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arxiv: 2605.18960 · v1 · pith:MT7H5WPKnew · submitted 2026-05-18 · 🌌 astro-ph.HE

Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. III: Hunting for electron-capture supernovae using nebular spectroscopy

Kaustav K. Das , Anders Jerkstrand , Mansi M. Kasliwal , Jesper Sollerman , Christoffer Fremling , Steve Schulze , Avishay Gal-Yam , Tomas Ahumada
show 13 more authors
Shreya Anand Bart van Baal Michael W. Coughlin Sofia Covarrubias Richard Dekany Nicholas Earley W. V. Jacobson-Gal\'an Joahan Castaneda Jaimes Frank J. Masci Yu-Jing Qin Reed Riddle Sam Rose Yashvi Sharma
This is my paper

Pith reviewed 2026-05-20 08:11 UTC · model grok-4.3

classification 🌌 astro-ph.HE
keywords electron-capture supernovaelow-luminosity Type IIP supernovaenebular spectroscopycore-collapse supernovaesuper-asymptotic giant branch starsinitial mass function
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The pith

Nebular spectra of low-luminosity Type IIP supernovae set an upper limit on the electron-capture supernova rate.

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

The authors analyze nebular spectra from 19 low-luminosity Type IIP supernovae observed 115 to 450 days after explosion to look for signatures of electron-capture supernovae from super-asymptotic giant branch stars. They identify a correlation between the width of the hydrogen alpha line and the supernova's peak luminosity, confirming these events are at the low-energy end of core-collapse explosions, but find no link to the duration of the plateau phase. Only one supernova shows the very narrow hydrogen lines expected from the weakest explosion models around 9 solar masses, and two candidates selected for lacking helium and oxygen lines still do not match the narrow line predictions. This leads to the conclusion that if electron-capture supernovae mostly come from this low-luminosity channel, their rate must be low, corresponding to a very narrow range of progenitor masses.

Core claim

Combining the ZTF sample with literature data, the paper shows that low-luminosity Type IIP supernovae rarely exhibit the extremely narrow nebular hydrogen lines predicted for electron-capture supernovae from the lowest-mass core collapses, allowing an upper limit on the ECSN rate of ≲ (5–8)×10² Gpc^{-3} yr^{-1} and a progenitor mass window ΔM_sAGB ≲ 0.02–0.06 M_⊙ if they arise predominantly through the LLIIP channel.

What carries the argument

The ECSN score, which identifies candidates by the absence of He- and O-shell emission lines in nebular spectra, together with the full width at half maximum of the H I λ6563 line as a measure of explosion energy.

If this is right

  • Low-luminosity Type IIP supernovae occupy the low-energy end of the core-collapse supernova population.
  • The lack of correlation between hydrogen line width and plateau duration indicates that envelope and core properties are decoupled.
  • Events with the extremely low energies predicted for ~9 solar mass progenitors are intrinsically rare.
  • An IMF slope of 2.1±1.2 is inferred for the progenitors of Type II supernovae.

Where Pith is reading between the lines

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

  • Current theoretical models for the nebular line widths in the weakest ECSNe may require refinement if no events match the predictions.
  • Electron-capture supernovae could occur through channels other than low-luminosity Type IIP events.
  • The narrow mass window suggests that only a small fraction of super-asymptotic giant branch stars successfully explode as electron-capture supernovae rather than forming white dwarfs.

Load-bearing premise

The assumption that the absence of extremely narrow nebular hydrogen lines in the candidate events rules out standard electron-capture supernova models depends on the accuracy of current theoretical predictions for line widths in the weakest explosions around 9 solar masses.

What would settle it

Detection of a low-luminosity Type IIP supernova that scores high on the ECSN criteria and also displays the extremely narrow H I λ6563 line widths predicted by the weakest explosion models would support a higher rate or confirm the model predictions.

Figures

Figures reproduced from arXiv: 2605.18960 by Anders Jerkstrand, Avishay Gal-Yam, Bart van Baal, Christoffer Fremling, Frank J. Masci, Jesper Sollerman, Joahan Castaneda Jaimes, Kaustav K. Das, Mansi M. Kasliwal, Michael W. Coughlin, Nicholas Earley, Reed Riddle, Richard Dekany, Sam Rose, Shreya Anand, Sofia Covarrubias, Steve Schulze, Tomas Ahumada, W. V. Jacobson-Gal\'an, Yashvi Sharma, Yu-Jing Qin.

Figure 1
Figure 1. Figure 1: Keck/LRIS nebular spectra of ZTF24abtczty/SN 2024abfl at 149 d, 334 d and 393 d after explosion. The black curve shows the spectrum smoothed with a Savitzky–Golay filter, while the faint gray curve indicates the raw data. Col￾ored vertical dashed lines mark the rest wavelengths of prominent nebular features, as labeled at the top (e.g. Mg i] λ4571, [O i] λλ6300, 6364, H i λ6563, [Ca ii] λλ7291, 7323, and [… view at source ↗
Figure 2
Figure 2. Figure 2: Left: H i λ6563 FWHM for all objects in our sample as a function of phase. The colored points represent the ZTF CLU LLIIP sample, the literature comparison sample is shown in gray, and low-luminosity literature events are highlighted in yellow. Right: Same objects but showing the fractional [O i] luminosity f[O I], as defined in Section 4.4. Dashed lines connect measurements of the same SN across different… view at source ↗
Figure 3
Figure 3. Figure 3: Nebular H i λ6563 FWHM as a function of phase and peak absolute magnitude for our sample. Points are color-coded by Mpeak, and the best-fit weighted plane (Sec￾tion 4.6) is shown in gray. The trend indicates that fainter SNe exhibit systematically narrower nebular H i lines. A two-dimensional projection of this relation is shown in the top-left panel of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Correlation of nebular observables with r-band light-curve properties. Each column shows a different nebular diagnostic as a function of phase: H i λ6563 FWHM (left), the [Ca ii]/[O i] ratio (middle), and the fractional [O i] luminosity f[O I] (right). The three rows use different photometric quantities for the color coding: peak absolute magnitude (top), plateau slope (middle), and plateau duration (botto… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of the ZTF LLIIP sample with literature LLIIP SNe and published nebular models. The colored points show the ZTF+CLU LLIIP SNe, and the yellow points show low-luminosity LLIIP events from the literature. Model tracks from Dessart et al. 2021b (D21), Dessart et al. 2025 (D25), Jerkstrand et al. 2014a (J14), and Jerkstrand et al. 2018 (J18) are shown for reference. The three panels present the evol… view at source ↗
Figure 6
Figure 6. Figure 6: Fractional [O i] flux as a function of phase for the ZTF LLIIP sample (colored symbols) and for literature com￾parison SNe (gray points). The background color shows the ZAMS mass predicted by our Gaussian Process regression model, which maps phase and fractional [O i] flux to MZAMS as described in Section 5. To characterise the progenitor population, for every SN we choose the spectrum closest to ∼350 d af… view at source ↗
Figure 7
Figure 7. Figure 7: Top left: distribution of ZAMS masses inferred for the ZTF LLIIP sample. Top right: cumulative distribution of the inferred masses, with Monte Carlo realizations shown in blue and the best-fit power-law slope α indicated. Bottom left: correlation between the fractional [O i] luminosity and the r-band peak magnitude. Bottom right: weak correlation between the inferred ZAMS mass and the r-band peak magnitude… view at source ↗
Figure 8
Figure 8. Figure 8: Required width of the sAGB ECSN window ∆MsAGB as a function of the CCSN threshold mass MCC,min. The blue shaded region indicates the range of ∆MsAGB val￾ues consistent with the empirical constraints. ample, ∆MsAGB ≈ 0.2–1.4 M⊙ at solar metallicity (e.g., Poelarends et al. 2008). Our inferred narrow progenitor interval is qualitatively consistent with Podsiadlowski et al. (2004), who argued that the mass ra… view at source ↗
Figure 9
Figure 9. Figure 9: Absolute-magnitude light curves of the three highest-scoring ECSN candidates compared with the ZTF LLIIP nebular-spectroscopic sample. The left and right panels show the r- and g-band light curves, respectively. Gray points show the other ZTF LLIIP SNe with nebular spectra, while colored points mark SN 2023bvj, SN 2024btj, and SN 2016bkv [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example of the nebular spectral analysis procedure for ZTF22abtjefa/SN 2022aaad at phase 332 d. Top panels show the original rest-frame, extinction-corrected spectrum with the fitted host-galaxy continuum (left) and the continuum-subtracted spectrum (right), with shaded regions indicating the line-poor windows used to define the continuum. Bottom left panels show the isolation of the [O i] λλ6300, 6363 fe… view at source ↗
Figure 11
Figure 11. Figure 11: Nebular spectra of ZTF22abtjefa/SN 2022aaad [PITH_FULL_IMAGE:figures/full_fig_p032_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Nebular spectra of ZTF22abtjefa/SN 2022aaad [PITH_FULL_IMAGE:figures/full_fig_p033_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Nebular spectra of ZTF24abtczty/SN 2024abfl. 5000 6000 7000 8000 9000 Wavelength (Å) 0.000 0.002 0.004 0.006 Mg I] Fe II+Mg I] [O I] [O I] He I [Fe II] [Ca II] [Fe II] O I Fe I Fe I Fe I O I [C I] [C I] [C I] Ca II Ca II Na I H I [Ni II] [Ni II] K I ZTF23aackjhs 221.0 d 7000 7250 7500 He I [Fe II] [Ca II] [Ni II] [Ni II] [Fe II] N o r m aliz e d F + o f f s e t [PITH_FULL_IMAGE:figures/full_fig_p034_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Nebular spectra of ZTF23aackjhs/SN 2023bvj [PITH_FULL_IMAGE:figures/full_fig_p034_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Nebular spectra of ZTF24aaejecr/SN 2024btj [PITH_FULL_IMAGE:figures/full_fig_p035_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Nebular spectra of ZTF23aanxrjm/SN 2023kmk [PITH_FULL_IMAGE:figures/full_fig_p036_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Nebular spectra of ZTF23aaquhaz/SN 2023mpz. 5000 6000 7000 8000 9000 Wavelength (Å) 0.000 0.002 0.004 0.006 0.008 Mg I] Fe II+Mg I] [O I] [O I] He I [Fe II] [Ca II] [Fe II] O I Fe I Fe I Fe I O I [C I] [C I] [C I] Ca II Ca II Na I H I [Ni II] [Ni II] K I ZTF22aakdbia 310.0 d 7000 7250 7500 He I [Fe II] [Ca II] [Ni II] [Ni II] [Fe II] N o r m aliz e d F + o f f s e t [PITH_FULL_IMAGE:figures/full_fig_p037… view at source ↗
Figure 18
Figure 18. Figure 18: Nebular spectra of ZTF22aakdbia/SN 2022jzc [PITH_FULL_IMAGE:figures/full_fig_p037_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Nebular spectra of ZTF22abssiet/SN 2022zmb. 5000 6000 7000 8000 9000 Wavelength (Å) 0.0005 0.0000 0.0005 0.0010 0.0015 0.0020 Mg I] Fe II+Mg I] [O I] [O I] He I [Fe II] [Ca II] [Fe II] O I Fe I Fe I Fe I O I [C I] [C I] [C I] Ca II Ca II Na I H I [Ni II] [Ni II] K I ZTF22abvaetz 312.0 d 7000 7250 7500 He I [Fe II] [Ca II] [Ni II] [Ni II] [Fe II] N o r m aliz e d F + o f f s e t [PITH_FULL_IMAGE:figures/f… view at source ↗
Figure 20
Figure 20. Figure 20: Nebular spectra of ZTF22abvaetz/SN 2022aang [PITH_FULL_IMAGE:figures/full_fig_p038_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Nebular spectra of ZTF23aabksje/SN 2023azx. 0.0000 0.0005 0.0010 0.0015 Mg I] Fe II+Mg I] [O I] [O I] He I [Fe II] [Ca II] [Fe II] O I Fe I Fe I Fe I O I [C I] [C I] [C I] Ca II Ca II Na I H I [Ni II] [Ni II] K I ZTF23abgmhgw 165.0 d 5000 6000 7000 8000 9000 Wavelength (Å) 0.0000 0.0005 0.0010 0.0015 ZTF23abgmhgw 210.0 d He I [Fe II] [Ca II] [Ni II] [Ni II] [Fe II] 7000 7250 7500 N o r m aliz e d F + o f … view at source ↗
Figure 22
Figure 22. Figure 22: Nebular spectra of ZTF23abgmhgw/SN 2023vci [PITH_FULL_IMAGE:figures/full_fig_p039_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Nebular spectra of ZTF22abyivoq/SN 2022acko [PITH_FULL_IMAGE:figures/full_fig_p040_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Nebular spectra of ZTF23abnogui/SN 2023wcr [PITH_FULL_IMAGE:figures/full_fig_p041_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Nebular spectra of ZTF23abpbuha/SN 2023usp. 0.0000 0.0005 0.0010 0.0015 0.0020 0.0025 Mg I] Fe II+Mg I] [O I] [O I] He I [Fe II] [Ca II] [Fe II] O I Fe I Fe I Fe I O I [C I] [C I] [C I] Ca II Ca II Na I H I [Ni II] [Ni II] K I ZTF23absscow 250.0 d 5000 6000 7000 8000 9000 Wavelength (Å) 0.0000 0.0005 0.0010 0.0015 0.0020 0.0025 ZTF23absscow 342.0 d He I [Fe II] [Ca II] [Ni II] [Ni II] [Fe II] 7000 7250 75… view at source ↗
Figure 26
Figure 26. Figure 26: Nebular spectra of ZTF23absscow/SN 2023ywa [PITH_FULL_IMAGE:figures/full_fig_p042_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Nebular spectra of ZTF23abvgvab/SN 2023abim [PITH_FULL_IMAGE:figures/full_fig_p043_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Nebular spectra of ZTF24aaasazz/SN 2024ov. 5000 6000 7000 8000 9000 Wavelength (Å) 0.000 0.001 0.002 0.003 Mg I] Fe II+Mg I] [O I] [O I] He I [Fe II] [Ca II] [Fe II] O I Fe I Fe I Fe I O I [C I] [C I] [C I] Ca II Ca II Na I H I [Ni II] [Ni II] K I ZTF24aabppgn 125.0 d 7000 7250 7500 He I [Fe II] [Ca II] [Ni II] [Ni II] [Fe II] N o r m aliz e d F + o f f s e t [PITH_FULL_IMAGE:figures/full_fig_p044_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Nebular spectra of ZTF24aabppgn/SN 2024wp [PITH_FULL_IMAGE:figures/full_fig_p044_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: Nebular spectra of ZTF24aaezido/SN 2024cro [PITH_FULL_IMAGE:figures/full_fig_p045_30.png] view at source ↗
Figure 31
Figure 31. Figure 31: Nebular spectra of ZTF24aaplfjd/SN 2024jxm [PITH_FULL_IMAGE:figures/full_fig_p046_31.png] view at source ↗
read the original abstract

Electron-capture supernovae (ECSNe) may arise from ONeMg-core collapse in super-asymptotic giant branch (sAGB) stars near the low-mass core-collapse limit ($\approx\!8$--$10$\,\Msun). At early times, models predict that ECSNe resemble low-mass red supergiant iron-core-collapse SNe (FeCCSNe), making the two channels difficult to distinguish. Nebular spectroscopy, however, can reveal differences in ejecta composition. We present a systematic sample of nebular spectra of 19 low-luminosity Type IIP (LLIIP) SNe from the ZTF CLU survey, obtained 115$-$450\,d after explosion. Their low velocities expose narrow lines blended in brighter SNe, which we identify and model to constrain progenitor properties. We find a strong correlation between the FWHM of H\,\textsc{i}\,$\lambda$6563 and peak luminosity, showing that LLIIP SNe occupy the low-energy end of the core-collapse population, but no correlation with plateau duration, suggesting that envelope and core properties are not tightly linked. Only one SN reaches the extremely low H\,\textsc{i}\,$\lambda$6563 widths predicted for the weakest $\sim$9\,M$_\odot$ explosion models, implying that such low-energy events are intrinsically rare. Combining our sample with 118 literature nebular spectra of Type II SNe, we infer an IMF slope of $2.1\pm1.2$. We also introduce an `ECSN score'' based on the absence of He- and O-shell emission lines, and identify two plausible ECSN candidates, SN~2023bvj and SN~2024btj. However, neither shows the extremely narrow nebular lines predicted by current ECSN models. If ECSNe arise predominantly through the LLIIP channel, we infer an upper limit on the ECSN rate of $\lesssim (5$--$8)\times10^{2}\,\mathrm{Gpc^{-3}\,yr^{-1}}$, corresponding to a narrow sAGB progenitor mass window of $\Delta M_{\rm sAGB} \lesssim 0.02$--$0.06\,\mathrm{M_\odot}$.

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

1 major / 2 minor

Summary. The manuscript analyzes nebular spectra of 19 low-luminosity Type IIP supernovae from the ZTF CLU survey obtained 115-450 days post-explosion. It reports a strong correlation between H I λ6563 FWHM and peak luminosity (but none with plateau duration), introduces an empirically defined 'ECSN score' based on the absence of He- and O-shell emission lines, identifies two plausible ECSN candidates (SN 2023bvj and SN 2024btj), and combines the sample with 118 literature Type II spectra to infer an IMF slope of 2.1±1.2. Assuming ECSNe arise predominantly via the LLIIP channel, it derives an upper limit on the ECSN rate of ≲(5–8)×10² Gpc^{-3} yr^{-1}, implying a narrow sAGB progenitor mass window of ΔM_sAGB ≲0.02–0.06 M_⊙.

Significance. If the central claims hold, the work supplies a systematic observational sample at the faint end of the core-collapse population and places quantitative limits on the ECSN channel that are directly relevant to stellar evolution models near the 8–10 M_⊙ boundary. The reported FWHM–luminosity correlation and the introduction of an ECSN score constitute useful empirical advances, though the rate upper limit rests on theoretical line-width predictions.

major comments (1)
  1. [Abstract and rate-derivation section] Abstract and the section deriving the ECSN rate upper limit: the inference that the absence of extremely narrow H I λ6563 lines in the two high-ECSN-score candidates (SN 2023bvj and SN 2024btj) rules out standard ~9 M_⊙ ONeMg-core models (and thereby yields the ≲(5–8)×10² Gpc^{-3} yr^{-1} limit) depends on the robustness of current theoretical FWHM predictions; no sensitivity analysis to variations in explosion energy, 56Ni mixing, or residual H-envelope mass is presented, which is load-bearing for the rate and mass-window conclusions.
minor comments (2)
  1. [Methods] Provide the precise numerical definition and weighting of the 'ECSN score' (including which lines are required to be absent) so that the classification of the two candidates can be reproduced from the spectra.
  2. [Results] Clarify whether the IMF slope of 2.1±1.2 is derived solely from the combined sample or incorporates additional assumptions about the LLIIP fraction; state the exact fitting procedure and any selection-function corrections applied to the heterogeneous literature data.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and for highlighting the importance of assessing the robustness of the theoretical line-width predictions underlying our ECSN rate limit. We address the major comment below and outline planned revisions.

read point-by-point responses

  1. Referee: [Abstract and rate-derivation section] Abstract and the section deriving the ECSN rate upper limit: the inference that the absence of extremely narrow H I λ6563 lines in the two high-ECSN-score candidates (SN 2023bvj and SN 2024btj) rules out standard ~9 M_⊙ ONeMg-core models (and thereby yields the ≲(5–8)×10² Gpc^{-3} yr^{-1} limit) depends on the robustness of current theoretical FWHM predictions; no sensitivity analysis to variations in explosion energy, 56Ni mixing, or residual H-envelope mass is presented, which is load-bearing for the rate and mass-window conclusions.

    Authors: We agree that the rate upper limit and implied sAGB mass window rest on the assumption that current ECSN models reliably predict extremely narrow H I λ6563 lines for standard ~9 M_⊙ ONeMg-core progenitors. The manuscript cites specific model predictions (e.g., from the literature on low-energy ECSN explosions) showing FWHM values well below those observed in our sample, including the two high-ECSN-score candidates. While a comprehensive sensitivity study varying explosion energy, 56Ni mixing, and residual H-envelope mass was not included, the available models indicate that even moderate increases in these parameters do not produce the observed line widths without violating other constraints (such as the low luminosities and plateau properties). We will revise the rate-derivation section and abstract to explicitly discuss these model dependencies, add qualitative sensitivity considerations drawn from existing ECSN and low-energy FeCCSN simulations, and qualify the upper limit as model-dependent. This will make the load-bearing assumptions transparent without altering the core observational result that no object in the sample matches the narrowest predicted lines. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation uses independent observational spectra and external model comparisons

full rationale

The paper's central inference—an upper limit on the ECSN rate conditional on the LLIIP channel—rests on new ZTF nebular spectra of 19 LLIIP events, a measured FWHM-luminosity correlation, an empirically defined ECSN score from missing He/O lines, and comparison to cited theoretical predictions for line widths in ~9 M⊙ models. No step reduces a fitted parameter to a renamed prediction, defines a quantity in terms of itself, or relies on a load-bearing self-citation whose validity is internal to the present work. The IMF slope and rate limit are derived from the combined sample and model assumptions that remain falsifiable against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim depends on ZTF observational data, theoretical predictions for nebular line widths in ECSN models, the definition of the ECSN score, and the assumption that the LLIIP channel dominates ECSN production. The IMF slope is fitted to the combined sample.

free parameters (1)
  • IMF slope = 2.1 ± 1.2
    Derived by combining the 19-event sample with 118 literature spectra to infer the slope of 2.1±1.2.
axioms (1)
  • domain assumption Nebular spectra at 115-450 days reveal composition differences that distinguish ECSNe from iron-core-collapse SNe
    Invoked to justify using line absences and widths to identify ECSN candidates and constrain rates.
invented entities (1)
  • ECSN score no independent evidence
    purpose: Quantitative metric to flag plausible ECSN candidates from absence of He- and O-shell emission lines
    Newly introduced diagnostic whose calibration rests on model expectations rather than independent empirical validation.

pith-pipeline@v0.9.0 · 6075 in / 1596 out tokens · 71502 ms · 2026-05-20T08:11:57.065544+00:00 · methodology

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    Relation between the paper passage and the cited Recognition theorem.

    We find a strong correlation between the FWHM of H I λ6563 and peak luminosity... Only one SN reaches the extremely low H I λ6563 widths predicted for the weakest ∼9 M⊙ explosion models... If ECSNe arise predominantly through the LLIIP channel, we infer an upper limit on the ECSN rate of ≲(5–8)×10² Gpc⁻³ yr⁻¹, corresponding to a narrow sAGB progenitor mass window of ΔM_sAGB ≲0.02–0.06 M⊙.

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Works this paper leans on

130 extracted references · 130 canonical work pages · 4 internal anchors

  1. [1]

    M., Kochanek, C

    Adams, S. M., Kochanek, C. S., Prieto, J. L., et al. 2016, MNRAS, 460, 1645, doi: 10.1093/mnras/stw1059

    work page doi:10.1093/mnras/stw1059 2016

  2. [2]

    2023, MNRAS, 519, 248, doi: 10.1093/mnras/stac3234

    Ailawadhi, B., Dastidar, R., Misra, K., et al. 2023, MNRAS, 519, 248, doi: 10.1093/mnras/stac3234

    work page doi:10.1093/mnras/stac3234 2023

  3. [3]

    P., Dessart, L., Gutierrez, C

    Anderson, J. P., Dessart, L., Gutierrez, C. P., et al. 2014, MNRAS, 441, 671, doi: 10.1093/mnras/stu610

    work page doi:10.1093/mnras/stu610 2014

  4. [4]

    P., Dessart, L., Guti´ errez, C

    Anderson, J. P., Dessart, L., Guti´ errez, C. P., et al. 2018, Nature Astronomy, 2, 574, doi: 10.1038/s41550-018-0458-4

    work page doi:10.1038/s41550-018-0458-4 2018

  5. [5]

    E., Sugerman, B

    Andrews, J. E., Sugerman, B. E. K., Clayton, G. C., et al. 2011, ApJ, 731, 47, doi: 10.1088/0004-637X/731/1/47

    work page doi:10.1088/0004-637x/731/1/47 2011

  6. [6]

    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 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068

    work page doi:10.3847/1538-4357/ad2a49 2024

  7. [7]

    2024, MNRAS, 533, 1251, doi: 10.1093/mnras/stae1811

    Barmentloo, S., Jerkstrand, A., Iwamoto, K., et al. 2024, MNRAS, 533, 1251, doi: 10.1093/mnras/stae1811

    work page doi:10.1093/mnras/stae1811 2024

  8. [8]

    C., & Sesar, B

    Bellm, E. C., & Sesar, B. 2016, pyraf-dbsp: Reduction pipeline for the Palomar Double Beam Spectrograph, Astrophysics Source Code Library. http://ascl.net/1602.002

    work page 2016

  9. [9]

    C., Kulkarni, S

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

    work page doi:10.1088/1538-3873/aaecbe 2019

  10. [10]

    , keywords =

    Benetti, S., Turatto, M., Balberg, S., et al. 2001, MNRAS, 322, 361, doi: 10.1046/j.1365-8711.2001.04122.x

    work page doi:10.1046/j.1365-8711.2001.04122.x 2001

  11. [11]

    S., Milisavljevic, D., Margutti, R., et al

    Black, C. S., Milisavljevic, D., Margutti, R., et al. 2017, ApJ, 848, 5, doi: 10.3847/1538-4357/aa8999

    work page doi:10.3847/1538-4357/aa8999 2017

  12. [12]

    L., Schmidt, B

    Blanton, E. L., Schmidt, B. P., Kirshner, R. P., et al. 1995, AJ, 110, 2868, doi: 10.1086/117735

    work page doi:10.1086/117735 1995

  13. [13]

    2013, MNRAS, 433, 1871, doi: 10.1093/mnras/stt864 Low Luminosity Type IIP Supernovae19

    Bose, S., Kumar, B., Sutaria, F., et al. 2013, MNRAS, 433, 1871, doi: 10.1093/mnras/stt864 Low Luminosity Type IIP Supernovae19

    work page doi:10.1093/mnras/stt864 2013

  14. [14]

    2015, ApJ, 806, 160, doi: 10.1088/0004-637X/806/2/160

    Bose, S., Sutaria, F., Kumar, B., et al. 2015, ApJ, 806, 160, doi: 10.1088/0004-637X/806/2/160

    work page doi:10.1088/0004-637x/806/2/160 2015

  15. [15]

    A., Valenti, S., Horesh, A., et al

    Bostroem, K. A., Valenti, S., Horesh, A., et al. 2019, MNRAS, 485, 5120, doi: 10.1093/mnras/stz570

    work page doi:10.1093/mnras/stz570 2019

  16. [16]

    A., Dessart, L., Hillier, D

    Bostroem, K. A., Dessart, L., Hillier, D. J., et al. 2023, ApJL, 953, L18, doi: 10.3847/2041-8213/ace31c

    work page doi:10.3847/2041-8213/ace31c 2023

  17. [17]

    2009, MNRAS, 395, 472, doi: 10.1111/j.1365-2966.2009.14539.x

    Botticella, M. T., Pastorello, A., Smartt, S. J., et al. 2009, MNRAS, 398, 1041, doi: 10.1111/j.1365-2966.2009.15082.x

    work page doi:10.1111/j.1365-2966.2009.15082.x 2009

  18. [18]

    2007, in American Institute of Physics Conference Series, Vol

    Bufano, F., Benetti, S., Turatto, M., et al. 2007, in American Institute of Physics Conference Series, Vol. 924, The Multicolored Landscape of Compact Objects and Their Explosive Origins, ed. T. di Salvo, G. L. Israel, L. Piersant, L. Burderi, G. Matt, A. Tornambe, & M. T. Menna (AIP), 271–276, doi: 10.1063/1.2774869

    work page doi:10.1063/1.2774869 2007

  19. [19]

    2019, MNRAS, 485, 3153, doi: 10.1093/mnras/stz543

    Burrows, A., Radice, D., & Vartanyan, D. 2019, MNRAS, 485, 3153, doi: 10.1093/mnras/stz543

    work page doi:10.1093/mnras/stz543 2019

  20. [20]

    2024, ApJL, 964, L16, doi: 10.3847/2041-8213/ad319e

    Burrows, A., Wang, T., & Vartanyan, D. 2024, ApJL, 964, L16, doi: 10.3847/2041-8213/ad319e

    work page doi:10.3847/2041-8213/ad319e 2024

  21. [21]

    M., Eldridge, J

    Byrne, C. M., Eldridge, J. J., & Stanway, E. R. 2025, MNRAS, 537, 2433, doi: 10.1093/mnras/staf178

    work page doi:10.1093/mnras/staf178 2025

  22. [22]

    2021, arXiv e-prints, arXiv:2109.12943, doi: 10.48550/arXiv.2109.12943

    Callis, E., Fraser, M., Pastorello, A., et al. 2021, arXiv e-prints, arXiv:2109.12943, doi: 10.48550/arXiv.2109.12943

    work page doi:10.48550/arxiv.2109.12943 2021

  23. [23]

    V., Li, W., & Silverman, J

    Chornock, R., Filippenko, A. V., Li, W., & Silverman, J. M. 2010, ApJ, 713, 1363, doi: 10.1088/0004-637X/713/2/1363

    work page doi:10.1088/0004-637x/713/2/1363 2010

  24. [24]

    C., et al

    Clocchiatti, A., Benetti, S., Wheeler, J. C., et al. 1996, AJ, 111, 1286, doi: 10.1086/117874

    work page doi:10.1086/117874 1996

  25. [25]

    W., Bloom, J

    Coughlin, M. W., Bloom, J. S., Nir, G., et al. 2023, ApJS, 267, 31, doi: 10.3847/1538-4365/acdee1

    work page doi:10.3847/1538-4365/acdee1 2023

  26. [26]

    K., Kasliwal, M

    Das, K. K., Kasliwal, M. M., Fremling, C., et al. 2025, PASP, 137, 044203, doi: 10.1088/1538-3873/adcaeb

    work page doi:10.1088/1538-3873/adcaeb 2025

  27. [27]

    K., Kasliwal, M

    Das, K. K., Kasliwal, M. M., Sollerman, J., et al. 2026, PASP, 138, 024204, doi: 10.1088/1538-3873/ae33f5

    work page doi:10.1088/1538-3873/ae33f5 2026

  28. [28]

    2025, arXiv e-prints, arXiv:2501.01530

    Dastidar, R., Misra, K., Valenti, S., et al. 2025, arXiv e-prints, arXiv:2501.01530. https://arxiv.org/abs/2501.01530

    work page arXiv 2025

  29. [29]

    M., Tzanidakis, A., et al

    De, K., Kasliwal, M. M., Tzanidakis, A., et al. 2020, ApJ, 905, 58, doi: 10.3847/1538-4357/abb45c de Jaeger, T., Zheng, W., Stahl, B. E., et al. 2019, MNRAS, 490, 2799, doi: 10.1093/mnras/stz2714

    work page doi:10.3847/1538-4357/abb45c 2020

  30. [30]

    M., Riddle, R., et al

    Dekany, R., Smith, R. M., Riddle, R., et al. 2020, PASP, 132, 038001, doi: 10.1088/1538-3873/ab4ca2

    work page doi:10.1088/1538-3873/ab4ca2 2020

  31. [31]

    Dessart, L., & Hillier, D. J. 2020, A&A, 642, A33, doi: 10.1051/0004-6361/202038148

    work page doi:10.1051/0004-6361/202038148 2020

  32. [32]

    Janka, H. T. 2021a, A&A, 656, A61, doi: 10.1051/0004-6361/202141927 —. 2021b, A&A, 652, A64, doi: 10.1051/0004-6361/202140839

    work page doi:10.1051/0004-6361/202141927

  33. [33]

    2025, arXiv e-prints, arXiv:2507.05803, doi: 10.48550/arXiv.2507.05803

    Dessart, L., Kotak, R., Jacobson-Galan, W., et al. 2025, arXiv e-prints, arXiv:2507.05803, doi: 10.48550/arXiv.2507.05803

    work page doi:10.48550/arxiv.2507.05803 2025

  34. [34]

    Lau, H. H. B. 2015, MNRAS, 446, 2599, doi: 10.1093/mnras/stu2180

    work page doi:10.1093/mnras/stu2180 2015

  35. [35]

    A., et al

    Dong, Y., Valenti, S., Bostroem, K. A., et al. 2021, ApJ, 906, 56, doi: 10.3847/1538-4357/abc417

    work page doi:10.3847/1538-4357/abc417 2021

  36. [36]

    J., Djorgovski, S

    Drake, A. J., Djorgovski, S. G., Graham, M. J., et al. 2012, Central Bureau Electronic Telegrams, 3118, 1

    work page 2012

  37. [37]

    A., Mahabal, A., Masci, F

    Duev, D. A., Mahabal, A., Masci, F. J., et al. 2019, arXiv e-prints. https://arxiv.org/abs/1907.11259

    work page arXiv 2019

  38. [38]

    Binary Population and Spectral Synthesis Version 2.1: construction, observational verification and new results

    Eldridge, J. J., Stanway, E. R., Xiao, L., et al. 2017, PASA, 34, e058, doi: 10.1017/pasa.2017.51

    work page internal anchor Pith review doi:10.1017/pasa.2017.51 2017

  39. [39]

    D., Li, W., et al

    Elias-Rosa, N., Van Dyk, S. D., Li, W., et al. 2009, ApJ, 706, 1174, doi: 10.1088/0004-637X/706/2/1174

    work page doi:10.1088/0004-637x/706/2/1174 2009

  40. [40]

    , keywords =

    Elmhamdi, A., Danziger, I. J., Chugai, N., et al. 2003, MNRAS, 338, 939, doi: 10.1046/j.1365-8711.2003.06150.x

    work page doi:10.1046/j.1365-8711.2003.06150.x 2003

  41. [41]

    J., & Maeda, K

    Fang, Q., Moriya, T. J., & Maeda, K. 2025, ApJ, 986, 39, doi: 10.3847/1538-4357/adceae

    work page doi:10.3847/1538-4357/adceae 2025

  42. [42]

    V., et al

    Faran, T., Poznanski, D., Filippenko, A. V., et al. 2014, MNRAS, 442, 844, doi: 10.1093/mnras/stu955

    work page doi:10.1093/mnras/stu955 2014

  43. [43]

    Andrews, J. E. 2024, A&A, 687, L20, doi: 10.1051/0004-6361/202450440

    work page doi:10.1051/0004-6361/202450440 2024

  44. [44]

    Fransson, C., & Chevalier, R. A. 1989, ApJ, 343, 323, doi: 10.1086/167707

    work page doi:10.1086/167707 1989

  45. [45]

    , keywords =

    Fraser, M., Ergon, M., Eldridge, J. J., et al. 2011, MNRAS, 417, 1417, doi: 10.1111/j.1365-2966.2011.19370.x G´ omez, G., & L´ opez, R. 2000, AJ, 120, 367, doi: 10.1086/301419

    work page doi:10.1111/j.1365-2966.2011.19370.x 2011

  46. [46]

    J., Kulkarni, S

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

    work page doi:10.1088/1538-3873/ab006c 2019

  47. [47]

    Z., & Margutti, R

    Guillochon, J., Parrent, J., Kelley, L. Z., & Margutti, R. 2017, ApJ, 835, 64, doi: 10.3847/1538-4357/835/1/64

    work page doi:10.3847/1538-4357/835/1/64 2017

  48. [48]

    Chakradhari, N. K. 2008, Bulletin of the Astronomical Society of India, 36, 79 Guti´ errez, C. P., Anderson, J. P., Hamuy, M., et al. 2017, ApJ, 850, 89, doi: 10.3847/1538-4357/aa8f52 Guti´ errez, C. P., Pastorello, A., Jerkstrand, A., et al. 2020, MNRAS, 499, 974, doi: 10.1093/mnras/staa2763 Guti´ errez, J., Canal, R., & Garc´ ıa-Berro, E. 2005, A&A, 435...

    work page doi:10.3847/1538-4357/aa8f52 2008

  49. [49]

    , keywords =

    Hendry, M. A., Smartt, S. J., Maund, J. R., et al. 2005, MNRAS, 359, 906, doi: 10.1111/j.1365-2966.2005.08928.x 20Das et al

    work page doi:10.1111/j.1365-2966.2005.08928.x 2005

  50. [50]

    , keywords =

    Hendry, M. A., Smartt, S. J., Crockett, R. M., et al. 2006, MNRAS, 369, 1303, doi: 10.1111/j.1365-2966.2006.10374.x

    work page doi:10.1111/j.1365-2966.2006.10374.x 2006

  51. [51]

    A., Van Dyk, S

    Hiramatsu, D., Howell, D. A., Van Dyk, S. D., et al. 2021, Nature Astronomy, 5, 903, doi: 10.1038/s41550-021-01384-2

    work page doi:10.1038/s41550-021-01384-2 2021

  52. [52]

    2018, ApJ, 861, 63, doi: 10.3847/1538-4357/aac5f6

    Hosseinzadeh, G., Valenti, S., McCully, C., et al. 2018, ApJ, 861, 63, doi: 10.3847/1538-4357/aac5f6

    work page doi:10.3847/1538-4357/aac5f6 2018

  53. [53]

    2016, ApJ, 832, 139, doi: 10.3847/0004-637X/832/2/139

    Huang, F., Wang, X., Zampieri, L., et al. 2016, ApJ, 832, 139, doi: 10.3847/0004-637X/832/2/139

    work page doi:10.3847/0004-637x/832/2/139 2016

  54. [54]

    2018, MNRAS, 475, 3959, doi: 10.1093/mnras/sty066

    Huang, F., Wang, X.-F., Hosseinzadeh, G., et al. 2018, MNRAS, 475, 3959, doi: 10.1093/mnras/sty066

    work page doi:10.1093/mnras/sty066 2018

  55. [55]

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

    work page doi:10.1109/mcse.2007.55 2007

  56. [56]

    2013, A&A, 555, A142, doi: 10.1051/0004-6361/201220496

    Inserra, C., Pastorello, A., Turatto, M., et al. 2013, A&A, 555, A142, doi: 10.1051/0004-6361/201220496

    work page doi:10.1051/0004-6361/201220496 2013

  57. [57]

    2012, Central Bureau Electronic Telegrams, 3338, 1 Jacobson-Gal´ an, W

    Itagaki, K., Noguchi, T., Nakano, S., et al. 2012, Central Bureau Electronic Telegrams, 3338, 1 Jacobson-Gal´ an, W. V., Dessart, L., Davis, K. W., et al. 2025, ApJ, 992, 100, doi: 10.3847/1538-4357/adfa23 J¨ ager, Zolt´ an, J., Vink´ o, J., B´ ır´ o, B. I., et al. 2020, MNRAS, 496, 3725, doi: 10.1093/mnras/staa1743

    work page doi:10.3847/1538-4357/adfa23 2012

  58. [58]

    T., M¨ uller, B., Kitaura, F

    Janka, H. T., M¨ uller, B., Kitaura, F. S., & Buras, R. 2008, A&A, 485, 199, doi: 10.1051/0004-6361:20079334

    work page doi:10.1051/0004-6361:20079334 2008

  59. [59]

    E., Adams, S

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

    work page doi:10.3847/2041-8213/ab2c05 2019

  60. [60]

    J., et al

    Jerkstrand, A., Ergon, M., Smartt, S. J., et al. 2015a, A&A, 573, A12, doi: 10.1051/0004-6361/201423983

    work page doi:10.1051/0004-6361/201423983

  61. [61]

    T., et al

    Jerkstrand, A., Ertl, T., Janka, H. T., et al. 2018, MNRAS, 475, 277, doi: 10.1093/mnras/stx2877

    work page doi:10.1093/mnras/stx2877 2018

  62. [62]

    2011, A&A, 530, A45, doi: 10.1051/0004-6361/201015937

    Jerkstrand, A., Fransson, C., & Kozma, C. 2011, A&A, 530, A45, doi: 10.1051/0004-6361/201015937

    work page doi:10.1051/0004-6361/201015937 2011

  63. [63]

    2012, A&A, 546, A28, doi: 10.1051/0004-6361/201219528

    Jerkstrand, A., Fransson, C., Maguire, K., et al. 2012, A&A, 546, A28, doi: 10.1051/0004-6361/201219528

    work page doi:10.1051/0004-6361/201219528 2012

  64. [64]

    J., Fraser, M., et al

    Jerkstrand, A., Smartt, S. J., Fraser, M., et al. 2014a, MNRAS, 439, 3694, doi: 10.1093/mnras/stu221 —. 2014b, MNRAS, 439, 3694, doi: 10.1093/mnras/stu221

    work page doi:10.1093/mnras/stu221

  65. [65]

    J., Sollerman, J., et al

    Jerkstrand, A., Smartt, S. J., Sollerman, J., et al. 2015b, MNRAS, 448, 2482, doi: 10.1093/mnras/stv087 —. 2015c, MNRAS, 448, 2482, doi: 10.1093/mnras/stv087

    work page doi:10.1093/mnras/stv087

  66. [66]

    2013, ApJ, 772, 150, doi: 10.1088/0004-637X/772/2/150

    Jones, S., Hirschi, R., Nomoto, K., et al. 2013, ApJ, 772, 150, doi: 10.1088/0004-637X/772/2/150

    work page doi:10.1088/0004-637x/772/2/150 2013

  67. [67]

    M., Cannella, C., Bagdasaryan, A., et al

    Kasliwal, M. M., Cannella, C., Bagdasaryan, A., et al. 2019, PASP, 131, 038003, doi: 10.1088/1538-3873/aafbc2

    work page doi:10.1088/1538-3873/aafbc2 2019

  68. [68]

    M., Fremling, C., Yan, L., et al

    Kasliwal, M. M., Fremling, C., Yan, L., et al. 2024, Transient Name Server AstroNote, 340, 1

    work page 2024

  69. [69]

    D., Foley, R

    Kilpatrick, C. D., Foley, R. J., Jacobson-Gal´ an, W. V., et al. 2023, ApJL, 952, L23, doi: 10.3847/2041-8213/ace4ca

    work page doi:10.3847/2041-8213/ace4ca 2023

  70. [70]

    S., Janka, H

    Kitaura, F. S., Janka, H. T., & Hillebrandt, W. 2006, A&A, 450, 345, doi: 10.1051/0004-6361:20054703

    work page doi:10.1051/0004-6361:20054703 2006

  71. [71]

    2022, MNRAS, 514, 4173, doi: 10.1093/mnras/stac1518

    Kozyreva, A., Janka, H.-T., Kresse, D., Taubenberger, S., & Baklanov, P. 2022, MNRAS, 514, 4173, doi: 10.1093/mnras/stac1518

    work page doi:10.1093/mnras/stac1518 2022

  72. [72]

    C., Filippenko, A

    Leonard, D. C., Filippenko, A. V., Ganeshalingam, M., et al. 2006, Nature, 440, 505, doi: 10.1038/nature04558

    work page doi:10.1038/nature04558 2006

  73. [73]

    D., Filippenko, A

    Li, W., Van Dyk, S. D., Filippenko, A. V., et al. 2006, ApJ, 641, 1060, doi: 10.1086/499916

    work page doi:10.1086/499916 2006

  74. [74]

    , keywords =

    Maguire, K., Jerkstrand, A., Smartt, S. J., et al. 2012, MNRAS, 420, 3451, doi: 10.1111/j.1365-2966.2011.20276.x

    work page doi:10.1111/j.1365-2966.2011.20276.x 2012

  75. [75]

    J., Laher, R

    Masci, F. J., Laher, R. R., Rusholme, B., et al. 2019, PASP, 131, 018003, doi: 10.1088/1538-3873/aae8ac

    work page internal anchor Pith review doi:10.1088/1538-3873/aae8ac 2019

  76. [76]

    J., Eldridge, J

    Mattila, S., Smartt, S. J., Eldridge, J. J., et al. 2008, ApJL, 688, L91, doi: 10.1086/595587

    work page doi:10.1086/595587 2008

  77. [77]

    R., Smartt, S

    Maund, J. R., Smartt, S. J., & Danziger, I. J. 2005, MNRAS, 364, L33, doi: 10.1111/j.1745-3933.2005.00100.x

    work page doi:10.1111/j.1745-3933.2005.00100.x 2005

  78. [78]

    A., et al

    Meza-Retamal, N., Dong, Y., Bostroem, K. A., et al. 2024, ApJ, 971, 141, doi: 10.3847/1538-4357/ad4d55

    work page doi:10.3847/1538-4357/ad4d55 2024

  79. [79]

    1980, PASJ, 32, 303 M¨ uller-Bravo, T

    Miyaji, S., Nomoto, K., Yokoi, K., & Sugimoto, D. 1980, PASJ, 32, 303 M¨ uller-Bravo, T. E., Guti´ errez, C. P., Sullivan, M., et al. 2020, MNRAS, 497, 361, doi: 10.1093/mnras/staa1932

    work page doi:10.1093/mnras/staa1932 1980

  80. [80]

    2024, MNRAS, 528, 4209, doi: 10.1093/mnras/stae170

    Murai, Y., Tanaka, M., Kawabata, M., et al. 2024, MNRAS, 528, 4209, doi: 10.1093/mnras/stae170

    work page doi:10.1093/mnras/stae170 2024

Showing first 80 references.