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Time-Dependent Radiation Transport Simulations of Infrared Echoes from Dust-Shrouded Luminous Transients

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A single ratio, the transient rise time divided by the dust photosphere's light-crossing time, decides whether a dust-shrouded transient produces a delayed infrared echo or an infrared rise that precedes its ultraviolet escape.

desk verdict First time-dependent dust-echo simulations with a genuinely useful fast/slow-rise dichotomy; the AT2018cow fit leans on a gray-opacity approximation that deserves a multigroup check. read the letter →

arxiv 2501.13157 v1 pith:AJ4TKBSF submitted 2025-01-22 astro-ph.HE astro-ph.SR

Semih Tuna , Brian D. Metzger , Yan-Fei Jiang , Christopher J. White This is my paper · ORCID
classification astro-ph.HEastro-ph.SR
keywords infraredechoesdustsublimationradiationtransportfastblueopticaltransientstidaldisruptioneventssupernovaeAT2018cowgrayopacity
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

This paper sets out to show that the infrared light curve of a transient hidden inside a dusty shell is governed by one ratio: the transient's rise time near the sublimation luminosity, $t_{\rm rise}$, against the light-crossing time of the dust photosphere, $t_{\rm lc}$. In the slow-rise limit the reprocessed infrared escapes by diffusion before the ultraviolet, rising early and peaking around $t_{\rm rise}$; in the fast-rise limit the ultraviolet stays hidden until the dust is destroyed and the infrared arrives as a delayed echo lasting about $2t_{\rm lc}$. The paper simulates both regimes, including a torus-shaped dust distribution, and uses the fast-rise echo to fit the early infrared excess of AT2018cow. A sympathetic reader would care because the ratio turns the shape of an infrared light curve into a direct clock for the dust photosphere's size and a probe of the environment around supernovae, tidal disruption events, and fast blue optical transients.

What carries the argument

The carrying mechanism is the ratio $t_{\rm rise}/t_{\rm lc}$, with $t_{\rm lc}=R_{\rm ph,0}/c$ the light-crossing time of the initial dust photosphere and $t_{\rm rise}$ the time the transient spends near the photosphere-sublimation luminosity $L_{\rm thin}$. It enters through the sublimation-front speed $v_{\rm sub}/c=t_{\rm lc}/(t_{\rm lc}+2t_{\rm rise})$ and the diffusion/escape criterion $t_{\rm esc}$, which translate into the two distinct light-curve families summarized in the paper's Table 1. Numerically, the argument is carried by a customized angular grid that adds purely radial rays to the radiation-transport discretization, so that in the frequency-integrated calculation outward radial rays can be counted as the transient's UV light and all non-radial rays as reprocessed IR radiation.

What would settle it

Run the same fast-rise spherical model with frequency-dependent opacities instead of gray averages: if the infrared light curve then peaks on the transient's own rise timescale rather than lasting about two light-crossing times, the dichotomy is wrong.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a dichotomy. In axisymmetric time-dependent radiation transport with gray opacities, the speed of the dust sublimation front is $v_{\rm sub}/c=t_{\rm lc}/(t_{\rm lc}+2t_{\rm rise})$, so the ratio $t_{\rm rise}/t_{\rm lc}$ decides whether reprocessed photons can diffuse ahead of the front. For slow-rising transients ($t_{\rm rise}\gg t_{\rm lc}$) the infrared light curve rises before the UV/optical escapes and peaks on a timescale $\sim t_{\rm rise}$; for fast-rising transients ($t_{\rm rise}\ll t_{\rm lc}$) the UV and IR begin to escape around the same time, but the reprocessed energy arrives as an echo lasting $\sim 2t_{\rm lc}$, long after the transient's peak. In a torus geometry the echo shape depends on viewing angle: equatorial observers see flatter, dimmer, longer-lived emission similar to the spherical shell, while polar observers see earlier, brighter, shorter-lived emission. Applied to AT2018cow, a spherical fast-rise model with a raised sublimation temperature of 1300 K reproduces the early IR excess, supporting a dust-echo origin.

Load-bearing premise

The loaded assumption is that the code can tell the transient's direct light from the dust's reradiated infrared simply by the direction the rays travel, and that the averaged opacities get ultraviolet absorption roughly right.

Editorial extensions

If this is right

  • Slow-rising dusty transients should emit detectable infrared before their ultraviolet/optical light escapes, so the IR rise can serve as an early signal for events that optical surveys catch later.
  • Fast-rising transients should leave an infrared echo lasting about $2t_{\rm lc}$ even after the transient has faded, so monitoring IR after peak reveals the size of the pre-explosion dust photosphere.
  • For dusty tori, polar observers see earlier, brighter, shorter-lived infrared while equatorial observers see flatter, longer-lived emission; fitting both shapes can constrain viewing angle and torus opening angle.
  • The AT2018cow infrared excess is reproduced as a fast-rise dust echo, favoring the picture in which at least some fast blue optical transients explode inside opaque dusty media.

Reading between the lines

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

  • By extension, the same ratio should organize infrared echoes across explosion classes: luminous red novae and classical novae in the slow-rise regime should show IR rising before optical, while the fastest tidal disruption events should show the two-light-crossing echo; stacking IR light curves by explosion class would test this directly.
  • The torus models imply that a population of dusty transients viewed from random angles should show a spread in IR peak luminosity and delay of about a factor of two, making the echo an inclination diagnostic independent of jet orientation.
  • If the AT2018cow requirement of a raised sublimation temperature is generic, the infrared echo becomes a grain-composition probe: future events whose echoes demand $T_{\rm sub}$ well above 1000 K would favor refractory or carbonaceous grains in the pre-explosion environment.
  • The appendix's late-time scaling $L_{\rm IR,thin}\propto t^{-p}$ could be inverted against observed IR echo decays to measure the outer density profile index $p$ of the surrounding medium, extending the method beyond the optically thick phase.
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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

3 major / 4 minor

Summary. This paper presents 2D axisymmetric, time-dependent gray radiation transport simulations of dust-shrouded optical/UV transients, using Athena++ with tabulated dust opacities, to predict the bolometric IR light curves produced as a rising transient sublimates its surrounding dust. The central physical result is a dichotomy controlled by the ratio trise/tlc: for fast-rising transients the reprocessed radiation arrives as an IR echo with duration ~2 tlc and luminosity ~Lthin trise/(2 tlc), while for slow-rising transients the IR emission tracks the transient, peaking near Lthin on a timescale ~trise. Spherical and torus dust geometries are compared, and the models are applied to the IR excess of AT2018cow.

Significance. If the results hold, the paper provides a simple and physically motivated diagnostic for inferring the presence and geometry of dusty circumstellar material from IR light curves, and it gives a concrete dust-echo interpretation for AT2018cow. The simulations reproduce the analytic scalings in both regimes (e.g., vsub ~ 0.68c versus the predicted ~0.72c, and the slow-rise onset time close to Eq. (17)), which is a genuine quantitative check and a strength of the paper. The work is also transparent about its gray-opacity approximation and the artificial opacity cutoff. However, the quantitative LIR predictions and the AT2018cow fit rest on an unvalidated directional decomposition of the radiation field and on a post-processing time-delay formula that, as written, appears to be in error; these issues need to be addressed before the central quantitative claims can be accepted.

major comments (3)
  1. [Sec. 3.1 and Sec. 3.5] The time-delay mapping in Eq. (23) appears incorrect as written. For a point source at the origin and a thin shell at radius r, a photon reprocessed at position r_vec at simulation time t_e reaches the observer at tobs = t_e - (n_hat·r_vec)/c (with tobs = 0 for a direct photon from the origin), so t_e = tobs + (n_hat·r_vec)/c. Equation (23) instead gives t_e = tobs + 2r/c - (n_hat·r_vec)/c, which equals the correct expression only at the near point n_hat·r_vec = r and yields negative arrival times for far-side points when inverted. Since this mapping is used to construct all light curves in Figs. 5, 7, 9, and 10, the reported peak times, shapes, and the AT2018cow fit could be affected. Please correct the formula and the definition of tobs, or explicitly verify that the simulations used the correct mapping.
  2. The separation of the radiation field into 'UV' radial rays and 'IR' non-radial rays is a load-bearing assumption that is never validated. Direct source radiation that is scattered, or that leaks into non-radial directions because of the gray transport and the acknowledged factor-of-3-10 underestimate of UV opacity (Sec. 6), would be misclassified as reprocessed IR and would contaminate LIR and the inferred luminosities such as LIR ~ 0.1 Lthin in SPHERE FAST. Please quantify this leakage, e.g., by comparing the non-radial luminosity with the independently predicted Lthin trise/(2 tlc), or by running at least one multigroup/frequency-dependent test in the fast-rise regime.
  3. The AT2018cow application is not an independent validation of the dust-echo interpretation. The sublimation temperature is raised from ~1000 K to 1300 K specifically to satisfy the fast-rise requirement in Eq. (16) (see the discussion near Eq. (25)), the comparison is restricted to the first ~20 days because of the opacity cutoff in Sec. 3.4, and the first two data points are excluded. Please present the sensitivity of the fit to the free parameters (Tsub, trise, Xd, Delta) or explicitly label the model as an illustrative proof of concept rather than as a successful quantitative fit.
minor comments (4)
  1. The text states that 'purely radial (cos zeta = 0) rays travel along straight paths', which contradicts Eq. (18), where cos zeta = r_hat·n_hat; radial rays correspond to cos zeta = ±1. Please correct this typo.
  2. It would improve reproducibility to state explicitly how the control parameters {LQ, Lpk, trise} determine t0 and t1 in Eq. (20), since the paper refers to this inversion but does not give it.
  3. The measurement sphere is said to be at r ~ Rph,0, but the figure caption mentions 1.2 Rph,0; please state the exact radius in the text for clarity.
  4. The manuscript uses both 'AT2018cow' and 'AT2018COW'; please standardize the spelling.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the fast/slow IR-echo scalings are derived from stated transport assumptions and checked against independent radiation-transport simulations; the AT2018cow section is an openly parameterized fit rather than a renamed prediction.

full rationale

The paper's central derivation chain is self-contained. The analytic scalings (Eqs. 10, 15-17) are order-of-magnitude estimates built from the assumed rise law, the sublimation condition, and the light-crossing time; they do not import the simulation outputs as inputs. The simulations are then used as an independent numerical test, e.g. Sec. 4.1 reports LIR ~ 0.1 Lthin, 'consistent with analytic expectations (Eq. (16))'. The gray-opacity and ray-direction split in Sec. 3.1 is a stated approximation and a genuine correctness risk, but it is not circular: the UV/IR separation by ray direction is an identification used to post-process the same transport solution, not an assumption that by itself forces the predicted scaling or the fast/slow dichotomy. The AT2018cow comparison is a fit, with Tsub = 1300 K, trise, and viewing angle adjusted to match the observed excess; the paper does not dress this fit up as an independent prediction of those parameters, and the dust-echo interpretation is anchored to external Perley et al. (2019) data plus a consistency condition (Eq. 25). Self-citations to Metzger & Perley (2023) motivate the AT2018cow application and some grain-size/sublimation-temperature choices, but the central regime dichotomy and the light-curve predictions do not reduce to those citations. No step in the derivation is equivalent by construction to its own input.

Assumptions & free parameters 10 free parameters · 9 assumptions · 0 invented entities

The central claim rests on a chain of modeling choices: the gray opacity treatment, the artificial opacity cutoff, the direction-based UV/IR separation, and the assumed density and light-curve forms. The fast/slow-rise scaling itself is derived analytically with stated assumptions, but the quantitative AT2018cow application adds tuned parameters (Tsub, trise, viewing angle). No new physical entities are introduced.

free parameters (10)
  • Tsub = 1000 K (fiducial); 1300 K (COW models)
    Dust sublimation temperature; raised to 1300 K for AT2018cow because lower values are inconsistent with the observed IR luminosity (Sec. 5).
  • trise = 1.9 d (SPHERE FAST), 19 d (SPHERE SLOW), 0.8 d (COW SPHERE), 1.5 d (COW TORUS)
    Transient rise time near Lthin; COW values chosen to fit AT2018cow.
  • rho0 = 1e-16 g cm^-3
    Density normalization of the external medium; degenerate with dust-to-gas ratio.
  • p = 3
    Radial power-law index of density profile, motivated by TDE and FBOT CSM.
  • Xd = 0.1
    Dust-to-gas mass ratio, about 10 times solar; degenerate with density normalization.
  • Delta = infinity (sphere) or 0.2 (torus)
    Angular width of torus; chosen to explore geometry.
  • amax = 1 micron
    Maximum dust grain size; chosen based on FBOT and SNR precursor estimates.
  • Lpk = 4e42 erg/s (SPHERE models); about 1e44 erg/s (AT2018cow models)
    Peak transient luminosity; for AT2018cow taken from observations.
  • tpk = 7.1 d (SPHERE models); about 3 d (AT2018cow models)
    Peak timescale; for AT2018cow taken from observations.
  • theta_obs = 14.3, 59.1, 87.2 degrees; equatorial preferred for AT2018cow
    Observer viewing angle for torus models; equatorial angles fit AT2018cow better.
assumptions (9)
  • standard math Radiation hydrodynamics equations as implemented in Athena++ (Jiang 2021)
    The simulation relies on the correctness of the implicit radiation transport module; treated as a trusted solver from prior literature.
  • domain assumption Rosseland mean (gray) opacity approximation
    Opacities are frequency-integrated, which the paper states underestimates UV absorption by a factor of 3-10 (Sec. 6).
  • domain assumption Dust sublimation at Tsub with no reformation
    Dust is destroyed when temperature exceeds Tsub and does not reform on the transient timescale (Sec. 3.3).
  • ad hoc to paper Power-law density profile with torus modulation (Eq. 21)
    The external medium is parameterized as rho proportional to r^-p with a Gaussian angular cut; chosen for tractability and motivated by TDE and FBOT environments.
  • ad hoc to paper Transient light-curve shape (Eq. 20)
    The central source follows (1 + t/t0)^2 exp(-t/t1); a specific assumed rise and decay law.
  • ad hoc to paper Opacity cutoff beyond the initial photosphere (Sec. 3.4)
    Opacity is artificially set to zero outside Rph,0 to restrict results to optically-thick reprocessing; directly shapes the computed light curves.
  • ad hoc to paper Radial/non-radial ray classification as UV/IR (Sec. 3.1)
    In the gray code, direct and reprocessed radiation are separated geometrically rather than by frequency.
  • domain assumption Neglect of radiation pressure on the gas (Eq. 3)
    Argument that acceleration timescale is much longer than the transient rise time; if invalid, the dust shell would move during the echo.
  • domain assumption Instantaneous thermal coupling of dust and gas (Eq. 4)
    Heating timescale is about 3 seconds, so dust and gas temperature equilibrium is assumed.

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Cite this review

Pith. "Pith review of Time-Dependent Radiation Transport Simulations of Infrared Echoes from Dust-Shrouded Luminous Transients." pith.science (2026). https://pith.science/paper/AJ4TKBSF

@misc{pith2026250113157,
  author       = {Pith},
  title        = {Pith review of: Time-Dependent Radiation Transport Simulations of Infrared Echoes from Dust-Shrouded Luminous Transients},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AJ4TKBSF}},
  note         = {Machine review of arXiv:2501.13157}
}
abstract

A wide range of stellar explosions, including supernovae (SNe), tidal disruption events (TDE), and fast blue optical transients (FBOT), can occur in dusty environments initially opaque to the transient's optical/UV light, becoming visible only once the dust is destroyed by the transient's rising luminosity. We present axisymmetric time-dependent radiation transport simulations of dust-shrouded transients with \texttt{Athena++} and tabulated gray opacities, which predict the light-curves of the dust-reprocessed infrared (IR) radiation. The luminosity and timescale of the IR light-curve depends on whether the transient rises rapidly or slowly compared to the light crossing-time of the photosphere, $t_{\rm lc}$. For slow-rising transients ($t_{\rm rise} \gg t_{\rm lc}$) such as SNe, the reprocessed IR radiation diffuses outwards through the dust shell faster than the sublimation front expands; the IR light-curve therefore begins rising prior to the escape of UV/optical light, but peaks on a timescale $\sim t_{\rm rise}$ shorter than the transient duration. By contrast, for fast-rising transients ($t_{\rm rise} \ll t_{\rm lc}$) such as FBOTs and some TDEs, the finite light-travel time results in the reprocessed radiation arriving as an ``echo'' lasting much longer than the transient itself (despite the dust photosphere having already being destroyed by peak light). We explore the effects of the system geometry by considering a torus-shaped distribution of dust. The IR light-curves seen by observers in the equatorial plane of the torus resemble those for a spherical dust shell, while polar observers see faster-rising, brighter and shorter-lived emission. We successfully model the IR excess seen in AT2018cow as a dust echo, supporting the presence of an opaque dusty medium surrounding FBOTs prior to explosion.

Figures

Figures reproduced from arXiv: 2501.13157 by the authors.

Figure 1
Figure 1. Classes of optical/UV transients in the space of peak luminosity and duration. A black curve denotes the boundary tlc = trise (Eq. (13), for Tsub = 1500 K) separating events for which the reprocessed emission from a optically￾thick dust shell surrounding the progenitor will out the tran￾sient itself as an “echo” (tlc ≫ trise) versus cases in which light-crossing delays are negligible (tlc ≪ trise) and hence the IR l… view at source ↗
Figure 2
Figure 2. Schematic illustration of the intrinsic transient luminosity Ltr(t) (solid blue line), escaping optical/UV light-curve LUV(t) (dashed blue line), and dust-reprocessed IR light-curve LIR(t) for an assumed dust density profile ρ ∝ r −p . For LIR, pink and red colors represent NIR and far-IR emission from hot dust close to the sublimation temperature and cooler dust, respectively. The separate panels depicts the two re… view at source ↗
Figure 3
Figure 3. Rosseland-mean of the extinction opacity at den￾sities ρ = 10−18 g cm−3 (blue), ρ = 10−16 g cm−3 (orange), ρ = 10−14 g cm−3 (green), for assumed maximum grain sizes amax = 10−5 cm (dotted), amax = 3 × 10−5 cm (dashed), and amax = 10−4 cm (solid). The opacity values are normalized to a dust mass fraction Xd = 1, but we use Xd = 0.1 in our simulations. The opacity drops off sharply above Tsub ≈ 103 K corresponding to … view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Radius-time diagrams showing (clockwise, starting from the top left) the evolution of the local radial luminosity L = 4πr2Fr, gas temperature, Tgas, optical depth to infinity τ ≡ R ∞ r ρκdr, and Rosseland opacity κ. A red line depicts the photosphere (τ = 1) while a gr…
Figure 5
Figure 5. Figure 5: Left panels: Light-curves from the fiducial fast-rise model SPHERE FAST (top) compared to the otherwise equivalent slow-rise model SPHERE SLOW (bottom). We compare the intrinsic luminosity of the optical/UV transient Ltr (green lines; Eq. (20)) as injected at the cente…
Figure 6
Figure 6. Figure 6: Two-dimensional (r, θ) snapshots of the model TORUS FAST, taken at four times after the onset of the tran￾sient as marked. We show the reprocessed IR radiation en￾ergy density urep (panel (a)), luminosity L = 4πr2Fr (panel (b)), opacity (panel (c)), and gas temperature…
Figure 7
Figure 7. Figure 7: Isotropic luminosities L = 4πr2F for TORUS FAST (covering angle ∆ = 0.2), color indexed according to observer angle θobs, as measured from the symmetry axis, i.e. θobs = 0 corresponds to an observer viewing the system from the pole. (Escaping) UV and IR light-curves ar…
Figure 8
Figure 8. Figure 8: Snapshots of TORUS FAST showing reprocessed energy flux (calculated using non-radial rays). Blue labels mark the time after explosion in units of tlc. The color plot shows the magnitude of the energy flux scaled to acT 4 sub, with Tsub = 103 K, while streamlines show t…
Figure 9
Figure 9. Figure 9: Top panel: IR light-curves for observers with viewing angles θobs = 14.3, θobs = 59.1 an θobs = 87.2 degrees measured from the torus polar axis. Bottom panel: The sum of reprocessed energy flux FIR over area elements Ai on the sphere, that contribute to the light-curve…
Figure 10
Figure 10. Figure 10: IR light curves of models COW SPHERE (blue dashed line) and COW TORUS (solid lines), compared to the observed IR excess of AT2018cow (Perley et al. 2019) for different observer viewing angles. We do not attempt to fit the first two data points (shown in gray) since th…

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Forward citations

Cited by 3 Pith papers

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Reference graph

Works this paper leans on

72 extracted references · 12 canonical work pages · cited by 3 Pith papers

  1. [1]

    D., van Velzen, S., Horesh, A., & Zauderer, B

    Alexander, K. D., van Velzen, S., Horesh, A., & Zauderer, B. A. 2020, SSRv, 216, 81, doi: 10.1007/s11214-020-00702-w

    doi:10.1007/s11214-020-00702-w 2020

  2. [2]

    E., & Smith, N

    Andrews, J. E., & Smith, N. 2018, MNRAS, 477, 74, doi: 10.1093/mnras/sty584

    doi:10.1093/mnras/sty584 2018

  3. [3]

    1987, ApJ, 320, 537, doi: 10.1086/165571

    Barvainis, R. 1987, ApJ, 320, 537, doi: 10.1086/165571

    doi:10.1086/165571 1987

  4. [4]

    M., Chevalier, R

    Berger, E., Soderberg, A. M., Chevalier, R. A., et al. 2009, ApJ, 699, 1850, doi: 10.1088/0004-637X/699/2/1850

    doi:10.1088/0004-637x/699/2/1850 2009

  5. [5]

    P., Zhu, Z., et al

    Birnstiel, T., Dullemond, C. P., Zhu, Z., et al. 2018, ApJL, 869, L45, doi: 10.3847/2041-8213/aaf743

    doi:10.3847/2041-8213/aaf743 2018

  6. [6]

    K., Nicholl, M., Berger, E., et al

    Blanchard, P. K., Nicholl, M., Berger, E., et al. 2017, ApJ, 843, 106, doi: 10.3847/1538-4357/aa77f7 20 Semih Tuna, B. D. Metzger

    doi:10.3847/1538-4357/aa77f7 2017

  7. [7]

    F., & Huffman, D

    Bohren, C. F., & Huffman, D. R. 1998, Absorption and Scattering of Light by Small Particles (John Wiley and

    work page 1998

  8. [8]

    E., Bedin, L

    Bond, H. E., Bedin, L. R., Bonanos, A. Z., et al. 2009, ApJL, 695, L154, doi: 10.1088/0004-637X/695/2/L154

    work page doi:10.1088/0004-637x/695/2/l154 2009

Show all 72 references
  1. [9]

    2022, arXiv e-prints, arXiv:2210.04115, doi: 10.48550/arXiv.2210.04115

    Cao, X.-M., Wang, S.-Q., Gan, W.-P., & Li, J.-Y. 2022, arXiv e-prints, arXiv:2210.04115, doi: 10.48550/arXiv.2210.04115

    2022 doi arXiv

  2. [10]

    A., & Fransson, C

    Chevalier, R. A., & Fransson, C. 1994, ApJ, 420, 268, doi: 10.1086/173557

    1994 doi

  3. [11]

    Crotts, A. P. S. 1988, ApJL, 333, L51, doi: 10.1086/185286

    1988 doi

  4. [12]

    W., & Gammie, C

    Davis, S. W., & Gammie, C. F. 2020, ApJ, 888, 94, doi: 10.3847/1538-4357/ab5950

    2020 doi

  5. [13]

    2017, ApJL, 841, L8, doi: 10.3847/2041-8213/aa7130

    Dou, L., Wang, T., Yan, L., et al. 2017, ApJL, 841, L8, doi: 10.3847/2041-8213/aa7130

    2017 doi

  6. [14]

    T., & Lee, H

    Draine, B. T., & Lee, H. M. 1984, ApJ, 285, 89, doi: 10.1086/162480

    1984 doi

  7. [15]

    R., Chornock, R., Soderberg, A

    Drout, M. R., Chornock, R., Soderberg, A. M., Sanders, N. E., et al. 2014, ApJ, 794, 23, doi: 10.1088/0004-637X/794/1/23

    2014 doi

  8. [16]

    P., Juhasz, A., Pohl, A., et al

    Dullemond, C. P., Juhasz, A., Pohl, A., et al. 2012, RADMC-3D: A multi-purpose radiative transfer tool, Astrophysics Source Code Library, record ascl:1202.015

    2012

  9. [17]

    1985, ApJ, 297, 719, doi: 10.1086/163571

    Dwek, E. 1985, ApJ, 297, 719, doi: 10.1086/163571

    1985 doi

  10. [18]

    R., & Kochanek, C

    Evans, C. R., & Kochanek, C. S. 1989, ApJL, 346, L13, doi: 10.1086/185567

    1989 doi

  11. [19]

    C., & Metzger, B

    Generozov, A., Stone, N. C., & Metzger, B. D. 2015, MNRAS, 453, 775, doi: 10.1093/mnras/stv1607

    2015 doi

  12. [20]

    R., Meikle, W

    Graham, J. R., Meikle, W. P. S., Selby, M. J., et al. 1983, Nature, 304, 709, doi: 10.1038/304709a0

    1983 doi

  13. [21]

    2007, ApJ, 662, 1119, doi: 10.1086/517913

    Heng, K., Lazzati, D., & Perna, R. 2007, ApJ, 662, 1119, doi: 10.1086/517913

    2007 doi

  14. [22]

    Hinkle, J. T. 2022, arXiv e-prints, arXiv:2210.15681, doi: 10.48550/arXiv.2210.15681

    2022 doi arXiv

  15. [23]

    Ho, A. Y. Q., Phinney, E. S., Ravi, V., et al. 2019, ApJ, 871, 73, doi: 10.3847/1538-4357/aaf473

    2019 doi

  16. [24]

    Ho, A. Y. Q., Margalit, B., Bremer, M., et al. 2021, arXiv e-prints, arXiv:2110.05490. https://arxiv.org/abs/2110.05490

    2021 arXiv

  17. [25]

    2013, A&A Rv, 21, 59, doi: 10.1007/s00159-013-0059-2

    Ivanova, N., Justham, S., Chen, X., et al. 2013, A&A Rv, 21, 59, doi: 10.1007/s00159-013-0059-2

    2013 doi

  18. [26]

    1997, MNRAS, 287, 799, doi: 10.1093/mnras/287.4.799 Ivezi´ c,ˇZ., Kahn, S

    Ivezic, Z., & Elitzur, M. 1997, MNRAS, 287, 799, doi: 10.1093/mnras/287.4.799 Ivezi´ c,ˇZ., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c

    1997 doi

  19. [27]

    Jaffe, W., Meisenheimer, K., R¨ ottgering, H. J. A., et al. 2004, Nature, 429, 47, doi: 10.1038/nature02531

    2004 doi

  20. [28]

    E., Adams, S

    Jencson, J. E., Adams, S. M., Bond, H. E., et al. 2019, ApJL, 880, L20, doi: 10.3847/2041-8213/ab2c05

    2019 doi

  21. [29]

    2021, ApJ, 911, 31, doi: 10.3847/1538-4357/abe772

    Jiang, N., Wang, T., Hu, X., et al. 2021, ApJ, 911, 31, doi: 10.3847/1538-4357/abe772

    2021 doi

  22. [30]

    2019, ApJ, 871, 15, doi: 10.3847/1538-4357/aaf6b2

    Jiang, N., Wang, T., Mou, G., et al. 2019, ApJ, 871, 15, doi: 10.3847/1538-4357/aaf6b2

    2019 doi

  23. [31]

    2021, ApJS, 253, 49, doi: 10.3847/1538-4365/abe303

    Jiang, Y.-F. 2021, ApJS, 253, 49, doi: 10.3847/1538-4365/abe303

    2021 doi

  24. [32]

    2017, ApJ, 843, 68, doi: 10.3847/1538-4357/aa77b0

    Blaes, O. 2017, ApJ, 843, 68, doi: 10.3847/1538-4357/aa77b0

    2017 doi

  25. [33]

    Kaur, K., & Stone, N. C. 2024, arXiv e-prints, arXiv:2405.18500, doi: 10.48550/arXiv.2405.18500

    2024 doi arXiv

  26. [34]

    Kochanek, C. S. 2011, ApJ, 743, 73, doi: 10.1088/0004-637X/743/1/73 —. 2023, arXiv e-prints, arXiv:2305.11936, doi: 10.48550/arXiv.2305.11936

    2011 doi arXiv

  27. [35]

    2009, ApJ, 701, 105, doi: 10.1088/0004-637X/701/1/105

    Komossa, S., Zhou, H., Rau, A., et al. 2009, ApJ, 701, 105, doi: 10.1088/0004-637X/701/1/105

    2009 doi

  28. [36]

    C., Reynolds, T

    Kool, E. C., Reynolds, T. M., Mattila, S., et al. 2020, MNRAS, 498, 2167, doi: 10.1093/mnras/staa2351

    2020 doi

  29. [37]

    R., Harrison, F

    Kulkarni, S. R., Harrison, F. A., Grefenstette, B. W., et al. 2021, arXiv e-prints, arXiv:2111.15608, doi: 10.48550/arXiv.2111.15608

    2021 doi arXiv

  30. [38]

    Lu, W., Kumar, P., & Evans, N. J. 2016, MNRAS, 458, 575, doi: 10.1093/mnras/stw307

    2016 doi

  31. [39]

    D., Chornock, R., et al

    Margutti, R., Metzger, B. D., Chornock, R., et al. 2019, ApJ, 872, 18, doi: 10.3847/1538-4357/aafa01

    2019 doi

  32. [40]

    2024, ApJ, 961, 211, doi: 10.3847/1538-4357/ad18bb

    Masterson, M., De, K., Panagiotou, C., et al. 2024, ApJ, 961, 211, doi: 10.3847/1538-4357/ad18bb

    2024 doi

  33. [41]

    Matsumoto, T., & Metzger, B. D. 2022, ApJ, 936, 114, doi: 10.3847/1538-4357/ac892c

    2022 doi

  34. [42]

    2018, Science, 361, 482, doi: 10.1126/science.aao4669

    Mattila, S., P´ erez-Torres, M., Efstathiou, A., et al. 2018, Science, 361, 482, doi: 10.1126/science.aao4669

    2018 doi

  35. [43]

    Metzger, B. D. 2010, MNRAS, 409, 284, doi: 10.1111/j.1365-2966.2010.17308.x —. 2022, arXiv e-prints, arXiv:2203.04331. https://arxiv.org/abs/2203.04331

    2010

  36. [44]

    D., & Perley, D

    Metzger, B. D., & Perley, D. A. 2023, ApJ, 944, 74, doi: 10.3847/1538-4357/acae89

    2023 doi

  37. [45]

    A., et al

    Newsome, M., Arcavi, I., Howell, D. A., et al. 2023, arXiv e-prints, arXiv:2305.03767, doi: 10.48550/arXiv.2305.03767

    2023 doi arXiv

  38. [46]

    1984, A&A, 136, 306

    Pearce, G., & Evans, A. 1984, A&A, 136, 306

    1984

  39. [47]

    D., & Tomida, K

    Pejcha, O., Metzger, B. D., & Tomida, K. 2016a, MNRAS, 455, 4351, doi: 10.1093/mnras/stv2592 —. 2016b, MNRAS, 461, 2527, doi: 10.1093/mnras/stw1481

    doi

  40. [48]

    D., Tyles, J

    Pejcha, O., Metzger, B. D., Tyles, J. G., & Tomida, K. 2017, ApJ, 850, 59, doi: 10.3847/1538-4357/aa95b9

    2017 doi

  41. [49]

    A., Mazzali, P

    Perley, D. A., Mazzali, P. A., Yan, L., et al. 2019, MNRAS, 484, 1031, doi: 10.1093/mnras/sty3420 Infrared Echoes from Dust-Shrouded Luminous Transients 21

    2019 doi

  42. [50]

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

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

    2002 doi

  43. [51]

    B., Hollenbach, D., Beckwith, S., et al

    Pollack, J. B., Hollenbach, D., Beckwith, S., et al. 1994, ApJ, 421, 615, doi: 10.1086/173677

    1994 doi

  44. [52]

    2012, MNRAS, 423, L92, doi: 10.1111/j.1745-3933.2012.01264.x

    Quataert, E., & Shiode, J. 2012, MNRAS, 423, L92, doi: 10.1111/j.1745-3933.2012.01264.x

    2012

  45. [53]

    M., Bregman, J., Witteborn, F

    Rank, D. M., Bregman, J., Witteborn, F. C., et al. 1988, ApJL, 325, L1, doi: 10.1086/185096

    1988 doi

  46. [54]

    Rees, M. J. 1988, Nature, 333, 523, doi: 10.1038/333523a0

    1988 doi

  47. [55]

    O., et al

    Sagiv, I., Gal-Yam, A., Ofek, E. O., et al. 2014, AJ, 147, 79, doi: 10.1088/0004-6256/147/4/79

    2014 doi

  48. [56]

    2019, ApJ, 884, 58, doi: 10.3847/1538-4357/ab3f2a

    Segev, R., Sabach, E., & Soker, N. 2019, ApJ, 884, 58, doi: 10.3847/1538-4357/ab3f2a

    2019 doi

  49. [57]

    Smith, N., & Arnett, W. D. 2014, ApJ, 785, 82, doi: 10.1088/0004-637X/785/2/82

    2014 doi

  50. [58]

    2006, MNRAS, 373, 733, doi: 10.1111/j.1365-2966.2006.11056.x

    Soker, N., & Tylenda, R. 2006, MNRAS, 373, 733, doi: 10.1111/j.1365-2966.2006.11056.x

    2006

  51. [59]

    L., Crotts, A

    Sokoloski, J. L., Crotts, A. P. S., Lawrence, S., & Uthas, H. 2013, ApJL, 770, L33, doi: 10.1088/2041-8205/770/2/L33

    2013 doi

  52. [60]

    2015, arXiv e-prints, arXiv:1503.03757, doi: 10.48550/arXiv.1503.03757

    Spergel, D., Gehrels, N., Baltay, C., et al. 2015, arXiv e-prints, arXiv:1503.03757, doi: 10.48550/arXiv.1503.03757

    2015 doi arXiv

  53. [61]

    M., Tomida, K., White, C

    Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, ApJS, 249, 4, doi: 10.3847/1538-4365/ab929b

    2020 doi

  54. [62]

    C., & Metzger, B

    Stone, N. C., & Metzger, B. D. 2016, MNRAS, 455, 859, doi: 10.1093/mnras/stv2281

    2016 doi

  55. [63]

    2020, ApJ, 898, 129, doi: 10.3847/1538-4357/ab9f2c

    Sun, L., Jiang, N., Wang, T., et al. 2020, ApJ, 898, 129, doi: 10.3847/1538-4357/ab9f2c

    2020 doi

  56. [64]

    A., Prieto, J

    Thompson, T. A., Prieto, J. L., Stanek, K. Z., et al. 2009, ApJ, 705, 1364, doi: 10.1088/0004-637X/705/2/1364

    2009 doi

  57. [65]

    Tuna, S., & Metzger, B. D. 2023, ApJ, 955, 125, doi: 10.3847/1538-4357/acef17

    2023 doi

  58. [66]

    2011, A&A, 528, A114, doi: 10.1051/0004-6361/201016221

    Tylenda, R., Hajduk, M., Kami´ nski, T., et al. 2011, A&A, 528, A114, doi: 10.1051/0004-6361/201016221

    2011 doi

  59. [67]

    2003, ApJ, 586, 1338, doi: 10.1086/367818 Van Dyk, S

    Ueta, T., & Meixner, M. 2003, ApJ, 586, 1338, doi: 10.1086/367818 Van Dyk, S. D. 2013, AJ, 146, 24, doi: 10.1088/0004-6256/146/2/24 van Velzen, S., Mendez, A. J., Krolik, J. H., & Gorjian, V. 2016, ApJ, 829, 19, doi: 10.3847/0004-637X/829/1/19 van Velzen, S., Pasham, D. R., Ko...

    2003 doi

  60. [68]

    Kara, E. A. 2021, SSRv, 217, 63, doi: 10.1007/s11214-021-00835-6

    2021 doi

  61. [69]

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

    2000 doi

  62. [70]

    2023, arXiv e-prints, arXiv:2306.15080, doi: 10.48550/arXiv.2306.15080

    Werner, N., ˇR ´ ıpa, J., Th¨ one, C., et al. 2023, arXiv e-prints, arXiv:2306.15080, doi: 10.48550/arXiv.2306.15080

    2023 doi arXiv

  63. [71]

    2010, ApJL, 717, L62, doi: 10.1088/2041-8205/717/1/L62

    Yoon, S.-C., & Cantiello, M. 2010, ApJL, 717, L62, doi: 10.1088/2041-8205/717/1/L62

    2010 doi

  64. [72]

    2021, MNRAS, 508, 453, doi: 10.1093/mnras/stab2517

    Zhu, Z., Jiang, Y.-F., Baehr, H., et al. 2021, MNRAS, 508, 453, doi: 10.1093/mnras/stab2517

    2021 doi

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