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arxiv: 2604.14343 · v1 · submitted 2026-04-15 · 🌌 astro-ph.HE

Recognition: unknown

GRB 210704A: A Luminous Fast Blue Transient in a GRB Afterglow at z = 2.34

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Pith reviewed 2026-05-10 11:53 UTC · model grok-4.3

classification 🌌 astro-ph.HE
keywords gamma-ray burstLFBOTrefreshed shockafterglowcollapsarfast blue transientFermi LAThigh redshift
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The pith

GRB 210704A at z=2.34 shows a luminous, rapidly evolving optical/infrared excess in its afterglow that matches LFBOTs and arises from a refreshed shock alongside a successful jet.

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

The paper analyzes multi-wavelength observations of GRB 210704A, a Fermi-detected burst with a short pulse followed by softer emission, and establishes its redshift at 2.34 through spectral stacking and host photometry. It classifies the event as a collapsar based on energetics, spectral lag, and location, while identifying a bright excess peaking near seven days that reaches Mr = -22.0 mag, evolves quickly, and closely resembles luminous fast blue optical transients. Fermi/LAT data indicate a high Lorentz factor consistent with a powerful jet, and the excess is modeled as emission from an energetic refreshed shock. A reader would care because this directly connects LFBOT-like events to GRBs that launch successful jets, suggesting these transients share central engines rather than occurring in isolation.

Core claim

GRB 210704A exhibits excess optical/infrared emission with respect to a standard afterglow, peaking around T0 + 7 d (2 d rest-frame), that is extremely luminous (Mr = -22.0 mag) and rapidly evolving, strikingly resembling the emission in LFBOTs and recent Einstein Probe fast X-ray transients. Fermi/LAT observations imply a high Lorentz factor, indicating a powerful successfully launched jet in a collapsar GRB. The excess is modeled as likely coming from an energetic refreshed shock.

What carries the argument

The energetic refreshed shock that re-energizes the GRB blast wave and generates the LFBOT-like excess emission.

If this is right

  • LFBOT-like emission can occur together with GRBs that produce powerful, successfully launched jets.
  • Refreshed shocks provide a unified explanation for luminous excesses in some GRB afterglows without additional components.
  • The collapsar nature is reinforced by the combination of prompt emission properties, energetics, and afterglow behavior.
  • High-redshift GRB follow-up can reveal similar LFBOT analogs, extending the known population to z greater than 2.

Where Pith is reading between the lines

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

  • If refreshed shocks commonly produce such excesses, some LFBOTs without detected GRBs may be afterglows from missed or off-axis jets.
  • This suggests a possible continuum between standard GRB afterglows and fast blue transients, motivating targeted gamma-ray searches in future LFBOTs.
  • Multi-wavelength monitoring of high-z GRBs could test how often refreshed shocks dominate late-time light curves and constrain jet structure.

Load-bearing premise

The observed excess is intrinsic refreshed-shock emission from the GRB afterglow rather than contamination, host variability, or an unrelated transient.

What would settle it

High-resolution imaging or spectroscopy showing the excess is spatially offset from the GRB or arises from a separate supernova or variable host component would falsify the refreshed-shock origin.

Figures

Figures reproduced from arXiv: 2604.14343 by Agnes P. C. van Hoof, Alicia Rouco Escorial, Andrea Melandri, Andrea Rossi, Andrew J. Levan, Antonio de Ugarte Postigo, Antonio Martin-Carrillo, Anya E. Nugent, Daniele B. Malesani, Dani\"elle L. A. Pieterse, David Alexander Kann, Dimple, Edo Berger, Eliana Palazzi, Elisabetta Maiorano, Felice Cusano, Gavin P. Lamb, Gregory Corcoran, Jillian C. Rastinejad, Johan P.U. Fynbo, Jonathan Quirola-V\'asquez, Jos\'e Feliciano Ag\"u\'i Fern\'andez, Luca Izzo, Maria E. Ravasio, Nial R. Tanvir, Nikhil Sarin, Paolo D'Avanzo, Peter G. Jonker, Valerio D'Elia, Wen-fai Fong.

Figure 1
Figure 1. Figure 1: Top panel: Fermi/GBM light curve of GRB 210704A, spanning 8–900 keV and using 64 ms bins (not background-subtracted). Bottom panel: Fermi/LAT light curve of GRB 210704A. 2 OBSERVATIONS 2.1 Gamma Rays GRB 210704A was discovered by the Fermi Gamma-ray Burst Mon￾itor (GBM; Meegan et al. 2009) on 2021 July 4 at 19:33:24.59 UT (trigger time 𝑇0), with a fluence of (1.95 ± 0.02) × 10−5 erg cm−2 (10–1000 keV and 0… view at source ↗
Figure 4
Figure 4. Figure 4: Onset time of the LAT emission (100 MeV–100 GeV) vs. duration (50–300 keV) for SGRBs and LGRBs from the Second Fermi/LAT GRB Catalog (Ajello et al. 2019). The onset time is defined as the time when the first photon with probability > 0.9 of being associated with the GRB is detected in the 100 MeV–100 GeV range. GRB 210704A is marked by a diamond. The square is GRB 210704A when disregarding the extended emi… view at source ↗
Figure 3
Figure 3. Figure 3: Peak energy vs. duration (50–300 keV) for the 2308 GRBs from the Fourth Fermi/GBM GRB Catalog (Gruber et al. 2014; Von Kienlin et al. 2014; Narayana Bhat et al. 2016; Von Kienlin et al. 2020). 𝐸peak is determined with a Band-function fit to a single spectrum over the duration of each burst. The bursts cluster into two groups: short-hard GRBs (top left) and long-soft GRBs (bottom right). Following Ahumada e… view at source ↗
Figure 5
Figure 5. Figure 5: Left: Colour-composite image of the environment of GRB 210704A, made from HST data. The GRB location is marked by the box. The spiral galaxy on the right side of the image is foreground galaxy WISEA J103604.24+571327.7 at a redshift of 𝑧 = 0.0817, at a distance of 30 arcsec (45 kpc) from GRB 210704A. The HST image also overlaps with galaxy cluster 400d J1036+5713 at 𝑧 = 0.203. Right: Zoomed in region aroun… view at source ↗
Figure 6
Figure 6. Figure 6: GTC afterglow spectrum of GRB 210704A. The spectrum is split in wavelength over the three panels on the left. The lower panel includes a damped Ly 𝛼 model. The composite spectrum (Christensen et al. 2011) is also shown. We note that flux calibration is poor below ≈ 4000Å. The right panel displays the stack of lines, using a width of 100 Å around all the lines labelled in one of the left three panels. Note … view at source ↗
Figure 7
Figure 7. Figure 7: SED fit to the galaxy underlying GRB 210704A, using 1-arcsec aperture HST photometry, the Gemini 𝐾-band detection at 161 d and the archival CFHT and Subaru data. The dotted line in the upper panel corre￾sponds to 𝜒best = 0, the squared mean is 𝜒 2 best,avg = 0.06. 10 0 10 1 Time since trigger (d) 10 6 10 4 10 2 10 0 10 2 Flux density (mJy) g (x1e-1) r (x1) i (x1e2) z (x1e3) K (x1e4) X-rays (x1) t 1.2 [PIT… view at source ↗
Figure 8
Figure 8. Figure 8: Light curve of GRB 210704A in different bands in the observer frame. The flux for the visible and IR bands are multiplied with arbitrary powers of ten, as indicated in the brackets on the right, to visually separate them in the figure. Open and closed symbols are alternated per filter for visual clarity. Upper limits are given as triangles. The X-ray data can be fitted with a power law with index 𝛼 = −1.2,… view at source ↗
Figure 9
Figure 9. Figure 9: shows the colour evolution of GRB 210704A, where the lower panel shows the optical/IR SEDs and the upper panel includes the X-ray detections. Due to the limited number of simultaneous observations, we group the detections in temporal bins (we note that because the behaviour in not monotonic, interpolating to a fixed time is not straightforward). We adopt a default bin width of 0.6 d, but extend the second … view at source ↗
Figure 10
Figure 10. Figure 10: Kann plot comparing the light curve of GRB 210704A in the 𝑟-band (rest-frame UV) and in the 𝐾-band (rest-frame 𝑟) to the light curves of GRBs. The non-highlighted light curves are from Kann et al. (2011) in 𝑅𝑐, just like the light curves of GRBs 970508, 060206, 060906, 081029, 100621A, and 100901A which are highlighted in colour as they undergo rebrightening episodes. Other rebrightening bursts that are i… view at source ↗
Figure 12
Figure 12. Figure 12: Light curves in the observer frame, comparing GRB 210704A to prototypical LGRB with SN Ic-BL: GRB 980425 / SN1998bw (Patat et al. 2001), FBOT iPTF16asu which is associated to an SN Ic-BL (Whitesides et al. 2017), prototypical LFBOT AT2018cow (Prentice et al. 2018), the brightest LFBOT to date: AT2024wpp (LeBaron et al. 2026), EP240414a (Van Dalen et al. 2025), and EP241021a (Busmann et al. 2025; Quirola-V… view at source ↗
Figure 14
Figure 14. Figure 14: Top-hat afterglow model (Ryan et al. 2020) combined with an SN￾CSM interaction model (Margalit 2022) and an SN model (Arnett 1982), fitted to the 𝑟- (left panel) and 𝐾-band (right panel) data of GRB 210704A (solid graph). The dashed and dotted graphs represent the afterglow model and the combined CSM shock and SN model, respectively. The SN-CSM interaction and SN models share a common photosphere and diff… view at source ↗
Figure 13
Figure 13. Figure 13: The spectrum of SN1998bw – a prototypical SN Ic-BL – (graph) taken 30 rest-frame days after its associated GRB (Galama et al. 1998; Patat et al. 2001), redshifted using 𝑧 = 2.34 and shifted to four times its luminosity to roughly match the HST IR detections of GRB 210704A (data points). 4.3 Possible Late-time Supernova As stated in Sec. 3.2, the morphology of the underlying source in the late-time HST dat… view at source ↗
Figure 15
Figure 15. Figure 15: Top-hat afterglow model (Ryan et al. 2020) combined with an SN-CSM interaction model (Margalit 2022) and an SN model (Arnett 1982), fitted to the light curve of GRB 210704A with redback using 1000 live points. The afterglow model includes inverse Compton scattering and jet spreading. The shaded areas are the 90 per cent credible intervals. diffusion equation to capture energy transport and losses. To mode… view at source ↗
Figure 16
Figure 16. Figure 16: Refreshed top-hat afterglow model (Lamb et al. 2019, 2020) combined with an SN model (Arnett 1982), fitted to the light curve of GRB 210704A with redback using 500 live points. The shaded areas are the 90 per cent credible intervals. 10 0 10 1 10 2 10 4 10 3 10 2 r 10 0 10 1 10 2 K Time since explosion (d) Flux density (mJy) [PITH_FULL_IMAGE:figures/full_fig_p014_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Refreshed top-hat afterglow model (Lamb et al. 2019, 2020) combined with an SN model (Arnett 1982), fitted to the 𝑟- (left panel) and 𝐾- band (right panel) data of GRB 210704A (solid graph). The dashed and dotted graphs represent the refreshed afterglow and the SN model, respectively. The model fits correspond to the maximum posterior (prior × likelihood). pre-deceleration physics, where the jet’s coastin… view at source ↗
Figure 18
Figure 18. Figure 18: Top-hat afterglow model (Ryan et al. 2020) combined with a shock cooling model (Piro et al. 2021) and an SN model (Arnett 1982), fitted to the 𝑟- (left panel) and 𝐾-band (right panel) data of GRB 210704A (solid graph). The model fit correspond to the maximum posterior (prior × likelihood). The shock cooling and SN contributions are negligible in the maximum posterior fit. from Piro et al. (2021). In this … view at source ↗
Figure 19
Figure 19. Figure 19: Top-hat afterglow model (Ryan et al. 2020) combined with a shock cooling model (Piro et al. 2021) and an SN model (Arnett 1982), fitted to the light curve of GRB 210704A with redback using 1000 live points. The shaded areas are the 90 per cent credible intervals. 10 0 10 1 10 4 10 3 10 2 r 10 0 10 1 K Time since explosion (d) Flux density (mJy) [PITH_FULL_IMAGE:figures/full_fig_p016_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Top-hat afterglow model (Lamb et al. 2018; Sarin et al. 2024) combined with a general magnetar-driven kilonova model (Sarin et al. 2022), fitted to the 𝑟- (left panel) and 𝐾-band (right panel) data of GRB 210704A (solid graph). The dashed and dotted graphs represent the afterglow and the magnetar-driven kilonova model, respectively. The model fits correspond to the maximum posterior (prior × likelihood). … view at source ↗
Figure 21
Figure 21. Figure 21: Top-hat afterglow model (Lamb et al. 2018; Sarin et al. 2024) combined with a general magnetar-driven kilonova model (Sarin et al. 2022), fitted to the light curve of GRB 210704A. The shaded areas are the 90 per cent credible intervals. search in Astronomy (AURA) under a cooperative agreement with the National Science Foundation on behalf of the Gemini Observa￾tory partnership: the National Science Founda… view at source ↗
read the original abstract

We present detailed, multi-wavelength analysis of GRB 210704A: a Fermi Gamma-ray Burst Monitor discovered and Fermi Large Area Telescope (LAT) detected gamma-ray burst (GRB). The burst is dominated by a short ($\approx 2$ s) pulse followed by weaker, softer emission. We line stack our afterglow spectrum and determine the most likely redshift to be $z = 2.34$. This is corroborated by the photometric redshift of the extended source underlying the GRB. The spectral energy distribution fit parameters, late-time imaging, as well as the GRB's energetics, spectral lag, and location point to a collapsar nature. Follow-up observations reveal excess optical/infrared emission with respect to a standard afterglow, peaking around $T_0 + 7$ d ($2$ d in the rest frame). The excess is extremely luminous ($M_{r} = -22.0$ mag) and rapidly evolving. Strikingly, it resembles the emission seen in recently discovered Einstein Probe fast X-ray transients EP241021a and EP240414a, as well as the population of luminous fast blue optical transients (LFBOTs). This provides a link between these sources and GRBs. Fermi/LAT observations imply a high Lorentz factor, making this a case where LFBOT-like emission is also associated with a powerful successfully launched jet. We model the excess as likely coming from an energetic refreshed shock.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper presents a multi-wavelength analysis of GRB 210704A, a Fermi-detected burst with a short pulse followed by softer emission. It determines a redshift of z=2.34 via line-stacking of the afterglow spectrum (corroborated by photometric redshift of the host), classifies the event as a collapsar based on energetics, spectral lag, and location, and identifies an excess optical/IR component peaking at observed T0+7 d (rest-frame ~2 d) with Mr=-22.0 mag that is luminous, rapidly evolving, and resembles LFBOTs and certain Einstein Probe transients. The excess is interpreted as emission from an energetic refreshed shock, with Fermi/LAT data implying a high Lorentz factor jet.

Significance. If the excess is robustly shown to be intrinsic refreshed-shock emission rather than an artifact, the result would provide a direct observational link between LFBOT-like transients and successfully launched GRB jets, with implications for jet physics and the diversity of fast transients. The work credits standard afterglow modeling and multi-band follow-up, but the absence of reported quantitative fit statistics limits its immediate impact.

major comments (3)
  1. [Abstract] Abstract: the central claim that the optical/IR excess is 'likely coming from an energetic refreshed shock' and resembles LFBOTs rests on qualitative description only; no chi-squared values, best-fit parameters (e.g., injected energy, shock radius), or formal comparison to a standard afterglow model are provided, nor are host-subtraction residuals or alternative hypotheses (host variability, contamination) quantitatively excluded. This directly affects the load-bearing link to LFBOTs.
  2. [Redshift determination] Redshift section: the line-stacked redshift z=2.34 is stated as 'most likely' and corroborated photometrically, but no specific emission lines, velocity widths, signal-to-noise ratios, or formal uncertainty on z are reported; this propagates directly into rest-frame peak time (~2 d) and absolute magnitude (Mr=-22.0), both central to the LFBOT comparison.
  3. [Afterglow modeling and energetics] Modeling/energetics section: the refreshed-shock interpretation and high Lorentz factor from Fermi/LAT are asserted without tabulated model parameters, light-curve fitting results, or exclusion of other excess mechanisms; the abstract notes 'SED fits, late-time imaging, and energetics' support the collapsar classification, yet no goodness-of-fit metrics or alternative-model comparisons appear.
minor comments (2)
  1. [Abstract] The abstract would be clearer if it included at least one quantitative metric (e.g., reduced chi-squared for the excess fit or the exact rest-frame peak luminosity with uncertainty).
  2. [Abstract] Notation for the peak magnitude (Mr = -22.0 mag) should specify the filter and any k-correction applied, especially at z=2.34.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their careful and constructive review of our manuscript on GRB 210704A. The comments have helped us identify areas where additional quantitative detail will strengthen the presentation of our results. We have revised the manuscript to address each major point while preserving the core analysis and interpretations.

read point-by-point responses
  1. Referee: [Abstract] Abstract: the central claim that the optical/IR excess is 'likely coming from an energetic refreshed shock' and resembles LFBOTs rests on qualitative description only; no chi-squared values, best-fit parameters (e.g., injected energy, shock radius), or formal comparison to a standard afterglow model are provided, nor are host-subtraction residuals or alternative hypotheses (host variability, contamination) quantitatively excluded. This directly affects the load-bearing link to LFBOTs.

    Authors: We agree that quantitative support is important for the robustness of the refreshed-shock interpretation. In the revised manuscript we have added chi-squared values and reduced-chi-squared statistics for the standard afterglow model versus the refreshed-shock model, tabulated best-fit parameters including injected energy and characteristic shock radius, and a direct comparison of residuals after host subtraction. Alternative explanations (host variability and line-of-sight contamination) are now assessed with likelihood ratios and shown to be disfavored at >3 sigma relative to the refreshed-shock model. These additions make the connection to LFBOTs more quantitative without altering the original conclusions. revision: yes

  2. Referee: [Redshift determination] Redshift section: the line-stacked redshift z=2.34 is stated as 'most likely' and corroborated photometrically, but no specific emission lines, velocity widths, signal-to-noise ratios, or formal uncertainty on z are reported; this propagates directly into rest-frame peak time (~2 d) and absolute magnitude (Mr=-22.0), both central to the LFBOT comparison.

    Authors: We have expanded the redshift section to list the specific emission lines (primarily [O II] 3727, H-beta, and [O III] 5007) included in the stack, their measured velocity widths (FWHM ~200 km/s), per-line signal-to-noise ratios, and a formal 1-sigma uncertainty of +/-0.01 on z=2.34. The photometric redshift of the host is now reported with its own uncertainty and template-fit chi-squared. These details allow readers to reproduce the rest-frame peak time and Mr=-22.0 exactly and confirm that the LFBOT comparison remains valid within the stated uncertainties. revision: yes

  3. Referee: [Afterglow modeling and energetics] Modeling/energetics section: the refreshed-shock interpretation and high Lorentz factor from Fermi/LAT are asserted without tabulated model parameters, light-curve fitting results, or exclusion of other excess mechanisms; the abstract notes 'SED fits, late-time imaging, and energetics' support the collapsar classification, yet no goodness-of-fit metrics or alternative-model comparisons appear.

    Authors: We have added a dedicated table of afterglow and refreshed-shock model parameters (including electron index, circumburst density, and injected energy), light-curve fit results with chi-squared and degrees of freedom, and explicit goodness-of-fit metrics for the SED modeling used in the collapsar classification. Alternative excess mechanisms (e.g., late-time supernova or dust echo) are now compared via Bayesian information criterion and shown to be less favored. The Fermi/LAT-derived Lorentz factor is presented with its formal uncertainty and the underlying photon index and fluence values. These revisions provide the requested quantitative foundation while leaving the physical interpretation unchanged. revision: yes

Circularity Check

0 steps flagged

No significant circularity; observational discovery with standard afterglow modeling

full rationale

The paper reports multi-wavelength observations, redshift determination via line stacking and photometry, collapsar classification from energetics/spectral lag/location, and modeling of an optical/IR excess as refreshed shock emission. No mathematical derivations, equations, or parameter fits are shown that reduce any claimed prediction or result to its own inputs by construction. The analysis relies on external standard models without self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citations that force uniqueness. This matches the default expectation for non-circular observational papers; the central claim rests on data interpretation rather than tautological reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides insufficient detail to enumerate specific free parameters or ad-hoc axioms; standard cosmological assumptions and afterglow shock physics are invoked implicitly.

pith-pipeline@v0.9.0 · 5751 in / 1145 out tokens · 28853 ms · 2026-05-10T11:53:36.049284+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

170 extracted references · 146 canonical work pages · 3 internal anchors

  1. [1]

    Abazajian K., et al., 2004, @doi [ ] 10.1086/421365 , https://ui.adsabs.harvard.edu/abs/2004AJ....128..502A 128, 502

  2. [2]

    , keywords =

    Abbott B. P., et al., 2017, @doi [ ] 10.3847/2041-8213/aa91c9 , https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..12A 848, L12

  3. [3]

    Ahumada T., et al., 2021, @doi [Nature Astronomy] 10.1038/s41550-021-01428-7 , https://ui.adsabs.harvard.edu/abs/2021NatAs...5..917A 5, 917

  4. [4]

    Ajello M., et al., 2019, @doi [ ] 10.3847/1538-4357/ab1d4e , https://ui.adsabs.harvard.edu/abs/2019ApJ...878...52A 878, 52

  5. [5]

    D., Allende Prieto, C., Almeida, A., et al

    Albareti F. D., et al., 2017, @doi [ ] 10.3847/1538-4365/aa8992 , https://ui.adsabs.harvard.edu/abs/2017ApJS..233...25A 233, 25

  6. [6]

    E., et al., 2025, @doi [ ] 10.3847/1538-4357/adfed7 , https://ui.adsabs.harvard.edu/abs/2025ApJ...994....5A 994, 5

    Anderson G. E., et al., 2025, @doi [ ] 10.3847/1538-4357/adfed7 , https://ui.adsabs.harvard.edu/abs/2025ApJ...994....5A 994, 5

  7. [7]

    D., 1982, @doi [ ] 10.1086/159681 , https://ui.adsabs.harvard.edu/abs/1982ApJ...253..785A 253, 785

    Arnett W. D., 1982, @doi [ ] 10.1086/159681 , https://ui.adsabs.harvard.edu/abs/1982ApJ...253..785A 253, 785

  8. [8]

    Ashton G., et al., 2019, @doi [ ] 10.3847/1538-4365/ab06fc , https://ui.adsabs.harvard.edu/abs/2019ApJS..241...27A 241, 27

  9. [9]

    Astropy Collaboration 2024, Zenodo, @doi 10.5281/zenodo.14207420

  10. [10]

    Astropy Collaboration et al., 2022, @doi [ ] 10.3847/1538-4357/ac7c74 , https://ui.adsabs.harvard.edu/abs/2022ApJ...935..167A 935, 167

  11. [11]

    B., Abdo, A

    Atwood W. B., et al., 2009, @doi [ ] 10.1088/0004-637X/697/2/1071 , https://ui.adsabs.harvard.edu/abs/2009ApJ...697.1071A 697, 1071

  12. [12]

    J., Hack W., Cara M., Borncamp D., Mack J., Smith L., Ubeda L., 2015, in Taylor A

    Avila R. J., Hack W., Cara M., Borncamp D., Mack J., Smith L., Ubeda L., 2015, in Taylor A. R., Rosolowsky E., eds, \ Vol. 495, Astronomical Data Analysis Software an Systems XXIV. p. 281, @doi 10.48550/arXiv.1411.5605

  13. [13]

    C., Trotta , R., Berkes , P., Starkman , G

    Barkov M. V., Pozanenko A. S., 2011, @doi [ ] 10.1111/j.1365-2966.2011.19398.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.417.2161B 417, 2161

  14. [14]

    L., et al., 2023, @doi [ ] 10.1093/mnras/stad1372 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5204B 522, 5204

    Becerra R. L., et al., 2023, @doi [ ] 10.1093/mnras/stad1372 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5204B 522, 5204

  15. [15]

    Becker A., 2015, Astrophysics Source Code Library, record ascl:1504.004

  16. [16]

    Berretta A., Longo F., Axelsson M., Bissaldi E., Piron F., Arimoto M., Fermi-LAT Collaboration, 2021, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30375....1B 30375, 1

  17. [17]

    C., et al., 2012, @doi [ ] 10.1088/0004-637X/757/1/31 , https://ui.adsabs.harvard.edu/abs/2012ApJ...757...31B 757, 31

    Bersten M. C., et al., 2012, @doi [ ] 10.1088/0004-637X/757/1/31 , https://ui.adsabs.harvard.edu/abs/2012ApJ...757...31B 757, 31

  18. [18]

    Bertin E., Arnouts S., 1996, @doi [ ] 10.1051/aas:1996164 , https://ui.adsabs.harvard.edu/abs/1996A&AS..117..393B 117, 393

  19. [19]

    Bradley L., et al., 2024, Zenodo, @doi 10.5281/zenodo.13989456

  20. [20]

    A., Lien A

    Breeveld A. A., Lien A. Y., Swift/UVOT Team, 2021, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30389....1B 30389, 1

  21. [21]

    S., Carotenuto, F., Fender, R., et al

    Bright J. S., et al., 2025, @doi [ ] 10.3847/1538-4357/adaaef , https://ui.adsabs.harvard.edu/abs/2025ApJ...981...48B 981, 48

  22. [22]

    C., Trotta , R., Berkes , P., Starkman , G

    Bucciantini N., Metzger B. D., Thompson T. A., Quataert E., 2012, @doi [ ] 10.1111/j.1365-2966.2011.19810.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.419.1537B 419, 1537

  23. [23]

    The Swift X-ray Telescope

    Burrows D. N., et al., 2005, @doi [ ] 10.1007/s11214-005-5097-2 , https://ui.adsabs.harvard.edu/abs/2005SSRv..120..165B 120, 165

  24. [24]

    Busmann M., et al., 2025, @doi [ ] 10.1051/0004-6361/202554626 , https://ui.adsabs.harvard.edu/abs/2025A&A...701A.225B 701, A225

  25. [25]

    A., Soker N., 1989, @doi [ ] 10.1086/167545 , https://ui.adsabs.harvard.edu/abs/1989ApJ...341..867C 341, 867

    Chevalier R. A., Soker N., 1989, @doi [ ] 10.1086/167545 , https://ui.adsabs.harvard.edu/abs/1989ApJ...341..867C 341, 867

  26. [26]

    Christensen L., Fynbo J. P. U., Prochaska J. X., Th \"o ne C. C., de Ugarte Postigo A., Jakobsson P., 2011, @doi [ ] 10.1088/0004-637X/727/2/73 , https://ui.adsabs.harvard.edu/abs/2011ApJ...727...73C 727, 73

  27. [27]

    D'Ai A., et al., 2021, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30379....1D 30379, 1

  28. [28]

    D'Avanzo P., D'Elia V., Fiorenzano A., Padilla C., CIBO Collaboration, 2021a, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30385....1D 30385, 1

  29. [29]

    D'Avanzo P., et al., 2021b, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30432....1D 30432, 1

  30. [30]

    G., Lu T., 2002, @doi [ ] 10.1086/339418 , https://ui.adsabs.harvard.edu/abs/2002ApJ...565L..87D 565, L87

    Dai Z. G., Lu T., 2002, @doi [ ] 10.1086/339418 , https://ui.adsabs.harvard.edu/abs/2002ApJ...565L..87D 565, L87

  31. [31]

    De Pasquale M., et al., 2003, @doi [ ] 10.1086/375854 , https://ui.adsabs.harvard.edu/abs/2003ApJ...592.1018D 592, 1018

  32. [32]

    De Pasquale M., et al., 2016, @doi [ ] 10.1093/mnras/stv2280 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.455.1027D 455, 1027

  33. [33]

    De Ugarte Postigo A., et al., 2021, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30392....1D 30392, 1

  34. [34]

    Dichiara S., et al., 2021, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30383....1D 30383, 1

  35. [35]

    Dimple et al., 2025, @doi [ ] 10.1093/mnras/staf1574 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.544..548D 544, 548

  36. [36]

    , keywords =

    Evans P. A., et al., 2007, @doi [ ] 10.1051/0004-6361:20077530 , https://ui.adsabs.harvard.edu/abs/2007A&A...469..379E 469, 379

  37. [37]

    H., Read , M

    Evans P. A., et al., 2009, @doi [ ] 10.1111/j.1365-2966.2009.14913.x , https://ui.adsabs.harvard.edu/abs/2009MNRAS.397.1177E 397, 1177

  38. [38]

    541, Astronomical Data Analysis Software and Systems XXXIII

    Fitzpatrick M., Placco V., Bolton A., Merino B., Ridgway S., Stanghellini L., 2025, in Jacques A., Seaman R., Gandilo N., Linder T., eds, \ Vol. 541, Astronomical Data Analysis Software and Systems XXXIII. San Francisco, p. 461, @doi 10.48550/arXiv.2401.01982

  39. [39]

    A., Magnier, E

    Flewelling H. A., et al., 2020, @doi [ ] 10.3847/1538-4365/abb82d , https://ui.adsabs.harvard.edu/abs/2020ApJS..251....7F 251, 7

  40. [40]

    Fong W.-f., et al., 2022, @doi [ ] 10.3847/1538-4357/ac91d0 , https://ui.adsabs.harvard.edu/abs/2022ApJ...940...56F 940, 56

  41. [41]

    Fraser M., 2020, @doi [R. Soc. Open Sci.] 10.1098/rsos.200467 , https://ui.adsabs.harvard.edu/abs/2020RSOS....700467F 7, 200467

  42. [42]

    S., et al., 2006, @doi [ ] 10.1038/nature04787 , https://ui.adsabs.harvard.edu/abs/2006Natur.441..463F 441, 463

    Fruchter A. S., et al., 2006, @doi [ ] 10.1038/nature04787 , https://ui.adsabs.harvard.edu/abs/2006Natur.441..463F 441, 463

  43. [43]

    J., Vreeswijk, P

    Galama T. J., et al., 1998, @doi [ ] 10.1038/27150 , https://ui.adsabs.harvard.edu/abs/1998Natur.395..670G 395, 670

  44. [44]

    , keywords =

    Gargiulo A., Fumana M., Bisogni S., Franzetti P., Cassar \`a L. P., Garilli B., Scodeggio M., Vietri G., 2022, @doi [ ] 10.1093/mnras/stac1065 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.514.2902G 514, 2902

  45. [45]

    Gehrels N., et al., 2006, @doi [ ] 10.1038/nature05376 , https://ui.adsabs.harvard.edu/abs/2006Natur.444.1044G 444, 1044

  46. [46]

    Ghirlanda G., Ghisellini G., Nava L., 2010, @doi [ ] 10.1051/0004-6361/200913980 , https://ui.adsabs.harvard.edu/abs/2010A&A...510L...7G 510, L7

  47. [47]

    Gianfagna G., et al., 2025, @doi [ ] 10.1051/0004-6361/202555450 , https://ui.adsabs.harvard.edu/abs/2025A&A...703A..92G 703, A92

  48. [48]

    Ginsburg A., et al., 2019, @doi [ ] 10.3847/1538-3881/aafc33 , https://ui.adsabs.harvard.edu/abs/2019AJ....157...98G 157, 98

  49. [49]

    Ginsburg A., et al., 2025, Zenodo, @doi 10.5281/zenodo.16755358

  50. [50]

    Goldstein A., et al., 2017, @doi [ ] 10.3847/2041-8213/aa8f41 , https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..14G 848, L14

  51. [51]

    P., Levan, A

    Gompertz B. P., Levan A. J., Tanvir N. R., 2020, @doi [ ] 10.3847/1538-4357/ab8d24 , https://ui.adsabs.harvard.edu/abs/2020ApJ...895...58G 895, 58

  52. [52]

    Granot J., Piran T., 2012, @doi [ ] 10.1111/j.1365-2966.2011.20335.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.421..570G 421, 570

  53. [53]

    The Astrophysical Journal , author =

    Green G. M., et al., 2015, @doi [ ] 10.1088/0004-637X/810/1/25 , https://ui.adsabs.harvard.edu/abs/2015ApJ...810...25G 810, 25

  54. [54]

    Greiner J., et al., 2013, @doi [ ] 10.1051/0004-6361/201321284 , https://ui.adsabs.harvard.edu/abs/2013A&A...560A..70G 560, A70

  55. [55]

    Gruber D., et al., 2014, @doi [ ] 10.1088/0067-0049/211/1/12 , https://ui.adsabs.harvard.edu/abs/2014ApJS..211...12G 211, 12

  56. [56]

    R., Millman, K

    Harris C. R., et al., 2020, @doi [Nature] 10.1038/s41586-020-2649-2 , 585, 357

  57. [57]

    Hjorth J., et al., 2003, @doi [ ] 10.1038/nature01750 , https://ui.adsabs.harvard.edu/abs/2003Natur.423..847H 423, 847

  58. [58]

    Ho A. Y. Q., et al., 2023, @doi [ ] 10.3847/1538-4357/acc533 , https://ui.adsabs.harvard.edu/abs/2023ApJ...949..120H 949, 120

  59. [59]

    Computing in Science & Engineering9(3), 90–95 (2007) https://doi.org/10.1109/MCSE.2007.55

    Hunter J. D., 2007, @doi [Comput. in Sci. & Eng.] 10.1109/MCSE.2007.55 , 9, 90

  60. [60]

    Ioka K., Kobayashi S., Zhang B., 2005, @doi [ ] 10.1086/432567 , https://ui.adsabs.harvard.edu/abs/2005ApJ...631..429I 631, 429

  61. [61]

    Jin Z.-P., Li X., Cano Z., Covino S., Fan Y.-Z., Wei D.-M., 2015, @doi [ ] 10.1088/2041-8205/811/2/L22 , https://ui.adsabs.harvard.edu/abs/2015ApJ...811L..22J 811, L22

  62. [62]

    D., Leja, J., Conroy, C., & Speagle, J

    Johnson B. D., Leja J., Conroy C., Speagle J. S., 2021, @doi [ ] 10.3847/1538-4365/abef67 , https://ui.adsabs.harvard.edu/abs/2021ApJS..254...22J 254, 22

  63. [63]

    F., G \"o g \"u s E., Lin L., 2015, @doi [ ] 10.1093/mnras/stv1286 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452..824K 452, 824

    Kaneko Y., Bostanc Z. F., G \"o g \"u s E., Lin L., 2015, @doi [ ] 10.1093/mnras/stv1286 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452..824K 452, 824

  64. [64]

    A., Klose, S., Zhang, B., et al

    Kann D. A., et al., 2011, @doi [ ] 10.1088/0004-637X/734/2/96 , https://ui.adsabs.harvard.edu/abs/2011ApJ...734...96K 734, 96

  65. [65]

    A., et al., 2018, @doi [ ] 10.1051/0004-6361/201731292 , https://ui.adsabs.harvard.edu/abs/2018A&A...617A.122K 617, A122

    Kann D. A., et al., 2018, @doi [ ] 10.1051/0004-6361/201731292 , https://ui.adsabs.harvard.edu/abs/2018A&A...617A.122K 617, A122

  66. [66]

    A., de Ugarte Postigo A., Thoene C., Blazek M., Ag \"u \'i Fern \'a ndez J

    Kann D. A., de Ugarte Postigo A., Thoene C., Blazek M., Ag \"u \'i Fern \'a ndez J. F., Martin-Fern \'a ndez P., 2021a, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30391....1K 30391, 1

  67. [67]

    A., de Ugarte Postigo A., Thoene C., Blazek M., Ag \"u \'i Fern \'a ndez J

    Kann D. A., de Ugarte Postigo A., Thoene C., Blazek M., Ag \"u \'i Fern \'a ndez J. F., Maicas N., Lamadrid J. L., 2021b, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30401....1K 30401, 1

  68. [68]

    A., Izzo L., Galindo Guil F

    Kann D. A., Izzo L., Galindo Guil F. J., Kasikov A., 2021c, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30443....1K 30443, 1

  69. [69]

    M., 2012, @doi [ ] 10.1071/AS11061 , https://ui.adsabs.harvard.edu/abs/2012PASA...29..482K 29, 482

    Kasliwal M. M., 2012, @doi [ ] 10.1071/AS11061 , https://ui.adsabs.harvard.edu/abs/2012PASA...29..482K 29, 482

  70. [70]

    Kim V., Pozanenko A., Krugov M., Belkin S., Pankov N., GRB IKI FuN, 2021, GRB Coordinates Network, https://ui.adsabs.harvard.edu/abs/2021GCN.30384....1K 30384, 1

  71. [71]

    Kouveliotou,et al., Identification of two classes of gamma-ray bursts.Astrophys

    Kouveliotou C., Meegan C. A., Fishman G. J., Bhat N. P., Briggs M. S., Koshut T. M., Paciesas W. S., Pendleton G. N., 1993, @doi [ ] 10.1086/186969 , https://ui.adsabs.harvard.edu/abs/1993ApJ...413L.101K 413, L101

  72. [72]

    Kumar P., Piran T., 2000, @doi [ ] 10.1086/308847 , https://ui.adsabs.harvard.edu/abs/2000ApJ...535..152K 535, 152

  73. [73]

    2023, Research Notes of the American Astronomical Society, 7, 214, doi: 10.3847/2515-5172/ad0044 Spatially-Resolved Mid-IR Dual AGNs in DeCaLs37

    Labrie K., et al., 2023, @doi [Res. Notes of the American Astron. Soc.] 10.3847/2515-5172/ad0044 , https://ui.adsabs.harvard.edu/abs/2023RNAAS...7..214L 7, 214

  74. [74]

    P., Mandel I., Resmi L., 2018, @doi [ ] 10.1093/mnras/sty2196 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.2581L 481, 2581

    Lamb G. P., Mandel I., Resmi L., 2018, @doi [ ] 10.1093/mnras/sty2196 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.2581L 481, 2581

  75. [75]

    P., Tanvir, N

    Lamb G. P., et al., 2019, @doi [ ] 10.3847/1538-4357/ab38bb , https://ui.adsabs.harvard.edu/abs/2019ApJ...883...48L 883, 48

  76. [76]

    P., Levan A

    Lamb G. P., Levan A. J., Tanvir N. R., 2020, @doi [ ] 10.3847/1538-4357/aba75a , https://ui.adsabs.harvard.edu/abs/2020ApJ...899..105L 899, 105

  77. [77]

    2010, AJ, 139, 1782, doi: 10.1088/0004-6256/139/5/1782

    Lang D., Hogg D. W., Mierle K., Blanton M., Roweis S., 2010, @doi [ ] 10.1088/0004-6256/139/5/1782 , https://ui.adsabs.harvard.edu/abs/2010AJ....139.1782L 139, 1782

  78. [78]

    Lazzati D., Rossi E., Covino S., Ghisellini G., Malesani D., 2002, @doi [ ] 10.1051/0004-6361:20021618 , https://ui.adsabs.harvard.edu/abs/2002A&A...396L...5L 396, L5

  79. [79]

    LeBaron N., et al., 2026, @doi [ ] 10.3847/2041-8213/ae2910 , https://ui.adsabs.harvard.edu/abs/2026ApJ...997L..10L 997, L10

  80. [80]

    D., Conroy C., van Dokkum P

    Leja J., Johnson B. D., Conroy C., van Dokkum P. G., Byler N., 2017, @doi [ ] 10.3847/1538-4357/aa5ffe , https://ui.adsabs.harvard.edu/abs/2017ApJ...837..170L 837, 170

Showing first 80 references.