538-4357/aa5ffa

    doi:10.3847/1538-4357/aa5ffa 2017

  8. [8]

    2019, MNRAS, 482, 5430, doi: 10.1093/mnras/sty3110

    Beniamini, P., & Nakar, E. 2019, MNRAS, 482, 5430, doi: 10.1093/mnras/sty3110

    doi:10.1093/mnras/sty3110 2019

Show all 94 references
  1. [9]

    2020, GRB Coordinates Network, 27930, 1

    Bissaldi, E., Lesage, S., & Fermi GBM Team. 2020, GRB Coordinates Network, 27930, 1

    2020

  2. [10]

    K., Villar, V

    Blanchard, P. K., Villar, V. A., Chornock, R., et al. 2024, Nature Astronomy, 8, 774, doi: 10.1038/s41550-024-02237-4

    2024 doi

  3. [11]

    D., & Znajek, R

    Blandford, R. D., & Znajek, R. L. 1977, MNRAS, 179, 433, doi: 10.1093/mnras/179.3.433

    1977 doi

  4. [12]

    Bloom, J. S. 2022, Research Notes of the American Astronomical Society, 6, 220, doi: 10.3847/2515-5172/ac9b5a

    2022 doi

  5. [13]

    S., Rhodes, L., Farah, W., et al

    Bright, J. S., Rhodes, L., Farah, W., et al. 2023, Nature Astronomy, 7, 986, doi: 10.1038/s41550-023-01997-9

    2023 doi

  6. [14]

    2011, ApJ, 740, 100, doi: 10.1088/0004-637X/740/2/100

    Bromberg, O., Nakar, E., Piran, T., & Sari, R. 2011, ApJ, 740, 100, doi: 10.1088/0004-637X/740/2/100

    2011 doi

  7. [15]

    2023, ApJL, 946, L31, doi: 10.3847/2041-8213/acc39c

    Burns, E., Svinkin, D., Fenimore, E., et al. 2023, ApJL, 946, L31, doi: 10.3847/2041-8213/acc39c

    2023 doi

  8. [16]

    2023, Science Advances, 9, eadj2778, doi: 10.1126/sciadv.adj2778

    Cao, Z., Aharonian, F., An, Q., et al. 2023, Science Advances, 9, eadj2778, doi: 10.1126/sciadv.adj2778

    2023 doi

  9. [17]

    J., Valeev, A

    Castro-Tirado, A. J., Valeev, A. F., Vinokurov, A., et al. 2022, GRB Coordinates Network, 31423, 1

    2022

  10. [18]

    J., Hu, Y., Fernandez-Garcia, E., et al

    Castro-Tirado, A. J., Hu, Y., Fernandez-Garcia, E., et al. 2019, GRB Coordinates Network, 23708, 1

    2019

  11. [19]

    B., Frail, D

    Cenko, S. B., Frail, D. A., Harrison, F. A., et al. 2010, ApJ, 711, 641, doi: 10.1088/0004-637X/711/2/641

    2010 doi

  12. [20]

    A., Wijers, R

    Curran, P. A., Wijers, R. A. M. J., Heemskerk, M. H. M., et al. 2008, A&A, 490, 1047, doi: 10.1051/0004-6361:200810545

    2008 doi

  13. [21]

    2022, ApJL, 940, L4, doi: 10.3847/2041-8213/ac98c8 D’Avanzo, P., Heintz, K

    Dado, S., & Dar, A. 2022, ApJL, 940, L4, doi: 10.3847/2041-8213/ac98c8 D’Avanzo, P., Heintz, K. E., de Ugarte Postigo, A., et al. 2018a, GRB Coordinates Network, 23246, 1 D’Avanzo, P., Bolmer, J., D’Elia, V., et al. 2018b, GRB Coordinates Network, 22555, 1 de Ugarte Postigo, A...

    2022 doi

  14. [22]

    2024, arXiv e-prints, arXiv:2407.15940

    Finke, J., & Razzaque, S. 2024, arXiv e-prints, arXiv:2407.15940. https://arxiv.org/abs/2407.15940

    2024 arXiv

  15. [23]

    A., Kulkarni, S

    Frail, D. A., Kulkarni, S. R., Sari, R., et al. 2001, ApJL, 562, L55, doi: 10.1086/338119

    2001 doi

  16. [24]

    2016, GRB Coordinates Network, 20111, 1 —

    Frederiks, D., Golenetskii, S., Aptekar, R., et al. 2016, GRB Coordinates Network, 20111, 1 —. 2018a, GRB Coordinates Network, 22546, 1 —. 2018b, GRB Coordinates Network, 23011, 1 —. 2018c, GRB Coordinates Network, 23240, 1 —. 2019a, GRB Coordinates Network, 24715, 1 —. 2019b,...

    2016

  17. [25]

    L., et al

    Frederiks, D., Svinkin, D., Lysenko, A. L., et al. 2023b, ApJL, 949, L7, doi: 10.3847/2041-8213/acd1eb

    doi

  18. [26]

    D., Hurley, K., Svinkin, D

    Frederiks, D. D., Hurley, K., Svinkin, D. S., et al. 2013, ApJ, 779, 151, doi: 10.1088/0004-637X/779/2/151

    2013 doi

  19. [27]

    D., Smartt, S

    Fulton, M. D., Smartt, S. J., Rhodes, L., et al. 2023, ApJL, 946, L22, doi: 10.3847/2041-8213/acc101

    2023 doi

  20. [28]

    Fynbo, J. P. U., de Ugarte Postigo, A., D’Elia, V., et al. 2018, GRB Coordinates Network, 23356, 1

    2018

  21. [29]

    Fynbo, J. P. U., de Ugarte Postigo, A., Xu, D., et al. 2022, GRB Coordinates Network, 31359, 1

    2022

  22. [30]

    2004, ApJ, 611, 1005, doi: 10.1086/422091

    Gehrels, N., Chincarini, G., Giommi, P., et al. 2004, ApJ, 611, 1005, doi: 10.1086/422091

    2004 doi

  23. [31]

    2022, MNRAS, 511, 1694, doi: 10.1093/mnras/stac015

    Gupta, R., Gupta, S., Chattopadhyay, T., et al. 2022, MNRAS, 511, 1694, doi: 10.1093/mnras/stac015

    2022 doi

  24. [32]

    2003, ApJ, 597, 699, doi: 10.1086/378493

    Hjorth, J., Møller, P., Gorosabel, J., et al. 2003, ApJ, 597, 699, doi: 10.1086/378493

    2003 doi

  25. [33]

    A., et al

    Izzo, L., de Ugarte Postigo, A., Kann, D. A., et al. 2018, GRB Coordinates Network, 23488, 1 GRB 221009A and Apparently Energetic GRBs 15

    2018

  26. [34]

    2022, GRB Coordinates Network, 32291, 1

    Izzo, L., D’Elia, V., de Ugarte Postigo, A., et al. 2022, GRB Coordinates Network, 32291, 1

    2022

  27. [35]

    2024, The Astrophysical Journal, 961, 212, doi: 10.3847/1538-4357/ad02f0

    Tchekhovskoy, A. 2024, The Astrophysical Journal, 961, 212, doi: 10.3847/1538-4357/ad02f0

    2024 doi

  28. [36]

    A., Agayeva, S., Aivazyan, V., et al

    Kann, D. A., Agayeva, S., Aivazyan, V., et al. 2023, ApJL, 948, L12, doi: 10.3847/2041-8213/acc8d0

    2023 doi

  29. [37]

    2024, ApJ, 971, 56, doi: 10.3847/1538-4357/ad5ce1 Kr¨ uhler, T., Malesani, D., Fynbo, J

    Kong, D.-F., Wang, X.-G., Zheng, W., et al. 2024, ApJ, 971, 56, doi: 10.3847/1538-4357/ad5ce1 Kr¨ uhler, T., Malesani, D., Fynbo, J. P. U., et al. 2015, A&A, 581, A125, doi: 10.1051/0004-6361/201425561

    2024 doi

  30. [38]

    R., Djorgovski, S

    Kulkarni, S. R., Djorgovski, S. G., Ramaprakash, A. N., et al. 1998, Nature, 393, 35, doi: 10.1038/29927

    1998 doi

  31. [39]

    2023, ApJL, 949, L4, doi: 10.3847/2041-8213/accf93

    Lan, L., Gao, H., Li, A., et al. 2023, ApJL, 949, L4, doi: 10.3847/2041-8213/accf93

    2023 doi

  32. [40]

    D., Margutti, R., et al

    Laskar, T., Alexander, K. D., Margutti, R., et al. 2023, ApJL, 946, L23, doi: 10.3847/2041-8213/acbfad

    2023 doi

  33. [41]

    2022, GRB Coordinates Network, 31360, 1

    Lesage, S., Meegan, C., & Fermi Gamma-ray Burst Monitor Team. 2022, GRB Coordinates Network, 31360, 1

    2022

  34. [42]

    S., et al

    Lesage, S., Veres, P., Briggs, M. S., et al. 2023, ApJL, 952, L42, doi: 10.3847/2041-8213/ace5b4 LHAASO Collaboration, Cao, Z., Aharonian, F., et al. 2023, Science, 380, 1390, doi: 10.1126/science.adg9328

    2023 doi

  35. [43]

    2022, GRB Coordinates Network, 32152, 1

    Lysenko, A., Frederiks, D., Ridnaia, A., et al. 2022, GRB Coordinates Network, 32152, 1

    2022

  36. [44]

    I., & Woosley, S

    MacFadyen, A. I., & Woosley, S. E. 1999, ApJ, 524, 262, doi: 10.1086/307790

    1999 doi

  37. [45]

    2006, A&A, 455, 423, doi: 10.1051/0004-6361:20054728

    Maiorano, E., Masetti, N., Palazzi, E., et al. 2006, A&A, 455, 423, doi: 10.1051/0004-6361:20054728

    2006 doi

  38. [46]

    B., Vielfaure, J

    Malesani, D. B., Vielfaure, J. B., Izzo, L., et al. 2020, GRB Coordinates Network, 29100, 1

    2020

  39. [47]

    B., Levan, A

    Malesani, D. B., Levan, A. J., Izzo, L., et al. 2023, arXiv e-prints, arXiv:2302.07891, doi: 10.48550/arXiv.2302.07891

    2023 doi arXiv

  40. [48]

    N., et al

    Meegan, C., Lichti, G., Bhat, P. N., et al. 2009, ApJ, 702, 791, doi: 10.1088/0004-637X/702/1/791

    2009 doi

  41. [49]

    2011, MNRAS, 413, 2031, doi: 10.1111/j.1365-2966.2011.18280.x

    Bucciantini, N., & Quataert, E. 2011, MNRAS, 413, 2031, doi: 10.1111/j.1365-2966.2011.18280.x

    2011

  42. [50]

    2024, arXiv e-prints, arXiv:2412.09476, doi: 10.48550/arXiv.2412.09476 O’Connor, B., Troja, E., Ryan, G., et al

    Kong, E. 2024, arXiv e-prints, arXiv:2412.09476, doi: 10.48550/arXiv.2412.09476 O’Connor, B., Troja, E., Ryan, G., et al. 2023, Science Advances, 9, eadi1405, doi: 10.1126/sciadv.adi1405

    2024 doi arXiv

  43. [51]

    B., Fynbo, J

    Palmerio, J., Malesani, D. B., Fynbo, J. P. U., et al. 2022, GRB Coordinates Network, 31480, 1

    2022

  44. [52]

    S., Pozanenko, A

    Pankov, N. S., Pozanenko, A. S., Minaev, P. Y., et al. 2023, Astronomy Letters, 49, 81, doi: 10.1134/S1063773723030052 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2014, A&A, 571, A16, doi: 10.1051/0004-6361/201321591

    2023 doi

  45. [53]

    2023, GRB Coordinates Network, 33288, 1

    Poolakkil, S., Meegan, C., & Fermi GBM Team. 2023, GRB Coordinates Network, 33288, 1

    2023

  46. [54]

    2021, ApJ, 913, 60, doi: 10.3847/1538-4357/abf24d

    Poolakkil, S., Preece, R., Fletcher, C., et al. 2021, ApJ, 913, 60, doi: 10.3847/1538-4357/abf24d

    2021 doi

  47. [55]

    2023, ApJ, 947, 53, doi: 10.3847/1538-4357/acc57d

    Ren, J., Wang, Y., Zhang, L.-L., & Dai, Z.-G. 2023, ApJ, 947, 53, doi: 10.3847/1538-4357/acc57d

    2023 doi

  48. [56]

    Rhoads, J. E. 1997, ApJL, 487, L1, doi: 10.1086/310876

    1997 doi

  49. [57]

    2022, GRB Coordinates Network, 31429, 1

    Ridnaia, A., Frederiks, D., Lysenko, A., et al. 2022, GRB Coordinates Network, 31429, 1

    2022

  50. [58]

    2020, GRB Coordinates Network, 28323, 1

    Ridnaia, A., Golenetskii, S., Aptekar, R., et al. 2020, GRB Coordinates Network, 28323, 1

    2020

  51. [59]

    D., Kann, D

    Rossi, A., Frederiks, D. D., Kann, D. A., et al. 2022, A&A, 665, A125, doi: 10.1051/0004-6361/202243225

    2022 doi

  52. [60]

    A., Palmerio, J., et al

    Saccardi, A., Kann, D. A., Palmerio, J., et al. 2023, GRB Coordinates Network, 33281, 1

    2023

  53. [61]

    Q., Kawai, N., et al

    Sakamoto, T., Lamb, D. Q., Kawai, N., et al. 2005, ApJ, 629, 311, doi: 10.1086/431235 S´ anchez-Ram ´ ırez, Lang, R. G., Pozanenko, A., et al. 2024, A&A, 692, A3, doi: 10.1051/0004-6361/202449783

    2005 doi

  54. [62]

    2023, MNRAS, 522, L56, doi: 10.1093/mnrasl/slad038

    Sato, Y., Murase, K., Ohira, Y., & Yamazaki, R. 2023, MNRAS, 522, L56, doi: 10.1093/mnrasl/slad038

    2023 doi

  55. [63]

    2024, A&A, 684, L2, doi: 10.1051/0004-6361/202346336

    Savchenko, V., Ubertini, P., Bazzano, A., et al. 2024, A&A, 684, L2, doi: 10.1051/0004-6361/202346336

    2024 doi

  56. [64]

    E., et al

    Schady, P., Xu, D., Heintz, K. E., et al. 2019, GRB Coordinates Network, 23632, 1

    2019

  57. [65]

    1978, Annals of Statistics, 6, 461

    Schwarz, G. 1978, Annals of Statistics, 6, 461

    1978

  58. [66]

    2024, arXiv e-prints

    Sears, H., Chornock, R., Blanchard, P., et al. 2024, arXiv e-prints. https://arxiv.org/abs/2412.02663

    2024 arXiv

  59. [67]

    J., Alexander, K

    Shrestha, M., Sand, D. J., Alexander, K. D., et al. 2023, ApJL, 946, L25, doi: 10.3847/2041-8213/acbd50

    2023 doi

  60. [68]

    P., O’Connor, B., Cenko, S

    Srinivasaragavan, G. P., O’Connor, B., Cenko, S. B., et al. 2023, ApJL, 949, L39, doi: 10.3847/2041-8213/accf97

    2023 doi

  61. [69]

    2018, GRB Coordinates Network, 23495, 1 —

    Svinkin, D., Golenetskii, S., Aptekar, R., et al. 2018, GRB Coordinates Network, 23495, 1 —. 2019, GRB Coordinates Network, 25974, 1 —. 2020a, GRB Coordinates Network, 27867, 1 —. 2020b, GRB Coordinates Network, 28872, 1

    2018

  62. [70]

    2021, GRB Coordinates Network, 30276, 1

    Svinkin, D., Golenetskii, S., Frederiks, D., et al. 2021, GRB Coordinates Network, 30276, 1

    2021

  63. [71]

    2021, GRB Coordinates Network, 30771, 1

    Tanvir, N., Rossi, A., Xu, D., et al. 2021, GRB Coordinates Network, 30771, 1

    2021

  64. [72]

    2023, ApJL, 956, L23, doi: 10.3847/2041-8213/acfaff

    Tavani, M., Piano, G., Bulgarelli, A., et al. 2023, ApJL, 956, L23, doi: 10.3847/2041-8213/acfaff

    2023 doi

  65. [73]

    C., de Ugarte Postigo, A., Agui Fernandez, J

    Thoene, C. C., de Ugarte Postigo, A., Agui Fernandez, J. F., et al. 2023, GRB Coordinates Network, 35373, 1

    2023

  66. [74]

    2022, GRB Coordinates Network, 31433, 1 16 Atteia et al

    Tsvetkova, A., Frederiks, D., Lysenko, A., et al. 2022, GRB Coordinates Network, 31433, 1 16 Atteia et al

    2022

  67. [75]

    2022, GRB Coordinates Network, 31436, 1

    Tsvetkova, A., & KW Team. 2022, GRB Coordinates Network, 31436, 1

    2022

  68. [76]

    2017, ApJ, 850, 161, doi: 10.3847/1538-4357/aa96af

    Tsvetkova, A., Frederiks, D., Golenetskii, S., et al. 2017, ApJ, 850, 161, doi: 10.3847/1538-4357/aa96af

    2017 doi

  69. [77]

    2018, GRB Coordinates Network, 23363, 1 —

    Tsvetkova, A., Golenetskii, S., Aptekar, R., et al. 2018, GRB Coordinates Network, 23363, 1 —. 2019, GRB Coordinates Network, 23637, 1

    2018

  70. [78]

    2021, ApJ, 908, 83, doi: 10.3847/1538-4357/abd569

    Tsvetkova, A., Frederiks, D., Svinkin, D., et al. 2021, ApJ, 908, 83, doi: 10.3847/1538-4357/abd569

    2021 doi

  71. [79]

    D., et al

    Ursi, A., Tavani, M., Frederiks, D. D., et al. 2020, ApJ, 904, 133, doi: 10.3847/1538-4357/abc2d4

    2020 doi

  72. [80]

    B., Arabsalmani, M., Heintz, K

    Vielfaure, J. B., Arabsalmani, M., Heintz, K. E., et al. 2019, GRB Coordinates Network, 26553, 1

    2019

  73. [81]

    B., Vergani, S

    Vielfaure, J. B., Vergani, S. D., Japelj, J., et al. 2020a, A&A, 641, A30, doi: 10.1051/0004-6361/202038316

    doi

  74. [82]

    M., Kann, D

    Vreeswijk, P. M., Kann, D. A., Heintz, K. E., et al. 2018, GRB Coordinates Network, 22996, 1 ˇR ´ ıpa, J., Takahashi, H., Fukazawa, Y., et al. 2023, A&A, 677, L2, doi: 10.1051/0004-6361/202346128

    2018 doi

  75. [83]

    2016, arXiv e-prints, arXiv:1610.06892, doi: 10.48550/arXiv.1610.06892

    Wei, J., Cordier, B., Antier, S., et al. 2016, arXiv e-prints, arXiv:1610.06892, doi: 10.48550/arXiv.1610.06892

    2016 doi arXiv

  76. [84]

    A., Kennea, J

    Williams, M. A., Kennea, J. A., Dichiara, S., et al. 2023, ApJL, 946, L24, doi: 10.3847/2041-8213/acbcd1

    2023 doi

  77. [85]

    E., & Bloom, J

    Woosley, S. E., & Bloom, J. S. 2006, ARA&A, 44, 507, doi: 10.1146/annurev.astro.43.072103.150558

    2006 arXiv

  78. [86]

    2024, arXiv e-prints, arXiv:2411.12850, doi: 10.48550/arXiv.2411.12850

    Wu, Z.-F., Damoulakis, M., Beniamini, P., & Giannios, D. 2024, arXiv e-prints, arXiv:2411.12850, doi: 10.48550/arXiv.2411.12850

    2024 doi arXiv

  79. [87]

    P., Izzo, L., et al

    Xu, D., Zhu, Z. P., Izzo, L., et al. 2022, GRB Coordinates Network, 32141, 1

    2022

  80. [88]

    B., Kann, D

    Xu, D., Vielfaure, J. B., Kann, D. A., et al. 2020, GRB Coordinates Network, 28847, 1

    2020

  81. [89]

    2021, GRB Coordinates Network, 30357, 1

    Xu, D., Izzo, L., de Ugarte Postigo, A., et al. 2021, GRB Coordinates Network, 30357, 1

    2021

  82. [90]

    2023, ApJL, 947, L11, doi: 10.3847/2041-8213/acc84b

    Yang, J., Zhao, X.-H., Yan, Z., et al. 2023, ApJL, 947, L11, doi: 10.3847/2041-8213/acc84b

    2023 doi

  83. [91]

    2021, GRB Coordinates Network, 29673, 1

    Yao, Y., Miller, A., Ho, A., & Perley, D. 2021, GRB Coordinates Network, 29673, 1

    2021

  84. [92]

    T., Murase, K., Ioka, K., et al

    Zhang, B. T., Murase, K., Ioka, K., et al. 2023, ApJL, 947, L14, doi: 10.3847/2041-8213/acc79f

    2023 doi

  85. [93]

    2020, ApJ, 900, 112, doi: 10.3847/1538-4357/aba43a

    Zhao, W., Zhang, J.-C., Zhang, Q.-X., et al. 2020, ApJ, 900, 112, doi: 10.3847/1538-4357/aba43a

    2020 doi

  86. [94]

    P., Izzo, L., Fu, S

    Zhu, Z. P., Izzo, L., Fu, S. Y., et al. 2021, GRB Coordinates Network, 30692, 1

    2021

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