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The BTSbot-nearby discovery of SN 2024jlf: rapid, autonomous follow-up probes interaction in an 18.5 Mpc Type IIP supernova

T0 review · 3 major / 4 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A normal Type IIP supernova at 18.5 Mpc shows its red supergiant progenitor shed mass at an elevated rate in its final years, with the flash features caught just 17 hours after first light by an autonomous follow-up system.

desk verdict A genuinely new autonomous follow-up demonstration wrapped around a well-observed early flash event, with a mass-loss inference that is real but softer than the abstract implies. read the letter →

arxiv 2501.18686 v2 pith:FEMIRLFL submitted 2025-01-30 astro-ph.HE

Nabeel Rehemtulla , W. V. Jacobson-Galán , Avinash Singh , Adam A. Miller , Charles D. Kilpatrick , K-Ryan Hinds , Chang Liu , Steve Schulze
show 22 more authors
Jesper Sollerman Theophile Jegou du Laz Tomás Ahumada Katie Auchettl S. J. Brennan Michael W. Coughlin Christoffer Fremling Anjasha Gangopadhyay Daniel A. Perley Nikolaus Z. Prusinski Josiah Purdum Yu-Jing Qin Sara Romagnoli Jennifer Shi Jacob L. Wise Tracy X. Chen Steven L. Groom David O. Jones Mansi M. Kasliwal Roger Smith Niharika Sravan Shrinivas R. Kulkarni
This is my paper · ORCID
classification astro-ph.HE
keywords time-domainastronomyskysurveyssupernovaeTypeIIPcircumstellarmediumflashspectroscopypre-supernovamasslossautonomousfollow-up
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 tries to establish that SN 2024jlf, an otherwise normal Type IIP supernova 18.5 Mpc away, had a red supergiant progenitor that lost mass at an enhanced rate in the final years before core collapse. The evidence is a set of narrow, short-lived flash ionization lines (H$\alpha$, He II, C IV) that appear in spectra beginning 0.7 days after first light and vanish within 1.8 days. Matching those features and the early light curve to two independent radiation-hydrodynamics model grids yields a pre-explosion mass-loss rate between $10^{-4}$ and $10^{-3}\,M_\odot\,\mathrm{yr}^{-1}$, with the enhanced-loss phase lasting roughly one to five years. The paper also presents BTSbot-nearby, an automated system that identified the supernova and triggered follow-up within minutes, arguing that such automation is what makes observing these ephemeral phases possible. If the inference is right, normal Type IIP progenitors can undergo brief, intense mass-loss episodes shortly before explosion without producing a fully interacting supernova.

What carries the argument

The central object is the flash ionization phase itself: narrow, short-lived emission lines of He II, C IV, and H$\alpha$ that form when the supernova shock photoionizes dense circumstellar gas, and recombination emits briefly before the expanding ejecta overwhelm it. The flash duration $\tau$ and the line set are the observable handle on the CSM density and extent. The argument is carried by matching these features, plus the early multi-band light curve, to grids from two independent radiative-hydrodynamics codes (CMFGEN and STELLA); the mass-loss rate and its duration follow from the best-matched models under an assumed steady, spherical wind velocity. The BTSbot-nearby pipeline is the enabling mechanism that catches the phase while it lasts.

What would settle it

A high-resolution spectrum of the flash lines taken during the 1.3--1.8 day window would measure the line width and profile; a width implying $v_w$ far from 10--50 km/s, or an asymmetric or multiple-component profile, would invalidate the assumed wind speed and with it the derived mass-loss rate and duration. Spectropolarimetry showing significant polarization during the flash phase would indicate non-spherical CSM, breaking the smooth spherical model on which the quoted $\dot{M}$ range depends.

Watch

Extended reading notes

Core claim

In the paper's own terms, SN 2024jlf is a normal Type IIP supernova at distance $18.45 \pm 3.66$ Mpc that nevertheless shows flash ionization features: weak, narrow emission in H$\alpha$, He II $\lambda4686$, and C IV that persist for $1.3 < \tau < 1.8$ days. The fast rise ($>4$ mag/day) and the short flash duration point to a dense, close-in circumstellar envelope. Comparing the spectral series and optical/UV light curves with the best-matched CMFGEN model (mdot1em3) and with the best-matched STELLA grid model gives mass-loss rates of $10^{-3}$ and $10^{-4}\,M_\odot\,\mathrm{yr}^{-1}$ respectively, and rules out rates above $10^{-2}$ and below $10^{-5}$ in those grids; the paper therefore quotes $10^{-4} < \dot{M} < 10^{-3}\,M_\odot\,\mathrm{yr}^{-1}$. Using the inferred IIn-feature duration and adopted wind velocities, the enhanced mass-loss phase lasted at least 1 year (CMFGEN, $v_w=50$ km/s) to about 5 years (STELLA, $v_w=10$ km/s). The discovery was enabled by BTSbot-nearby, which triggered spectroscopy 7 minutes after alert, i.e., 0.7 days after first light, with no human having viewed the candidate before the observation concluded.

Load-bearing premise

The inference assumes the flash lines come from recombination in a smooth, spherically symmetric stellar wind moving at one fixed speed (50 km/s for the CMFGEN interpretation, 10 km/s for STELLA); if the circumstellar matter is clumpy, disk-like, or moves at a very different speed, the quoted mass-loss rate and its one-to-five-year duration would shift outside the quoted range.

Editorial extensions

If this is right

  • If the inference holds, a normal-appearing Type IIP can have a progenitor that shed mass at $10^{-4}$ to $10^{-3}\,M_\odot\,\mathrm{yr}^{-1}$ in its final one to five years, implying late-stage mass-loss enhancement is not restricted to strongly interacting supernovae.
  • Automated target-of-opportunity follow-up with roughly 7-minute latency makes the first hours after first light routinely observable for nearby supernovae, where human scanning typically introduces about a day of delay.
  • The short flash duration sets SN 2024jlf apart from the typical flashing SNe found by Bruch et al. (2023) (median $\tau$ about 5 days), suggesting a population of very short-lived, weaker flash events that automated programs are needed to catch.
  • The two independent model grids disagree on the early optical/UV rise and the plateau brightness; if each is trusted for what it fits best, the CSM mass-loss rate is nevertheless constrained to one order of magnitude even though neither model reproduces all observations.
  • Rapid public release of automated classifications and spectra would allow larger facilities to follow up the same transients, extending the method beyond low-resolution spectral data.

Reading between the lines

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

  • The quoted mass-loss range rests on the adopted wind velocities (50 and 10 km/s) and on spherical, smooth CSM; if the true wind is slower, clumpier, or disk-like, the inferred rate could move outside $10^{-4}$ to $10^{-3}\,M_\odot\,\mathrm{yr}^{-1}$. The paper acknowledges this but does not measure the geometry, so this is the main systematic caveat.
  • A direct test is to measure the flash-line profiles at high resolution during the 1.3--1.8 day window: line widths and any P Cygni absorption would give the wind velocity and break the degeneracy between mass-loss rate and wind speed.
  • The same BTSbot-nearby selection, applied over a few seasons, should yield a sample of short-$\tau$ flashing supernovae; the distribution of $\tau$ among normal IIPs would test whether 1--2 day flashes are rare or just rarely caught.
  • Because the STELLA best fit suggests a low ZAMS mass and modest $^{56}$Ni while the CMFGEN model reproduces the plateau brightness well, a joint fit to both grids could recover progenitor radius and explosion-energy degeneracies rather than treating each grid separately.
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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. The paper presents SN 2024jlf, a Type IIP supernova at 18.5 Mpc, discovered by the newly introduced BTSbot-nearby autonomous follow-up program. Spectroscopic observations began 0.7 days after first light and reveal weak flash ionization features (H-alpha, He II, C IV) lasting 1.3–1.8 days. The authors match the spectral series and light curve to grids from CMFGEN (Dessart et al. 2017; Dessart & Jacobson-Galan 2023) and STELLA (Moriya et al. 2023), inferring a mass-loss rate of 10^-4–10^-3 M_sun/yr and an enhanced mass-loss phase lasting 1–5 years before explosion. The paper also characterizes the BTSbot-nearby filter's purity and latency, demonstrating a 7-minute spectroscopic follow-up latency for this event.

Significance. The early-time dataset is valuable: sub-day spectroscopy, deep pre-first-light non-detections, and a well-sampled flash-feature decline provide a strong empirical constraint on the duration of the CSM interaction (1.3–1.8 d). If the mass-loss inference holds, SN 2024jlf adds to the growing sample of RSG progenitors with enhanced pre-explosion mass loss. The BTSbot-nearby latency demonstration is a practical contribution to autonomous transient follow-up. The paper is generally careful with data reduction and transparently discusses the limitations of its model matches, although the central mass-loss claim inherits assumptions that need sharper treatment.

major comments (3)
  1. [Sec. 6.2 / Abstract] The quoted mass-loss rate range (10^-4 < Mdot < 10^-3 M_sun/yr) is presented as a constraint on the progenitor, but it is explicitly conditional on the smooth, spherical, constant-velocity wind assumed in both model grids. The wind velocity is not measured: the only high-resolution spectrum (WiFeS, R=3000) was obtained at +1.8 d after the flash features had disappeared, and the earlier spectra have R~100–350. Because recombination line emissivity scales with density squared, a clumpy or porous CSM could produce similar flash features with a substantially lower mean mass-loss rate. The paper should quantify this systematic uncertainty (e.g., by comparing line luminosities or adopting a filling-factor parameter) or rephrase the result as a constraint on the CSM density/column rather than on Mdot.
  2. [Secs. 4–5] The range 10^-4–10^-3 M_sun/yr is the union of the two best-fit grid values (CMFGEN mdot1em3 at 10^-3; STELLA best model at 10^-4), not a continuous bound. The paper does not show that intermediate values are excluded; for the STELLA grid, only the single best chi^2 model is reported, despite the availability of ~228,000 models. The paper should present the distribution of Mdot among statistically acceptable models (e.g., within a Delta-chi^2 threshold) to demonstrate that intermediate mass-loss rates are either excluded or admitted. Without this, the central mass-loss constraint is not well supported as a range.
  3. [Sec. 6.2] The duration of enhanced mass-loss (t_Mdot = v_sh * t_IIn / v_w, giving 1–5 yr) depends on the indirectly inferred t_IIn = 0.96 ± 0.34 d, which is scaled from SN 2013fs using the JG24 procedure. The paper acknowledges this inference is imperfect, but the 'last year before explosion' claim in the abstract rests on it. The scaling has not been validated for SN 2024jlf, and the uncertainty in t_IIn is not propagated into the quoted 1–5 yr range (which is instead mostly set by the assumed v_w values of 50 and 10 km/s). The authors should present this duration as a model-dependent estimate with an explicit discussion of the t_IIn scaling uncertainty.
minor comments (4)
  1. [Sec. 3.4] The sentence describing the t_IIn scaling ('Following this procedure with our +1.3 day ALFOSC and the +1.9 day SN 2013fs spectrum') would benefit from stating that the ratio is based on spectral similarity at those epochs, not on a single spectral comparison, to avoid over-interpretation.
  2. [Figure 3] The caption says 'Left panel (and shaded region in right panel)' but it is not immediately clear which shaded region is meant; consider labeling the early-time inset more explicitly.
  3. [Sec. 6.3] Typo: 'Sourthern Astrophysical Research (SOAR)' should be 'Southern Astrophysical Research'.
  4. [Sec. 3.2] The notation 'EW_Na ID' is a bit awkward; consider writing 'EW(Na I D)' or 'the Na I D equivalent width' for clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the mass-loss inference is an inverse-modeling match to independent published grids, with assumptions (wind velocity, tIIn scaling) explicitly flagged rather than assumed into the conclusion.

full rationale

The paper's central inference—that SN 2024jlf's flash features imply an enhanced pre-explosion mass-loss rate of 10^-4 < Mdot < 10^-3 M_sun/yr—is obtained by matching observed spectra and light curves to grids from two independent radiative-hydrodynamics codes (CMFGEN via Dessart et al. 2017 and Dessart & Jacobson-Galan 2023; STELLA via Moriya et al. 2023). Neither grid is fitted to SN 2024jlf; the observed flash-line identifications, their duration (1.3 < tau < 1.8 d), and the photometric rise are independent inputs. The adopted wind velocities (50 km/s for CMFGEN, 10 km/s for STELLA) are assumptions, but they are explicit modeling choices, not quantities derived from the target data, so no equation reduces the conclusion to its own input. The tIIn estimate is scaled from SN 2013fs using the JG24 procedure, and the paper openly calls it an 'indirect inference' that is 'imperfect'; this is an empirical analogy rather than a circular derivation. Self-citations are present (Dessart & Jacobson-Galan 2023; JG24), but the argument does not rest on them alone: the STELLA grid is independent, and the observed flash features and light curve are external constraints. The paper also acknowledges that neither model reproduces all observed properties and that the Mdot grid has coarse granularity. These are limitations on precision and model dependence, not evidence that the derivations assume their conclusions. Therefore no circular step is exhibited.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

No new particles, forces, or physical entities are introduced. The analysis uses standard supernova physics and published model grids, with the main externally supplied inputs being the wind velocity assumption, the Tully-Fisher distance, the Na I D reddening estimate, and the two precomputed radiation-hydrodynamics model grids.

free parameters (2)
  • Wind velocity v_w = 50 km/s (CMFGEN), 10 km/s (STELLA)
    Adopted by hand, not measured. Directly enters t_Mdot = v_sh t_IIn / v_w and affects the CSM density normalization; changing v_w shifts the inferred duration of enhanced mass loss.
  • First-light epoch t_fl = 60457.62 +/- 0.054 MJD
    Obtained by joint power-law fits to early gri photometry and used as phase zero for all epochs. It is well constrained by pre-first-light non-detections, so it is not ad hoc.
assumptions (5)
  • domain assumption Flash ionization lines arise from recombination of photoionized circumstellar material close to the progenitor star
    Standard flash-spectroscopy interpretation invoked in Section 1 and used to infer CSM density from the presence and duration of He II, C IV, and H-alpha lines.
  • domain assumption The CSM can be modeled as a spherical, smooth, exponential wind with a constant mass-loss rate
    Both the CMFGEN and STELLA grids adopt this geometry; a clumpy or asymmetric CSM could change the inferred mass-loss rate.
  • domain assumption The adopted distance modulus and host reddening are correct
    The Tully-Fisher distance to NGC 5690 and the Na I D-based A_V,host set the absolute scale of light curve fits and model comparisons.
  • domain assumption The two published model grids adequately sample the relevant progenitor and CSM parameter space
    The mass-loss range is inferred from the best-matching grid points; if the true CSM lies outside these grids, the constraint could miss the true value.
  • ad hoc to paper The t_IIn for SN 2024jlf can be estimated by scaling the evolution of SN 2013fs by the ratio of similar spectral epochs
    This is the JG24 Section 3.2 procedure used in Section 3.4. The paper calls the inference indirect and imperfect, but it feeds the lower limit on the duration of enhanced mass loss.

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Cite this review

Pith. "Pith review of The BTSbot-nearby discovery of SN 2024jlf: rapid, autonomous follow-up probes interaction in an 18.5 Mpc Type IIP supernova." pith.science (2026). https://pith.science/paper/FEMIRLFL

@misc{pith2026250118686,
  author       = {Pith},
  title        = {Pith review of: The BTSbot-nearby discovery of SN 2024jlf: rapid, autonomous follow-up probes interaction in an 18.5 Mpc Type IIP supernova},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FEMIRLFL}},
  note         = {Machine review of arXiv:2501.18686}
}
abstract

We present observations of the Type IIP supernova (SN) 2024jlf, including spectroscopy beginning just 0.7 days ($\sim$17 hours) after first light. Rapid follow-up was enabled by the new $\texttt{BTSbot-nearby}$ program, which involves autonomously triggering target-of-opportunity requests for new transients in Zwicky Transient Facility data that are coincident with nearby ($D<60$ Mpc) galaxies and identified by the $\texttt{BTSbot}$ machine learning model. Early photometry and non-detections shortly prior to first light show that SN 2024jlf initially brightened by $>$4 mag/day, quicker than $\sim$90% of Type II SNe. Early spectra reveal weak flash ionization features: narrow, short-lived ($1.3 < \tau ~\mathrm{[d]} < 1.8$) emission lines of H$\alpha$, He II, and C IV. Assuming a wind velocity of $v_w=50$ km s$^{-1}$, these properties indicate that the red supergiant progenitor exhibited enhanced mass-loss in the last year before explosion. We constrain the mass-loss rate to $10^{-4} < \dot{M}~\mathrm{[M_\odot~yr^{-1}]} < 10^{-3}$ by matching observations to model grids from two independent radiative hydrodynamics codes. $\texttt{BTSbot-nearby}$ automation minimizes spectroscopic follow-up latency, enabling the observation of ephemeral early-time phenomena exhibited by transients.

Figures

Figures reproduced from arXiv: 2501.18686 by the authors.

Figure 1
Figure 1. Comparison of spectroscopic follow-up latency (∆tspec) distributions for automated (red, teal) and manual (black) follow-up of nearby (D < 60 Mpc) SNe discovered in ZTF data. ∆tspec is the time between a SN passing a ZTF alert filter and the first spectrum being taken. Rely￾ing on human-triggered follow-up often incurs ∆tspec ≳ 1 day of latency whereas automated follow-up can expedite ∆tspec by an order of magnitude… view at source ↗
Figure 2
Figure 2. Na ID absorption doublet (shaded gray region) in a continuum-normalized spectrum of SN 2024jlf (solid black line). The large equivalent width, measured to be 0.815 ± 0.033 ˚A, suggests significant extinction by the host galaxy. The velocity axis is defined relative to the Na I D2 line at the host redshift from SDSS. The sodium gas producing the absorption appears to move with v = 80 km s−1 relative to the host nucle… view at source ↗
Figure 3
Figure 3. Multi-band light curve of SN 2024jlf and best fit model light curves. Left panel (and shaded region in right panel): SN 2024jlf rises exceptionally rapidly in all bands and begins to fade in the UV ∼24 hours after tf l (dashed black line). The STELLA model better reproduces the first five days after tf l, but both models significantly underestimate the optical brightness ∼24 hours after tf l. Right panel: As a norma… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Comparison of SN 2024jlf (teal) g-band light curve properties with those of a SN II sample (gray; Hinds et al., in prep.). SNe with poor light curve coverage (see Hinds et al. for definition) only have upper limits on rise time, which are shown as arrowheads. SN 2024jl…
Figure 5
Figure 5. Figure 5: Joint power-law fits to the early gri photometry of SN 2024jlf (same colors and markers as [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Full spectral series of SN 2024jlf (colored lines) and best matched model spectra (gray lines). SN 2024jlf is a normal SN IIP showing flash features starting from a spec￾trum acquired just +0.7 days after tf l (see [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Early spectra of SN 2024jlf (colored lines) and best matched model (gray lines) around regions showing flash features. He II, C IV, Hα, and possibly Hβ can be seen in narrow, short-lived emission. The WiFeS spectrum is shown un-smoothed (light green) and smoothed with …
Figure 9
Figure 9. Figure 9: The disappearance of the Hα flash feature viewed from early 2D spectra. Left column: the calibrated 2D spectra in the vicinity of the Hα emission; Center column: models of the SN continua and the global background, including the host galaxy continua and the sky backgro…

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Works this paper leans on

154 extracted references · 6 canonical work pages

  1. [1]

    2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems

    Abadi, M., Agarwal, A., Barham, P., et al. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/

    2015

  2. [2]

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

    Albareti, F. D., Allende Prieto, C., Almeida, A., et al. 2017, ApJS, 233, 25, doi: 10.3847/1538-4365/aa8992

    doi:10.3847/1538-4365/aa8992 2017

  3. [3]

    E., Pearson, J., Hosseinzadeh, G., et al

    Andrews, J. E., Pearson, J., Hosseinzadeh, G., et al. 2024, ApJ, 965, 85, doi: 10.3847/1538-4357/ad2a49

    doi:10.3847/1538-4357/ad2a49 2024

  4. [4]

    2021, Transient Name Server Classification Report, 2021-1233, 1 Astropy Collaboration, Robitaille, T

    Jacobson-Galan, W. 2021, Transient Name Server Classification Report, 2021-1233, 1 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 Astropy Collaboration, Price-Whelan,...

    doi:10.1051/0004-6361/201322068 2021

  5. [5]

    2017, extinction v0.3.0, Zenodo, doi: 10.5281/zenodo.804967

    Barbary, K. 2017, extinction v0.3.0, Zenodo, doi: 10.5281/zenodo.804967

    doi:10.5281/zenodo.804967 2017

  6. [6]

    M., Smith, R

    Barnsley, R. M., Smith, R. J., & Steele, I. A. 2012, Astronomische Nachrichten, 333, 101, doi: 10.1002/asna.201111634

    work page doi:10.1002/asna.201111634 2012

  7. [7]

    R., Davies, B., Smith, N., et al

    Beasor, E. R., Davies, B., Smith, N., et al. 2020, MNRAS, 492, 5994, doi: 10.1093/mnras/staa255

    doi:10.1093/mnras/staa255 2020

  8. [8]

    2015, HOTPANTS: High Order Transform of PSF ANd Template Subtraction, Astrophysics Source Code Library, record ascl:1504.004

    Becker, A. 2015, HOTPANTS: High Order Transform of PSF ANd Template Subtraction, Astrophysics Source Code Library, record ascl:1504.004

    2015

Show all 154 references
  1. [9]

    2011, Computing in Science Engineering, 13, 31, doi: 10.1109/MCSE.2010.118

    Behnel, S., Bradshaw, R., Citro, C., et al. 2011, Computing in Science Engineering, 13, 31, doi: 10.1109/MCSE.2010.118

    2011 doi

  2. [10]

    C., Kulkarni, S

    Bellm, E. C., Kulkarni, S. R., Graham, M. J., et al. 2019a, PASP, 131, 018002, doi: 10.1088/1538-3873/aaecbe

    doi

  3. [11]

    C., Kulkarni, S

    Bellm, E. C., Kulkarni, S. R., Barlow, T., et al. 2019b, PASP, 131, 068003, doi: 10.1088/1538-3873/ab0c2a

    doi

  4. [12]

    K., Margutti, R., et al

    Berger, E., Keating, G. K., Margutti, R., et al. 2023, The Astrophysical Journal Letters, 951, L31, doi: 10.3847/2041-8213/ace0c4

    2023 doi

  5. [13]

    2020, Experiment Tracking with Weights and Biases

    Biewald, L. 2020, Experiment Tracking with Weights and Biases. https://www.wandb.com/

    2020

  6. [14]

    D., Walters, R., et al

    Blagorodnova, N., Neill, J. D., Walters, R., et al. 2018, PASP, 130, 035003, doi: 10.1088/1538-3873/aaa53f

    2018 doi

  7. [15]

    2000, ApJ, 532, 1132, doi: 10.1086/308588

    Iwamoto, K. 2000, ApJ, 532, 1132, doi: 10.1086/308588

    2000 doi

  8. [16]

    I., Eastman, R., Bartunov, O

    Blinnikov, S. I., Eastman, R., Bartunov, O. S., Popolitov, V. A., & Woosley, S. E. 1998, ApJ, 496, 454, doi: 10.1086/305375

    1998 doi

  9. [17]

    I., R¨ opke, F

    Blinnikov, S. I., R¨ opke, F. K., Sorokina, E. I., et al. 2006, A&A, 453, 229, doi: 10.1051/0004-6361:20054594

    2006 doi

  10. [18]

    S., Richards, J

    Bloom, J. S., Richards, J. W., Nugent, P. E., et al. 2012, PASP, 124, 1175, doi: 10.1086/668468

    2012 doi

  11. [19]

    Boian, I., & Groh, J. H. 2019, A&A, 621, A109, doi: 10.1051/0004-6361/201833779 —. 2020, MNRAS, 496, 1325, doi: 10.1093/mnras/staa1540

    2019 doi

  12. [20]

    2019, AJ, 158, 257, doi: 10.3847/1538-3881/ab5182

    Boone, K. 2019, AJ, 158, 257, doi: 10.3847/1538-3881/ab5182

    2019 doi

  13. [21]

    A., Pearson, J., Shrestha, M., et al

    Bostroem, K. A., Pearson, J., Shrestha, M., et al. 2023, ApJL, 956, L5, doi: 10.3847/2041-8213/acf9a4

    2023 doi

  14. [22]

    W., Poznanski, D., et al

    Brink, H., Richards, J. W., Poznanski, D., et al. 2013, MNRAS, 435, 1047, doi: 10.1093/mnras/stt1306

    2013 doi

  15. [23]

    M., Baliber, N., Bianco, F

    Brown, T. M., Baliber, N., Bianco, F. B., et al. 2013, PASP, 125, 1031, doi: 10.1086/673168

    2013 doi

  16. [24]

    J., Gal-Yam, A., Schulze, S., et al

    Bruch, R. J., Gal-Yam, A., Schulze, S., et al. 2021, ApJ, 912, 46, doi: 10.3847/1538-4357/abef05

    2021 doi

  17. [25]

    J., Gal-Yam, A., Yaron, O., et al

    Bruch, R. J., Gal-Yam, A., Yaron, O., et al. 2023, ApJ, 952, 119, doi: 10.3847/1538-4357/acd8be

    2023 doi

  18. [26]

    G., et al

    Bullivant, C., Smith, N., Williams, G. G., et al. 2018, MNRAS, 476, 1497, doi: 10.1093/mnras/sty045

    2018 doi

  19. [27]

    1989, AJ, 97, 1622, doi: 10.1086/115104

    Capaccioli, M., Della Valle, M., D’Onofrio, M., & Rosino, L. 1989, AJ, 97, 1622, doi: 10.1086/115104

    1989 doi

  20. [28]

    M., Camilleri, R., et al

    Carr, A., Davis, T. M., Camilleri, R., et al. 2024, PASA, 41, e068, doi: 10.1017/pasa.2024.74

    2024 doi

  21. [29]

    2021, AJ, 162, 231, doi: 10.3847/1538-3881/ac0ef1

    Carrasco-Davis, R., Reyes, E., Valenzuela, C., et al. 2021, AJ, 162, 231, doi: 10.3847/1538-3881/ac0ef1

    2021 doi

  22. [30]

    B., Fox, D

    Cenko, S. B., Fox, D. B., Moon, D.-S., et al. 2006, PASP, 118, 1396, doi: 10.1086/508366

    2006 doi

  23. [31]

    C., Magnier, E

    Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv e-prints, arXiv:1612.05560, doi: 10.48550/arXiv.1612.05560

    2016 doi arXiv

  24. [32]

    J., Vogt, F

    Childress, M. J., Vogt, F. P. A., Nielsen, J., & Sharp, R. G. 2014, Ap&SS, 349, 617, doi: 10.1007/s10509-013-1682-0

    2014 doi

  25. [33]

    2015, Keras, https://keras.io

    Chollet, F., et al. 2015, Keras, https://keras.io

    2015

  26. [34]

    Chugai, N. N. 1991, Soviet Astronomy Letters, 17, 210

    1991

  27. [35]

    N., & Utrobin, V

    Chugai, N. N., & Utrobin, V. P. 2023, Astronomy Letters, 49, 639, doi: 10.1134/S1063773723350013

    2023 doi

  28. [36]

    2025, Transient Name Server AstroNote, 55, 1

    Cook, D., Mazzarella, J., Helou, G., et al. 2025, Transient Name Server AstroNote, 55, 1

    2025

  29. [37]

    O., Mazzarella, J

    Cook, D. O., Mazzarella, J. M., Helou, G., et al. 2023, ApJS, 268, 14, doi: 10.3847/1538-4365/acdd06

    2023 doi

  30. [38]

    W., Bloom, J

    Coughlin, M. W., Bloom, J. S., Nir, G., et al. 2023, ApJS, 267, 31, doi: 10.3847/1538-4365/acdee1 da Costa-Luis, C. 2019, The Journal of Open Source Software, 4, 1277, doi: 10.21105/joss.01277

    2023 doi

  31. [39]

    W., Taggart, K., Tinyanont, S., et al

    Davis, K. W., Taggart, K., Tinyanont, S., et al. 2023, MNRAS, 523, 2530, doi: 10.1093/mnras/stad1433

    2023 doi

  32. [40]

    M., Tzanidakis, A., et al

    De, K., Kasliwal, M. M., Tzanidakis, A., et al. 2020, ApJ, 905, 58, doi: 10.3847/1538-4357/abb45c SN 2024jlf & BTSbot-nearby 21 de Vaucouleurs, G., de Vaucouleurs, A., Corwin, Herold G., J., et al. 1991, Third Reference Catalogue of Bright Galaxies

    2020 doi

  33. [41]

    M., Riddle, R., et al

    Dekany, R., Smith, R. M., Riddle, R., et al. 2020, PASP, 132, 038001, doi: 10.1088/1538-3873/ab4ca2 D’Elia, V., Fiore, F., Perna, R., et al. 2009, ApJ, 694, 332, doi: 10.1088/0004-637X/694/1/332

    2020 doi

  34. [42]

    J., & Audit, E

    Dessart, L., Hillier, D. J., & Audit, E. 2017, A&A, 605, A83, doi: 10.1051/0004-6361/201730942

    2017 doi

  35. [43]

    Dessart, L., & Jacobson-Gal´ an, W. V. 2023, Astronomy and Astrophysics, 677, A105, doi: 10.1051/0004-6361/202346754

    2023 doi

  36. [44]

    2024, arXiv e-prints, arXiv:2412.14406, doi: 10.48550/arXiv.2412.14406

    Dickinson, D., Milisavljevic, D., Garretson, B., et al. 2024, arXiv e-prints, arXiv:2412.14406, doi: 10.48550/arXiv.2412.14406

    2024 doi

  37. [45]

    2007, Ap&SS, 310, 255, doi: 10.1007/s10509-007-9510-z

    Dopita, M., Hart, J., McGregor, P., et al. 2007, Ap&SS, 310, 255, doi: 10.1007/s10509-007-9510-z

    2007 doi

  38. [46]

    2010, Ap&SS, 327, 245, doi: 10.1007/s10509-010-0335-9

    Dopita, M., Rhee, J., Farage, C., et al. 2010, Ap&SS, 327, 245, doi: 10.1007/s10509-010-0335-9

    2010 doi

  39. [47]

    M., & Singer, L

    Duev, D., Shin, K. M., & Singer, L. 2021, dmitryduev/penquins: a python client for dmitryduev/kowalski, v2.1.2, Zenodo, doi: 10.5281/zenodo.5651471

    2021 doi

  40. [48]

    A., & van der Walt, S

    Duev, D. A., & van der Walt, S. J. 2021, arXiv e-prints, arXiv:2111.12142, doi: 10.48550/arXiv.2111.12142

    2021 doi arXiv

  41. [49]

    A., Mahabal, A., Masci, F

    Duev, D. A., Mahabal, A., Masci, F. J., et al. 2019, MNRAS, 489, 3582, doi: 10.1093/mnras/stz2357

    2019 doi

  42. [50]

    2019, Publications of the Astronomical Society of the Pacific, 131, 075004, doi: 10.1088/1538-3873/ab1d78

    Fabricant, D., Fata, R., Epps, H., et al. 2019, Publications of the Astronomical Society of the Pacific, 131, 075004, doi: 10.1088/1538-3873/ab1d78

    2019 doi

  43. [51]

    G., Hora, J

    Fazio, G. G., Hora, J. L., Allen, L. E., et al. 2004, The Astrophysical Journal Supplement Series, 154, 10, doi: 10.1086/422843

    2004 doi

  44. [52]

    Fitzpatrick, E. L. 1999, PASP, 111, 63, doi: 10.1086/316293

    1999 doi

  45. [53]

    2016, A&A, 593, A68, doi: 10.1051/0004-6361/201628275

    Fremling, C., Sollerman, J., Taddia, F., et al. 2016, A&A, 593, A68, doi: 10.1051/0004-6361/201628275

    2016 doi

  46. [54]

    A., Sharma, Y., et al

    Fremling, C., Miller, A. A., Sharma, Y., et al. 2020, ApJ, 895, 32, doi: 10.3847/1538-4357/ab8943

    2020 doi

  47. [55]

    J., Coughlin, M

    Fremling, C., Hall, X. J., Coughlin, M. W., et al. 2021, ApJL, 917, L2, doi: 10.3847/2041-8213/ac116f

    2021 doi

  48. [56]

    I., & Aleo, P

    Gagliano, A., Contardo, G., Foreman-Mackey, D., Malz, A. I., & Aleo, P. D. 2023, ApJ, 954, 6, doi: 10.3847/1538-4357/ace326

    2023 doi

  49. [57]

    O., et al

    Gal-Yam, A., Arcavi, I., Ofek, E. O., et al. 2014, Nature, 509, 471, doi: 10.1038/nature13304

    2014 doi

  50. [58]

    2020, ApJ, 889, 170, doi: 10.3847/1538-4357/ab6328

    Gangopadhyay, A., Misra, K., Hiramatsu, D., et al. 2020, ApJ, 889, 170, doi: 10.3847/1538-4357/ab6328

    2020 doi

  51. [59]

    2022, ApJ, 930, 127, doi: 10.3847/1538-4357/ac6187

    Gangopadhyay, A., Misra, K., Hosseinzadeh, G., et al. 2022, ApJ, 930, 127, doi: 10.3847/1538-4357/ac6187

    2022 doi

  52. [60]

    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

  53. [61]

    M., Brasseur, C

    Ginsburg, A., Sip˝ ocz, B. M., Brasseur, C. E., et al. 2019, AJ, 157, 98, doi: 10.3847/1538-3881/aafc33

    2019 doi

  54. [62]

    E., et al

    Ginsburg, A., Sipcz, B., Brasseur, C. E., et al. 2024, astropy/astroquery: v0.4.7, v0.4.7, Zenodo, doi: 10.5281/zenodo.10799414

    2024 doi

  55. [63]

    A., Bildsten, L., & Paxton, B

    Goldberg, J. A., Bildsten, L., & Paxton, B. 2019, ApJ, 879, 3, doi: 10.3847/1538-4357/ab22b6

    2019 doi

  56. [64]

    K., et al

    Gomez, S., Berger, E., Blanchard, P. K., et al. 2020, ApJ, 904, 74, doi: 10.3847/1538-4357/abbf49

    2020 doi

  57. [65]

    A., Berger, E., et al

    Gomez, S., Villar, V. A., Berger, E., et al. 2023, ApJ, 949, 113, doi: 10.3847/1538-4357/acc535

    2023 doi

  58. [66]

    2024, scipy/scipy: SciPy 1.14.1, v1.14.1, Zenodo, doi: 10.5281/zenodo.13352243 Gonz´ alez, M., Audit, E., & Huynh, P

    Gommers, R., Virtanen, P., Haberland, M., et al. 2024, scipy/scipy: SciPy 1.14.1, v1.14.1, Zenodo, doi: 10.5281/zenodo.13352243 Gonz´ alez, M., Audit, E., & Huynh, P. 2007, A&A, 464, 429, doi: 10.1051/0004-6361:20065486

    2024 doi

  59. [67]

    J., Kulkarni, S

    Graham, M. J., Kulkarni, S. R., Bellm, E. C., et al. 2019, PASP, 131, 078001, doi: 10.1088/1538-3873/ab006c

    2019 doi

  60. [68]

    2018, The Journal of Open Source Software, 3, 695, doi: 10.21105/joss.00695

    Green, G. 2018, The Journal of Open Source Software, 3, 695, doi: 10.21105/joss.00695

    2018 doi

  61. [69]

    2024, gregreen/dustmaps: v1.0.13, v1.0.13, Zenodo, doi: 10.5281/zenodo.10517733

    Green, G., Edenhofer, G., Krughoff, S., et al. 2024, gregreen/dustmaps: v1.0.13, v1.0.13, Zenodo, doi: 10.5281/zenodo.10517733

    2024 doi

  62. [70]

    2003, ApJ, 582, 905, doi: 10.1086/344689

    Hamuy, M. 2003, ApJ, 582, 905, doi: 10.1086/344689

    2003 doi

  63. [71]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357362, doi: 10.1038/s41586-020-2649-2

    2020 doi

  64. [72]

    J., & Dessart, L

    Hillier, D. J., & Dessart, L. 2012, MNRAS, 424, 252, doi: 10.1111/j.1365-2966.2012.21192.x

    2012

  65. [73]

    Hinds, K., Sollerman, J., Fremling, C., Perley, D., & Laz, T. D. 2024, Transient Name Server Discovery Report, 2024-1682, 1

    2024

  66. [74]

    2020, Transient Name Server Classification Report, 2020-1922, 1

    Hiramatsu, D., Arcavi, I., Zimmerman, E., et al. 2020, Transient Name Server Classification Report, 2020-1922, 1

    2020

  67. [75]

    2023, ApJL, 955, L8, doi: 10.3847/2041-8213/acf299

    Hiramatsu, D., Tsuna, D., Berger, E., et al. 2023, ApJL, 955, L8, doi: 10.3847/2041-8213/acf299

    2023 doi

  68. [76]

    A., McCully, C., et al

    Hosseinzadeh, G., Howell, D. A., McCully, C., et al. 2024, Transient Name Server Classification Report, 2024-1686, 1

    2024

  69. [77]

    A., & Global Supernova Project

    Howell, D. A., & Global Supernova Project. 2017, in American Astronomical Society Meeting Abstracts, Vol. 230, American Astronomical Society Meeting Abstracts #230, 318.03

    2017

  70. [78]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    2007 doi

  71. [79]

    2024, ApJ, 970, 96, doi: 10.3847/1538-4357/ad3de8 22 Rehemtulla et al

    Irani, I., Morag, J., Gal-Yam, A., et al. 2024, ApJ, 970, 96, doi: 10.3847/1538-4357/ad3de8 22 Rehemtulla et al

    2024 doi

  72. [80]

    2020, Transient Name Server Classification Report, 2020-1621, 1 Jacobson-Gal´ an, W

    Izzo, L., Angus, C., Hjorth, J., & Gall, C. 2020, Transient Name Server Classification Report, 2020-1621, 1 Jacobson-Gal´ an, W. V., Dessart, L., Jones, D. O., et al. 2022, ApJ, 924, 15, doi: 10.3847/1538-4357/ac3f3a Jacobson-Gal´ an, W. V., Dessart, L., Margutti, R., et al. 2...

    2020 doi

  73. [81]

    O., Foley, R

    Jones, D. O., Foley, R. J., Narayan, G., et al. 2021, ApJ, 908, 143, doi: 10.3847/1538-4357/abd7f5

    2021 doi

  74. [82]

    E., et al

    Kaiser, N., Aussel, H., Burke, B. E., et al. 2002, in SPIE Conference Series, Vol. 4836, Survey and Other Telescope Technologies and Discoveries, ed. J. A. Tyson & S. Wolff, 154–164, doi: 10.1117/12.457365

    2002 doi

  75. [83]

    2016, ApJ, 818, 3, doi: 10.3847/0004-637X/818/1/3

    Khazov, D., Yaron, O., Gal-Yam, A., et al. 2016, ApJ, 818, 3, doi: 10.3847/0004-637X/818/1/3

    2016 doi

  76. [84]

    D., & Foley, R

    Kilpatrick, C. D., & Foley, R. J. 2018, MNRAS, 481, 2536, doi: 10.1093/mnras/sty2435

    2018 doi

  77. [85]

    L., Rigault, M., Neill, J

    Kim, Y. L., Rigault, M., Neill, J. D., et al. 2022, PASP, 134, 024505, doi: 10.1088/1538-3873/ac50a0

    2022 doi

  78. [86]

    2016, in IOS Press, 87–90, doi: 10.3233/978-1-61499-649-1-87

    Kluyver, T., Ragan-Kelley, B., P´ erez, F., et al. 2016, in IOS Press, 87–90, doi: 10.3233/978-1-61499-649-1-87

    2016 doi

  79. [87]

    2010, in SPIE Conference Series, Vol

    Martin, C., Moore, A., Morrissey, P., et al. 2010, in SPIE Conference Series, Vol. 7735, Ground-based and Airborne Instrumentation for Astronomy III, ed. I. S. McLean, S. K. Ramsay, & H. Takami, 77350M, doi: 10.1117/12.858227

    2010 doi

  80. [88]

    J., Laher, R

    Masci, F. J., Laher, R. R., Rusholme, B., et al. 2019, PASP, 131, 018003, doi: 10.1088/1538-3873/aae8ac —. 2023, arXiv e-prints, arXiv:2305.16279, doi: 10.48550/arXiv.2305.16279

    2019 doi arXiv

  81. [89]

    V., Barth, A

    Matheson, T., Filippenko, A. V., Barth, A. J., et al. 2000, The Astronomical Journal, 120, 1487, doi: 10.1086/301518

    2000 doi

  82. [90]

    A., Yao, Y., Bulla, M., et al

    Miller, A. A., Yao, Y., Bulla, M., et al. 2020, ApJ, 902, 47, doi: 10.3847/1538-4357/abb13b

    2020 doi

  83. [91]

    C., Nordin, J., et al

    Miranda, N., Freytag, J. C., Nordin, J., et al. 2022, A&A, 665, A99, doi: 10.1051/0004-6361/202243668

    2022 doi

  84. [92]

    J., & Singh, A

    Moriya, T. J., & Singh, A. 2024, PASJ, 76, 1050, doi: 10.1093/pasj/psae070

    2024 doi

  85. [93]

    Blinnikov, S. I. 2023, Publications of the Astronomical Society of Japan, 75, 634645, doi: 10.1093/pasj/psad024

    2023 doi

  86. [94]

    C., et al

    Morrissey, P., Matuszewski, M., Martin, D. C., et al. 2018, The Astrophysical Journal, 864, 93, doi: 10.3847/1538-4357/aad597

    2018 doi

  87. [95]

    S., Biswas, R., & Hloˇ zek, R

    Muthukrishna, D., Narayan, G., Mandel, K. S., Biswas, R., & Hloˇ zek, R. 2019, PASP, 131, 118002, doi: 10.1088/1538-3873/ab1609 Nasa High Energy Astrophysics Science Archive Research Center (Heasarc). 2014, HEAsoft: Unified Release of FTOOLS and XANADU, Astrophysics Source Cod...

    2019 doi

  88. [96]

    S., Ruiz, M

    Niemela, V. S., Ruiz, M. T., & Phillips, M. M. 1985, ApJ, 289, 52, doi: 10.1086/162863

    1985 doi

  89. [97]

    2019, A&A, 631, A147, doi: 10.1051/0004-6361/201935634

    Nordin, J., Brinnel, V., van Santen, J., et al. 2019, A&A, 631, A147, doi: 10.1051/0004-6361/201935634

    2019 doi

  90. [98]

    B., Cohen, J

    Oke, J. B., Cohen, J. G., Carr, M., et al. 1995, Publications of the Astronomical Society of the Pacific, 107, 375, doi: 10.1086/133562 pandas development team, T. 2024, pandas-dev/pandas: Pandas, v2.2.3, Zenodo, doi: 10.5281/zenodo.13819579

    1995 doi

  91. [99]

    2006, MNRAS, 370, 1752, doi: 10.1111/j.1365-2966.2006.10587.x

    Pastorello, A., Sauer, D., Taubenberger, S., et al. 2006, MNRAS, 370, 1752, doi: 10.1111/j.1365-2966.2006.10587.x

    2006

  92. [100]

    J., et al

    Pearson, J., Hosseinzadeh, G., Sand, D. J., et al. 2023, ApJ, 945, 107, doi: 10.3847/1538-4357/acb8a9

    2023 doi

  93. [101]

    Pejcha, O., & Prieto, J. L. 2015, ApJ, 806, 225, doi: 10.1088/0004-637X/806/2/225

    2015 doi

  94. [102]

    Perez, F., & Granger, B. E. 2007, Computing in Science and Engineering, 9, 21, doi: 10.1109/MCSE.2007.53

    2007 doi

  95. [103]

    A., Fremling, C., Sollerman, J., et al

    Perley, D. A., Fremling, C., Sollerman, J., et al. 2020, ApJ, 904, 35, doi: 10.3847/1538-4357/abbd98

    2020 doi

  96. [104]

    A., Sollerman, J., Schulze, S., et al

    Perley, D. A., Sollerman, J., Schulze, S., et al. 2022, ApJ, 927, 180, doi: 10.3847/1538-4357/ac478e

    2022 doi

  97. [105]

    M., Simon, J

    Phillips, M. M., Simon, J. D., Morrell, N., et al. 2013, ApJ, 779, 38, doi: 10.1088/0004-637X/779/1/38

    2013 doi

  98. [106]

    S., Steele, I

    Piascik, A. S., Steele, I. A., Bates, S. D., et al. 2014, in SPIE Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K

    2014

  99. [107]

    Ramsay, I. S. McLean, & H. Takami, 91478H, doi: 10.1117/12.2055117

    doi

  100. [108]

    Filippenko, A. V. 2011, MNRAS, 415, L81, doi: 10.1111/j.1745-3933.2011.01084.x

    2011

  101. [109]

    X., Hennawi, J

    Prochaska, J. X., Hennawi, J. F., Westfall, K. B., et al. 2020, Journal of Open Source Software, 5, 2308, doi: 10.21105/joss.02308

    2020 doi

  102. [110]

    Rehemtulla, N., Fremling, C., Perley, D., & Laz, T. D. 2025, Transient Name Server Discovery Report, 2025-50, 1

    2025

  103. [111]

    2023, Transient Name Server AstroNote, 265, 1 SN 2024jlf & BTSbot-nearby 23

    Rehemtulla, N., Miller, A., Fremling, C., et al. 2023, Transient Name Server AstroNote, 265, 1 SN 2024jlf & BTSbot-nearby 23

    2023

  104. [112]

    A., Jegou Du Laz, T., et al

    Rehemtulla, N., Miller, A. A., Jegou Du Laz, T., et al. 2024a, ApJ, 972, 7, doi: 10.3847/1538-4357/ad5666

    doi

  105. [113]

    C., et al

    Rest, A., Stubbs, C., Becker, A. C., et al. 2005, ApJ, 634, 1103, doi: 10.1086/497060

    2005 doi

  106. [114]

    D., Blagorodnova, N., et al

    Rigault, M., Neill, J. D., Blagorodnova, N., et al. 2019, A&A, 627, A115, doi: 10.1051/0004-6361/201935344

    2019 doi

  107. [115]

    Roming, P. W. A., Kennedy, T. E., Mason, K. O., et al. 2005, SSRv, 120, 95, doi: 10.1007/s11214-005-5095-4

    2005 doi

  108. [116]

    L., Mateo, M., & Saha, A

    Schechter, P. L., Mateo, M., & Saha, A. 1993, PASP, 105, 1342, doi: 10.1086/133316

    1993 doi

  109. [117]

    F., & Finkbeiner, D

    Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103, doi: 10.1088/0004-637X/737/2/103

    2011 doi

  110. [118]

    2021, ApJS, 255, 29, doi: 10.3847/1538-4365/abff5e

    Schulze, S., Yaron, O., Sollerman, J., et al. 2021, ApJS, 255, 29, doi: 10.3847/1538-4365/abff5e

    2021 doi

  111. [119]

    2024, arXiv e-prints, arXiv:2409.02054, doi: 10.48550/arXiv.2409.02054

    Schulze, S., Gal-Yam, A., Dessart, L., et al. 2024, arXiv e-prints, arXiv:2409.02054, doi: 10.48550/arXiv.2409.02054

    2024 doi arXiv

  112. [120]

    A., Sollerman, J., et al

    Sharma, Y., Mahabal, A. A., Sollerman, J., et al. 2024, arXiv e-prints, arXiv:2412.08601, doi: 10.48550/arXiv.2412.08601

    2024 doi arXiv

  113. [121]

    H., Mauerhan, J

    Shivvers, I., Groh, J. H., Mauerhan, J. C., et al. 2015, ApJ, 806, 213, doi: 10.1088/0004-637X/806/2/213

    2015 doi

  114. [122]

    A., Sand, D

    Shrestha, M., Bostroem, K. A., Sand, D. J., et al. 2024, ApJL, 972, L15, doi: 10.3847/2041-8213/ad6907

    2024 doi

  115. [123]

    J., et al

    Singh, A., Kumar, B., Moriya, T. J., et al. 2019, ApJ, 882, 68, doi: 10.3847/1538-4357/ab3050

    2019 doi

  116. [124]

    S., Moriya, T

    Singh, A., Teja, R. S., Moriya, T. J., et al. 2024, ApJ, 975, 132, doi: 10.3847/1538-4357/ad7955

    2024 doi

  117. [125]

    W., Smartt, S

    Smith, K. W., Smartt, S. J., Young, D. R., et al. 2020, PASP, 132, 085002, doi: 10.1088/1538-3873/ab936e

    2020 doi

  118. [126]

    2021, A&A, 655, A105, doi: 10.1051/0004-6361/202141374

    Sollerman, J., Yang, S., Schulze, S., et al. 2021, A&A, 655, A105, doi: 10.1051/0004-6361/202141374

    2021 doi

  119. [127]

    G., Tully, R

    Sorce, J. G., Tully, R. B., Courtois, H. M., et al. 2014, MNRAS, 444, 527, doi: 10.1093/mnras/stu1450

    2014 doi

  120. [128]

    T., Ganot, N., Irani, I., et al

    Soumagnac, M. T., Ganot, N., Irani, I., et al. 2020, ApJ, 902, 6, doi: 10.3847/1538-4357/abb247

    2020 doi

  121. [129]

    A., Smith, R

    Steele, I. A., Smith, R. J., Rees, P. C., et al. 2004, in SPIE Conference Series, Vol. 5489, Ground-based Telescopes, ed. J. Oschmann, Jacobus M., 679–692, doi: 10.1117/12.551456

    2004 doi

  122. [130]

    2024, ApJL, 965, L14, doi: 10.3847/2041-8213/ad3337

    Stein, R., Mahabal, A., Reusch, S., et al. 2024, ApJL, 965, L14, doi: 10.3847/2041-8213/ad3337

    2024 doi

  123. [131]

    A., Adamson, A., Blakeslee, J

    Street, R. A., Adamson, A., Blakeslee, J. P., et al. 2020, in SPIE Conference Series, Vol. 11449, Observatory Operations: Strategies, Processes, and Systems VIII, ed. D. S. Adler, R. L. Seaman, & C. R. Benn, 1144925, doi: 10.1117/12.2559986

    2020 doi

  124. [132]

    D., Taddia, F., Burns, C

    Stritzinger, M. D., Taddia, F., Burns, C. R., et al. 2018, A&A, 609, A135, doi: 10.1051/0004-6361/201730843

    2018 doi

  125. [133]

    Janka, H. T. 2016, ApJ, 821, 38, doi: 10.3847/0004-637X/821/1/38

    2016 doi

  126. [134]

    2019, ApJ, 876, 19, doi: 10.3847/1538-4357/ab12d0

    Szalai, T., Vink´ o, J., K¨ onyves-T´ oth, R., et al. 2019, ApJ, 876, 19, doi: 10.3847/1538-4357/ab12d0

    2019 doi

  127. [135]

    J., & Gagliano, A

    Taggart, K., Tinyanont, S., Foley, R. J., & Gagliano, A. 2021, The Astronomer’s Telegram, 14959, 1

    2021

  128. [136]

    J., Valenti, S., et al

    Tartaglia, L., Sand, D. J., Valenti, S., et al. 2018, ApJ, 853, 62, doi: 10.3847/1538-4357/aaa014

    2018 doi

  129. [137]

    J., Groh, J

    Tartaglia, L., Sand, D. J., Groh, J. H., et al. 2021, ApJ, 907, 52, doi: 10.3847/1538-4357/abca8a

    2021 doi

  130. [138]

    S., Singh, A., Basu, J., et al

    Teja, R. S., Singh, A., Basu, J., et al. 2023, ApJL, 954, L12, doi: 10.3847/2041-8213/acef20

    2023 doi

  131. [139]

    Terreran, G., Jacobson-Galan, W., & Blanchard, P. K. 2020, The Astronomer’s Telegram, 14115, 1

    2020

  132. [140]

    V., Groh, J

    Terreran, G., Jacobson-Gal´ an, W. V., Groh, J. H., et al. 2022, ApJ, 926, 20, doi: 10.3847/1538-4357/ac3820

    2022 doi

  133. [141]

    A., Roberts, C

    Tohuvavohu, A., Kennea, J. A., Roberts, C. J., et al. 2024, ApJL, 975, L19, doi: 10.3847/2041-8213/ad87ce

    2024 doi

  134. [142]

    Tonry, J. L. 2011, PASP, 123, 58, doi: 10.1086/657997

    2011 doi

  135. [143]

    L., Denneau, L., Heinze, A

    Tonry, J. L., Denneau, L., Heinze, A. N., et al. 2018, PASP, 130, 064505, doi: 10.1088/1538-3873/aabadf

    2018 doi

  136. [144]

    B., & Fisher, J

    Tully, R. B., & Fisher, J. R. 1977, A&A, 54, 661 van der Walt, S. J., Crellin-Quick, A., & Bloom, J. S. 2019, Journal of Open Source Software, 4, 1247, doi: 10.21105/joss.01247 Van Rossum, G., & Drake, F. L. 2009, Python 3 Reference Manual (Scotts Valley, CA: CreateSpace)

    1977 doi

  137. [145]

    A., Berger, E., Miller, G., et al

    Villar, V. A., Berger, E., Miller, G., et al. 2019, ApJ, 884, 83, doi: 10.3847/1538-4357/ab418c

    2019 doi

  138. [146]

    A., Hosseinzadeh, G., Berger, E., et al

    Villar, V. A., Hosseinzadeh, G., Berger, E., et al. 2020, ApJ, 905, 94, doi: 10.3847/1538-4357/abc6fd

    2020 doi

  139. [147]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

    2020 doi

  140. [148]

    2024, TomWagg/software-citation-station: v1.2, v1.2, Zenodo, doi: 10.5281/zenodo.13225824

    Wagg, T., Broekgaarden, F., & G ültekin, K. 2024, TomWagg/software-citation-station: v1.2, v1.2, Zenodo, doi: 10.5281/zenodo.13225824

    2024 doi

  141. [149]

    Wagg, T., & Broekgaarden, F. S. 2024, arXiv e-prints, arXiv:2406.04405. https://arxiv.org/abs/2406.04405 Wes McKinney. 2010, in Proceedings of the 9th Python in Science Conference, ed. St´ efan van der Walt & Jarrod Millman, 56 – 61, doi: 10.25080/Majora-92bf1922-00a

    2024

  142. [150]

    L., et al

    Yang, S., Sollerman, J., Strotjohann, N. L., et al. 2021, A&A, 655, A90, doi: 10.1051/0004-6361/202141244

    2021 doi

  143. [151]

    A., Gal-Yam, A., et al

    Yaron, O., Perley, D. A., Gal-Yam, A., et al. 2017, Nature Physics, 13, 510, doi: 10.1038/nphys4025

    2017 doi

  144. [152]

    G., Adelman, J., Anderson, John E., J., et al

    York, D. G., Adelman, J., Anderson, John E., J., et al. 2000, AJ, 120, 1579, doi: 10.1086/301513 24 Rehemtulla et al

    2000 doi

  145. [153]

    2020, MNRAS, 498, 84, doi: 10.1093/mnras/staa2273

    Zhang, J., Wang, X., J´ ozsef, V., et al. 2020, MNRAS, 498, 84, doi: 10.1093/mnras/staa2273

    2020 doi

  146. [154]

    A., Irani, I., Chen, P., et al

    Zimmerman, E. A., Irani, I., Chen, P., et al. 2024, Nature, 627, 759, doi: 10.1038/s41586-024-07116-6

    2024 doi

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