Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. I: Luminosity Function, Volumetric Rate

Kaustav K. Das Thanks: E-mail: kdas@astro.caltech.edu Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Mansi M. Kasliwal Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Christoffer Fremling Affiliation: Caltech Optical Observatories, California Institute of Technology, Pasadena, CA 91125, USA Jesper Sollerman Affiliation: The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691 Stockholm, Sweden Daniel A. Perley Affiliation: Astrophysics Research Institute, Liverpool John Moores University, IC2, Liverpool L3 5RF, UK Kishalay De Affiliation: MIT-Kavli Institute for Astrophysics and Space Research 77 Massachusetts Ave. Cambridge, MA 02139, USA Anastasios Tzanidakis Affiliation: Department of Astronomy and the DiRAC Institute, University of Washington, 3910 15th Avenue NE, Seattle, WA 98195, USA Tawny Sit Affiliation: Department of Astronomy, The Ohio State University, Columbus, OH 43210, USA Scott Adams Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Shreya Anand Affiliation: Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305-4085, USA Tomas Ahumuda Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Igor Andreoni Affiliation: Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255, USA Seán Brennan Affiliation: The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691 Stockholm, Sweden Thomas Brink Affiliation: Department of Astronomy, University of California, Berkeley, CA 94720-3411, USA Rachel J. Bruch Affiliation: Department of Particle Physics and Astrophysics, Weizmann Institute of Science, 234 Herzl St, 76100 Rehovot, Israel Ping Chen Affiliation: Department of Particle Physics and Astrophysics, Weizmann Institute of Science, 234 Herzl St, 76100 Rehovot, Israel Matthew R. Chu Affiliation: Department of Astronomy, University of California, Berkeley, CA 94720-3411, USA David O. Cook Affiliation: IPAC, California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA Sofia Covarrubias Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Aishwarya Dahiwale Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Nicholas Earley Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Anna Y. Q. Ho Affiliation: Department of Astronomy, Cornell University, Ithaca, NY 14853, USA Avishay Gal-Yam Affiliation: Department of Particle Physics and Astrophysics, Weizmann Institute of Science, 234 Herzl St, 76100 Rehovot, Israel Anjasha Gangopadhyay Affiliation: The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691 Stockholm, Sweden Erica Hammerstein Affiliation: Department of Astronomy, University of Maryland, College Park, MD 20742, USA K-Ryan Hinds Affiliation: Astrophysics Research Institute, Liverpool John Moores University, IC2, Liverpool L3 5RF, UK Viraj Karambelkar Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Yihan Kong Affiliation: Institute for Astronomy, University of Hawaii at Manoa, Honolulu S. R. Kulkarni Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Theophile Jegou du Laz Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Chang Liu Affiliation: Department of Physics and Astronomy, Northwestern University, 2145 Sheridan Rd, Evanston, IL 60208, USA Affiliation: Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), 1800 Sherman Ave., Evanston, IL 60201, USA William Meynardie Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Adam A. Miller Affiliation: Department of Physics and Astronomy, Northwestern University, 2145 Sheridan Rd, Evanston, IL 60208, USA Affiliation: Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), 1800 Sherman Ave., Evanston, IL 60201, USA Affiliation: NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA Guy Nir Affiliation: Department of Astronomy, University of California, Berkeley, CA 94720-3411, USA Kishore C. Patra Affiliation: Department of Astronomy, University of California, Berkeley, CA 94720-3411, USA Priscila J. Pessi Affiliation: The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691 Stockholm, Sweden R. Michael Rich Affiliation: Department of Physics & Astronomy, Univ. of California Los Angeles, 430 Portola Plaza, Los Angeles, CA 90095-1547, USA Nabeel Rehemtulla Affiliation: Department of Physics and Astronomy, Northwestern University, 2145 Sheridan Rd, Evanston, IL 60208, USA Affiliation: Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), 1800 Sherman Ave., Evanston, IL 60201, USA Affiliation: NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611, USA Sam Rose Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Ben Rusholme Affiliation: IPAC, California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA Steve Schulze Affiliation: Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), 1800 Sherman Ave., Evanston, IL 60201, USA Yashvi Sharma Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Avinash Singh Affiliation: The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691 Stockholm, Sweden Roger Smith Affiliation: Caltech Optical Observatories, California Institute of Technology, Pasadena, CA 91125, USA Robert Stein Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Milan Sharma Mandigo-Stoba Affiliation: Department of Physics & Astronomy, Univ. of California Los Angeles, 430 Portola Plaza, Los Angeles, CA 90095-1547, USA Nora L. Strotjohann Affiliation: Department of Particle Physics and Astrophysics, Weizmann Institute of Science, 234 Herzl St, 76100 Rehovot, Israel Yu-Jing Qin Affiliation: Cahill Center for Astrophysics, California Institute of Technology, MC 249-17, 1200 E California Boulevard, Pasadena, CA, 91125, USA Jacob Wise Affiliation: Astrophysics Research Institute, Liverpool John Moores University, IC2, Liverpool L3 5RF, UK Avery Wold Affiliation: IPAC, California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA Lin Yan Affiliation: Caltech Optical Observatories, California Institute of Technology, Pasadena, CA 91125, USA Yi Yang Affiliation: Department of Physics, Tsinghua University, Beijing, 100084, China Yuhan Yao Affiliation: Department of Astronomy, University of California, Berkeley, CA 94720-3411, USA Erez Zimmerman Affiliation: Department of Particle Physics and Astrophysics, Weizmann Institute of Science, 234 Herzl St, 76100 Rehovot, Israel

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 $8-12$ $\mathrm{M}_{\odot}$ 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.

I Introduction

Core-collapse supernovae (CCSNe), the explosive endpoints of massive stars, drive chemical evolution in galaxies, provide impetus for the formation of new generations of stars, and leave behind neutron star or black hole remnants. Despite the discovery of over 10,000 SNe, the fate of stars at the low-mass end of CCSNe, with progenitor initial masses of $\approx 8$ –12 $\mathrm{M}_{\odot}$ , is not well understood [50, 107, e.g., see]. Exploring this mass range is important, because it probes the boundary between whether a star has a large enough mass to form a neutron star from its central core or, instead, leaves behind a white dwarf. Also, it is uncertain whether stars in this mass range undergo iron core-collapse from Red Supergiants (RSG) or if electron capture in super-Asymptotic Giant Branch (sAGB) stars drives the SN explosions [79, 53, 61, 59].

Stars in the $8–12$ $\mathrm{M}_{\odot}$ range exhibit a distinctly different core structure compared to those with higher progenitor mass, characterized by significantly lower compactness [107]. The evolution of such stars is more complex to model than at $>12$ $\mathrm{M}_{\odot}$ due to the development of thermal pulses that are numerically challenging to follow, and highly uncertain late-time mass-loss [74, 118, e.g.,]. The lowest mass CCSNe have steep density gradients outside their degenerate cores, liberate less gravitational binding energy, have lower neutrino luminosities, and ultimately are associated with lower luminosities, explosion energy and nickel yield [50, 77, 14, 32, 105, 13, 98, 4, 15].

It is critical to understand this faint end of the core-collapse luminosity function, constrain their rates and couple the stellar evolution initial mass function (IMF) to the known SN populations. Determining the SN rate and tying it to the mass boundary between stars that do and do not explode as SNe is vital for determining the number of neutron stars, galactic chemical evolution and dust evolution models, and the total energy released by SNe into the environment. The CCSN distribution traces the formation of massive stars and the CCSN rate should match the massive star formation rate using the known star-formation rate density and IMF, given our current understanding of stellar evolution. However, there is an apparent discrepancy between the two absolute rates with the star-formation implying a much higher CCSN rate than is observed. This ongoing debate has been discussed extensively in the literature [45, 12, 44, 17, 119, 52, e.g.,]. One possibility is that this disagreement arises from a missing population of low-luminosity CCSNe.

One such class of faint CCSNe is low-luminosity Type IIP SNe (LLIIP SNe), which likely represent the explosions of $8-12$ $\mathrm{M}_{\odot}$ progenitor stars. LLIIP SNe exhibit a faint peak luminosity, defined here as $M_{\textrm{r,peak}}\geq-16$ mag, motivated by prior literature [104, e.g.,]. Recent studies, based on pre-explosion images of SNe 2005cs, 2008bk, 2018aoq, 2022acko [72, 63, 69, 82, 117] also show that SNe with $r$ -band peak $>-16$ mag have progenitor mass estimates less than 11 $\mathrm{M}_{\odot}$ , while those with brighter peak have progenitor masses $>$ 12 M_☉(see Figure 12 in Appendix A). The nickel mass ( $\sim 0.005$ M_☉) of LLIIP SNe is an order of magnitude lower than that typically found in standard Type II SNe [92, e.g.,]. Additionally, LLIIP SNe are characterized by slow expansion velocities (approximately 1300 $-$ 2500 km s^-1 at 50 days after the explosion), indicating low explosion energies. The low energy and low nickel mass could be explained by the explosion of $8-12$ M_☉ progenitors. This is also favored by lightcurve simulations [88, 36], evolutionary numerical simulations of low mass RSGs [64], and nebular spectroscopy [53]. A correlation is also observed between the explosion energy, nickel mass and progenitor mass estimates from pre-SN imaging [32], where lower-mass progenitors ( $<12$ $\mathrm{M}_{\odot}$ ) are associated with lower nickel mass ( $<0.01$ M_☉) and lower explosion energy ( $<2\times 10^{50}$ erg s^-1).

However, there are only about a dozen such objects presented in the literature [112, 104, 49, 78, 89, 120, 114, 60, 10, 109, 26]. For a Salpeter IMF [95], around 50% of the potential CCSN progenitors reside in the $8-12$ $\mathrm{M}_{\odot}$ mass range. Given this fact, it is surprising that such SNe are rarely detected. What do half of the stars that undergo core-collapse explode as? It is likely that this deficit of discovery can be explained by the connection between these relatively low-mass progenitors and the occurrence of low-luminosity SNe, which are more difficult to detect and classify.

In this series of three papers, we present the analysis of a sample of 36 LLIIP SNe detected by Zwicky Transient Facility (ZTF) as part of the Census of the Local Universe (CLU) Survey, which roughly triples the number of existing LLIIP SNe in the literature. The focus of the first paper is to estimate their luminosity function and volumetric rates.

To place these results in a broader context, we also present a sample of 330 Type IIP SNe, obtained from the largest systematic volume-limited SN survey to date. The previous Type II SNe sample from a volume-limited sample survey includes 62 Type II SNe from the Lick Observatory Supernova Search [62, LOSS;]. Other Type II samples from systematics surveys include 50 Type II SNe from the Panoramic Survey Telescope and Rapid Response System [97, PS1;], 34 Type IIP SNe from the Sloan Digital Sky Survey II Supernova Survey [108, SDSS-II SNS;] and 21 Type II SNe from the Caltech Core-collapse SN Project [3, CCCP;].

In Section II.2, we define the sample selection criteria. The data obtained and the extinction correction method are described in Sections III and IV, respectively. In Sections V and VI, we estimate the luminosity function and the volumetric rate of LLIIP SNe and compare with the overall CLU Type IIP SN population. We discuss the implications of the measured LLIIP rates in Section VII and conclude in Section VIII. In companion Papers II and III, we will present the analysis of their lightcurves and nebular spectra, respectively.

II Sample Selection

II.1 Census of the Local Universe

ZTF conducts an optical time-domain survey with the 48-inch Schmidt telescope (P48) situated at Palomar Observatory [7, 40, 29]. The ZTF Census of the Local Universe (CLU) experiment aims to build a comprehensive spectroscopic sample of transients in the local Universe. To accomplish this, we match the hosts of these transients with the galaxy catalog from the Census of the Local Universe [19]. This catalog contains around 234,500 galaxies with established distances, compiled from pre-existing spectroscopic galaxy surveys and the CLU-H $\alpha$ survey (refer to Cook et al. 19 for further details).

The CLU experiment is designed as a volume-limited SN survey where sources at less than 150 Mpc ( $z\leq 0.033$ ) and spatially coincident with (within 30 kpc) or visibly associated with a galaxy in the CLU catalog were assigned for spectroscopic follow-up for classification. For more detailed discussion of the CLU experiment filtering, see De et al. [28]. The filter is implemented on the Global Relay of Observatories Watching Transients Happen (GROWTH) Marshal [55] and the Fritz Marshal [115, 20], both of which are web portals designed for vetting and coordinating transient follow-up observations.

After passing through the CLU filter, the transients are allocated for spectroscopic classification using: the Spectral Energy Distribution Machine [8, 56, SEDM;] at the Palomar 60-inch telescope, the Double-Beam Spectrograph [81, DBSP;] at the Palomar 200-inch Hale telescope, and the Low-Resolution Imaging Spectrometer [80, LRIS;]. In this paper, we consider SNe saved to the CLU experiment starting from 1 October 2018 until 1 April 2024. We exclude the targets saved to ZTF in 2020 as the CLU experiment was inactive during that year. A total of 1745 SNe saved to CLU were classified during this interval.

Refer to caption — Figure 1: Example of SN light curves and the GP interpolations. The apparent magnitudes have been corrected for Galactic and host extinction. The lower panels show the slope of the light curves, i.e., the derivative of the GP fits, in units of mag per day. The vertical dashed red line shows the epoch of the first detection. The vertical dashed grey line shows the epoch of last non-detection. The vertical solid red and blue lines show the epochs of peak $r$ - and $g$ -band magnitudes, respectively.

II.2 Sample of LLIIP SNe

We apply the following selection criteria on the ZTF SN sample obtained from the CLU survey:

1.

Type II Classification: From the CLU sample, 727 candidates classified as Type II (including subtypes IIP, II, IIL, II-norm, and II-pec) were selected.
2.

Peak Magnitude: A Gaussian process fit was applied to the forced photometry light curves, and the sample was restricted to those with a peak magnitude $m_{peak_{r}}<20$ mag, resulting in 719 candidates. The peak magnitude and other lightcurve parameters are measured on this GP fit (see Section III.1 for details.). The 20 mag cut is chosen as the pipeline recovery efficiency and classification completeness drops below 80% for alerts fainter than 20 mag (see Section V).
3.
In order to constrain the $r$ -band peak, we first require that the candidates have more than 10 detections, reducing the sample to 626. We also require that the candidates pass any one of the following conditions:
1. (a)
  
  Candidates with at least one detection before the peak and one after the peak, and for which the duration between the last non-detection and the first detection ( $t_{firstdet}-t_{lastnon-det}$ ) was $\leq 20$ days.
2. (b)
  
  If $t_{firstdet}-t_{lastnon-det}\geq 20$ days, then candidates with more than 10 pre-peak detections and more than 2 post-peak detections, as well as a magnitude difference $mag_{firstdet}-mag_{peak}>1$ mag, were considered.
3. (c)
  
  For candidates where the first detection corresponds to the peak, the last non-detection had to occur within 10 days of the first detection.
394 candidates pass this quality cut.
4.

Plateau Criterion: Candidates were required to show a plateau phase, defined as a duration of $\geq$ 40 days with a drop of less than 1 magnitude, resulting in a sample of 330 candidates.
5.

Peak Absolute Magnitude: To obtain the absolute magnitudes, we use the distance modulus calculated from the host redshift assuming a cosmological model with $\Omega_{M}=0.3$ , $\Omega_{\Lambda}=0.7$ , and $h=0.7$ . For galaxies closer than 25 Mpc, we correct for the Virgo, Great Attractor, and Shapley supercluster infall based on the NASA Extragalactic Database object page [41, NED¹¹ 1 https://ned.ipac.caltech.edu/;]. We ignore corrections due to peculiar motions for galaxies farther than 25 Mpc. If the peculiar motion of the galaxy is $\sim 300$ km s^-1, then the error of the peak magnitude is $\leq 0.1$ mag. Table A lists the distances used for the nearby galaxies and their references. We apply a K-correction of $2.5\times$ log(1 + z) for all the SNe in our sample. The LLIIP sample is restricted to those with a peak absolute magnitude less luminous than $-16$ mag, leaving 48 LLIIP SN candidates.
6.

Host Extinction Correction: Finally, after correcting for host galaxy extinction (see Section IV for details), 36 LLIIP SNe remained in the final sample.

The summary of the selection criteria and the sample are provided in Tables 1 and 2 respectively. The distribution of the number of SNe as a function of peak absolute magnitude is shown in Figure 2. A complementary ZTF experiment is the magnitude-limited Bright Transient Survey [39, 84, BTS;], aimed at classifying transients brighter than a peak apparent magnitude of 18.5 mag without any redshift cut. 62 SNe in our sample are also part of the Type II SN sample of the BTS survey (Y. Qin et al., in prep.; K. Hinds et al., in prep.)

Table 1:

Step	Criteria	# Candidates
1	SNe in CLU classified as Type II (IIP, II, II-norm, II-pec)	727
2	Gaussian process fit to forced photometry lightcurves, $m_{peak_{r}}$ $<$ 20 mag	719
3	Number of detections ( $N_{pre-peak}$ + $N_{post-peak}$ ) $>$ 10
3A	If first detection is the peak, require that the last non-detection is within 10 days of first detection
3B	$\geq$ 1 detection before peak and $\geq$ 1 detection after peak and $t_{firstdet}-t_{lastnon-det}\leq 20$ days	394
3C	If $t_{firstdet}-t_{lastnon-det}\geq 20$ days, $N_{pre-peak}$ $>10$ , $N_{post-peak}$ $>2$ , $mag_{firstdet}-mag_{peak}>$ 1 mag
4	Criteria for plateau $:$ There should be a duration of 40 days with a drop of $<$ 1 mag	330
5	LLIIP: Peak absolute magnitude $\geq$ -16 mag (no host extinction)	48
	LLIIP: Peak absolute magnitude $\geq$ -16 mag (after host extinction)	36

Table 2: Summary table of the CLU Type II SN sample. Only the first 45 rows are shown here. The full table is shown in the Appendix D. The peak absolute magnitudes have been measured by assuming Milky Way extinction (

\mathrm{A_{V,MW}}

) and host galaxy extinction (

\mathrm{A_{V,host}}

) as described in Section IV. The

t_{\rm expl}

column shows the explosion epoch. The Peak mag

{}_{\textrm{r}}

shows the peak

r

-band absolute magnitude. A machine-readable version of the full sample table is available in DOI:10.5281/zenodo.14538857.

Name	RA	Dec	Redshift	$t_{\rm exp}$	1st detection	Peak mag ${}_{\textrm{r}}$	$A_{V,MW}$	$A_{V,\text{host}}$
	(hh:mm:ss)	(dd:mm:ss)		(MJD)	(MJD)	(mag)	(mag)	(mag)
ZTF18abnxfve/AT2018lrz	22:29:52.72	+36:43:50.1	0.025	58347.8	58349.3	-15.94	0.30	0.00
ZTF18abrzbtb/SN2018ggu	07:43:04.67	+50:17:22.2	0.019	59120.0	59120.5	-15.66	0.19	0.00
ZTF19aaabzpt/SN2018lab	06:16:26.51	-21:22:32.4	0.009	58479.3	58487.3	-15.81	0.24	0.71
ZTF19aalycsv/AT2019txj	10:48:39.19	+76:48:05.3	0.023	58543.3	58546.2	-15.27	0.07	0.00
ZTF19aamwhat/SN2019bzd	14:47:32.03	-19:45:57.7	0.008	58559.7	58568.4	-15.83	0.24	0.07
ZTF19aatmadu/AT2019esn	14:51:56.11	+51:15:51.2	0.027	58602.4	58605.4	-16.00	0.06	0.00
ZTF19abalqkq/AT2019khq	17:50:41.76	+14:49:26.3	0.014	58645.9	58650.3	-15.76	0.26	1.63
ZTF19abctzkc/AT2019tti	00:18:59.83	+08:46:28.2	0.019	58644.4	58646.4	-15.67	0.55	0.21
ZTF19abejaiy/SN2019krp	14:07:33.70	+14:38:03.3	0.017	58670.2	58671.2	-15.45	0.04	0.00
ZTF19ablfoqa/AT2019tya	02:09:38.03	+01:33:06.7	0.032	58694.0	58694.5	-15.93	0.08	0.00
ZTF19abllxfy/AT2019ttl	21:52:43.47	+38:56:00.8	0.020	58682.9	58690.3	-15.36	0.88	0.00
ZTF19acalxgp/AT2019qiq	23:44:56.09	-04:16:33.1	0.029	58735.4	58737.3	-15.86	0.12	0.00
ZTF21aabfwwl/SN2021iy	11:18:31.68	-06:16:40.5	0.014	59217.9	59219.4	-15.55	0.14	0.00
ZTF21aaobkmg/SN2021eui	19:20:55.80	+43:07:14.6	0.015	59273.5	59276.5	-15.34	0.28	0.00
ZTF21aaqgmjt/SN2021gmj	10:38:47.27	+53:30:30.3	0.003	59292.3	59293.2	-15.02	0.06	0.00
ZTF21abbomrf/AT2020ghq	14:45:20.59	+38:44:18.5	0.015	59347.8	59349.2	-15.72	0.03	0.00
ZTF21abglcxm/SN2021qcs	15:29:22.82	-12:14:54.4	0.011	59377.3	59378.2	-15.65	0.43	0.42
ZTF21acdoyqt/SN2021zgm	18:35:48.34	+22:27:45.2	0.013	59479.2	59480.1	-15.51	0.44	0.00
ZTF22aaahubo/AT2022cru	08:23:26.30	-04:55:06.5	0.023	59575.4	59600.3	-15.89	0.12	0.00
ZTF22aakdbia/SN2022jzc	12:05:28.66	+50:31:36.8	0.002	59714.3	59715.2	-14.91	0.05	0.57
ZTF22aanrqje/SN2022mji	09:42:54.06	+31:51:03.6	0.004	59732.7	59741.2	-15.00	0.05	0.85
ZTF22aaywnyg/SN2022pru	11:59:07.65	+52:41:58.5	0.004	59787.7	59797.2	-15.42	0.07	0.00
ZTF22aazmrpx/SN2022raj	02:03:17.52	+29:14:04.9	0.012	59798.4	59800.4	-15.20	0.15	0.00
ZTF22abssiet/SN2022zmb	10:38:43.18	+56:33:14.4	0.014	59885.5	59887.5	-15.72	0.02	0.07
ZTF22abvaetz/SN2022aang	07:59:21.83	+18:06:40.9	0.016	59894.5	59901.5	-15.44	0.08	0.00
ZTF22abyivoq/SN2022acko	03:19:38.98	-19:23:42.8	0.006	59917.8	59922.2	-15.83	0.08	0.00
ZTF23aaarmtb/SN2023qh	09:07:15.44	+37:12:54.8	0.024	59947.4	59957.4	-15.89	0.06	0.00
ZTF23aackjhs/SN1995al	09:50:56.03	+33:33:11.0	0.005	59989.8	59992.3	-14.88	0.04	0.07
ZTF23aagkajy/AT2023gdt	10:30:10.64	+43:21:23.4	0.014	60050.2	60051.2	-15.42	0.03	0.71
ZTF23aajrmfh/SN2023ijd	12:36:32.47	+11:13:19.7	0.007	60078.2	60079.0	-15.39	0.09	0.00
ZTF23aamzlzc/AT2023kne	17:25:19.11	+58:49:02.6	0.028	60095.4	60097.3	-15.81	0.10	0.00
ZTF23aaqwpio/AT2023nca	16:39:26.36	+11:12:45.3	0.023	60129.3	60135.2	-15.61	0.14	0.00
ZTF23aasyvbf/SN2023nmh	00:37:38.73	-04:16:53.2	0.020	60142.4	60144.5	-15.93	0.10	0.00
ZTF23abnogui/SN2023wcr	12:23:31.29	+74:57:01.3	0.005	60240.5	60244.5	-15.58	0.09	0.00
ZTF23abvommm/SN2023acbr	02:27:03.18	-09:25:02.3	0.016	60300.7	60305.2	-15.57	0.08	0.00
ZTF24aabppgn/SN2024wp	11:24:39.25	+14:56:52.9	0.014	60320.5	60325.5	-15.52	0.10	0.00
ZTF24aahwfsa/AT2024fas	10:51:55.36	+37:35:23.9	0.026	60393.4	60396.4	-15.80	0.04	0.00
ZTF24aaplfjd/SN2024jxm	0:58:01.36	+30:42:23.84	0.016	60460.0	60460.5	-16.00	0.18	0.00

III Data

In this section, we describe the photometric and spectroscopic data used.

III.1 Optical photometry

We perform forced point-spread function photometry on the ZTF difference images at the location of these SNe using the ZTF forced-photometry service developed by Masci et al. [66], Masci et al. [67] in $g$ and $r$ bands. For this work, we consider anything less than a 3 $\sigma$ detection an upper limit. We used a Gaussian Process (GP) algorithm²² 2 https://george.readthedocs.io/ [2] to interpolate the photometric measurements and measure the slope. Figure 1 shows the lightcurves of SNe 2023wcr and 2024wcs as examples. The vertical dashed red line shows the epoch of first detection, and the vertical dashed grey line shows the epoch of last non-detection. The explosion epoch is defined to be midway between the epoch of first detection and last non-detection. The epochs of peak $r$ - and $g$ -band magnitudes are shown by the vertical solid red and blue lines. The photometry data, lightcurve plots, and spectroscopy of all SNe in the sample will be made available on WISEREP and Zenodo after publication.

III.2 Optical spectroscopy

For each transient, at least one spectrum is usually obtained close to the peak luminosity to establish a spectroscopic classification as described in Section II.1. We employ the SuperNova Identification [9, SNID;] code for these classifications. For spectra that show host galaxy contamination, we utilize superfit [46]. The reduction of DBSP spectra follows the procedures outlined in Bellm and Sesar [6] and Roberson et al. [91], while the SEDM data reductions are detailed in Rigault et al. [90]. The LRIS spectra were reduced using the automated lpipe [83] pipeline.

We also make use of spectra from the Alhambra Faint Object Spectrograph and Camera at the Nordic Optical Telescope [30, NOT;] and from the Spectrograph for the Rapid Acquisition of Transients [85, SPRAT;] on the Liverpool Telescope. The NOT data reduction utilized the PyNOT³³ 3 https://github.com/jkrogager/PyNOT and PypeIt [87] pipelines. The SPRAT data is processed using a pipeline based on FrodoSpec [5].

IV Extinction Correction

Extinction is divided into two components: the first component represents dust extinction from the Milky Way, while the second component accounts for extinction originating from the host galaxies of the SNe. To correct for Galactic extinction, we employ the reddening maps provided by Schlafly and Finkbeiner [100], the extinction law described by Cardelli et al. [18] and a value of $R_{V}=3.1$ .

To estimate the host-galaxy extinction, we use the average $g-r$ color curve of the sample. First, we measure the Na I D absorption lines of the host environment [86, 106] when a high SNR SN spectrum is available. To measure the template $g-r$ color, we discard objects with Na I D EW $>$ 1 Å. The 1 $\sigma$ scatter is 0.35 mag. The average color and standard deviation (1 $\sigma$ range) are shown by the black solid line and blue shaded region, respectively, in Figure 3. This template is available on Zenodo. We then measure the $A_{\textrm{V,host}}$ assuming that the SN color exceeding the 1 $\sigma$ $g-r$ template is caused by host extinction. 80 out of the 330 Type IIP SNe has $A_{\textrm{V,host}}>0$ . The sample color curve after this correction is shown in the right panel of Figure 3. The remaining 1 $\sigma$ scatter in the $g-r$ color is likely due to intrinsic properties of the SNe, which could be dominated by photospheric temperature differences [27, e.g., see]. Table 2 lists the measured $A_{\textrm{V,host}}$ values.

V Luminosity function

With the use of our systematic and controlled ZTF CLU event sample, we determine a luminosity function for Type IIP and LLIIP SNe. The CLU LLIIP sample nearly triples the current number of published LLIIP SNe in the literature. Our final sample comprises 330 Type IIP SNe, with 36 ( $\approx$ 11%) LLIIP SNe ( $M_{r}>-16$ mag).

We use the $r$ -band photometry to measure the SN II luminosity function and volumetric rate. A volume correction is applied to the luminosity function by weighting each object by $V_{max}=1/D^{3}_{max}$ , where $D_{max}$ ( $\propto 10^{\frac{1}{5}(20-M_{r},peak)}$ ) is the farthest distance at which a transient can be detected given a limiting magnitude of 20 mag. For sources detectable past 150 Mpc, $D_{max}$ was set to the maximum CLU experiment distance of 150 Mpc.

We also correct for the CLU spectroscopic completeness and the ZTF pipeline recovery efficiency factor as they are dependent on the apparent magnitude. Of the total of 2064 transients that are saved in CLU, 1745 (84.6%) transients are classified. The overall completeness of the CLU survey as a function of peak apparent magnitude is indicated by the dashed black line in Figure 4. There are 79 transients for which we are uncertain whether they are real transients on the basis of their spectroscopic and photometric properties. If we exclude these, the overall completeness is $\sim$ 87% (solid black line). Also, we note that the long-lived nature and relative ease of identifying the H $\alpha$ P-Cygni feature make Type IIP events easier to classify. So the Type IIP SNe completeness is likely to be higher than the solid black line. As a conservative estimate, for the rest of the paper, we use the spectroscopic completeness depicted by the dashed black line.

The ZTF image subtraction pipeline has two potential sources of inefficiency relevant to this calculation. In each science image, the pipeline actively masks pixels affected by quality criteria, such as saturation from high brightness, cosmic rays, and defective pixels. This dynamic masking does not have a significant effect as Type IIP SNe have long durations ( $\sim$ 100 days) and are eventually detected by the pipeline. A significant issue relates to the decreased efficiency of the image subtraction algorithm for faint alerts and on bright galaxy backgrounds. Here, we model the efficiency of the ZTF pipeline as a function of the alert flux and the ratio of background surface brightness to target flux. To estimate this, we check whether alert photometry exists for a given epoch for which there is a $\geq 5\sigma$ forced photometry detection. The local host surface brightness is extracted using PS1 $r$ -band images with a 3 $"$ radius aperture. To estimate the conditional probability of the ZTF pipeline efficiency, we consider the binary data, $X$ , which represents whether the ZTF pipeline successfully recovers a transient in alerts ( $X=1$ ) or not ( $X=0$ ). We model $X$ using the Bernoulli distribution:

X\sim\text{Bern}(p),

where $p$ is parameterized by a logistic function dependent on the ratio ( $r$ ) of the host surface brightness and the target flux:

p(r)=\frac{1}{1+\exp(a(r-c))},

with $a$ and $c$ representing the model parameters that need to be determined. The logistic function was chosen because it smoothly transitions from 1 to 0 and captures the pipeline efficiency behavior as a function of $r$ . We then estimate the model parameters using the emcee package [34]. The best-fit values of $a$ and $c$ are $a=1.12^{+0.02}_{-0.02}$ and $c=0.21^{+0.03}_{-0.03}$ respectively. As expected, the recovery efficiency decreases as this ratio increases. The best-fit efficiency is $\sim 50\%$ when the host surface brightness and target fluxes are comparable.

We also measure the inefficiency as a function of the apparent magnitude alone, using

p(m)=\frac{1}{1+\exp(b(m-d))},

where $m$ is the alert apparent magnitude. The best-fit values obtained are $b=2.28^{+0.03}_{-0.04}$ mag^-1 and $d=20.57^{+0.01}_{-0.01}$ mag. The best fits, and corner plots of the MCMC fit are shown in Figure 5 and Appendix C, respectively. The best-fit efficiency decreases with an increase in target alert apparent magnitude and is $\approx$ 80% when the alert magnitude is 20 mag.

We then weight each SN by the corresponding classification completeness factor and the pipeline efficiency factor. The luminosity function thus obtained is shown in Figure 6 in blue. The luminosity function without volume correction is shown by the green dashed lines in Figure 6. After volume-, incompleteness- and pipeline-efficiency correction, the fraction of LLIIP SNe is $19^{+3}_{-4}\%$ of the entire Type II SN population (see Figure 9). The fraction of Type IIP SNe fainter than $-15$ mag and $-14.5$ are $<5\%$ and $<1.5\%$ respectively.

VI The Volumetric rate of Type IIP SNe using skysurvey

First, we create a template $r$ -band lightcurve by compiling the photometry data for all the Type IIP SNe in the sample and apply Gaussian Regression processing (Figure 7). We use the skysurvey code⁴⁴ 4 https://github.com/MickaelRigault/skysurvey to estimate the volumetric rate. skysurvey simulates light curves as they would be observed based on a light curve template (using sncosmo) and survey plan. The survey plan is constructed using the actual pointing schedule of ZTF, detailing observation times, the filters used, and the sky brightness. Additionally, the simulation incorporates data on CCD outlines to account for data losses caused by chip gaps.

For the selection of simulated SNe II from skysurvey, we use the forced photometry-based selection criteria outlined in Section II.2. We require at least two 5 $\sigma$ detections exceeding 20 mag in brightness. These criteria guarantee detection by the ZTF alert system, allowing the transient to pass the CLU filter, with a distance $<150$ Mpc to ensure appropriate CLU filter saving and spectroscopic follow-up. Additionally, the lightcurve quality cuts described in Section II.2 are employed to consistently filter the skysurvey sample.

We generate ZTF lightcurves of Type IIP SNe for a range of input volumetric rates ( $0.1$ – $10.0\times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ ) for the skysurvey simulation up to a distance of 150 Mpc, according to the luminosity function in Figure 6. At each rate, 30 survey plan simulations were conducted. For these simulations, we documented the number of transients that successfully passed the filtering process. The detection estimate for each rate was made by computing the mean number of transients detected from the simulations, with the standard deviation serving as the error estimate. Figure 8 shows the expected number of detected transients along with the fraction of simulations that yield our observed sample size within an error margin of 10% for a given input volumetric rate.

The fraction of simulations matching our sample size follows a skewed Gaussian distribution, which was used to estimate the rate and its uncertainty based on a 68% confidence interval. This analysis yields a volumetric rate of $(2.6_{-0.3}^{+0.3})\times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ .

This raw skysurvey rate already accounts for survey conditions such as weather and cadence as we used the real observing history of ZTF to simulate the light curves as they would have been observed. We now apply corrections for spectroscopic incompleteness, pipeline recovery efficiency and galaxy catalog incompleteness. As shown in Section V, the CLU experiment achieved an overall completeness rate of $\approx$ 84%.

In the CLU experiment, our methodology restricts the identification of SNe to those in galaxies where a spectroscopic redshift has been established. Therefore, we must correct the skysurvey rate of LLIIP events for galaxy catalog incompleteness. To correct for the incompleteness of the galaxy catalog, we used the redshift completeness factor derived from the ZTF Bright Transient Survey [39]. We use the redshifts and WISE 3.36 $\mu m$ absolute magnitudes (MW1) of the host galaxies of our sample and weight each event by 1/RCF(z,MW1) to estimate the effect of galaxy catalog incompleteness (see Figure 10). This leads to an underestimate of the volumetric rate by $\approx$ 19%.

Finally, as mentioned earlier, the ZTF image subtraction pipeline has a reduced efficiency of the image subtraction algorithm on bright galaxy backgrounds and fainter targets. We weight each event by the inverse of the Pipeline Efficiency Factor (1/PEF(M, host)). This leads to an underestimate of the rate by $\approx$ 6%.

Applying the corrections described above, we derive a corrected 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.

VII Discussion

VII.1 Comparison to the literature

The ZTF CLU sample of 330 SNe is the largest sample of Type IIP SNe from a systematic volume-limited survey to date. In Figure 11, we compare the luminosity function with the $r$ -band Type II Luminosity functions from previous systematic SN surveys $-$ 62 Type II SNe from the Lick Observatory Supernova Survey Search [62, LOSS;], 21 Type II SNe from the Caltech Core-Collapse Project [3, CCCP;], 50 Type II SNe from the Panoramic Survey Telescope and Rapid Response System [97, PS1;], 34 Type IIP SNe from the Sloan Digital Sky Survey II Supernova Survey [31, 108, SDSS-II SNS;]. We also compare with the sample of 241 Type II SNe from the ZTF BTS used to analyze the rise-time properties of Type II SNe (K. Hinds et al., in prep.). In comparison with the LOSS sample, which is corrected for luminosity bias, we show the volume- and host-extinction corrected distribution in solid black and the volume-corrected but not host-extinction-corrected distribution in dashed violet. Our distribution without host extinction correction matches better with the LOSS luminosity function. In the comparison with the PS1 and CCCP samples, the CLU sample in dashed green is not corrected for luminosity bias. We note that the PS1 $r$ -band sample was part of a flux-limited survey and the observed distribution follows what one would typically expect without a volume correction. The CCCP experiment was also a flux-limited SN experiment. The observed CCCP distribution falls close to the mean and the fainter end of the dispersion of the ZTF CLU sample without any volume correction. The SDSS Type IIP SN sample from D’Andrea et al. [31], which shows the $I$ -band magnitude at 50 days post-explosion, does not have any SN fainter than $-16$ mag. The volume-corrected distribution of the peak $r$ -band magnitude of the BTS Type II SN sample (K. Hinds et al., in prep.) in shaded green closely resembles the ZTF CLU distribution, with a higher fraction of fainter SNe in ZTF CLU sample without any volume correction (in orange), consistent with the experiment design.

We obtained a LLIIP fraction of $19^{+3}_{-4}\%$ of the host-extinction corrected sample of CLU Type II SNe. The fraction of LLII SNe is $\sim$ 45% for the LOSS volume-corrected luminosity function without any host-extinction correction. The fraction of LLIIP in the raw PS1 and CCCP sample distribution is $\sim 3\%$ and $\sim 9\%$ , respectively.

The volumetric rate for Type II SNe from the LOSS survey was $(4.4^{+1.4}_{-1.4})\times 10^{4}\ \textrm{Gpc}^{-3}\textrm{yr}^{-1}$ . Based on the LOSS survey statistics [62], Type IIP SNe are 70% of all Type II SNe, thus the Type IIP SN rate is $\sim$ $(3.1^{+1.0}_{-1.0})\times 10^{4}\ \textrm{Gpc}^{-3}\textrm{yr}^{-1}$ , which is consistent with the volumetric rate measured with the CLU sample. The Bright Transient Survey [84, BTS;] and the Sloan Digital Sky Survey II Supernova Survey [108, SDSS-II SNS;] measured an overall core-collapse SN rate. BTS measured a CCSN rate of $(10.1^{+5.3}_{-3.5})\times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ , and SDSS-II SNS measured a CCSN rate of $(10.6^{+1.9}_{-1.9})\times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ . If we assume Type II SNe constitute 57% of all CCSNe and Type IIP SNe are 70% of all Type II SNe [62], the Type IIP SN rates in BTS and SDSS-II would be $(4.0^{+2.1}_{-1.4})\times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ and $(4.2^{+0.8}_{-0.8})\times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ respectively, which is consistent with the ZTF CLU volumetric rate.

VII.2 Can LLIIP SNe account for the fate of all $8-12$ $\mathrm{M}_{\odot}$ stars?

The fraction of massive stars that explode in this mass range is given by:

\frac{\int_{M_{\text{min}}}^{12}\psi(M)\,dM}{\int_{M_{\text{min}}}^{M_{\text{max}}}\psi(M)\,dM},

where $M_{\text{min}}$ is the minimum progenitor mass that explodes, $M_{\text{max}}$ is the maximum progenitor mass of stars that explode, and $\psi(M)\propto M^{-2.35}$ for the Salpeter IMF. If we assume $M_{\text{max}}=40$ M_☉ and $M_{\text{min}}=8$ M_☉, $8-12$ $\mathrm{M}_{\odot}$ progenitors should account for $48\%$ of all massive stars that collapse. If we assume $M_{\text{max}}=100$ M_☉ and $M_{\text{min}}=8$ M_☉, $8-12$ M_☉ progenitors account for 44% of all massive stars that explode. However, even though LLIIP SNe are the main observational candidates for low-mass CCSNe, we find that LLIIP SNe constitutes only $19^{+3}_{-4}\%$ of all Type IIP SNe. If we assume that $40\%$ of all CCSNe are Type IIP SNe [62], LLIIP SNe are only $8^{+1}_{-2}\%$ of all CCSNe. The mean local star-formation rate density within $\sim$ 150 Mpc is $(17^{+7}_{-5})\times 10^{6}\ \mathrm{M_{\odot}\ Gpc^{-3}\ yr^{-1}}$ [45]. This is also in good agreement with the local cosmic star-formation rate (SFR) of $(18.5\pm 1.20)\times 10^{6}\ \mathrm{M_{\odot}\ Gpc^{-3}\ yr^{-1}}$ [43]. Assuming a SFR to SN rate conversion factor of 0.0088 [45], the local SN rate would be $\approx 14\times\mathrm{10^{4}\ yr^{-1}\ Gpc^{-3}}$ . Assuming a Salpeter IMF, the expected SN rate for $8-12$ $\mathrm{M}_{\odot}$ is $\approx 6.7\times 10^{4}\ \mathrm{yr^{-1}\ Gpc^{-3}}$ , which is more than a factor of $9.2^{+0.8}_{-0.7}$ higher than the ZTF CLU LLIIP SN rate. How can this inconsistency be accounted for?

Observational bias: A possible dominant effect is that many SNe are missed because they are too faint for current optical surveys. We can rule out that faint IIP SN ( $>-14.5$ mag) account for the missing SNe population as they account for $<1.5\%$ of the entire Type IIP SNe population. However, we note that ZTF CLU is sensitive to only 25 Mpc for potential SNe fainter than $-12$ mag and up to 40 Mpc for potential SNe fainter than $-13$ mag. If Type IIP SNe alone were the fate of all progenitors in this mass range, we require that all Type IIP SN fainter than $\sim-18$ mag originate from this progenitor mass range. The ZAMS mass for the entire sample will be estimated in Papers II and III. The other scenario is that current optical transient surveys like CLU might be missing a significant fraction of low-mass explosions due to extinction [52, 35, e.g., see]. Mattila et al. [68] estimate that locally $18.9^{+19.2}_{-9.5}\%$ of the CCSNe are missed by optical surveys. Jencson et al. [52] predict an even higher fraction, that $38.5^{+26.0}_{-21.9}\%$ of the CCSNe are missed by optical surveys. Jencson et al. [52] speculate that the objects SPIRITS16ix and SPIRITS16tn may represent a class of low-energy CCSNe arising preferentially in particularly extinguished environments. SPIRITS16tn is consistent with a LLIIP SN, heavily obscured by $A_{V}=7-9$ mag. Another source of observational bias against LLIIP SNe could be the CLU galaxy catalog, if LLIIP SNe preferentially occurs in small galaxies.

Explode as other classes of transients: We know that the majority ( $\sim$ 70%) of young massive stars live in interacting binary systems and the outer envelopes of $\sim$ 33% of massive stars are stripped [96, e.g., see]. Thus, it is possible that a significant fraction of $8-12$ $\mathrm{M}_{\odot}$ stars explode as stripped-envelope SNe in binary star systems. These stars with low-mass iron cores produce a low amount of Ni and ejecta mass [105, 98, 15, 76, 99]. Candidates for low nickel mass stripped-envelope SNe include double-peaked Type Ibc SNe such as SN 2021inl [48, 25], rapidly evolving Type IIb SNe with a half-life of less than 10 days [e.g., 42, 24, Fremling et al., in prep], ultra-stripped SNe such as SN 2023zaw [23, 75]. Moore et al. [75] measured a volumetric rate of rapidly-evolving SESNe of $\approx(2.5^{+2.5}_{-1.4}\pm 0.9)\times 10^{3}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ , which is only $\approx 5\%$ of the Type IIP SN rate. A systematic study of the lowest-nickel mass stripped-envelope SNe in CLU, including their rates and the nickel mass distribution will be explored in a future work. Also, $\sim 7-9$ $\mathrm{M}_{\odot}$ sAGB stars are predicted to undergo electron-capture SNe [79], which could be the origin of the class of Intermediate Luminosity Red Transients (ILRTs) [11, 1, 51, 94, 113, e.g., AT 2019abn, NGC 300 2008OT-1, SN 2008S;]. Observations indicate that ILRTs comprise $1-10\%$ of the overall CCSNe rate [110, 16, 54].

Stellar evolution and explosion models: The third explanation could be our limited theoretical understanding regarding the evolution and fate of $8-12$ $\mathrm{M}_{\odot}$ progenitors. Stars in this mass range have a qualitatively different core structure than at $>12$ $\mathrm{M}_{\odot}$ , with significantly lower compactness [107]. Their evolution is significantly more complex to model due to degeneracy effects. They develop thermal pulses that are numerically challenging to follow. Due to the steeply falling nature of the IMF, the lower mass limit $M_{\textrm{min}}$ strongly affects the fraction of massive stars that explode. For example, if $M_{\textrm{min}}=9.7$ $\mathrm{M}_{\odot}$ , then the fraction of massive stars $<12$ $\mathrm{M}_{\odot}$ is $\sim 25\%$ . To match the observed rate of LLIIP SNe, we require a minimum progenitor mass of $M_{\textrm{min}}=11.3^{+0.6}_{-0.7}\,M_{\odot}$ . The low rate of LLIIP SNe can be reasonably explained if a fraction of potential progenitors within this mass range either fail to undergo Fe-core collapse or do not successfully explode. This outcome is plausible if their cores fail to achieve the necessary physical conditions for core collapse. Another possibility in binary stellar evolution is stellar mergers. Sana et al. [96] predict that more than half of the progenitors of Type II SNe are merged stars or binary mass gainers. Thus, if single stars in the $8-12$ $\mathrm{M}_{\odot}$ merge, then we might instead observe the SN explosions of these progenitors that have a higher mass and undergo more luminous explosions [122, 121].

It is likely that one or more of the above factors play a role in explaining the fate of all the lowest-mass stars that undergo core collapse.

VIII Summary

In summary, we present the largest sample of 330 Type IIP SNe to derive the luminosity function to-date from a spectroscopic complete volume-limited SN survey. The sample is particularly critical to understand the low-luminosity population of CCSNe, with the sample of 36 LLIIP SNe ( $M_{\textrm{r,peak}}\geq-16$ mag) tripling the sample of LLIIP SNe in the literature. The key takeaways from the analysis are:

1.

The luminosity function peaks at $-17$ mag and does not show a significant population $>-14.5$ mag, accounting for $<$ 1.5 % of all Type IIP SNe. The fraction of LLIIP SNe is $19^{+3}_{-4}\%$ of the Type IIP SNe population and $8^{+1}_{-2}\%$ of the CCSN population (see Figure 6).
2.

We model the efficiency of the ZTF subtraction pipeline as a function of the alert flux and the ratio of background surface brightness to target flux. As expected, the recovery efficiency decreases as this ratio increases. The best-fit efficiency is $\sim 50\%$ when the host surface brightness and target fluxes are comparable. Similarly, the best-fit efficiency decreases with an increase in target alert apparent magnitude and is $\approx$ 80% when the target magnitude is 20 mag (see Figure 5).
3.

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 (see Figures 8, 9).
4.

The expected SN rate for $8-12$ $\mathrm{M}_{\odot}$ is $\approx 6.7\times 10^{4}\ \mathrm{yr^{-1}\ Gpc^{-3}}$ , which is more than a factor of $9.2^{+0.8}_{-0.7}$ higher than the ZTF CLU LLIIP SNe rate. While LLIIP SNe represent the explosions of the lowest massive stars that explode, they cannot account for all $8-12$ $\mathrm{M}_{\odot}$ progenitors.

The robust LLIIP rate measured in this work shows a significant discrepancy between the calculated and predicted volumetric rates of SNe in the low mass-end ( $8-12$ $\mathrm{M}_{\odot}$ ) of core-collapse SNe. Future deep surveys such as the Legacy Survey of Space and Time [47, LSST;] in synergy with high-cadenced surveys of ZTF could reveal a hidden population of $\geq-13$ mag CCSNe. To enhance the efficiency of spectroscopic completeness for faint SNe, we will conduct the ZTF Complete Astronomical Transient Survey within 150 Mpc [22, CATS150;]. CATS150 will utilize the high efficiency of the Next Generation Palomar Spectrograph (NGPS) on the Palomar 200-inch Hale Telescope to achieve spectroscopic completeness exceeding 95% for even the faintest SNe. Also, SN survey in the local universe carried out at longer wavelengths by ground-based surveys such as the Wide-field Infrared Transient Explorer [65, WINTER;], Dynamic REd All-sky Monitoring Survey [103, DREAMS;], Prime-focus Infrared Microlensing Experiment [58, PRIME;], Cryoscope and space-based telescopes such as the Nancy Grace Roman Space Telescope will be critical in constraining the missing SN population due to dust obscuration.

IX Data Availability

All the photometric and spectroscopic data of the SNe in the sample will be made available on WISEREP and Zenodo after publication. The meta-data of the sample, extinction template, and pipeline recovery efficiency are available as a machine-readable tables on Zenodo.

X Acknowldegement

We thank the anonymous referee for their constructive feedback, which helped improve the quality of this manuscript.

Based on observations obtained with the Samuel Oschin Telescope 48-inch and the 60-inch Telescope at the Palomar Observatory as part of the Zwicky Transient Facility project. ZTF is supported by the National Science Foundation under Grants No. AST-1440341 and AST-2034437 and a collaboration including current partners Caltech, IPAC, the Oskar Klein Center at Stockholm University, the University of Maryland, University of California, Berkeley , the University of Wisconsin at Milwaukee, University of Warwick, Ruhr University Bochum, Cornell University, Northwestern University and Drexel University. Operations are conducted by COO, IPAC, and UW.

Zwicky Transient Facility access for N.R., A.A.M., S.S., and C.L. was supported by Northwestern University and the Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA). N.R., C.L., and A.A.M. are supported by DoE award #DE-SC0025599.

S. Schulze is partially supported by LBNL Subcontract 7707915.

SED Machine is based upon work supported by the National Science Foundation under Grant No. 1106171.

The ZTF forced-photometry service was funded under the Heising-Simons Foundation grant #12540303 (PI: Graham).

The Gordon and Betty Moore Foundation, through both the Data-Driven Investigator Program and a dedicated grant, provided critical funding for SkyPortal .

This research has made use of the NASA/IPAC Extragalactic Database (NED), which is funded by the National Aeronautics and Space Administration and operated by the California Institute of Technology.

The Liverpool Telescope is operated on the island of La Palma by Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias with financial support from the UK Science and Technology Facilities Council.

The W. M. Keck Observatory is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.

References

[1] S. M. Adams, C. S. Kochanek, J. L. Prieto, X. Dai, B. J. Shappee, and K. Z. Stanek (2016) Almost gone: SN 2008S and NGC 300 2008OT-1 are fainter than their progenitors. MNRAS 460 (2), pp. 1645–1657. External Links: Document, 1511.07393 Cited by: §VII.2.
[2] S. Ambikasaran, D. Foreman-Mackey, L. Greengard, D. W. Hogg, and M. O’Neil (2015) Fast Direct Methods for Gaussian Processes. IEEE Transactions on Pattern Analysis and Machine Intelligence 38, pp. 252. External Links: Document, 1403.6015 Cited by: §III.1.
[3] I. Arcavi, A. Gal-Yam, S. B. Cenko, D. B. Fox, D. C. Leonard, D. Moon, D. J. Sand, A. M. Soderberg, M. Kiewe, O. Yaron, A. B. Becker, R. Scheps, G. Birenbaum, D. Chamudot, and J. Zhou (2012) Caltech Core-Collapse Project (CCCP) Observations of Type II Supernovae: Evidence for Three Distinct Photometric Subtypes. ApJ 756 (2), pp. L30. External Links: Document, 1206.2029 Cited by: §I, §VII.1.
[4] B. L. Barker, C. E. Harris, M. L. Warren, E. P. O’Connor, and S. M. Couch (2022) Connecting the Light Curves of Type IIP Supernovae to the Properties of Their Progenitors. ApJ 934 (1), pp. 67. External Links: Document, 2102.01118 Cited by: §I.
[5] R. M. Barnsley, R. J. Smith, and I. A. Steele (2012) A fully automated data reduction pipeline for the FRODOSpec integral field spectrograph. Astronomische Nachrichten 333 (2), pp. 101–117. External Links: Document, 1112.2574 Cited by: §III.2.
[6] E. C. Bellm and B. Sesar (2016) pyraf-dbsp: Reduction pipeline for the Palomar Double Beam Spectrograph. Note: Astrophysics Source Code Library External Links: 1602.002 Cited by: §III.2.
[7] E. C. Bellm, S. R. Kulkarni, M. J. Graham, R. Dekany, R. M. Smith, R. Riddle, F. J. Masci, G. Helou, T. A. Prince, S. M. Adams, C. Barbarino, T. Barlow, J. Bauer, R. Beck, J. Belicki, R. Biswas, N. Blagorodnova, D. Bodewits, B. Bolin, V. Brinnel, T. Brooke, B. Bue, M. Bulla, R. Burruss, S. B. Cenko, C. Chang, A. Connolly, M. Coughlin, J. Cromer, V. Cunningham, K. De, A. Delacroix, V. Desai, D. A. Duev, G. Eadie, T. L. Farnham, M. Feeney, U. Feindt, D. Flynn, A. Franckowiak, S. Frederick, C. Fremling, A. Gal-Yam, S. Gezari, M. Giomi, D. A. Goldstein, V. Z. Golkhou, A. Goobar, S. Groom, E. Hacopians, D. Hale, J. Henning, A. Y. Q. Ho, D. Hover, J. Howell, T. Hung, D. Huppenkothen, D. Imel, W. Ip, Ž. Ivezić, E. Jackson, L. Jones, M. Juric, M. M. Kasliwal, S. Kaspi, S. Kaye, M. S. P. Kelley, M. Kowalski, E. Kramer, T. Kupfer, W. Landry, R. R. Laher, C. Lee, H. W. Lin, Z. Lin, R. Lunnan, M. Giomi, A. Mahabal, P. Mao, A. A. Miller, S. Monkewitz, P. Murphy, C. Ngeow, J. Nordin, P. Nugent, E. Ofek, M. T. Patterson, B. Penprase, M. Porter, L. Rauch, U. Rebbapragada, D. Reiley, M. Rigault, H. Rodriguez, J. van Roestel, B. Rusholme, J. van Santen, S. Schulze, D. L. Shupe, L. P. Singer, M. T. Soumagnac, R. Stein, J. Surace, J. Sollerman, P. Szkody, F. Taddia, S. Terek, A. Van Sistine, S. van Velzen, W. T. Vestrand, R. Walters, C. Ward, Q. Ye, P. Yu, L. Yan, and J. Zolkower (2019) The Zwicky Transient Facility: System Overview, Performance, and First Results. PASP 131 (995), pp. 018002. External Links: Document, 1902.01932 Cited by: §II.1.
[8] N. Blagorodnova, J. D. Neill, R. Walters, S. R. Kulkarni, C. Fremling, S. Ben-Ami, R. G. Dekany, J. R. Fucik, N. Konidaris, R. Nash, C.-C. Ngeow, E. O. Ofek, D. O’ Sullivan, R. Quimby, A. Ritter, and K. E. Vyhmeister (2018) The SED Machine: A Robotic Spectrograph for Fast Transient Classification. PASP 130 (3), pp. 035003. External Links: 1710.02917, Document Cited by: §II.1.
[9] S. Blondin and J. L. Tonry (2007) Determining the Type, Redshift, and Phase of a Supernova Spectrum. In The Multicolored Landscape of Compact Objects and Their Explosive Origins, T. di Salvo, G. L. Israel, L. Piersant, L. Burderi, G. Matt, A. Tornambe, and M. T. Menna (Eds.), American Institute of Physics Conference Series, Vol. 924, pp. 312–321. External Links: astro-ph/0612512, Document Cited by: §III.2.
[10] K. A. Bostroem, L. Dessart, D. J. Hillier, M. Lundquist, J. E. Andrews, D. J. Sand, Y. Dong, S. Valenti, J. Haislip, E. T. Hoang, G. Hosseinzadeh, D. Janzen, J. E. Jencson, S. W. Jha, V. Kouprianov, J. Pearson, N. E. Meza Retamal, D. E. Reichart, M. Shrestha, C. Ashall, E. Baron, P. J. Brown, J. M. DerKacy, J. Farah, L. Galbany, J. I. González Hernández, E. Green, P. Hoeflich, D. A. Howell, L. A. Kwok, C. McCully, T. E. Müller-Bravo, M. Newsome, E. P. Gonzalez, C. Pellegrino, J. Rho, M. Rowe, M. Schwab, M. Shahbandeh, N. Smith, J. Strader, G. Terreran, S. D. Van Dyk, and S. Wyatt (2023) SN 2022acko: The First Early Far-ultraviolet Spectra of a Type IIP Supernova. ApJ 953 (2), pp. L18. External Links: Document, 2305.01654 Cited by: §I.
[11] M. T. Botticella, A. Pastorello, S. J. Smartt, W. P. S. Meikle, S. Benetti, R. Kotak, E. Cappellaro, R. M. Crockett, S. Mattila, M. Sereno, F. Patat, D. Tsvetkov, J. Th. van Loon, D. Abraham, I. Agnoletto, R. Arbour, C. Benn, G. di Rico, N. Elias-Rosa, D. L. Gorshanov, A. Harutyunyan, D. Hunter, V. Lorenzi, F. P. Keenan, K. Maguire, J. Mendez, M. Mobberley, H. Navasardyan, C. Ries, V. Stanishev, S. Taubenberger, C. Trundle, M. Turatto, and I. M. Volkov (2009) SN 2008S: an electron-capture SN from a super-AGB progenitor?. MNRAS 398 (3), pp. 1041–1068. External Links: Document, 0903.1286 Cited by: §VII.2.
[12] M. T. Botticella, S. J. Smartt, R. C. Kennicutt, E. Cappellaro, M. Sereno, and J. C. Lee (2012) A comparison between star formation rate diagnostics and rate of core collapse supernovae within 11 Mpc. A&A 537, pp. A132. External Links: Document, 1111.1692 Cited by: §I.
[13] A. Burrows and D. Vartanyan (2021) Core-collapse supernova explosion theory. Nature 589 (7840), pp. 29–39. External Links: Document, 2009.14157 Cited by: §I.
[14] A. Burrows, D. Radice, and D. Vartanyan (2019) Three-dimensional supernova explosion simulations of 9-, 10-, 11-, 12-, and 13-M ${}_{{\odot}}$ stars. MNRAS 485 (3), pp. 3153–3168. External Links: Document, 1902.00547 Cited by: §I.
[15] A. Burrows, T. Wang, and D. Vartanyan (2024) Physical Correlations and Predictions Emerging from Modern Core-Collapse Supernova Theory. arXiv e-prints, pp. arXiv:2401.06840. External Links: Document, 2401.06840 Cited by: §I, §VII.2.
[16] Y. -Z. Cai, A. Pastorello, M. Fraser, M. T. Botticella, N. Elias-Rosa, L. -Z. Wang, R. Kotak, S. Benetti, E. Cappellaro, M. Turatto, A. Reguitti, S. Mattila, S. J. Smartt, C. Ashall, S. Benitez, T. -W. Chen, A. Harutyunyan, E. Kankare, P. Lundqvist, P. A. Mazzali, A. Morales-Garoffolo, P. Ochner, G. Pignata, S. J. Prentice, T. M. Reynolds, X. -W. Shu, M. D. Stritzinger, L. Tartaglia, G. Terreran, L. Tomasella, S. Valenti, G. Valerin, G. -J. Wang, X. -F. Wang, L. Borsato, E. Callis, G. Cannizzaro, S. Chen, E. Congiu, M. Ergon, L. Galbany, A. Gal-Yam, X. Gao, M. Gromadzki, S. Holmbo, F. Huang, C. Inserra, K. Itagaki, Z. Kostrzewa-Rutkowska, K. Maguire, S. Margheim, S. Moran, F. Onori, A. Sagués Carracedo, K. W. Smith, J. Sollerman, A. Somero, B. Wang, and D. R. Young (2021) Intermediate-luminosity red transients: Spectrophotometric properties and connection to electron-capture supernova explosions. A&A 654, pp. A157. External Links: Document, 2108.05087 Cited by: §VII.2.
[17] E. Cappellaro, M. T. Botticella, G. Pignata, A. Grado, L. Greggio, L. Limatola, M. Vaccari, A. Baruffolo, S. Benetti, F. Bufano, M. Capaccioli, E. Cascone, G. Covone, D. De Cicco, S. Falocco, M. Della Valle, M. Jarvis, L. Marchetti, N. R. Napolitano, M. Paolillo, A. Pastorello, M. Radovich, P. Schipani, S. Spiro, L. Tomasella, and M. Turatto (2015) Supernova rates from the SUDARE VST-OmegaCAM search. I. Rates per unit volume. A&A 584, pp. A62. External Links: Document, 1509.04496 Cited by: §I.
[18] J. A. Cardelli, G. C. Clayton, and J. S. Mathis (1989) The Relationship between Infrared, Optical, and Ultraviolet Extinction. ApJ 345, pp. 245. External Links: Document Cited by: §IV.
[19] D. O. Cook, M. M. Kasliwal, A. Van Sistine, D. L. Kaplan, J. S. Sutter, T. Kupfer, D. L. Shupe, R. R. Laher, F. J. Masci, D. A. Dale, B. Sesar, P. R. Brady, L. Yan, E. O. Ofek, D. H. Reitze, and S. R. Kulkarni (2019) Census of the Local Universe (CLU) Narrowband Survey. I. Galaxy Catalogs from Preliminary Fields. ApJ 880 (1), pp. 7. External Links: Document, 1710.05016 Cited by: §II.1.
[20] M. W. Coughlin, J. S. Bloom, G. Nir, S. Antier, T. J. du Laz, S. van der Walt, A. Crellin-Quick, T. Culino, D. A. Duev, D. A. Goldstein, B. F. Healy, V. Karambelkar, J. Lilleboe, K. M. Shin, L. P. Singer, T. Ahumada, S. Anand, E. C. Bellm, R. Dekany, M. J. Graham, M. M. Kasliwal, I. Kostadinova, R. W. Kiendrebeogo, S. R. Kulkarni, S. Jenkins, N. LeBaron, A. A. Mahabal, J. D. Neill, B. Parazin, J. Peloton, D. A. Perley, R. Riddle, B. Rusholme, J. van Santen, J. Sollerman, R. Stein, D. Turpin, A. Wold, C. Amat, A. Bonnefon, A. Bonnefoy, M. Flament, F. Kerkow, S. Kishore, S. Jani, S. K. Mahanty, C. Liu, L. Llinares, J. Makarison, A. Olliéric, I. Perez, L. Pont, and V. Sharma (2023) A Data Science Platform to Enable Time-domain Astronomy. ApJS 267 (2), pp. 31. External Links: Document, 2305.00108 Cited by: §II.1.
[21] R. M. Crockett, S. J. Smartt, A. Pastorello, J. J. Eldridge, A. W. Stephens, J. R. Maund, and S. Mattila (2011) On the nature of the progenitors of three Type II-P supernovae: 2004et, 2006my and 2006ov. MNRAS 410 (4), pp. 2767–2786. External Links: Document, 0912.3302 Cited by: Appendix A.
[22] K. K. Das, M. M. Kasliwal, T. J. D. Laz, S. Covarrubias, C. Fremling, W. Jacobson-Galan, S. Rose, Y. Qin, D. Cook, J. Sollerman, A. Gangopadhay, A. Singh, I. Andreoni, J. Carney, D. Perley, K. Hinds, N. Rehemtulla, A. A. Miller, and S. Schulze (2025) ZTF Complete Astronomical Transient Survey within 150 Mpc (CATS150). Transient Name Server AstroNote 64, pp. 1. Cited by: §VIII.
[23] K. K. Das, C. Fremling, M. M. Kasliwal, S. Schulze, J. Sollerman, V. Karambelkar, S. Rose, S. Anand, I. Andreoni, M. Aubert, S. J. Brennan, S. B. Cenko, M. W. Coughlin, B. O’Connor, K. De, J. Fuller, M. Graham, E. Hammerstein, A. Haynie, K. -. Hinds, I. Kleiser, S. R. Kulkarni, Z. Lin, C. Liu, A. A. Mahabal, C. Martin, A. A. Miller, J. D. Neill, D. A. Perley, P. J. Pessi, N. Z. Prusinski, J. Purdum, V. Ravi, B. Rusholme, S. Wu, A. Wold, and L. Yan (2024) SN 2023zaw: An Ultrastripped, Nickel-poor Supernova from a Low-mass Progenitor. ApJ 969 (1), pp. L11. External Links: Document, 2403.08165 Cited by: §VII.2.
[24] K. K. Das, M. M. Kasliwal, C. Fremling, S. Yang, S. Schulze, J. Sollerman, T. Sit, K. De, A. Tzanidakis, D. A. Perley, S. Anand, I. Andreoni, C. Barbarino, K. Brudge, A. Drake, A. Gal-Yam, R. R. Laher, V. Karambelkar, S. R. Kulkarni, F. J. Masci, M. S. Medford, A. Polin, H. Reedy, R. Riddle, Y. Sharma, R. Smith, L. Yan, Y. Yang, and Y. Yao (2023) Probing the Low-mass End of Core-collapse Supernovae Using a Sample of Strongly-stripped Calcium-rich Type IIb Supernovae from the Zwicky Transient Facility. ApJ 959 (1), pp. 12. External Links: Document, 2210.05729 Cited by: §VII.2.
[25] K. K. Das, M. M. Kasliwal, J. Sollerman, C. Fremling, I. Irani, S. Leung, S. Yang, S. Wu, J. Fuller, S. Anand, I. Andreoni, C. Barbarino, T. G. Brink, K. De, A. Dugas, S. L. Groom, G. Helou, K. -. Hinds, A. Y. Q. Ho, V. Karambelkar, S. R. Kulkarni, D. A. Perley, J. Purdum, N. Regnault, S. Schulze, Y. Sharma, T. Sit, N. Sravan, G. P. Srinivasaragavan, R. Stein, K. Taggart, L. Tartaglia, A. Tzanidakis, A. Wold, L. Yan, Y. Yao, and J. Zolkower (2024) Probing Presupernova Mass Loss in Double-peaked Type Ibc Supernovae from the Zwicky Transient Facility. ApJ 972 (1), pp. 91. External Links: Document, 2306.04698 Cited by: §VII.2.
[26] R. Dastidar, K. Misra, S. Valenti, D. J. Sand, A. Pastorello, A. Reguitti, G. Pignata, S. Benetti, S. Bose, A. Gangopadhyay, M. Singh, L. Tomasella, J. E. Andrews, I. Arcavi, C. Ashall, C. Bilinski, K. A. Bostroem, D. A. H. Buckley, G. Cannizzaro, L. Chomiuk, E. Congiu, S. Dong, Y. Dong, N. Elias-Rosa, M. Fraser, C. Gall, M. Gromadzki, D. Hiramatsu, G. Hosseinzadeh, D. A. Howell, E. Y. Hsiao, C. McCully, N. Smith, and J. Strader (2025) SN 2018is: a low-luminosity Type IIP supernova with narrow hydrogen emission lines at early phases. arXiv e-prints, pp. arXiv:2501.01530. External Links: 2501.01530 Cited by: §I.
[27] T. de Jaeger, J. P. Anderson, L. Galbany, S. González-Gaitán, M. Hamuy, M. M. Phillips, M. D. Stritzinger, C. Contreras, G. Folatelli, C. P. Gutiérrez, E. Y. Hsiao, N. Morrell, N. B. Suntzeff, L. Dessart, and A. V. Filippenko (2018) Observed Type II supernova colours from the Carnegie Supernova Project-I. MNRAS 476 (4), pp. 4592–4616. External Links: Document, 1802.07254 Cited by: §IV.
[28] K. De, M. M. Kasliwal, A. Tzanidakis, U. C. Fremling, S. Adams, R. Aloisi, I. Andreoni, A. Bagdasaryan, E. C. Bellm, L. Bildsten, C. Cannella, D. O. Cook, A. Delacroix, A. Drake, D. Duev, A. Dugas, S. Frederick, A. Gal-Yam, D. Goldstein, V. Z. Golkhou, M. J. Graham, D. Hale, M. Hankins, G. Helou, A. Y. Q. Ho, I. Irani, J. E. Jencson, D. L. Kaplan, S. Kaye, S. R. Kulkarni, T. Kupfer, R. R. Laher, R. Leadbeater, R. Lunnan, F. J. Masci, A. A. Miller, J. D. Neill, E. O. Ofek, D. A. Perley, A. Polin, T. A. Prince, E. Quataert, D. Reiley, R. L. Riddle, B. Rusholme, Y. Sharma, D. L. Shupe, J. Sollerman, L. Tartaglia, R. Walters, L. Yan, and Y. Yao (2020) The Zwicky Transient Facility Census of the Local Universe. I. Systematic Search for Calcium-rich Gap Transients Reveals Three Related Spectroscopic Subclasses. ApJ 905 (1), pp. 58. External Links: Document, 2004.09029 Cited by: §II.1.
[29] R. Dekany, R. M. Smith, R. Riddle, M. Feeney, M. Porter, D. Hale, J. Zolkower, J. Belicki, S. Kaye, J. Henning, R. Walters, J. Cromer, A. Delacroix, H. Rodriguez, D. J. Reiley, P. Mao, D. Hover, P. Murphy, R. Burruss, J. Baker, M. Kowalski, K. Reif, P. Mueller, E. Bellm, M. Graham, and S. R. Kulkarni (2020) The Zwicky Transient Facility: Observing System. PASP 132 (1009), pp. 038001. External Links: Document Cited by: §II.1.
[30] A. A. Djupvik and J. Andersen (2010) The Nordic Optical Telescope. In Highlights of Spanish Astrophysics V, Astrophysics and Space Science Proceedings, Vol. 14, pp. 211. External Links: Document, 0901.4015 Cited by: §III.2.
[31] C. B. D’Andrea, M. Sako, B. Dilday, J. A. Frieman, J. Holtzman, R. Kessler, K. Konishi, D. P. Schneider, J. Sollerman, J. C. Wheeler, N. Yasuda, D. Cinabro, S. Jha, R. C. Nichol, H. Lampeitl, M. Smith, D. W. Atlee, B. Bassett, F. J. Castander, A. Goobar, R. Miquel, J. Nordin, L. Östman, J. L. Prieto, R. Quimby, A. G. Riess, and M. Stritzinger (2010) Type II-P Supernovae from the SDSS-II Supernova Survey and the Standardized Candle Method. ApJ 708 (1), pp. 661–674. External Links: Document, 0910.5597 Cited by: §VII.1.
[32] J. J. Eldridge, N. -Y. Guo, N. Rodrigues, E. R. Stanway, and L. Xiao (2019) Supernova lightCURVE POPulation Synthesis II: Validation against supernovae with an observed progenitor. PASA 36, pp. e041. External Links: Document, 1908.07762 Cited by: §I, §I.
[33] N. Elias-Rosa, S. D. Van Dyk, W. Li, J. M. Silverman, R. J. Foley, M. Ganeshalingam, J. C. Mauerhan, E. Kankare, S. Jha, A. V. Filippenko, J. E. Beckman, E. Berger, J. Cuillandre, and N. Smith (2011) The Massive Progenitor of the Possible Type II-Linear Supernova 2009hd in Messier 66. ApJ 742 (1), pp. 6. External Links: Document, 1108.2645 Cited by: Appendix A.
[34] D. Foreman-Mackey, D. W. Hogg, D. Lang, and J. Goodman (2013) emcee: The MCMC Hammer. PASP 125, pp. 306. External Links: Document, 1202.3665 Cited by: §V.
[35] O. D. Fox, H. Khandrika, D. Rubin, C. Casper, G. Z. Li, T. Szalai, L. Armus, A. V. Filippenko, M. F. Skrutskie, L. Strolger, and S. D. Van Dyk (2021) A Spitzer survey for dust-obscured supernovae. MNRAS 506 (3), pp. 4199–4209. External Links: Document, 2106.09733 Cited by: §VII.2.
[36] M. Fraser, M. Ergon, J. J. Eldridge, S. Valenti, A. Pastorello, J. Sollerman, S. J. Smartt, I. Agnoletto, I. Arcavi, S. Benetti, M. -T. Botticella, F. Bufano, A. Campillay, R. M. Crockett, A. Gal-Yam, E. Kankare, G. Leloudas, K. Maguire, S. Mattila, J. R. Maund, F. Salgado, A. Stephens, S. Taubenberger, and M. Turatto (2011) SN 2009md: another faint supernova from a low-mass progenitor. MNRAS 417 (2), pp. 1417–1433. External Links: Document, 1011.6558 Cited by: Appendix A, §I.
[37] M. Fraser, J. R. Maund, S. J. Smartt, R. Kotak, A. Lawrence, A. Bruce, S. Valenti, F. Yuan, S. Benetti, T. -W. Chen, A. Gal-Yam, C. Inserra, and D. R. Young (2014) On the progenitor of the Type IIP SN 2013ej in M74.. MNRAS 439, pp. L56–L60. External Links: Document, 1309.4268 Cited by: Appendix A.
[38] M. Fraser, K. Takáts, A. Pastorello, S. J. Smartt, S. Mattila, M. -T. Botticella, S. Valenti, M. Ergon, J. Sollerman, I. Arcavi, S. Benetti, F. Bufano, R. M. Crockett, I. J. Danziger, A. Gal-Yam, J. R. Maund, S. Taubenberger, and M. Turatto (2010) On the Progenitor and Early Evolution of the Type II Supernova 2009kr. ApJ 714 (2), pp. L280–L284. External Links: Document, 0912.2071 Cited by: Appendix A.
[39] C. Fremling, A. A. Miller, Y. Sharma, A. Dugas, D. A. Perley, K. Taggart, J. Sollerman, A. Goobar, M. L. Graham, J. D. Neill, J. Nordin, M. Rigault, R. Walters, I. Andreoni, A. Bagdasaryan, J. Belicki, C. Cannella, E. C. Bellm, S. B. Cenko, K. De, R. Dekany, S. Frederick, V. Z. Golkhou, M. J. Graham, G. Helou, A. Y. Q. Ho, M. M. Kasliwal, T. Kupfer, R. R. Laher, A. Mahabal, F. J. Masci, R. Riddle, B. Rusholme, S. Schulze, D. L. Shupe, R. M. Smith, S. van Velzen, L. Yan, Y. Yao, Z. Zhuang, and S. R. Kulkarni (2020) The Zwicky Transient Facility Bright Transient Survey. I. Spectroscopic Classification and the Redshift Completeness of Local Galaxy Catalogs. ApJ 895 (1), pp. 32. External Links: Document, 1910.12973 Cited by: §II.2, Figure 10, §VI.
[40] M. J. Graham, S. R. Kulkarni, E. C. Bellm, S. M. Adams, C. Barbarino, N. Blagorodnova, D. Bodewits, B. Bolin, P. R. Brady, S. B. Cenko, C. Chang, M. W. Coughlin, K. De, G. Eadie, T. L. Farnham, U. Feindt, A. Franckowiak, C. Fremling, S. Gezari, S. Ghosh, D. A. Goldstein, V. Z. Golkhou, A. Goobar, A. Y. Q. Ho, D. Huppenkothen, Ž. Ivezić, R. L. Jones, M. Juric, D. L. Kaplan, M. M. Kasliwal, M. S. P. Kelley, T. Kupfer, C. Lee, H. W. Lin, R. Lunnan, A. A. Mahabal, A. A. Miller, C. Ngeow, P. Nugent, E. O. Ofek, T. A. Prince, L. Rauch, J. van Roestel, S. Schulze, L. P. Singer, J. Sollerman, F. Taddia, L. Yan, Q. Ye, P. Yu, T. Barlow, J. Bauer, R. Beck, J. Belicki, R. Biswas, V. Brinnel, T. Brooke, B. Bue, M. Bulla, R. Burruss, A. Connolly, J. Cromer, V. Cunningham, R. Dekany, A. Delacroix, V. Desai, D. A. Duev, M. Feeney, D. Flynn, S. Frederick, A. Gal-Yam, M. Giomi, S. Groom, E. Hacopians, D. Hale, G. Helou, J. Henning, D. Hover, L. A. Hillenbrand, J. Howell, T. Hung, D. Imel, W. Ip, E. Jackson, S. Kaspi, S. Kaye, M. Kowalski, E. Kramer, M. Kuhn, W. Landry, R. R. Laher, P. Mao, F. J. Masci, S. Monkewitz, P. Murphy, J. Nordin, M. T. Patterson, B. Penprase, M. Porter, U. Rebbapragada, D. Reiley, R. Riddle, M. Rigault, H. Rodriguez, B. Rusholme, J. van Santen, D. L. Shupe, R. M. Smith, M. T. Soumagnac, R. Stein, J. Surace, P. Szkody, S. Terek, A. Van Sistine, S. van Velzen, W. T. Vestrand, R. Walters, C. Ward, C. Zhang, and J. Zolkower (2019) The Zwicky Transient Facility: Science Objectives. PASP 131 (1001), pp. 078001. External Links: Document, 1902.01945 Cited by: §II.1.
[41] G. Helou, B. F. Madore, M. Schmitz, M. D. Bicay, X. Wu, and J. Bennett (1991) The NASA/IPAC extragalactic database.. In Databases and On-line Data in Astronomy, M. A. Albrecht and D. Egret (Eds.), Astrophysics and Space Science Library, Vol. 171, pp. 89–106. External Links: Document Cited by: Table A, Appendix B, item 5.
[42] A. Y. Q. Ho, D. A. Perley, A. Gal-Yam, R. Lunnan, J. Sollerman, S. Schulze, K. K. Das, D. Dobie, Y. Yao, C. Fremling, S. Adams, S. Anand, I. Andreoni, E. C. Bellm, R. J. Bruch, K. B. Burdge, A. J. Castro-Tirado, A. Dahiwale, K. De, R. Dekany, A. J. Drake, D. A. Duev, M. J. Graham, G. Helou, D. L. Kaplan, V. Karambelkar, M. M. Kasliwal, E. C. Kool, S. R. Kulkarni, A. A. Mahabal, M. S. Medford, A. A. Miller, J. Nordin, E. Ofek, G. Petitpas, R. Riddle, Y. Sharma, R. Smith, A. J. Stewart, K. Taggart, L. Tartaglia, A. Tzanidakis, and J. M. Winters (2023) A Search for Extragalactic Fast Blue Optical Transients in ZTF and the Rate of AT2018cow-like Transients. ApJ 949 (2), pp. 120. External Links: Document, 2105.08811 Cited by: §VII.2.
[43] A. M. Hopkins and J. F. Beacom (2006) On the Normalization of the Cosmic Star Formation History. ApJ 651 (1), pp. 142–154. External Links: Document, astro-ph/0601463 Cited by: §VII.2.
[44] S. Horiuchi, J. F. Beacom, M. S. Bothwell, and T. A. Thompson (2013) Effects of Stellar Rotation on Star Formation Rates and Comparison to Core-collapse Supernova Rates. ApJ 769 (2), pp. 113. External Links: Document, 1302.0287 Cited by: §I.
[45] S. Horiuchi, J. F. Beacom, C. S. Kochanek, J. L. Prieto, K. Z. Stanek, and T. A. Thompson (2011) The Cosmic Core-collapse Supernova Rate Does Not Match the Massive-star Formation Rate. ApJ 738 (2), pp. 154. External Links: Document, 1102.1977 Cited by: §I, §VII.2.
[46] D. A. Howell, M. Sullivan, K. Perrett, T. J. Bronder, I. M. Hook, P. Astier, E. Aubourg, D. Balam, S. Basa, R. G. Carlberg, S. Fabbro, D. Fouchez, J. Guy, H. Lafoux, J. D. Neill, R. Pain, N. Palanque-Delabrouille, C. J. Pritchet, N. Regnault, J. Rich, R. Taillet, R. Knop, R. G. McMahon, S. Perlmutter, and N. A. Walton (2005) Gemini Spectroscopy of Supernovae from the Supernova Legacy Survey: Improving High-Redshift Supernova Selection and Classification. ApJ 634 (2), pp. 1190–1201. External Links: Document, astro-ph/0509195 Cited by: §III.2.
[47] Ž. Ivezić, J. A. Tyson, E. Acosta, R. Allsman, S. F. Anderson, J. Andrew, R. Angel, T. Axelrod, J. D. Barr, A. C. Becker, J. Becla, C. Beldica, R. D. Blandford, J. S. Bloom, K. Borne, W. N. Brandt, M. E. Brown, J. S. Bullock, D. L. Burke, S. Chandrasekharan, S. Chesley, C. F. Claver, A. Connolly, K. H. Cook, A. Cooray, K. R. Covey, C. Cribbs, R. Cutri, G. Daues, F. Delgado, H. Ferguson, E. Gawiser, J. C. Geary, P. Gee, M. Geha, R. R. Gibson, D. K. Gilmore, W. J. Gressler, C. Hogan, M. E. Huffer, S. H. Jacoby, B. Jain, J. G. Jernigan, R. L. Jones, M. Juric, S. M. Kahn, J. S. Kalirai, J. P. Kantor, R. Kessler, D. Kirkby, L. Knox, V. L. Krabbendam, S. Krughoff, S. Kulkarni, R. Lambert, D. Levine, M. Liang, K. Lim, R. H. Lupton, P. Marshall, S. Marshall, M. May, M. Miller, D. J. Mills, D. G. Monet, D. R. Neill, M. Nordby, P. O’Connor, J. Oliver, S. S. Olivier, K. Olsen, R. E. Owen, J. R. Peterson, C. E. Petry, F. Pierfederici, S. Pietrowicz, R. Pike, P. A. Pinto, R. Plante, V. Radeka, A. Rasmussen, S. T. Ridgway, W. Rosing, A. Saha, T. L. Schalk, R. H. Schindler, D. P. Schneider, G. Schumacher, J. Sebag, L. G. Seppala, I. Shipsey, N. Silvestri, J. A. Smith, R. C. Smith, M. A. Strauss, C. W. Stubbs, D. Sweeney, A. Szalay, J. J. Thaler, D. Vanden Berk, L. Walkowicz, M. Warner, B. Willman, D. Wittman, S. C. Wolff, W. M. Wood-Vasey, P. Yoachim, H. Zhan, and for the LSST Collaboration (2008) LSST: from Science Drivers to Reference Design and Anticipated Data Products. ArXiv e-prints. External Links: 0805.2366 Cited by: §VIII.
[48] W. V. Jacobson-Galán, P. Venkatraman, R. Margutti, D. Khatami, G. Terreran, R. J. Foley, R. Angulo, C. R. Angus, K. Auchettl, P. K. Blanchard, A. Bobrick, J. S. Bright, D. Brout, K. C. Chambers, C. D. Couch, D. A. Coulter, K. Clever, K. W. Davis, T. J. L. de Boer, L. DeMarchi, S. A. Dodd, D. O. Jones, J. Johnson, C. D. Kilpatrick, N. Khetan, Z. Lai, D. Langeroodi, C. -C. Lin, E. A. Magnier, D. Milisavljevic, H. B. Perets, J. D. R. Pierel, J. Raymond, S. Rest, A. Rest, R. Ridden-Harper, K. J. Shen, M. R. Siebert, C. Smith, K. Taggart, S. Tinyanont, F. Valdes, V. A. Villar, Q. Wang, S. K. Yadavalli, Y. Zenati, and A. Zenteno (2022) The Circumstellar Environments of Double-peaked, Calcium-strong Transients 2021gno and 2021inl. ApJ 932 (1), pp. 58. External Links: Document, 2203.03785 Cited by: §VII.2.
[49] Jr. Jäger, J. Vinkó, B. I. Bíró, T. Hegedüs, T. Borkovits, Sr. Jäger, A. P. Nagy, L. Molnár, and L. Kriskovics (2020) A low-luminosity core-collapse supernova very similar to SN 2005cs. MNRAS 496 (3), pp. 3725–3740. External Links: Document, 2007.07650 Cited by: §I.
[50] H. Janka (2012) Explosion Mechanisms of Core-Collapse Supernovae. Annual Review of Nuclear and Particle Science 62 (1), pp. 407–451. External Links: Document, 1206.2503 Cited by: §I, §I.
[51] J. E. Jencson, S. M. Adams, H. E. Bond, S. D. van Dyk, M. M. Kasliwal, J. Bally, N. Blagorodnova, K. De, C. Fremling, Y. Yao, A. Fruchter, D. Rubin, C. Barbarino, J. Sollerman, A. A. Miller, E. K. S. Hicks, M. A. Malkan, I. Andreoni, E. C. Bellm, R. Buchheim, R. Dekany, M. Feeney, S. Frederick, A. Gal-Yam, R. D. Gehrz, M. Giomi, M. J. Graham, W. Green, D. Hale, M. J. Hankins, M. Hanson, G. Helou, A. Y. Q. Ho, T. Hung, M. Jurić, M. R. Kendurkar, S. R. Kulkarni, R. M. Lau, F. J. Masci, J. D. Neill, K. Quin, R. L. Riddle, B. Rusholme, F. Sims, N. Smith, R. M. Smith, M. T. Soumagnac, Y. Tachibana, S. Tinyanont, R. Walters, S. Watson, and R. E. Williams (2019) Discovery of an Intermediate-luminosity Red Transient in M51 and Its Likely Dust-obscured, Infrared-variable Progenitor. ApJ 880 (2), pp. L20. External Links: Document, 1904.07857 Cited by: §VII.2.
[52] J. E. Jencson, M. M. Kasliwal, S. M. Adams, H. E. Bond, K. De, J. Johansson, V. Karambelkar, R. M. Lau, S. Tinyanont, S. D. Ryder, A. M. Cody, F. J. Masci, J. Bally, N. Blagorodnova, S. Castellón, C. Fremling, R. D. Gehrz, G. Helou, C. D. Kilpatrick, P. A. Milne, N. Morrell, D. A. Perley, M. M. Phillips, N. Smith, S. D. van Dyk, and R. E. Williams (2019) The SPIRITS Sample of Luminous Infrared Transients: Uncovering Hidden Supernovae and Dusty Stellar Outbursts in Nearby Galaxies. ApJ 886 (1), pp. 40. External Links: Document, 1901.00871 Cited by: §I, §VII.2.
[53] A. Jerkstrand, T. Ertl, H. -T. Janka, E. Müller, T. Sukhbold, and S. E. Woosley (2018) Emission line models for the lowest mass core-collapse supernovae - I. Case study of a 9 M ${}_{{\odot}}$ one-dimensional neutrino-driven explosion. MNRAS 475 (1), pp. 277–305. External Links: Document, 1710.04508 Cited by: §I, §I.
[54] V. R. Karambelkar, M. M. Kasliwal, N. Blagorodnova, J. Sollerman, R. Aloisi, S. G. Anand, I. Andreoni, T. G. Brink, R. Bruch, D. Cook, K. K. Das, K. De, A. Drake, A. V. Filippenko, C. Fremling, G. Helou, A. Ho, J. Jencson, D. Jones, R. R. Laher, F. J. Masci, K. C. Patra, J. Purdum, A. Reedy, T. Sit, Y. Sharma, A. Tzanidakis, S. J. van der Walt, Y. Yao, and C. Zhang (2023) Volumetric Rates of Luminous Red Novae and Intermediate-luminosity Red Transients with the Zwicky Transient Facility. ApJ 948 (2), pp. 137. External Links: Document, 2211.05141 Cited by: §VII.2.
[55] M. M. Kasliwal, C. Cannella, A. Bagdasaryan, T. Hung, U. Feindt, L. P. Singer, M. Coughlin, C. Fremling, R. Walters, D. Duev, R. Itoh, and R. M. Quimby (2019) The GROWTH Marshal: A Dynamic Science Portal for Time-domain Astronomy. PASP 131 (3), pp. 038003. External Links: 1902.01934, Document Cited by: §II.1.
[56] Y. -L. Kim, M. Rigault, J. D. Neill, M. Briday, Y. Copin, J. Lezmy, N. Nicolas, R. Riddle, Y. Sharma, M. Smith, J. Sollerman, and R. Walters (2022) New Modules for the SEDMachine to Remove Contaminations from Cosmic Rays and Non-target Light: BYECR and CONTSEP. PASP 134 (1032), pp. 024505. External Links: Document, 2203.01346 Cited by: §II.1.
[57] C. S. Kochanek, R. Khan, and X. Dai (2012) On Absorption by Circumstellar Dust, with the Progenitor of SN 2012aw as a Case Study. ApJ 759 (1), pp. 20. External Links: Document, 1208.4111 Cited by: Appendix A.
[58] I. Kondo, T. Sumi, N. Koshimoto, N. J. Rattenbury, D. Suzuki, and D. P. Bennett (2023) Prediction of Planet Yields by the PRime-focus Infrared Microlensing Experiment Microlensing Survey. AJ 165 (6), pp. 254. External Links: Document, 2304.04605 Cited by: §VIII.
[59] A. Kozyreva, P. Baklanov, S. Jones, G. Stockinger, and H. Janka (2021) Synthetic observables for electron-capture supernovae and low-mass core collapse supernovae. MNRAS 503 (1), pp. 797–814. External Links: Document, 2102.02575 Cited by: §I.
[60] A. Kozyreva, H. Janka, D. Kresse, S. Taubenberger, and P. Baklanov (2022) Low-luminosity type IIP supermnovae: SN 2005cs and SN 2020cxd as very low-energy iron core-collapse explosions. MNRAS 514 (3), pp. 4173–4189. External Links: Document, 2203.00473 Cited by: §I.
[61] S. Leung and J. Fuller (2020) Hydrodynamic Simulations of Pre-supernova Outbursts in Red Supergiants: Asphericity and Mass Loss. ApJ 900 (2), pp. 99. External Links: Document, 2007.11712 Cited by: §I.
[62] W. Li, J. Leaman, R. Chornock, A. V. Filippenko, D. Poznanski, M. Ganeshalingam, X. Wang, M. Modjaz, S. Jha, R. J. Foley, and N. Smith (2011) Nearby supernova rates from the Lick Observatory Supernova Search - II. The observed luminosity functions and fractions of supernovae in a complete sample. MNRAS 412, pp. 1441–1472. External Links: Document, 1006.4612 Cited by: §I, §VII.1, §VII.1, §VII.2.
[63] W. Li, S. D. Van Dyk, A. V. Filippenko, J. Cuillandre, S. Jha, J. S. Bloom, A. G. Riess, and M. Livio (2006) Identification of the Red Supergiant Progenitor of Supernova 2005cs: Do the Progenitors of Type II-P Supernovae Have Low Mass?. ApJ 641 (2), pp. 1060–1070. External Links: Document, astro-ph/0507394 Cited by: §I.
[64] S. M. Lisakov, L. Dessart, D. J. Hillier, R. Waldman, and E. Livne (2017) A study of the low-luminosity Type II-Plateau supernova 2008bk. MNRAS 466 (1), pp. 34–48. External Links: Document, 1611.06809 Cited by: §I.
[65] N. P. Lourie, J. W. Baker, R. S. Burruss, M. Egan, G. Fżrész, D. Frostig, A. A. Garcia-Zych, N. Ganciu, K. Haworth, E. Hinrichsen, M. M. Kasliwal, V. R. Karambelkar, A. Malonis, R. A. Simcoe, and J. Zolkower (2020) The wide-field infrared transient explorer (WINTER). In Ground-based and Airborne Instrumentation for Astronomy VIII, C. J. Evans, J. J. Bryant, and K. Motohara (Eds.), Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 11447, pp. 114479K. External Links: Document, 2102.01109 Cited by: §VIII.
[66] F. J. Masci, R. R. Laher, B. Rusholme, D. L. Shupe, S. Groom, J. Surace, E. Jackson, S. Monkewitz, R. Beck, D. Flynn, S. Terek, W. Landry, E. Hacopians, V. Desai, J. Howell, T. Brooke, D. Imel, S. Wachter, Q.-Z. Ye, H.-W. Lin, S. B. Cenko, V. Cunningham, U. Rebbapragada, B. Bue, A. A. Miller, A. Mahabal, E. C. Bellm, M. T. Patterson, M. Jurić, V. Z. Golkhou, E. O. Ofek, R. Walters, M. Graham, M. M. Kasliwal, R. G. Dekany, T. Kupfer, K. Burdge, C. B. Cannella, T. Barlow, A. Van Sistine, M. Giomi, C. Fremling, N. Blagorodnova, D. Levitan, R. Riddle, R. M. Smith, G. Helou, T. A. Prince, and S. R. Kulkarni (2019) The Zwicky Transient Facility: Data Processing, Products, and Archive. PASP 131 (1), pp. 018003. External Links: 1902.01872, Document Cited by: §III.1.
[67] F. J. Masci, R. R. Laher, B. Rusholme, D. Shupe, R. Paladini, S. Groom, A. Wold, A. A. Miller, and A. Drake (2023) A New Forced Photometry Service for the Zwicky Transient Facility. arXiv e-prints, pp. arXiv:2305.16279. External Links: Document, 2305.16279 Cited by: §III.1.
[68] S. Mattila, T. Dahlen, A. Efstathiou, E. Kankare, J. Melinder, A. Alonso-Herrero, M. Á. Pérez-Torres, S. Ryder, P. Väisänen, and G. Östlin (2012) Core-collapse Supernovae Missed by Optical Surveys. ApJ 756 (2), pp. 111. External Links: Document, 1206.1314 Cited by: §VII.2.
[69] S. Mattila, S. J. Smartt, J. J. Eldridge, J. R. Maund, R. M. Crockett, and I. J. Danziger (2008) VLT Detection of a Red Supergiant Progenitor of the Type II-P Supernova 2008bk. ApJ 688 (2), pp. L91. External Links: Document, 0809.0206 Cited by: §I.
[70] J. R. Maund, M. Fraser, S. J. Smartt, M. T. Botticella, C. Barbarino, M. Childress, A. Gal-Yam, C. Inserra, G. Pignata, D. Reichart, B. Schmidt, J. Sollerman, F. Taddia, L. Tomasella, S. Valenti, and O. Yaron (2013) Supernova 2012ec: identification of the progenitor and early monitoring with PESSTO.. MNRAS 431, pp. L102–L106. External Links: Document, 1302.0170 Cited by: Appendix A.
[71] J. R. Maund, E. Reilly, and S. Mattila (2014) A late-time view of the progenitors of five Type IIP supernovae. MNRAS 438 (2), pp. 938–958. External Links: Document, 1302.7152 Cited by: Appendix A.
[72] J. R. Maund, S. J. Smartt, and I. J. Danziger (2005) The progenitor of SN 2005cs in the Whirlpool Galaxy. MNRAS 364 (1), pp. L33–L37. External Links: Document, astro-ph/0507502 Cited by: §I.
[73] J. R. Maund and S. J. Smartt (2009) The Disappearance of the Progenitors of Supernovae 1993J and 2003gd. Science 324 (5926), pp. 486. External Links: Document, 0903.3772 Cited by: Appendix A.
[74] S. Miyaji, K. Nomoto, K. Yokoi, and D. Sugimoto (1980) Supernova triggered by electron captures.. PASJ 32, pp. 303–329. Cited by: §I.
[75] T. Moore, J. Gillanders, M. Nicholl, M. Huber, S. Smartt, S. Srivastav, H. Stevance, T. Chen, K. Chambers, J. Anderson, M. Fulton, S. Oates, C. Angus, G. Pignata, N. Erasmus, H. Gao, J. Bulger, C. Lin, T. Lowe, E. Magnier, P. Minguez, C. Ngeow, X. Sheng, S. A. Sim, K. Smith, R. Wainscoat, S. Yang, D. Young, and K. Zeng (2024) SN 2023zaw: the low-energy explosion of an ultra-stripped star, with non-radioactive heating. arXiv e-prints, pp. arXiv:2405.13596. External Links: Document, 2405.13596 Cited by: §VII.2.
[76] T. J. Moriya, P. A. Mazzali, N. Tominaga, S. Hachinger, S. I. Blinnikov, T. M. Tauris, K. Takahashi, M. Tanaka, N. Langer, and P. Podsiadlowski (2017) Light-curve and spectral properties of ultrastripped core-collapse supernovae leading to binary neutron stars. MNRAS 466 (2), pp. 2085–2098. External Links: Document, 1612.02882 Cited by: §VII.2.
[77] B. Müller (2016) The Status of Multi-Dimensional Core-Collapse Supernova Models. PASA 33, pp. e048. External Links: Document, 1608.03274 Cited by: §I.
[78] T. E. Müller-Bravo, C. P. Gutiérrez, M. Sullivan, A. Jerkstrand, J. P. Anderson, S. González-Gaitán, J. Sollerman, I. Arcavi, J. Burke, L. Galbany, A. Gal-Yam, M. Gromadzki, D. Hiramatsu, G. Hosseinzadeh, D. A. Howell, C. Inserra, E. Kankare, A. Kozyreva, C. McCully, M. Nicholl, S. Smartt, S. Valenti, and D. R. Young (2020) The low-luminosity Type II SN 2016aqf: a well-monitored spectral evolution of the Ni/Fe abundance ratio. MNRAS 497 (1), pp. 361–377. External Links: Document, 2006.15028 Cited by: §I.
[79] K. Nomoto (1984) Evolution of 8-10 solar mass stars toward electron capture supernovae. I - Formation of electron-degenerate O + NE + MG cores.. ApJ 277, pp. 791–805. External Links: Document Cited by: §I, §VII.2.
[80] J. B. Oke, J. G. Cohen, M. Carr, J. Cromer, A. Dingizian, F. H. Harris, S. Labrecque, R. Lucinio, W. Schaal, H. Epps, and J. Miller (1995) The Keck Low-Resolution Imaging Spectrometer. PASP 107, pp. 375. External Links: Document Cited by: §II.1.
[81] J. B. Oke and J. E. Gunn (1982) An Efficient Low Resolution and Moderate Resolution Spectrograph for the Hale Telescope. PASP 94, pp. 586. External Links: Document Cited by: §II.1.
[82] D. O’Neill, R. Kotak, M. Fraser, S. A. Sim, S. Benetti, S. J. Smartt, S. Mattila, C. Ashall, E. Callis, N. Elias-Rosa, M. Gromadzki, and S. J. Prentice (2019) A progenitor candidate for the type II-P supernova SN 2018aoq in NGC 4151. A&A 622, pp. L1. External Links: Document, 1812.04988 Cited by: Appendix A, §I.
[83] D. A. Perley (2019) Fully Automated Reduction of Longslit Spectroscopy with the Low Resolution Imaging Spectrometer at the Keck Observatory. PASP 131 (8), pp. 084503. External Links: 1903.07629, Document Cited by: §III.2.
[84] D. A. Perley, J. Sollerman, S. Schulze, Y. Yao, C. Fremling, A. Gal-Yam, A. Y. Q. Ho, Y. Yang, E. C. Kool, I. Irani, L. Yan, I. Andreoni, D. Baade, E. C. Bellm, T. G. Brink, T. Chen, A. Cikota, M. W. Coughlin, A. Dahiwale, R. Dekany, D. A. Duev, A. V. Filippenko, P. Hoeflich, M. M. Kasliwal, S. R. Kulkarni, R. Lunnan, F. J. Masci, J. R. Maund, M. S. Medford, R. Riddle, P. Rosnet, D. L. Shupe, N. L. Strotjohann, A. Tzanidakis, and W. Zheng (2022) The Type Icn SN 2021csp: Implications for the Origins of the Fastest Supernovae and the Fates of Wolf-Rayet Stars. ApJ 927 (2), pp. 180. External Links: Document, 2111.12110 Cited by: §II.2, §VII.1.
[85] A. S. Piascik, I. A. Steele, S. D. Bates, C. J. Mottram, R. J. Smith, R. M. Barnsley, and B. Bolton (2014) SPRAT: Spectrograph for the Rapid Acquisition of Transients. In Ground-based and Airborne Instrumentation for Astronomy V, S. K. Ramsay, I. S. McLean, and H. Takami (Eds.), Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, pp. 91478H. External Links: Document Cited by: §III.2.
[86] D. Poznanski, J. X. Prochaska, and J. S. Bloom (2012) An empirical relation between sodium absorption and dust extinction. MNRAS 426 (2), pp. 1465–1474. External Links: Document, 1206.6107 Cited by: §IV.
[87] J. Prochaska, J. Hennawi, K. Westfall, R. Cooke, F. Wang, T. Hsyu, F. Davies, E. Farina, and D. Pelliccia (2020) PypeIt: The Python Spectroscopic Data Reduction Pipeline. The Journal of Open Source Software 5 (56), pp. 2308. External Links: Document, 2005.06505 Cited by: §III.2.
[88] M. L. Pumo, L. Zampieri, S. Spiro, A. Pastorello, S. Benetti, E. Cappellaro, G. Manicò, and M. Turatto (2017) Radiation-hydrodynamical modelling of underluminous Type II plateau supernovae. MNRAS 464 (3), pp. 3013–3020. External Links: Document, 1610.02981 Cited by: §I.
[89] A. Reguitti, M. L. Pumo, P. A. Mazzali, A. Pastorello, G. Pignata, N. Elias-Rosa, S. J. Prentice, T. Reynolds, S. Benetti, O. Rodrìguez, S. Mattila, and H. Kuncarayakti (2021) Low-luminosity Type II supernovae - III. SN 2018hwm, a faint event with an unusually long plateau. MNRAS 501 (1), pp. 1059–1071. External Links: Document, 2011.11297 Cited by: §I.
[90] M. Rigault, J. D. Neill, N. Blagorodnova, A. Dugas, M. Feeney, R. Walters, V. Brinnel, Y. Copin, C. Fremling, J. Nordin, and J. Sollerman (2019) Fully automated integral field spectrograph pipeline for the SEDMachine: pysedm. A&A 627, pp. A115. External Links: 1902.08526, Document Cited by: §III.2.
[91] M. Roberson, C. Fremling, and M. Kasliwal (2022) DBSP_DRP: A Python package for automated spectroscopic data reduction of DBSP data. The Journal of Open Source Software 7 (70), pp. 3612. External Links: Document, 2107.12339 Cited by: §III.2.
[92] Ó. Rodríguez, N. Meza, J. Pineda-García, and M. Ramirez (2021) The iron yield of normal Type II supernovae. MNRAS 505 (2), pp. 1742–1774. External Links: Document, 2105.03268 Cited by: §I.
[93] Ó. Rodríguez (2022) Luminosity distribution of Type II supernova progenitors. MNRAS 515 (1), pp. 897–913. External Links: Document, 2206.12974 Cited by: Appendix A.
[94] S. Rose, R. M. Lau, J. E. Jencson, M. M. Kasliwal, K. De, M. E. Ressler, O. D. Fox, and M. J. Hankins (2024) Investigating the Electron Capture Supernova Candidate AT 2019abn with JWST Spectroscopy. arXiv e-prints, pp. arXiv:2407.20430. External Links: Document, 2407.20430 Cited by: §VII.2.
[95] E. E. Salpeter (1955) The Luminosity Function and Stellar Evolution.. ApJ 121, pp. 161. External Links: Document Cited by: §I.
[96] H. Sana, S. E. de Mink, A. de Koter, N. Langer, C. J. Evans, M. Gieles, E. Gosset, R. G. Izzard, J. -B. Le Bouquin, and F. R. N. Schneider (2012) Binary Interaction Dominates the Evolution of Massive Stars. Science 337 (6093), pp. 444. External Links: Document, 1207.6397 Cited by: §VII.2, §VII.2.
[97] N. E. Sanders, A. M. Soderberg, S. Gezari, M. Betancourt, R. Chornock, E. Berger, R. J. Foley, P. Challis, M. Drout, R. P. Kirshner, R. Lunnan, G. H. Marion, R. Margutti, R. McKinnon, D. Milisavljevic, G. Narayan, A. Rest, E. Kankare, S. Mattila, S. J. Smartt, M. E. Huber, W. S. Burgett, P. W. Draper, K. W. Hodapp, N. Kaiser, R. P. Kudritzki, E. A. Magnier, N. Metcalfe, J. S. Morgan, P. A. Price, J. L. Tonry, R. J. Wainscoat, and C. Waters (2015) Toward Characterization of the Type IIP Supernova Progenitor Population: A Statistical Sample of Light Curves from Pan-STARRS1. ApJ 799 (2), pp. 208. External Links: Document, 1404.2004 Cited by: §I, §VII.1.
[98] M. A. Sandoval, W. R. Hix, O. E. B. Messer, E. J. Lentz, and J. A. Harris (2021) Three-dimensional Core-collapse Supernova Simulations with 160 Isotopic Species Evolved to Shock Breakout. ApJ 921 (2), pp. 113. External Links: Document, 2106.01389 Cited by: §I, §VII.2.
[99] R. Sawada, K. Kashiyama, and Y. Suwa (2022) On the Energy Source of Ultrastripped Supernovae. ApJ 927 (2), pp. 223. External Links: Document, 2112.10782 Cited by: §VII.2.
[100] E. F. Schlafly and D. P. Finkbeiner (2011) Measuring Reddening with Sloan Digital Sky Survey Stellar Spectra and Recalibrating SFD. ApJ 737, pp. 103. External Links: Document, 1012.4804 Cited by: §IV.
[101] M. Shrestha, J. Pearson, S. Wyatt, D. J. Sand, G. Hosseinzadeh, K. A. Bostroem, J. E. Andrews, Y. Dong, E. Hoang, D. Janzen, J. E. Jencson, M. Lundquist, D. Mehta, N. M. Retamal, S. Valenti, J. C. Rastinejad, P. Daly, D. Porter, J. Hinz, S. Self, B. Weiner, G. G. Williams, D. Hiramatsu, D. A. Howell, C. McCully, E. P. Gonzalez, C. Pellegrino, G. Terreran, M. Newsome, J. Farah, K. Itagaki, S. W. Jha, L. Kwok, N. Smith, M. Schwab, J. Rho, and Y. Yang (2024) Evidence of Weak Circumstellar Medium Interaction in the Type II SN 2023axu. ApJ 961 (2), pp. 247. External Links: Document, 2310.00162 Cited by: Table A.
[102] S. J. Smartt (2015) Observational Constraints on the Progenitors of Core-Collapse Supernovae: The Case for Missing High-Mass Stars. PASA 32, pp. e016. External Links: Document, 1504.02635 Cited by: Appendix A.
[103] J. Soon, D. Adams, K. De, A. Galla, M. Hankins, M. M. Kasliwal, A. M. Moore, S. M. Adams, J. Antoszewski, M. Ashley, A. Babul, J. Bland-Hawthorn, J. Cooke, O. De Marco, A. Delacroix, H. Devillepoix, S. C. Ellis, K. C. Freeman, D. Hale, A. Heger, J. E. Jencson, R. M. Lau, D. McKenna, E. Ofek, S. Ryder, R. Simcoe, J. L. Sokoloski, R. Soria, R. M. Smith, and T. D. Travouillon (2020) Wide-field dynamic astronomy in the near-infrared with Palomar Gattini-IR and DREAMS. In Advances in Optical Astronomical Instrumentation 2019, S. C. Ellis and C. d’Orgeville (Eds.), Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 11203, pp. 1120307. External Links: Document Cited by: §VIII.
[104] S. Spiro, A. Pastorello, M. L. Pumo, L. Zampieri, M. Turatto, S. J. Smartt, S. Benetti, E. Cappellaro, S. Valenti, I. Agnoletto, G. Altavilla, T. Aoki, E. Brocato, E. M. Corsini, A. Di Cianno, N. Elias-Rosa, M. Hamuy, K. Enya, M. Fiaschi, G. Folatelli, S. Desidera, A. Harutyunyan, D. A. Howell, A. Kawka, Y. Kobayashi, B. Leibundgut, T. Minezaki, H. Navasardyan, K. Nomoto, S. Mattila, A. Pietrinferni, G. Pignata, G. Raimondo, M. Salvo, B. P. Schmidt, J. Sollerman, J. Spyromilio, S. Taubenberger, G. Valentini, S. Vennes, and Y. Yoshii (2014) Low luminosity Type II supernovae - II. Pointing towards moderate mass precursors. MNRAS 439 (3), pp. 2873–2892. External Links: Document, 1401.5426 Cited by: §I, §I.
[105] G. Stockinger, H. -T. Janka, D. Kresse, T. Melson, T. Ertl, M. Gabler, A. Gessner, A. Wongwathanarat, A. Tolstov, S. -C. Leung, K. Nomoto, and A. Heger (2020) Three-dimensional models of core-collapse supernovae from low-mass progenitors with implications for Crab. MNRAS 496 (2), pp. 2039–2084. External Links: Document, 2005.02420 Cited by: §I, §VII.2.
[106] M. D. Stritzinger, F. Taddia, C. R. Burns, M. M. Phillips, M. Bersten, C. Contreras, G. Folatelli, S. Holmbo, E. Y. Hsiao, P. Hoeflich, G. Leloudas, N. Morrell, J. Sollerman, and N. B. Suntzeff (2018) The Carnegie Supernova Project I. Methods to estimate host-galaxy reddening of stripped-envelope supernovae. A&A 609, pp. A135. External Links: Document, 1707.07615 Cited by: §IV.
[107] T. Sukhbold, T. Ertl, S. E. Woosley, J. M. Brown, and H. -T. Janka (2016) Core-collapse Supernovae from 9 to 120 Solar Masses Based on Neutrino-powered Explosions. ApJ 821 (1), pp. 38. External Links: Document, 1510.04643 Cited by: §I, §I, §VII.2.
[108] M. Taylor, D. Cinabro, B. Dilday, L. Galbany, R. R. Gupta, R. Kessler, J. Marriner, R. C. Nichol, M. Richmond, D. P. Schneider, and J. Sollerman (2014) The Core Collapse Supernova Rate from the SDSS-II Supernova Survey. ApJ 792 (2), pp. 135. External Links: Document, 1407.0999 Cited by: §I, §VII.1, §VII.1.
[109] R. S. Teja, J. A. Goldberg, D. K. Sahu, G. C. Anupama, A. Singh, V. Swain, and V. Bhalerao (2024) SN 2021wvw: A Core-collapse Supernova at the Subluminous, Slower, and Shorter End of Type IIPs. ApJ 974 (1), pp. 44. External Links: Document, 2407.13207 Cited by: §I.
[110] T. A. Thompson, J. L. Prieto, K. Z. Stanek, M. D. Kistler, J. F. Beacom, and C. S. Kochanek (2009) A New Class of Luminous Transients and a First Census of their Massive Stellar Progenitors. ApJ 705 (2), pp. 1364–1384. External Links: Document, 0809.0510 Cited by: §VII.2.
[111] L. Tomasella, E. Cappellaro, M. Fraser, M. L. Pumo, A. Pastorello, G. Pignata, S. Benetti, F. Bufano, M. Dennefeld, A. Harutyunyan, T. Iijima, A. Jerkstrand, E. Kankare, R. Kotak, L. Magill, V. Nascimbeni, P. Ochner, A. Siviero, S. Smartt, J. Sollerman, V. Stanishev, F. Taddia, S. Taubenberger, M. Turatto, S. Valenti, D. E. Wright, and L. Zampieri (2013) Comparison of progenitor mass estimates for the Type IIP SN 2012A. MNRAS 434 (2), pp. 1636–1657. External Links: Document, 1305.5789 Cited by: Appendix A.
[112] M. Turatto, P. A. Mazzali, T. R. Young, K. Nomoto, K. Iwamoto, S. Benetti, E. Cappellaro, I. J. Danziger, D. F. de Mello, M. M. Phillips, N. B. Suntzeff, A. Clocchiatti, A. Piemonte, B. Leibundgut, R. Covarrubias, J. Maza, and J. Sollerman (1998) The Peculiar Type II Supernova 1997D: A Case for a Very Low ${}^{56}$ Ni Mass. ApJ 498 (2), pp. L129–L133. External Links: Document, astro-ph/9803216 Cited by: §I.
[113] G. Valerin, A. Pastorello, A. Reguitti, S. Benetti, Y. -Z. Cai, T. -W. Chen, D. Eappachen, N. Elias-Rosa, M. Fraser, A. Gangopadhyay, E. Y. Hsiao, D. A. Howell, C. Inserra, L. Izzo, J. Jencson, E. Kankare, R. Kotak, P. A. Mazzali, K. Misra, G. Pignata, S. J. Prentice, D. J. Sand, S. J. Smartt, M. D. Stritzinger, L. Tartaglia, S. Valenti, J. P. Anderson, J. E. Andrews, R. C. Amaro, S. Brennan, F. Bufano, E. Callis, E. Cappellaro, R. Dastidar, M. Della Valle, A. Fiore, M. D. Fulton, L. Galbany, T. Heikkilä, D. Hiramatsu, E. Karamehmetoglu, H. Kuncarayakti, G. Leloudas, M. Lundquist, C. McCully, T. E. Müller-Bravo, M. Nicholl, P. Ochner, E. Padilla Gonzalez, E. Paraskeva, C. Pellegrino, A. Rau, D. E. Reichart, T. M. Reynolds, R. Roy, I. Salmaso, M. Singh, M. Turatto, L. Tomasella, S. Wyatt, and D. R. Young (2025) A study in scarlet: I. Photometric properties of a sample of intermediate-luminosity red transients. A&A 695, pp. A42. External Links: Document, 2407.21671 Cited by: §VII.2.
[114] G. Valerin, M. L. Pumo, A. Pastorello, A. Reguitti, N. Elias-Rosa, C. P. Gútierrez, E. Kankare, M. Fraser, P. A. Mazzali, D. A. Howell, R. Kotak, L. Galbany, S. C. Williams, Y. -Z. Cai, I. Salmaso, V. Pinter, T. E. Müller-Bravo, J. Burke, E. Padilla Gonzalez, D. Hiramatsu, C. McCully, M. Newsome, and C. Pellegrino (2022) Low luminosity Type II supernovae - IV. SN 2020cxd and SN 2021aai, at the edges of the sub-luminous supernovae class. MNRAS 513 (4), pp. 4983–4999. External Links: Document, 2203.03988 Cited by: Table A, §I.
[115] S. van der Walt, A. Crellin-Quick, and J. Bloom (2019) SkyPortal: An Astronomical Data Platform. The Journal of Open Source Software 4 (37), pp. 1247. External Links: Document Cited by: §II.1.
[116] S. D. Van Dyk, K. A. Bostroem, W. Zheng, T. G. Brink, O. D. Fox, J. E. Andrews, A. V. Filippenko, Y. Dong, E. Hoang, G. Hosseinzadeh, D. Janzen, J. E. Jencson, M. J. Lundquist, N. Meza, D. Milisavljevic, J. Pearson, D. J. Sand, M. Shrestha, S. Valenti, and D. A. Howell (2023) Identifying the SN 2022acko progenitor with JWST. MNRAS 524 (2), pp. 2186–2194. External Links: Document, 2302.00274 Cited by: Appendix A, Table A.
[117] S. D. Van Dyk, K. A. Bostroem, W. Zheng, T. G. Brink, O. D. Fox, J. E. Andrews, A. V. Filippenko, Y. Dong, E. Hoang, G. Hosseinzadeh, D. Janzen, J. E. Jencson, M. J. Lundquist, N. Meza, D. Milisavljevic, J. Pearson, D. J. Sand, M. Shrestha, S. Valenti, and D. A. Howell (2023) Identifying the SN 2022acko progenitor with JWST. MNRAS 524 (2), pp. 2186–2194. External Links: Document, 2302.00274 Cited by: §I.
[118] S. E. Woosley and A. Heger (2015) The Remarkable Deaths of 9-11 Solar Mass Stars. ApJ 810 (1), pp. 34. External Links: Document, 1505.06712 Cited by: §I.
[119] L. Xiao and J. J. Eldridge (2015) Core-collapse supernova rate synthesis within 11 Mpc. MNRAS 452 (3), pp. 2597–2605. External Links: Document, 1506.07908 Cited by: §I.
[120] S. Yang, J. Sollerman, N. L. Strotjohann, S. Schulze, R. Lunnan, E. Kool, C. Fremling, D. Perley, E. Ofek, T. Schweyer, E. C. Bellm, M. M. Kasliwal, F. J. Masci, M. Rigault, and Y. Yang (2021) A low-energy explosion yields the underluminous Type IIP SN 2020cxd. A&A 655, pp. A90. External Links: Document, 2107.13439 Cited by: §I.
[121] E. Zapartas, S. E. de Mink, S. Justham, N. Smith, M. Renzo, and A. de Koter (2021) Effect of binary evolution on the inferred initial and final core masses of hydrogen-rich, Type II supernova progenitors. A&A 645, pp. A6. External Links: Document, 2002.07230 Cited by: §VII.2.
[122] E. Zapartas, S. E. de Mink, S. Justham, N. Smith, A. de Koter, M. Renzo, I. Arcavi, R. Farmer, Y. Götberg, and S. Toonen (2019) The diverse lives of progenitors of hydrogen-rich core-collapse supernovae: the role of binary interaction. A&A 631, pp. A5. External Links: Document, 1907.06687 Cited by: §VII.2.

Appendix A Progenitor Mass from pre-explosion images Vs SN peak magnitude

Here, we show the distribution of the Zero-Age Main-Sequence (ZAMS) mass estimated for Type II SNe from pre-explosion images. We can see that all LLIIP SNe have progenitor mass estimates of less than $<11$ $\mathrm{M}_{\odot}$ while SNe brighter than $-16$ mag have more massive progenitors. The SNe shown here are: SN 2003gd [73, 102], SN 2005cs [71, 102], SN 2009md [36, 102], SN 2006my [71, 102], SN 2012A [111, 102], SN 2013ej [37, 102], SN 2004et [21, 102], SN 2008bk [71, 102], SN 2004A [71, 102], SN 2012aw[57, 102], SN 2009hd [33, 102], SN 2009kr [38, 102], SN 2012ec [70, 102], SN 2018aoq [82], SN 2022acko [116]. A positive correlation between progenitor luminosity and $V-$ band magnitude at 50 days since explosion is also reported in Rodríguez [93].

Appendix B Distance correction for nearby galaxies

Table A lists the distance measurements used for galaxies closer than $25$ Mpc. We correct for the Virgo, Great Attractor, and Shapley supercluster infall based on the NASA Extragalactic Database object page [41, NED⁵⁵ 5 https://ned.ipac.caltech.edu/;].

Table A: Distance measurements used for galaxies

<25

Mpc.

Name	Distance (Mpc)
SN 2021gmj/ZTF21aaqgmjt^a	13.10
SN 2022acko/ZTF22abyivoq^b	23.40
SN 2023axu/ZTF23aabngtm^c	13.68
SN 2023hlf/ZTF23aaitpjv^d	9.54
SN 2022aagp/ZTF22abtspsw^d	25.15
SN 2023ijd/ZTF23aajrmfh^d	14.94
SN 2022jzc/ZTF22aakdbia^d	19.64

^aValerin et al. [114]; ^bVan Dyk et al. [116]; ^cShrestha et al. [101]; ^dHelou et al. [41].

Appendix C ZTF Pipeline Recovery Efficiency Fits

The corner plot for the MCMC fit to

p(m)=\frac{1}{1+\exp(a(m-c))},

where m is the alert apparent magnitude is shown in Figure 13. The best-fit values are $a=2.28^{+0.03}_{-0.04}$ and $c=20.57^{+0.01}_{-0.01}$ .

The corner plot for the MCMC fit to

p(r)=\frac{1}{1+\exp(a(r-c))},

where $r$ is the ratio of the alert apparent magnitude to the local surface brightness is shown in Figure 13. The best-fit values are $a=1.12^{+0.02}_{-0.02}$ and $c=0.21^{+0.03}_{-0.03}$ .

Appendix D Sample of CLU Type II SNe

The full version of the sample summary table is shown in Table B.

Table B: Summary table of the CLU Type II SN sample. The peak absolute magnitudes have been measured by assuming Milky Way extinction (

\mathrm{A_{V,MW}}

) and host galaxy extinction (

\mathrm{A_{V,host}}

) as described in Section IV. The

t_{\rm expl}

column shows the explosion epoch. The Peak mag

{}_{\textrm{r}}

shows the peak

r

-band absolute magnitude. A machine-readable version of the full sample table is available in DOI:10.5281/zenodo.14538857.

Name	RA	Dec	Redshift	$t_{\rm expl}$	1st detection	Peak mag ${}_{\textrm{r}}$	$A_{V,MW}$	$A_{V,\text{host}}$
	(hh:mm:ss)	(dd:mm:ss)		(MJD)	(MJD)	(mag)	(mag)	(mag)
ZTF18aaegvyd/SN2019env	09:09:35.63	+29:58:14.4	0.024	58606.7	58608.2	-17.03	0.07	0.00
ZTF18aaqkywr/SN2022giv	12:23:14.92	+51:11:28.3	0.031	59655.9	59670.4	-16.77	0.05	0.00
ZTF18aaqpowv/SN2024iwm	11:28:17.83	+26:58:26.37	0.032	60443.2	60444.2	-17.42	0.05	0.00
ZTF18aaszvfn/SN2021iaw	14:23:07.88	+50:13:14.9	0.027	59306.4	59307.3	-16.77	0.06	0.00
ZTF18aawgrxz/SN2021lmp	15:04:17.64	+48:55:10.7	0.027	59338.9	59339.3	-16.85	0.04	0.42
ZTF18aaxzlmy/SN2018lrq	13:34:51.37	+34:03:20.5	0.025	58218.8	58219.3	-16.62	0.03	0.99
ZTF18abbpxik/SN2018cqp	15:22:50.32	+08:04:49.1	0.032	58282.7	58283.2	-18.08	0.10	0.71
ZTF18abbpyvk/SN2018cqi	13:57:40.91	+17:30:29.1	0.022	58284.7	58286.2	-18.29	0.09	0.07
ZTF18abcpmwh/SN2018cur	12:59:09.12	+37:19:00.1	0.015	58289.7	58291.2	-17.56	0.04	0.00
ZTF18abdbysy/SN2018cyg	15:34:08.48	+56:41:48.6	0.011	58294.8	58295.2	-17.08	0.05	2.05
ZTF18abdfwcy/SN2018cwa	12:42:43.85	+34:06:26.3	0.014	58290.8	58295.2	-17.27	0.05	0.00
ZTF18abdjmfh/SN2018dct	16:15:08.80	+26:33:36.2	0.030	58298.8	58300.3	-18.72	0.12	1.27
ZTF18abjkryl/SN2018dzc	18:23:09.26	+21:14:56.4	0.010	58308.4	58308.4	-16.30	0.48	0.49
ZTF18abjndhh/SN2018ecj	11:32:43.02	+62:25:57.9	0.013	58322.2	58324.2	-17.03	0.03	1.34
ZTF18abjovdz/SN2018dht	20:55:19.05	+00:32:18.6	0.024	58309.9	58312.4	-18.36	0.23	1.48
ZTF18abklbam/SN2018elp	14:31:19.10	+21:17:24.7	0.031	58329.2	58331.2	-18.00	0.13	0.00
ZTF18abmdpwe/SN2018evy	18:22:38.17	+15:41:47.6	0.018	58339.2	58340.2	-18.06	0.54	0.00
ZTF18abnxfve/SN2018lrz	22:29:52.72	+36:43:50.1	0.025	58347.8	58349.3	-15.94	0.30	0.00
ZTF18abokyfk/SN2018fif	00:09:26.55	+47:21:14.7	0.017	58350.8	58351.4	-17.12	0.30	0.00
ZTF18abrpkdw/SN2018lsa	21:45:07.20	+15:35:09.0	0.031	58355.8	58357.2	-16.49	0.29	0.00
ZTF18abrzbtb/SN2018ggu	07:43:04.67	+50:17:22.2	0.019	59120.0	59120.5	-15.66	0.19	0.00
ZTF18abvmlow/SN2018lcd	01:51:10.65	-03:29:24.9	0.017	58479.2	58483.2	-18.02	0.08	0.00
ZTF18abvvmdf/SN2018gts	16:36:47.39	+55:44:08.8	0.030	58373.2	58374.2	-18.02	0.05	0.92
ZTF18abwisaz/SN2024kan	22:49:07.02	+32:47:53.12	0.028	60454.5	60456.4	-17.45	0.36	0.00
ZTF18abxbmcl/SN2018hcp	08:22:57.66	+27:42:10.5	0.020	58377.0	58378.5	-19.19	0.10	2.05
ZTF18abzrgim/SN2018gvt	23:07:32.54	+23:00:20.9	0.021	58386.8	58388.3	-17.49	0.63	0.00
ZTF18acbvhit/SN2018hle	03:39:28.11	-13:07:02.4	0.014	58418.7	58422.4	-16.80	0.15	0.21
ZTF18aceqrcy/SN2018joy	10:19:40.32	+12:50:37.7	0.032	58428.0	58429.5	-17.77	0.18	0.99
ZTF18acrflch/SN2018jgy	03:06:59.88	-06:45:14.0	0.029	58445.8	58448.3	-17.38	0.23	0.00
ZTF18acrkaks/SN2018jwq	05:17:54.17	+08:54:51.3	0.030	58438.4	58439.4	-16.51	0.93	0.00
ZTF18acrtvmm/SN2018jfp	03:17:56.27	-00:10:10.7	0.023	58448.8	58450.3	-17.55	0.20	0.00
ZTF18acsxkov/SN2018kds	00:10:09.06	-04:42:33.2	0.030	58449.7	58456.2	-16.68	0.09	0.00
ZTF18acurdnh/SN2018jvr	10:07:20.35	+04:04:49.2	0.029	58458.5	58461.5	-16.38	0.05	0.00
ZTF18acurqaw/SN2018hwm	08:21:28.20	+03:09:52.3	0.009	58432.5	58441.4	-17.05	0.07	3.39
ZTF18acyybvg/SN2018kkv	11:34:33.85	+22:31:09.2	0.032	58470.5	58471.5	-16.69	0.06	0.00
ZTF18adachwf/SN2018lev	15:13:08.16	+41:15:49.2	0.029	58472.2	58475.5	-16.94	0.06	0.14
ZTF18adazblo/SN2018ldu	09:09:32.49	+20:24:25.1	0.027	58476.4	58480.4	-16.67	0.11	0.00
ZTF18adbclkd/SN2018kpo	03:40:43.06	-06:25:23.9	0.017	58478.3	58481.3	-17.28	0.15	0.00
ZTF19aaabzpt/SN2018lab	06:16:26.51	-21:22:32.4	0.009	58479.3	58487.3	-15.81	0.24	0.71
ZTF19aadnxnl/SN2019va	13:35:14.68	+44:45:58.6	0.009	58498.5	58502.5	-16.87	0.05	0.00
ZTF19aadnxog/SN2019vb	13:14:23.07	+30:29:06.6	0.020	58498.5	58503.5	-18.04	0.04	0.00
ZTF19aailepg/SN2019amt	11:17:52.25	+30:09:23.5	0.030	58518.4	58522.4	-17.11	0.05	0.00
ZTF19aajwkbb/SN2019bsw	10:05:06.10	-16:24:21.3	0.027	58502.5	58511.4	-16.75	0.14	0.00
ZTF19aaklqoi/SN2021adnr	08:57:54.94	+20:07:12.6	0.031	59514.0	59518.5	-17.27	0.08	1.20
ZTF19aakpxfm/SN2019aor	07:24:57.85	-27:31:53.7	0.025	58476.4	58476.4	-17.94	0.83	0.00
ZTF19aalycsv/SN2019txj	10:48:39.19	+76:48:05.3	0.023	58543.3	58546.2	-15.27	0.07	0.00
ZTF19aamftfu/SN2019cag	02:23:21.52	+53:23:41.3	0.025	58551.6	58556.1	-17.30	0.53	0.00

Table B: Continued.

Name	RA	Dec	Redshift	$t_{\rm expl}$	1st detection	Peak mag ${}_{\textrm{r}}$	$A_{V,MW}$	$A_{V,\text{host}}$
	(hh:mm:ss)	(dd:mm:ss)		(MJD)	(MJD)	(mag)	(mag)	(mag)
ZTF19aamwhat/SN2019bzd	14:47:32.03	-19:45:57.7	0.008	58559.7	58568.4	-15.83	0.24	0.07
ZTF19aanfnvl/SN2019crk	10:10:31.69	+10:02:37.0	0.032	58561.7	58567.2	-17.77	0.12	0.00
ZTF19aanhhal/SN2019cec	13:41:40.74	+55:40:10.7	0.026	58561.9	58562.3	-17.23	0.03	0.00
ZTF19aaniore/SN2019ceg	16:27:51.64	+62:41:32.5	0.030	58564.5	58567.5	-17.31	0.09	0.00
ZTF19aanqzle/SN2019cmm	11:18:07.29	+75:08:50.8	0.023	58570.2	58572.2	-16.03	0.16	0.00
ZTF19aanrrqu/SN2019clp	12:13:39.83	+16:07:24.4	0.024	58569.8	58572.3	-18.02	0.10	0.00
ZTF19aapafit/SN2019cvz	16:30:54.08	+46:35:18.4	0.019	58576.5	58577.4	-17.05	0.04	0.00
ZTF19aaqxosb/SN2019dok	13:46:51.76	+16:16:53.7	0.019	58588.3	58589.2	-16.64	0.07	0.00
ZTF19aarjfqe/SN2019dvd	12:31:06.89	+00:27:54.6	0.021	58592.8	58596.3	-16.93	0.06	0.00
ZTF19aarykkb/SN2019dzk	17:13:21.95	-09:57:52.1	0.024	58595.4	58598.3	-18.17	1.54	0.00
ZTF19aatmadu/SN2019esn	14:51:56.11	+51:15:51.2	0.027	58602.4	58605.4	-16.00	0.06	0.00
ZTF19aauishy/SN2019evl	13:31:01.26	+34:09:12.5	0.023	58609.3	58612.2	-16.45	0.03	0.00
ZTF19aavrcew/SN2019fyw	13:07:18.44	+02:00:11.5	0.019	58625.2	58633.2	-17.78	0.08	0.00
ZTF19aavtcjs/SN2019gss	15:24:44.44	+68:43:44.3	0.022	58630.3	58633.3	-17.89	0.07	2.19
ZTF19aawgxdn/SN2019gmh	16:31:03.16	+41:09:14.2	0.031	58634.3	58635.2	-17.39	0.03	0.00
ZTF19aaykqsk/SN2019hci	11:16:03.35	-00:31:55.9	0.026	58641.7	58643.2	-17.02	0.17	0.00
ZTF19aayrosj/SN2019hrb	20:54:12.08	+10:33:01.1	0.015	58642.5	58644.5	-17.64	0.26	1.70
ZTF19aazudta/SN2019hqm	17:36:56.18	+21:06:17.1	0.024	58648.8	58650.3	-16.98	0.23	0.00
ZTF19aazyvub/SN2019hnl	23:43:10.25	-02:56:58.6	0.023	58649.5	58651.5	-17.13	0.09	0.00
ZTF19abaamsd/SN2019ifm	17:23:41.33	+52:00:36.1	0.024	58652.8	58653.3	-17.33	0.09	0.00
ZTF19abajxet/SN2019hyk	14:17:57.85	+26:25:17.6	0.015	58655.7	58657.2	-17.77	0.06	0.00
ZTF19abalqkq/SN2019khq	17:50:41.76	+14:49:26.3	0.014	58645.9	58650.3	-15.76	0.26	1.63
ZTF19abbnamr/SN2019iex	23:51:03.63	+20:08:43.7	0.014	58659.0	58660.5	-17.40	0.20	0.00
ZTF19abbwfgp/SN2019ikb	17:13:17.71	+43:47:03.5	0.026	58660.8	58661.3	-18.16	0.05	0.00
ZTF19abctzkc/SN2019tti	00:18:59.83	+08:46:28.2	0.019	58644.4	58646.4	-15.67	0.55	0.21
ZTF19abejaiy/SN2019krp	14:07:33.70	+14:38:03.3	0.017	58670.2	58671.2	-15.45	0.04	0.00
ZTF19abjrjdw/SN2019mkr	17:11:05.78	+05:51:07.3	0.022	58687.7	58689.2	-17.56	0.43	0.00
ZTF19abjsmmv/SN2019mor	15:38:38.04	+36:57:31.0	0.019	58693.3	58694.2	-17.13	0.06	0.00
ZTF19ablfoqa/SN2019tya	02:09:38.03	+01:33:06.7	0.032	58694.0	58694.5	-15.93	0.08	0.00
ZTF19abllxfy/SN2019ttl	21:52:43.47	+38:56:00.8	0.020	58682.9	58690.3	-15.36	0.88	0.00
ZTF19abpyqog/SN2019oba	19:57:03.47	+50:11:20.1	0.031	58708.3	58711.2	-17.22	0.40	0.00
ZTF19abqhobb/SN2019nvm	17:25:38.66	+59:26:48.2	0.018	58713.7	58714.2	-17.59	0.08	0.00
ZTF19abqrhvt/SN2019nyk	00:15:15.20	-08:11:21.8	0.021	58713.4	58715.4	-18.09	0.10	0.00
ZTF19abqrhvy/SN2019odf	22:48:44.69	+27:34:18.5	0.032	58714.9	58715.4	-18.18	0.14	0.00
ZTF19abrnjwi/SN2019omb	00:12:39.66	+05:30:32.0	0.028	58717.9	58719.4	-17.20	0.07	0.00
ZTF19abwztsb/SN2019pjs	18:04:40.34	+21:38:04.2	0.007	58731.7	58734.2	-16.23	0.29	0.00
ZTF19abzmoov/SN2019qba	15:37:44.70	+22:25:36.4	0.025	58735.6	58737.1	-16.68	0.12	0.00
ZTF19acalxgp/SN2019qiq	23:44:56.09	-04:16:33.1	0.029	58735.4	58737.3	-15.86	0.12	0.00
ZTF19acblhxc/SN2019rho	02:12:49.18	-06:42:05.9	0.017	58753.4	58756.4	-17.21	0.07	0.21
ZTF19acbpqlh/SN2019rpn	21:19:41.19	+37:31:19.3	0.026	58709.8	58711.2	-16.28	0.46	0.00
ZTF19acbwejj/SN2019upq	14:29:08.13	+27:27:00.6	0.014	58754.1	58758.1	-17.80	0.05	0.00
ZTF19acewuwn/SN2019ssl	23:20:17.26	+35:29:35.3	0.027	58771.7	58772.2	-16.96	0.27	0.00
ZTF19acftfav/SN2019ssi	23:30:56.12	+15:29:29.8	0.013	58773.7	58774.2	-16.51	0.19	0.00
ZTF19acgbkzr/SN2019szo	00:19:56.64	+15:05:36.1	0.026	58774.8	58775.3	-16.73	0.13	0.00
ZTF19acgzwbm/SN2019tba	04:55:09.84	-16:09:01.3	0.020	58770.5	58776.5	-16.34	0.18	0.00
ZTF19aclobbu/SN2019twk	02:23:05.28	+46:52:56.7	0.018	58787.3	58788.3	-17.63	0.49	0.00

Table B: Continued.

Name	RA	Dec	Redshift	$t_{\rm expl}$	1st detection	Peak mag ${}_{\textrm{r}}$	$A_{V,MW}$	$A_{V,\text{host}}$
	(hh:mm:ss)	(dd:mm:ss)		(MJD)	(MJD)	(mag)	(mag)	(mag)
ZTF19acnphay/SN2019ubr	06:25:52.32	+64:44:38.4	0.014	58769.5	58772.5	-16.57	0.32	2.40
ZTF19acrcxri/SN2019ult	23:58:47.97	+14:44:31.3	0.027	58795.1	58797.1	-18.42	0.11	0.00
ZTF19acryurj/SN2019ust	00:54:22.41	+31:40:12.6	0.022	58798.2	58800.3	-18.10	0.17	0.00
ZTF19actnwtn/SN2019vdl	09:29:31.80	+44:25:20.0	0.025	58803.5	58804.5	-17.35	0.06	0.57
ZTF19actnyae/SN2019vdm	11:26:24.51	+22:37:11.0	0.032	58804.0	58805.5	-16.88	0.04	0.00
ZTF19acvtrxj/SN2019vjl	09:49:23.69	+01:08:46.6	0.025	58808.5	58812.6	-17.58	0.23	0.07
ZTF19acwrrvg/SN2019vsr	02:01:57.73	+44:48:32.1	0.027	58816.7	58819.2	-18.66	0.21	0.00
ZTF19acxgwvo/SN2019wbd	23:20:35.17	-00:52:51.0	0.015	58815.7	58820.2	-17.20	0.10	0.35
ZTF19acyjjni/SN2019xbm	13:07:13.99	+58:08:03.3	0.028	58823.0	58831.6	-16.94	0.04	0.42
ZTF19acykzsk/SN2019wqj	02:11:37.09	+34:02:28.7	0.021	58823.2	58827.2	-16.31	0.27	0.07
ZTF19acytcsg/SN2019wvz	10:20:28.67	+50:28:04.5	0.032	58832.5	58833.4	-17.60	0.03	0.00
ZTF19adakmbh/SN2019xgi	21:55:25.49	+34:30:37.8	0.018	58834.1	58837.1	-17.90	0.46	1.63
ZTF20aarenrz/SN2021qyy	11:44:29.64	+69:43:45.4	0.009	59388.8	59390.2	-16.55	0.03	0.00
ZTF21aaabwem/SN2020aeqx	13:13:00.02	+06:10:12.1	0.032	59211.0	59215.5	-16.79	0.10	0.00
ZTF21aaagypx/SN2021V	11:13:07.92	+05:04:19.3	0.027	59213.4	59216.4	-17.43	0.20	0.00
ZTF21aabfwwl/SN2021iy	11:18:31.68	-06:16:40.5	0.014	59217.9	59219.4	-15.55	0.14	0.00
ZTF21aabygea/SN2021os	12:02:54.08	+05:36:53.1	0.019	59219.5	59221.4	-17.28	0.05	0.00
ZTF21aaeoqxf/SN2021aek	11:59:48.02	-21:23:13.0	0.022	59225.5	59227.5	-17.61	0.13	0.00
ZTF21aaeqwov/SN2021htp	07:43:04.75	+50:17:19.4	0.019	59119.0	59119.5	-16.39	0.19	0.00
ZTF21aafepon/SN2021ass	01:50:10.12	+27:38:42.7	0.012	59230.6	59231.1	-16.24	0.23	0.07
ZTF21aafkwtk/SN2021apg	13:41:19.24	+24:29:43.9	0.027	59229.5	59231.4	-16.98	0.03	0.00
ZTF21aagtqna/SN2021brb	18:05:15.31	+46:52:56.0	0.023	59238.5	59248.5	-16.84	0.11	0.00
ZTF21aahgspm/SN2021cah	02:48:30.72	+50:45:36.0	0.016	59250.7	59251.1	-17.43	0.98	0.00
ZTF21aaipypa/SN2021cgu	11:03:03.71	+05:05:53.4	0.025	59252.4	59253.4	-18.34	0.10	0.00
ZTF21aakvroo/SN2021cwe	15:45:31.20	+30:09:43.3	0.032	59257.0	59258.5	-17.11	0.09	0.00
ZTF21aaluqkp/SN2021dhx	11:05:10.37	-15:21:10.1	0.025	59263.3	59264.3	-16.99	0.14	0.00
ZTF21aalxurx/SN2021dqs	13:44:05.74	+43:04:18.0	0.027	59259.9	59264.4	-16.33	0.03	0.00
ZTF21aanjvng/SN2021dvk	08:07:38.41	+08:56:24.2	0.030	59269.3	59271.2	-16.65	0.07	0.00
ZTF21aantsla/SN2021ech	12:06:20.42	+37:00:47.1	0.021	59274.3	59275.2	-16.44	0.06	0.00
ZTF21aanzcuj/SN2021enz	12:07:05.23	+42:59:18.3	0.024	59275.9	59276.3	-16.69	0.04	0.00
ZTF21aaobkmg/SN2021eui	19:20:55.80	+43:07:14.6	0.015	59273.5	59276.5	-15.34	0.28	0.00
ZTF21aapkcmr/SN2021fnj	14:23:42.67	+28:20:45.7	0.030	59285.4	59290.4	-17.91	0.05	0.42
ZTF21aapliyn/SN2021foj	13:45:26.60	+47:55:05.3	0.028	59286.3	59291.3	-17.11	0.08	0.00
ZTF21aaqgmjt/SN2021gmj	10:38:47.27	+53:30:30.3	0.003	59292.3	59293.2	-15.02	0.06	0.00
ZTF21aaqjmps/SN2021gvm	13:30:01.22	+13:24:40.2	0.025	59292.8	59294.3	-18.10	0.07	0.00
ZTF21aaqldsj/SN2021hac	14:12:30.45	+34:33:00.3	0.032	59293.3	59294.3	-16.95	0.04	0.00
ZTF21aaqugxm/SN2021hdt	11:34:45.73	+42:57:55.3	0.019	59299.4	59300.3	-18.12	0.07	0.00
ZTF21aaqyifh/SN2021hqe	09:41:30.96	+10:38:22.8	0.019	59297.3	59301.2	-16.86	0.06	0.71
ZTF21aaqyuun/SN2021hkf	11:44:23.27	+08:10:41.4	0.019	59300.3	59302.3	-16.38	0.07	0.00
ZTF21aardvtn/SN2021htp	07:43:04.76	+50:17:19.6	0.019	59119.0	59119.5	-16.42	0.19	0.00
ZTF21aasksnl/SN2021mju	16:41:47.59	+19:21:53.5	0.028	59292.0	59301.4	-16.08	0.21	0.42
ZTF21aavhnpk/SN2021jsf	20:50:21.44	+01:08:29.5	0.028	59313.0	59317.4	-17.33	0.29	0.00
ZTF21aaxtzzj/SN2021kqj	11:04:58.16	+30:01:46.8	0.029	59331.8	59334.3	-17.72	0.07	1.41
ZTF21aazhegf/SN2021llp	09:29:31.17	+25:33:25.0	0.033	59339.2	59340.2	-17.75	0.09	0.00
ZTF21abbomrf/SN2020ghq	14:45:20.59	+38:44:18.5	0.015	59347.8	59349.2	-15.72	0.03	0.00
ZTF21abcacpa/SN2021mtb	20:03:44.13	+49:59:34.7	0.027	59350.9	59353.4	-16.45	0.39	0.00
ZTF21abcmzvk/SN2021nli	14:02:12.66	-18:45:16.1	0.030	59354.2	59356.2	-17.62	0.25	0.00

Table B: Continued.

Name	RA	Dec	Redshift	$t_{\rm expl}$	1st detection	Peak mag ${}_{\textrm{r}}$	$A_{V,MW}$	$A_{V,\text{host}}$
	(hh:mm:ss)	(dd:mm:ss)		(MJD)	(MJD)	(mag)	(mag)	(mag)
ZTF21abcpbqd/SN2014gz	14:15:50.78	+01:52:57.4	0.026	59359.3	59359.3	-17.72	0.13	0.49
ZTF21abfiuqf/SN2021pla	16:05:38.19	+69:35:41.2	0.024	59375.4	59376.3	-17.00	0.08	0.21
ZTF21abfjaxa/SN2021pkh	12:48:41.97	+26:25:06.7	0.023	59372.2	59373.2	-17.43	0.03	2.05
ZTF21abfjdev/SN2021pqj	11:05:35.47	+19:41:22.2	0.032	59366.2	59367.2	-16.99	0.07	0.00
ZTF21abfoytp/SN2021pnh	15:50:50.63	+22:14:16.1	0.031	59373.8	59376.3	-16.68	0.17	0.49
ZTF21abgilzj/SN2021qcr	17:10:21.60	-03:13:49.7	0.029	59289.0	59295.4	-18.12	1.14	0.00
ZTF21abglcxm/SN2021qcs	15:29:22.82	-12:14:54.4	0.011	59377.3	59378.2	-15.65	0.43	0.42
ZTF21abhhrpj/SN2021qiu	21:45:50.08	+15:11:01.3	0.029	59380.4	59381.4	-17.88	0.24	0.00
ZTF21abiblpl/SN2021qzi	20:45:13.87	-05:37:09.9	0.027	59389.4	59391.3	-17.30	0.17	0.00
ZTF21abioeyq/SN2021rhk	14:03:02.40	+08:45:56.4	0.023	59394.7	59395.2	-17.74	0.07	0.00
ZTF21abjcjmc/SN2021skn	16:24:49.00	+39:44:04.7	0.030	59398.3	59399.2	-18.02	0.03	0.00
ZTF21abjcliz/SN2021skm	16:16:56.05	+21:48:35.8	0.031	59371.8	59372.3	-18.31	0.22	1.34
ZTF21abkajar/SN2021svy	13:09:21.83	+30:55:20.5	0.017	59402.7	59403.2	-17.01	0.03	0.00
ZTF21ablvzhp/SN2021tiq	22:36:54.72	-12:33:41.9	0.024	59409.4	59411.4	-18.12	0.17	0.00
ZTF21abnlhxs/SN2021tyw	23:05:56.45	+14:21:27.8	0.013	59417.9	59419.4	-17.85	0.63	0.00
ZTF21abnudtb/SN2021txr	22:30:50.30	+36:33:48.2	0.026	59416.9	59418.4	-18.10	0.34	0.00
ZTF21abouuat/SN2021ucg	22:47:37.67	+39:52:59.4	0.017	59420.4	59422.4	-17.56	0.31	0.00
ZTF21abrluay/SN2021vfh	01:31:38.36	+31:59:23.7	0.025	59432.4	59434.4	-17.01	0.12	0.00
ZTF21abtephz/SN2021wun	15:46:31.98	+25:25:44.6	0.023	59425.7	59427.2	-16.86	0.13	0.14
ZTF21abvcxel/SN2021wvw	03:14:47.39	+40:15:47.7	0.010	59449.4	59449.4	-16.36	0.78	0.00
ZTF21abvcxid/SN2021xat	02:53:03.00	+42:51:07.0	0.032	59449.0	59451.5	-17.11	0.25	0.00
ZTF21acafqtj/SN2021yok	07:28:55.48	+20:35:09.9	0.015	59466.5	59469.5	-17.11	0.14	0.00
ZTF21accdiqz/SN2021ywg	02:58:44.41	+17:15:48.0	0.020	59469.4	59471.4	-17.49	1.08	1.56
ZTF21acceboj/SN2021yyg	05:16:21.03	-13:28:39.9	0.012	59471.0	59471.5	-16.51	0.40	0.00
ZTF21accwcrh/SN2021zco	03:39:13.31	+15:59:04.9	0.032	59472.9	59474.4	-17.50	0.66	0.00
ZTF21acdcxaf/SN2021zex	02:14:05.67	+05:10:35.5	0.031	59475.4	59476.4	-17.09	0.12	0.00
ZTF21acdezwk/SN2021zet	21:52:13.34	-23:22:37.0	0.032	59475.2	59477.2	-18.24	0.10	0.00
ZTF21acdoyqt/SN2021zgm	18:35:48.34	+22:27:45.2	0.013	59479.2	59480.1	-15.51	0.44	0.00
ZTF21acelnth/SN2021zzi	01:34:39.15	+55:25:12.4	0.025	59484.9	59485.3	-17.31	0.87	0.00
ZTF21acfajbc/SN2021aalq	09:50:40.54	+47:57:52.4	0.025	59487.0	59488.5	-18.21	0.02	0.00
ZTF21acgpjbx/SN2021aaqn	02:37:58.18	-01:49:53.2	0.028	59492.8	59494.3	-17.39	0.11	0.00
ZTF21acgqhru/SN2021aatd	00:59:04.17	-00:12:12.0	0.015	59492.8	59494.3	-16.63	0.07	0.00
ZTF21acgrrnl/SN2021aayf	06:22:08.33	+50:25:44.7	0.018	59492.9	59496.4	-16.31	0.39	0.00
ZTF21acgunkr/SN2021aaxs	08:33:35.18	+19:44:30.1	0.026	59490.5	59496.5	-17.77	0.09	0.00
ZTF21achkqhi/SN2021abpd	02:28:52.37	-05:29:14.2	0.031	59499.9	59501.4	-17.60	0.07	0.00
ZTF21achpqlr/SN2021abkm	18:22:37.63	+15:42:17.7	0.018	59496.7	59502.2	-16.84	0.54	0.00
ZTF21aciiaio/SN2021abqs	11:21:37.61	+20:09:02.1	0.013	59503.0	59504.5	-16.37	0.07	0.35
ZTF21acissla/SN2021achr	00:22:23.67	-19:47:37.0	0.025	59497.3	59503.3	-17.49	0.05	1.27
ZTF21acjglei/SN2021acma	02:30:33.57	+30:52:17.3	0.018	59514.8	59517.3	-16.61	0.25	0.07
ZTF21ackrkqq/SN2021addc	03:53:38.46	+37:15:47.3	0.019	59519.3	59521.3	-16.38	1.50	0.00
ZTF21aclmgzk/SN2021adxd	00:39:22.90	+02:48:14.5	0.018	59522.3	59524.2	-16.92	0.05	1.06
ZTF21acpqqgu/SN2021aewn	10:04:06.68	+31:11:02.7	0.021	59534.5	59536.5	-16.63	0.07	0.21
ZTF21acqxomi/SN2021afud	09:06:41.11	-10:00:29.0	0.025	59545.4	59550.4	-16.69	0.22	0.00
ZTF22aaacxkp/SN2022abq	13:22:56.82	+28:19:08.9	0.008	59599.2	59600.4	-16.55	0.05	0.00
ZTF22aaahubo/SN2022cru	08:23:26.30	-04:55:06.5	0.023	59575.4	59600.3	-15.89	0.12	0.00
ZTF22aaaowlo/SN2022ces	13:54:14.54	-01:26:34.6	0.024	59614.7	59623.5	-16.80	0.13	0.00
ZTF22aaevwec/SN2022gwg	13:50:25.69	+68:33:18.1	0.031	59675.4	59676.4	-17.67	0.05	0.28

Table B: Continued.

Name	RA	Dec	Redshift	$t_{\rm expl}$	1st detection	Peak mag ${}_{\textrm{r}}$	$A_{V,MW}$	$A_{V,\text{host}}$
	(hh:mm:ss)	(dd:mm:ss)		(MJD)	(MJD)	(mag)	(mag)	(mag)
ZTF22aafsqud/SN2022hql	13:48:06.22	+12:04:29.8	0.023	59682.8	59683.2	-17.00	0.09	0.00
ZTF22aagvgwl/SN2022hss	12:25:38.39	+07:11:33.0	0.025	59684.8	59687.4	-17.97	0.07	0.00
ZTF22aahhgjh/SN2022ihb	13:46:13.40	+23:05:10.9	0.030	59691.8	59693.2	-17.59	0.04	0.00
ZTF22aahyqkz/SN2022iob	19:10:37.07	+37:39:18.7	0.028	59684.5	59689.4	-17.62	0.48	0.92
ZTF22aaijrci/SN2022iyl	20:58:08.06	+00:27:10.0	0.030	59694.5	59698.4	-17.04	0.21	0.14
ZTF22aajipum/SN2022joe	14:29:20.63	-22:56:09.7	0.026	59704.3	59707.3	-16.54	0.27	0.14
ZTF22aajutqu/SN2022jux	08:07:22.25	+40:23:34.8	0.026	59711.2	59712.2	-17.56	0.16	0.00
ZTF22aajuufc/SN2022juw	08:30:31.46	+18:12:13.7	0.027	59711.7	59712.2	-16.98	0.10	0.00
ZTF22aakdbia/SN2022jzc	12:05:28.66	+50:31:36.8	0.002	59714.3	59715.2	-14.91	0.05	0.57
ZTF22aakdqqg/SN2022kad	14:58:43.33	+11:37:50.8	0.020	59713.4	59714.4	-17.82	0.10	0.00
ZTF22aanrqje/SN2022mji	09:42:54.06	+31:51:03.6	0.004	59732.7	59741.2	-15.00	0.05	0.85
ZTF22aaolwsd/SN2022mxv	23:51:05.12	+20:09:08.9	0.014	59747.4	59751.4	-18.10	0.20	0.00
ZTF22aapargp/SN2022niw	15:57:16.80	+19:28:28.1	0.033	59752.3	59753.3	-17.25	0.10	0.00
ZTF22aarskhm/SN2022ohx	20:46:37.43	-2:21:50.56	0.029	59760.9	59762.4	-17.07	0.18	0.28
ZTF22aarycqo/SN2022ojo	01:44:35.61	+37:41:50.7	0.019	59761.4	59765.4	-19.18	0.15	1.13
ZTF22aaslyzf/SN2022oor	15:04:29.79	+02:20:18.7	0.032	59767.2	59768.2	-17.05	0.14	0.00
ZTF22aasojye/SN2022omr	23:41:41.10	+50:02:58.9	0.023	59766.9	59768.4	-16.89	0.62	0.00
ZTF22aativsd/SN2022ovb	22:22:29.55	+36:00:17.9	0.018	59773.4	59774.4	-18.15	0.37	0.00
ZTF22aatpwfw/SN2022paf	22:05:26.42	-00:31:58.7	0.031	59774.4	59775.4	-16.93	0.28	0.00
ZTF22aattfmb/SN2022oyp	18:05:13.16	+46:52:49.5	0.023	59775.8	59776.3	-16.87	0.11	0.28
ZTF22aaudjgc/SN2022pfx	21:50:50.87	-00:50:48.9	0.027	59775.9	59778.4	-18.03	0.28	0.07
ZTF22aavbfhz/SN2022phi	1:20:15.03	+17:49:56.49	0.029	59779.4	59782.4	-16.60	0.21	0.00
ZTF22aavobvq/SN2022prv	15:40:07.76	+20:40:31.7	0.015	59781.7	59784.3	-18.26	0.17	0.00
ZTF22aaxzzoc/SN2022qhc	17:16:35.13	+07:19:44.4	0.022	59789.3	59791.2	-17.28	0.48	0.42
ZTF22aaywnyg/SN2022pru	11:59:07.65	+52:41:58.5	0.004	59787.7	59797.2	-15.42	0.07	0.00
ZTF22aazmrpx/SN2022raj	02:03:17.52	+29:14:04.9	0.012	59798.4	59800.4	-15.20	0.15	0.00
ZTF22abadzpo/SN2022rfz	17:22:20.39	+02:00:58.0	0.030	59799.8	59802.2	-18.65	0.49	0.00
ZTF22abbecow/SN2022rqg	16:56:14.62	+55:01:10.3	0.029	59801.7	59805.2	-16.20	0.07	0.00
ZTF22abfavpu/SN2022tmb	03:20:33.60	+37:29:54.8	0.019	59825.0	59825.5	-17.10	1.04	0.00
ZTF22abfwxtr/SN2022udq	00:05:55.84	+22:29:26.3	0.022	59834.7	59839.2	-16.96	0.20	0.00
ZTF22abfxkdm/SN2022ubb	23:08:58.34	+12:02:39.0	0.016	59834.3	59839.3	-16.57	0.24	0.00
ZTF22abgwgsv/SN2022vpm	17:22:32.88	+26:46:04.2	0.022	59842.7	59843.2	-17.80	0.12	0.00
ZTF22abhsxph/SN2022vyc	04:33:10.32	+76:34:05.3	0.026	59844.9	59846.4	-17.01	0.43	0.00
ZTF22abitour/SN2022wbr	04:29:15.65	+40:14:46.9	0.020	59838.0	59847.5	-16.34	1.67	0.00
ZTF22abkbjsb/SN2022vym	08:54:01.09	+18:41:18.1	0.015	59849.0	59853.5	-16.48	0.06	0.21
ZTF22abkhrkd/SN2022wol	01:51:27.78	+36:03:51.5	0.018	59853.9	59854.3	-16.02	0.21	0.00
ZTF22ablnrcv/SN2022xav	09:39:17.64	+32:18:38.3	0.023	59857.0	59858.5	-16.96	0.05	0.42
ZTF22abmsaxp/SN2022xjk	02:16:32.50	-11:20:59.4	0.013	59860.9	59861.4	-17.15	0.10	0.00
ZTF22abnujbv/SN2022xus	06:54:05.13	+08:34:13.3	0.009	59869.4	59871.4	-16.42	0.60	0.00
ZTF22abpxxil/SN2022yma	03:08:48.32	-07:02:06.5	0.029	59871.9	59873.3	-17.16	0.20	0.49
ZTF22abrexqa/SN2022yyz	19:07:01.55	+28:59:50.0	0.013	59880.1	59881.1	-17.52	0.64	0.00
ZTF22absqhkw/SN2022zkc	04:47:58.59	-16:39:37.0	0.032	59885.4	59887.4	-17.40	0.14	0.00
ZTF22abssiet/SN2022zmb	10:38:43.18	+56:33:14.4	0.014	59885.5	59887.5	-15.72	0.02	0.07
ZTF22abtcsyd/SN2022zxt	08:40:16.38	+56:02:36.0	0.026	59891.5	59893.4	-17.25	0.10	0.00
ZTF22abtspsw/SN2022aagp	09:10:41.90	+07:12:20.3	0.005	59895.4	59897.4	-17.09	0.12	0.00
ZTF22abulusd/SN2022aatx	09:15:15.32	+11:53:04.6	0.017	59899.4	59902.5	-17.27	0.08	0.49
ZTF22abvaetz/SN2022aang	07:59:21.83	+18:06:40.9	0.016	59894.5	59901.5	-15.44	0.08	0.00

Table B: Continued.

Name	RA	Dec	Redshift	$t_{\rm expl}$	1st detection	Peak mag ${}_{\textrm{r}}$	$A_{V,MW}$	$A_{V,\text{host}}$
	(hh:mm:ss)	(dd:mm:ss)		(MJD)	(MJD)	(mag)	(mag)	(mag)
ZTF22abxomzd/SN2022acbu	02:30:43.15	-02:55:56.9	0.019	59906.8	59910.2	-18.75	0.08	3.11
ZTF22abyivoq/SN2022acko	03:19:38.98	-19:23:42.8	0.006	59917.8	59922.2	-15.83	0.08	0.00
ZTF22abyivxh/SN2022acwe	02:28:07.20	-05:43:37.0	0.030	59917.8	59922.2	-17.22	0.07	0.00
ZTF22abyohff/SN2022acrl	11:34:21.03	+15:39:49.2	0.017	59913.5	59923.5	-16.32	0.12	0.00
ZTF22abyokkf/SN2022acri	14:34:19.17	+25:52:55.4	0.022	59915.0	59923.5	-17.22	0.08	0.00
ZTF22abzdzek/SN2022adtt	01:14:05.28	+38:07:05.2	0.027	59930.2	59932.1	-16.80	0.13	0.00
ZTF22abzqwmp/SN2022adth	10:15:39.94	+45:56:25.2	0.031	59932.8	59933.3	-16.84	0.02	0.00
ZTF22acahbko/SN2022advr	10:22:48.99	+03:45:18.7	0.033	59934.5	59935.4	-16.61	0.10	0.00
ZTF23aaaatjn/SN2023cf	04:26:49.49	+29:56:59.8	0.018	59945.8	59951.2	-18.51	1.26	0.00
ZTF23aaaigqy/SN2023fu	03:06:21.26	+36:01:11.1	0.016	59948.7	59957.1	-17.97	0.66	0.00
ZTF23aaarmtb/SN2023qh	09:07:15.44	+37:12:54.8	0.024	59947.4	59957.4	-15.89	0.06	0.00
ZTF23aaavxye/SN2023abq	15:34:33.34	+41:08:11.6	0.032	59962.7	59967.5	-17.63	0.07	0.00
ZTF23aaazdla/SN2023wn	13:36:04.57	-01:35:39.7	0.015	59962.5	59968.5	-16.79	0.09	0.00
ZTF23aabngtm/SN2023axu	06:45:55.32	-18:13:53.5	0.003	59970.3	59972.3	-17.27	1.06	0.00
ZTF23aabtmzm/SN2023blw	07:28:09.44	+52:28:17.5	0.022	59975.7	59979.2	-18.35	0.16	2.97
ZTF23aacdlsh/SN2023bmd	15:10:08.83	+46:06:23.2	0.020	59982.5	59984.5	-16.69	0.07	0.00
ZTF23aacjetk/SN2023buy	08:20:53.69	+39:14:29.7	0.029	59988.3	59991.2	-17.87	0.12	0.00
ZTF23aackdba/SN2023bql	08:11:27.82	+08:56:24.7	0.019	59984.8	59985.3	-16.51	0.07	0.28
ZTF23aackjhs/SN1995al	09:50:56.03	+33:33:11.0	0.005	59989.8	59992.3	-14.88	0.04	0.07
ZTF23aaflnok/SN2023fub	07:40:26.79	+25:08:00.2	0.029	60047.2	60049.2	-17.22	0.12	0.00
ZTF23aafumlg/SN2023fou	12:40:20.46	-10:02:55.1	0.026	60047.8	60050.3	-17.85	0.10	0.00
ZTF23aagkajy/SN2023gdt	10:30:10.64	+43:21:23.4	0.014	60050.2	60051.2	-15.42	0.03	0.71
ZTF23aagkutf/SN2023ghl	11:09:11.73	+53:21:43.9	0.027	60052.2	60053.2	-17.24	0.03	0.00
ZTF23aagqyym/SN2023gjg	09:07:17.96	+37:30:12.6	0.030	60054.2	60055.2	-17.08	0.05	0.00
ZTF23aahqvtz/SN2023gss	14:04:23.54	-27:08:58.1	0.021	60058.8	60059.3	-17.07	0.19	0.00
ZTF23aaiecnn/SN2023gxq	10:18:12.24	+34:40:19.7	0.029	60061.2	60062.2	-16.51	0.04	0.00
ZTF23aailjjs/SN2023hcp	16:48:42.72	+35:56:57.4	0.031	60062.4	60063.4	-17.91	0.05	0.00
ZTF23aaitpjv/SN2023hlf	12:26:26.17	+31:13:32.2	0.002	60053.3	60054.4	-16.53	0.05	5.09
ZTF23aajrmfh/SN2023ijd	12:36:32.47	+11:13:19.7	0.007	60078.2	60079.0	-15.39	0.09	0.00
ZTF23aajsjon/SN2023hzt	13:30:01.55	+75:34:09.0	0.030	60071.9	60076.4	-17.05	0.10	0.00
ZTF23aakirso/SN2023jid	22:40:43.23	+36:38:39.5	0.027	59994.8	60054.5	-16.12	0.42	0.00
ZTF23aamfqxc/SN2023jri	23:30:27.09	+30:13:11.2	0.015	60094.2	60097.4	-18.81	0.40	0.28
ZTF23aamqycj/SN2023jvi	13:31:22.27	+25:37:01.0	0.025	60094.8	60097.3	-17.03	0.03	0.00
ZTF23aamzlzc/SN2023kne	17:25:19.11	+58:49:02.6	0.028	60095.4	60097.3	-15.81	0.10	0.00
ZTF23aanymcl/SN2023kzz	17:17:06.29	-14:54:05.1	0.028	60106.8	60110.3	-17.24	1.26	0.00
ZTF23aanzmoz/SN2023kyi	19:16:43.95	-17:30:35.0	0.030	60105.9	60108.4	-17.00	0.33	0.00
ZTF23aaomzth/SN2023rpu	09:23:47.48	+42:11:12.1	0.014	60105.2	60113.2	-16.56	0.05	0.00
ZTF23aaphnyz/SN2023lkw	16:48:36.21	+41:36:02.6	0.031	60117.8	60118.2	-18.07	0.06	0.00
ZTF23aaqknaw/SN2023lzn	00:55:07.58	+31:32:47.6	0.018	60124.4	60128.4	-17.37	0.17	0.85
ZTF23aaqtckr/SN2023mpj	14:34:24.79	+02:53:04.9	0.030	60120.2	60120.3	-16.62	0.10	0.00
ZTF23aaqwpio/SN2023nca	16:39:26.36	+11:12:45.3	0.023	60129.3	60135.2	-15.61	0.14	0.00
ZTF23aasbvab/SN2023ngy	22:18:30.18	+29:14:41.0	0.021	60139.9	60140.3	-16.80	0.22	0.00
ZTF23aasrcyv/SN2023nlu	00:45:09.04	-09:37:38.3	0.020	60142.5	60143.4	-16.78	0.10	0.00
ZTF23aasyvbf/SN2023nmh	00:37:38.73	-04:16:53.2	0.020	60142.4	60144.5	-15.93	0.10	0.00
ZTF23aaxadel/SN2023pbg	00:14:54.90	+26:20:00.4	0.025	60166.9	60168.4	-16.88	0.11	0.00
ZTF23aazprcc/SN2023vhb	12:19:13.19	+22:25:42.4	0.022	60180.1	60181.1	-17.20	0.07	0.21
ZTF23abadrow/SN2023qxp	21:56:14.96	+02:10:27.8	0.028	60175.9	60178.4	-16.65	0.15	0.00

Table B: Continued.

Name	RA	Dec	Redshift	$t_{\rm expl}$	1st detection	Peak mag ${}_{\textrm{r}}$	$A_{V,MW}$	$A_{V,\text{host}}$
	(hh:mm:ss)	(dd:mm:ss)		(MJD)	(MJD)	(mag)	(mag)	(mag)
ZTF23abascqa/SN2023rbk	03:15:20.60	+41:36:53.5	0.020	60186.4	60187.4	-17.96	0.38	0.00
ZTF23abaxtlq/SN2023rix	02:47:56.84	+41:14:48.3	0.013	60191.9	60192.4	-16.37	0.23	0.00
ZTF23abbsxzs/SN2023rtq	04:51:46.59	+38:56:10.0	0.013	60193.4	60195.4	-17.56	2.68	0.57
ZTF23abbtkrv/SN2023rvo	08:49:16.37	+36:07:14.8	0.025	60193.0	60194.5	-17.05	0.09	0.00
ZTF23aberpzw/SN2023swf	21:05:58.52	-14:50:30.3	0.023	60201.7	60203.2	-16.66	0.17	0.00
ZTF23abhruov/SN2023ucx	03:59:54.88	+32:36:41.1	0.018	60210.4	60215.4	-17.08	0.65	1.91
ZTF23abhyroo/SN2023udb	04:25:05.77	-10:18:51.9	0.033	60219.0	60221.5	-17.25	0.25	0.00
ZTF23abhzfww/SN2023twg	08:45:54.52	+12:47:12.2	0.030	60214.5	60220.5	-17.62	0.09	0.00
ZTF23abiewbt/SN2023ujp	15:57:33.01	+20:02:54.2	0.033	60222.1	60223.1	-17.36	0.12	0.00
ZTF23abjwgre/SN2023vcg	23:56:05.87	+29:22:40.5	0.023	60231.3	60231.3	-16.70	0.17	0.00
ZTF23abkhajf/SN2023vcj	07:55:15.92	+53:44:47.7	0.025	60228.5	60232.5	-17.38	0.09	0.07
ZTF23abmoxlu/SN2023vog	09:45:09.63	+68:35:11.8	0.015	60237.4	60238.4	-17.59	0.28	0.00
ZTF23abndgbw/SN2023way	21:24:11.15	+15:59:22.9	0.018	60240.7	60242.2	-16.68	0.31	0.00
ZTF23abnogui/SN2023wcr	12:23:31.29	+74:57:01.3	0.005	60240.5	60244.5	-15.58	0.09	0.00
ZTF23abonlit/SN2023wuj	08:26:18.36	+02:55:28.6	0.031	60247.0	60248.5	-16.79	0.14	0.00
ZTF23abphqjk/SN2023xgn	03:02:48.82	-15:42:21.6	0.031	60253.9	60254.4	-17.42	0.15	0.00
ZTF23abqwald/SN2023xvo	11:20:24.29	+28:17:55.4	0.033	60234.0	60234.5	-16.90	0.05	0.21
ZTF23absdcgi/SN2023zcu	06:01:06.82	-23:40:29.3	0.006	60288.2	60289.3	-16.73	0.09	0.00
ZTF23abvommm/SN2023acbr	02:27:03.18	-09:25:02.3	0.016	60300.7	60305.2	-15.57	0.08	0.00
ZTF24aaabbse/SN2023achj	08:36:07.46	+25:06:47.0	0.023	60303.9	60311.3	-17.79	0.10	0.00
ZTF24aaarlvj/SN2024V	11:52:01.50	+57:41:37.4	0.031	60300.0	60308.5	-16.49	0.04	0.00
ZTF24aaasazz/SN2024ov	11:50:28.79	-18:34:42.8	0.023	60291.0	60291.6	-16.03	0.10	0.00
ZTF24aabppgn/SN2024wp	11:24:39.25	+14:56:52.9	0.014	60320.5	60325.5	-15.52	0.10	0.00
ZTF24aabpzuz/SN2024vs	07:51:07.27	+72:57:57.4	0.010	60317.9	60321.5	-16.70	0.08	0.00
ZTF24aabsmvc/SN2024ws	08:28:46.69	+73:45:08.6	0.012	60318.9	60322.3	-16.15	0.07	0.00
ZTF24aadkwni/SN2024aul	10:21:53.24	+00:17:44.3	0.021	60330.7	60335.4	-17.77	0.13	0.28
ZTF24aaejehf/SN2024bzq	11:44:33.94	+36:26:41.2	0.033	60346.4	60351.4	-17.97	0.05	0.00
ZTF24aaejjps/SN2024btx	11:54:55.45	+29:20:34.5	0.021	60346.4	60351.4	-16.04	0.06	0.00
ZTF24aaejvcx/SN2024atk	13:18:31.13	-14:36:39.1	0.010	60343.2	60351.4	-16.54	0.22	0.00
ZTF24aaemydm/SN2024chx	09:54:28.51	-18:38:10.8	0.013	60352.8	60354.2	-18.01	0.13	0.00
ZTF24aafqzur/SN2024daa	14:25:57.20	-02:23:32.2	0.031	60355.4	60355.5	-16.72	0.15	0.00
ZTF24aagiwoi/SN2024dhi	11:13:08.67	+05:04:28.3	0.027	60360.9	60363.4	-16.65	0.20	0.00
ZTF24aagupsf/SN2024egd	16:30:41.48	+44:30:40.0	0.032	60372.0	60374.5	-17.47	0.04	0.00
ZTF24aahalmb/SN2024ees	12:32:42.19	+14:32:27.4	0.024	60376.8	60379.4	-16.52	0.11	0.00
ZTF24aahewml/SN2024etq	18:13:41.23	+10:56:03.8	0.022	60381.0	60387.5	-16.60	0.51	0.00
ZTF24aahgmyj/SN2024epy	09:46:46.54	+13:31:53.4	0.024	60384.2	60388.2	-16.77	0.12	0.64
ZTF24aahmgck/SN2024faf	11:28:57.38	+73:02:11.9	0.021	60385.8	60388.3	-16.85	0.13	1.27
ZTF24aahwfsa/SN2024fas	10:51:55.36	+37:35:23.9	0.026	60393.4	60396.4	-15.80	0.04	0.00
ZTF24aajxppf/SN2024grw	17:58:21.63	+09:40:53.5	0.021	60415.5	60416.5	-18.01	0.48	0.00
ZTF24aakzive/SN2024hpg	21:31:43.20	+0:21:43.71	0.029	60427.7	60430.4	-17.53	0.14	0.00
ZTF24aalceob/SN2024hme	16:05:57.74	+27:11:21.00	0.031	60418.9	60423.4	-16.09	0.10	0.00
ZTF24aamzqsv/SN2024izq	9:24:57.24	+40:23:58.49	0.028	60440.2	60441.2	-16.29	0.04	0.00
ZTF24aaozxhx/SN2024jlf	14:37:42.32	+2:17:04.17	0.006	60457.3	60458.2	-16.99	0.11	0.00
ZTF24aaplfjd/SN2024jxm	0:58:01.36	+30:42:23.84	0.016	60460.0	60460.5	-16.00	0.18	0.00
ZTF24aarajmv/SN2024ldu	19:54:05.17	+49:56:47.25	0.025	60466.4	60469.4	-16.26	0.50	0.00
ZTF24aarvbxj/SN2024lby	20:22:40.78	-8:10:41.95	0.020	60472.4	60473.4	-17.56	0.17	0.00
ZTF24aasktmr/SN2024lss	16:31:22.18	+22:42:08.73	0.024	60479.8	60480.2	-17.19	0.13	0.00

Table B: Continued.

Name	RA	Dec	Redshift	$t_{\rm expl}$	1st detection	Peak mag ${}_{\textrm{r}}$	$A_{V,MW}$	$A_{V,\text{host}}$
	(hh:mm:ss)	(dd:mm:ss)		(MJD)	(MJD)	(mag)	(mag)	(mag)
ZTF24aatifzm/SN2024mxq	14:44:05.84	+9:16:47.52	0.031	60480.8	60484.2	-16.83	0.08	0.00

Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. I: Luminosity Function, Volumetric Rate

Abstract

I Introduction

II Sample Selection

II.1 Census of the Local Universe

II.2 Sample of LLIIP SNe

III Data

III.1 Optical photometry

III.2 Optical spectroscopy

IV Extinction Correction

V Luminosity function

VI The Volumetric rate of Type IIP SNe using skysurvey

VII Discussion

VII.1 Comparison to the literature

VII.2 Can LLIIP SNe account for the fate of all 8−128-12 M⊙\mathrm{M}_{\odot}stars?

VIII Summary

IX Data Availability

X Acknowldegement

References

Appendix A Progenitor Mass from pre-explosion images Vs SN peak magnitude

Appendix B Distance correction for nearby galaxies

Appendix C ZTF Pipeline Recovery Efficiency Fits

Appendix D Sample of CLU Type II SNe

VII.2 Can LLIIP SNe account for the fate of all $8-12$ $\mathrm{M}_{\odot}$ stars?