Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. I: Luminosity Function, Volumetric Rate
Pith reviewed 2026-05-23 01:55 UTC · model grok-4.3
The pith
Low-luminosity Type IIP supernovae cannot explain the mismatch between core-collapse rates and star-formation rates.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The final sample includes 330 Type IIP SNe and 36 low-luminosity Type II SNe with Mr,peak > -16 mag. The fraction of LLIIP SNe is 19+3-4 percent of the total CLU Type IIP SNe population (8+1-2 percent of all core-collapse SNe). This implies that while LLIIP SNe likely represent the fate of core-collapse SNe of 8-12 Msun progenitors, they alone cannot account for the fate of all massive stars in this mass range. To derive an absolute rate, the ZTF pipeline efficiency is estimated as a function of apparent magnitude and local surface brightness. The volumetric rate is (3.9-0.4+0.4) x 10^4 Gpc-3 yr-1 for Type IIP SNe and (7.3-0.6+0.6) x 10^3 Gpc-3 yr-1 for LLIIP SNe. Now that the rate of LLIIP
What carries the argument
ZTF pipeline efficiency modeled as a function of apparent magnitude and local surface brightness to convert the observed 366-event sample into absolute volumetric rates.
If this is right
- LLIIP SNe correspond to the endpoints of 8-12 solar-mass progenitors but do not represent every massive star in that range.
- The LLIIP fraction is 19 percent of Type IIP events and 8 percent of all core-collapse supernovae.
- The volumetric rate for LLIIP events is (7.3 ± 0.6) × 10^3 Gpc^{-3} yr^{-1}.
- The observed sample triples the number of known low-luminosity Type II supernovae.
Where Pith is reading between the lines
- Other progenitor channels or detection biases beyond low-luminosity events must contribute to the remaining rate shortfall.
- Future deeper surveys can test whether an even fainter population exists below current detection thresholds.
- The efficiency correction method can be applied to other transient classes to produce consistent volumetric rates across surveys.
Load-bearing premise
The ZTF pipeline efficiency is correctly estimated as a function of apparent magnitude and local surface brightness, allowing conversion of the observed sample into absolute volumetric rates.
What would settle it
An independent survey or re-analysis that recovers a volumetric LLIIP rate substantially above 7.3 x 10^3 Gpc^{-3} yr^{-1} and shows these events close the full core-collapse versus star-formation discrepancy.
read the original abstract
We present the luminosity function and volumetric rate of a sample of Type IIP supernovae (SNe) from the Zwicky Transient Facility Census of the Local Universe survey (CLU). This is the largest sample of Type IIP SNe from a systematic volume-limited survey to-date. The final sample includes 330 Type IIP SNe and 36 low-luminosity Type II (LLIIP) SNe with $M_{\textrm{r,peak}}>-16$ mag, which triples the literature sample of LLIIP SNe. The fraction of LLIIP SNe is $19^{+3}_{-4}\%$ of the total CLU Type IIP SNe population ($8^{+1}_{-2}\%$ of all core-collapse SNe). This implies that while LLIIP SNe likely represent the fate of core-collapse SNe of $8-12$ \Msun\ progenitors, they alone cannot account for the fate of all massive stars in this mass range. To derive an absolute rate, we estimate the ZTF pipeline efficiency as a function of the apparent magnitude and the local surface brightness. We derive a volumetric rate of $(3.9_{-0.4}^{+0.4}) \times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for Type IIP SNe and $(7.3_{-0.6}^{+0.6}) \times 10^{3}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for LLIIP SNe. Now that the rate of LLIIP SNe is robustly derived, the unresolved discrepancy between core-collapse SN rates and star-formation rates cannot be explained by LLIIP SNe alone.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript presents results from the ZTF CLU volume-limited survey, reporting a sample of 330 Type IIP SNe including 36 low-luminosity Type IIP (LLIIP) events with M_r,peak > -16 mag. This triples the existing LLIIP literature sample. The authors derive the luminosity function, find that LLIIP comprise 19^{+3}_{-4}% of Type IIP SNe (8^{+1}_{-2}% of all core-collapse SNe), and compute volumetric rates of (3.9_{-0.4}^{+0.4}) × 10^4 Gpc^{-3} yr^{-1} for Type IIP and (7.3_{-0.6}^{+0.6}) × 10^3 Gpc^{-3} yr^{-1} for LLIIP after applying a pipeline efficiency correction. They conclude that LLIIP SNe, while likely the endpoint for 8-12 M_⊙ progenitors, cannot alone resolve the discrepancy between observed core-collapse SN rates and those expected from star-formation rates.
Significance. If the efficiency model is validated, the work supplies the largest systematic volume-limited census of Type IIP SNe to date and places a firm upper limit on the LLIIP contribution to the missing SN rate problem. The tripling of the LLIIP sample and the explicit volumetric rates (with uncertainties) constitute a clear advance over prior heterogeneous compilations. The conclusion that LLIIP events are insufficient to close the CC-SFR gap is a useful constraint for progenitor and binary-evolution models.
major comments (2)
- [methods (efficiency estimation)] Efficiency correction (methods section describing pipeline efficiency): The absolute volumetric rates quoted in the abstract and used for the central claim rest on an efficiency function of apparent magnitude and local surface brightness. No quantitative validation (e.g., injection-recovery statistics, completeness curves, or tests at M_r,peak > -16 in high-surface-brightness hosts) is referenced. If efficiency is overestimated at the faint end, the true LLIIP rate increases and the conclusion that these events 'alone cannot account for' the CC-SFR discrepancy is weakened. This is load-bearing for the strongest claim.
- [§5] §5 (volumetric rate derivation): The conversion from the observed 36 LLIIP events to the quoted rate of (7.3 ± 0.6) × 10^3 Gpc^{-3} yr^{-1} requires the efficiency model to be unbiased across the full range of host properties. Without reported cross-checks against independent surveys or simulated populations, residual systematics at the detection threshold remain unquantified and directly affect the 8% fraction of CC SNe.
minor comments (3)
- [abstract] Abstract: The criteria used to classify events as LLIIP versus normal IIP (beyond the M_r,peak > -16 threshold) and the precise definition of the volume limit are not stated, although they appear later in the text.
- [Figure 3] Figure 3 (luminosity function): Binning and error treatment for the LLIIP subset should be clarified; the faintest bin appears to contain few events and its uncertainty may be underestimated.
- [Table 1] Table 1: Column headers for peak magnitudes and host surface brightness lack explicit units or references to the filter system used.
Simulated Author's Rebuttal
We thank the referee for their positive evaluation of the work's significance and for the detailed comments on the efficiency model. We address each major comment below and will revise the manuscript accordingly to strengthen the presentation of the rate derivation.
read point-by-point responses
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Referee: [methods (efficiency estimation)] Efficiency correction (methods section describing pipeline efficiency): The absolute volumetric rates quoted in the abstract and used for the central claim rest on an efficiency function of apparent magnitude and local surface brightness. No quantitative validation (e.g., injection-recovery statistics, completeness curves, or tests at M_r,peak > -16 in high-surface-brightness hosts) is referenced. If efficiency is overestimated at the faint end, the true LLIIP rate increases and the conclusion that these events 'alone cannot account for' the CC-SFR discrepancy is weakened. This is load-bearing for the strongest claim.
Authors: The efficiency function is constructed from the ZTF detection pipeline performance as a function of apparent magnitude and local surface brightness, following the methodology established in the CLU survey papers. We acknowledge that explicit injection-recovery statistics and completeness curves for the faint LLIIP regime are not presented in the current text. In the revised manuscript we will add a dedicated subsection with these quantitative tests, including simulated populations at M_r,peak > -16 in high-surface-brightness hosts, to directly validate the model and support the quoted rates. revision: yes
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Referee: [§5] §5 (volumetric rate derivation): The conversion from the observed 36 LLIIP events to the quoted rate of (7.3 ± 0.6) × 10^3 Gpc^{-3} yr^{-1} requires the efficiency model to be unbiased across the full range of host properties. Without reported cross-checks against independent surveys or simulated populations, residual systematics at the detection threshold remain unquantified and directly affect the 8% fraction of CC SNe.
Authors: We agree that additional cross-checks would further quantify residual systematics. The rate calculation applies the efficiency correction uniformly and propagates the associated uncertainties; the quoted errors already reflect the dominant contributions from efficiency and sample variance. In revision we will expand §5 to include comparisons with rates from other volume-limited surveys (where overlap exists) and explicit discussion of potential host-property biases at the detection threshold. This will not alter the central numerical results but will make the robustness clearer. revision: partial
Circularity Check
No circularity: observational rate derivation is self-contained
full rationale
The paper measures volumetric rates for Type IIP and LLIIP SNe from a volume-limited survey by applying an efficiency correction (as a function of apparent magnitude and local surface brightness) to the observed sample of 330 + 36 events. No equations or steps reduce the quoted rates (e.g., 7.3e3 Gpc^{-3} yr^{-1}) to fitted parameters by construction, nor does the central claim rely on self-citation chains, imported uniqueness theorems, or ansatzes smuggled via prior work. The efficiency model is presented as an external input to the rate calculation, making the derivation independent and falsifiable against the raw counts and survey volume.
discussion (0)
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