# The Hydrogen-Poor Superluminous Supernovae from the Zwicky Transient
Facility Phase-I Survey: I. Light Curves and Measurements

Z. H. Chen Physics Department and Tsinghua Center for Astrophysics (THCA),
Tsinghua University, Beijing, 100084, China  Lin Yan Caltech Optical
Observatories, California Institute of Technology, Pasadena, CA 91125, USA  T.
Kangas KTH Royal Institute of Technology, Stockholm, Sweden  R. Lunnan  The
Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova,
SE-106 91 Stockholm, Sweden  S. Schulze  The Oskar Klein Centre, Department of
Physics, Stockholm University, AlbaNova, SE-106 91 Stockholm, Sweden  J.
Sollerman The Oskar Klein Centre, Department of Astronomy, Stockholm
University, AlbaNova, SE-106 91 Stockholm, Sweden  D. A. Perley  Astrophysics
Research Institute, Liverpool John Moores University, 146 Brownlow Hill,
Liverpool L3 5RF, UK  T.-W. Chen The Oskar Klein Centre, Department of
Astronomy, Stockholm University, AlbaNova, SE-106 91 Stockholm, Sweden  K.
Taggart  Department of Astronomy and Astrophysics, University of California,
Santa Cruz, CA 95064, USA  K. R. Hinds  Astrophysics Research Institute,
Liverpool John Moores University, 146 Brownlow Hill, Liverpool L3 5RF, UK  A.
Gal-Yam  Department of particle physics and astrophysics, Weizmann Institute
of Science, 76100 Rehovot, Israel  X. F. Wang Physics Department and Tsinghua
Center for Astrophysics (THCA), Tsinghua University, Beijing, 100084, China
Beijing Planetarium, Beijing Academy of Sciences and Technology, Beijing,
100044, China  I. Andreoni  Cahill Center for Astrophysics, California
Institute of Technology, 1200 E. California Blvd. Pasadena, CA 91125, USA  E.
Bellm DIRAC Institute, Department of Astronomy, University of Washington, 3910
15th Avenue NE, Seattle, WA 98195, USA  J. S. Bloom Department of
Astrophysics, University of California, Berkeley, CA 94720-3411, USA  Lawrence
Berkeley National Laboratory, 1 Cyclotron Road, MS 50B-4206, Berkeley, CA
94720, USA  K. Burdge  Division of Physics, Mathematics, and Astronomy,
California Institute of Technology, Pasadena, CA 91125, USA  A. Burgos
Instituto de Astrofísica de Canarias, E-38 200 La Laguna, Tenerife, Spain  D.
Cook  IPAC, California Institute of Technology, 1200 E. California Blvd,
Pasadena, CA 91125, USA  A. Dahiwale  Division of Physics, Mathematics, and
Astronomy, California Institute of Technology, Pasadena, CA 91125, USA  K. De
Cahill Center for Astrophysics, California Institute of Technology, 1200 E.
California Blvd. Pasadena, CA 91125, USA  R. Dekany Caltech Optical
Observatories, California Institute of Technology, Pasadena, CA 91125, USA  A.
Dugas  Department of Physics and Astronomy, Watanabe 416, 2505 Correa Road,
Honolulu, HI 96822, USA  S. Frederik  Department of Astronomy, University of
Maryland, MD 20742-2421, USA  C. Fremling  Division of Physics, Mathematics,
and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA  M.
Graham  Cahill Center for Astrophysics, California Institute of Technology,
1200 E. California Blvd. Pasadena, CA 91125, USA  M. Hankins  Arkansas Tech
University, Russellville, AR 72801, USA  A. Ho  Department of Astronomy and
Miller Institute for Basic Research in Science, University of California,
Berkeley, CA, USA  J. Jencson  Steward Observatory, University of Arizona, 933
North Cherry Avenue, Tucson, AZ 85721-0065, USA  V. Karambelkar  Cahill Center
for Astrophysics, California Institute of Technology, 1200 E. California Blvd.
Pasadena, CA 91125, USA  M. Kasliwal  Division of Physics, Mathematics, and
Astronomy, California Institute of Technology, Pasadena, CA 91125, USA  S.
Kulkarni  Division of Physics, Mathematics, and Astronomy, California
Institute of Technology, Pasadena, CA 91125, USA  R. Laher IPAC, California
Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA  B.
Rusholme IPAC, California Institute of Technology, 1200 E. California Blvd,
Pasadena, CA 91125, USA  Y. Sharma  Division of Physics, Mathematics, and
Astronomy, California Institute of Technology, Pasadena, CA 91125, USA  F.
Taddia  Department of Physics and Astronomy, Aarhus University, Ny Munkegade
120, DK-8000 Aarhus C, Denmark  L. Tartaglia  INAF-Osservatorio Astronomico di
Padova, Vicolo dell’Osservatorio 5, I-35122 Padova, Italy  B. P. Thomas
Department of Astronomy, University of Texas at Austin, 2515 Speedway, Stop
C1400 Austin, TX 78712-1205, USA  A. Tzanidakis  Division of Physics,
Mathematics, and Astronomy, California Institute of Technology, Pasadena, CA
91125, USA  J. Van Roestel  Division of Physics, Mathematics, and Astronomy,
California Institute of Technology, Pasadena, CA 91125, USA  R. Walter  Cahill
Center for Astrophysics, California Institute of Technology, 1200 E.
California Blvd. Pasadena, CA 91125, USA  Y. Yang  Department of Astronomy,
University of California, Berkeley, CA 94720-3411, USA  Y. H. Yao  Cahill
Center for Astrophysics, California Institute of Technology, 1200 E.
California Blvd. Pasadena, CA 91125, USA  O. Yaron  Department of particle
physics and astrophysics, Weizmann Institute of Science, 76100 Rehovot, Israel
Z. H. Chen, Lin Yan<EMAIL_ADDRESS><EMAIL_ADDRESS>
(Accepted Nov. 1, 2022)

###### Abstract

During the Zwicky Transient Facility (ZTF) Phase-I operation, 78 hydrogen-poor
superluminous supernovae (SLSNe-I) were discovered in less than three years,
making up the largest sample from a single survey. This paper (Paper I)
presents the data, including the optical/ultraviolet light curves and
classification spectra, while Paper II in this series will focus on the
detailed analysis of the light curves and modeling. Our photometry is
primarily taken by the ZTF in the $g,r,i$ bands, and with additional data from
other ground-based facilities and Swift. The events of our sample cover a
redshift range of $z=0.06-0.67$, with a median and $1\sigma$ error (16% and
84% percentiles) $z_{\rm med}=0.265^{+0.143}_{-0.135}$. The peak luminosity
covers $-22.8\,{\rm mag}\leq M_{g,\rm peak}\leq-19.8$ mag, with a median value
of $-21.48^{+1.13}_{-0.61}$ mag. Their light curves evolve slowly with the
mean rest-frame rise time of $t_{\rm rise}=41.9\pm 17.8$ days. The luminosity
and time scale distributions suggest that low luminosity SLSNe-I with peak
luminosity $\sim-20$ mag or extremely fast rising events ($<10$ days) exist
but are rare. We confirm previous findings that slowly rising SLSNe-I also
tend to fade slowly. The rest-frame color and temperature evolution show large
scatters, suggesting that the SLSN-I population may have diverse spectral
energy distributions. The peak rest-frame color shows a moderate correlation
with the peak absolute magnitude, i.e. brighter SLSNe-I tend to have bluer
colors. With optical and ultraviolet photometry, we construct bolometric
luminosity and derive a bolometric correction relation generally applicable
for converting $g,r$-band photometry to bolometric luminosity for SLSNe-I.

Stars: supernovae: general

††journal: APJ††facilities: PO:1.2m, PO:1.5m, PO:Hale, Liverpool:2m,
NOT:2.56m, Keck:I, WHT:4.2m††software: Scikit-learn (Pedregosa et al., 2011),
FIREFLY (Wilkinson et al., 2017), george (Ambikasaran et al., 2015), HEASoft
(Nasa High Energy Astrophysics Science Archive Research Center, 2014,
https://heasarc.gsfc.nasa.gov/lheasoft/download.html), SEDM pipeline (Rigault
et al., 2019), pyraf-dbsp (Bellm & Sesar, 2016), DBSP_DRP (Roberson et al.,
2022), LPipe (Perley et al., 2019), Fpipe (Fremling et al., 2016), AutoPhOT
(Brennan & Fraser, 2022).

Draft

##  1 Introduction

Superluminous supernovae (SLSNe) are a rare class of stellar explosions first
discovered over fifteen years ago (i.e. SN 2005ap, Quimby et al., 2007). Their
peak luminosities ($10^{43-44}\,{\rm erg\,s^{-1}}$) are 10 – 100 times higher
than those of normal Type Ia and core-collapse supernovae (SNe). Their light
curves (LCs) usually evolve rather slowly, with rise times of $\sim 20-100$
days. The combination of these two features can not be explained by
conventional SN models, i.e. standard radioactive decay (Kasen, 2017). With
the discovery of the first several SLSNe (SN 2005ap, SN 2006gy, SN 2007bi & SN
2008es), it was quickly recognized that, like normal SNe, SLSNe can be divided
into two spectroscopic subclasses, one with hydrogen emission lines (SLSNe-II,
Miller et al., 2009; Gezari et al., 2009; Inserra et al., 2018b) and the other
without hydrogen (SLSNe-I, Quimby et al., 2007; Ofek et al., 2007; Gal-Yam et
al., 2009; Smith et al., 2007; Gal-Yam, 2012). In recent years, the subclass
of H-poor but Helium-rich SLSNe (SLSNe-Ib) were first identified by Quimby et
al. (2018), and later a sample by (Yan et al., 2020).

Three popular models have been proposed to explain the extraordinary radiative
power of SLSNe. One involves energy injection from a central engine, such as
the spin-down of a fast-rotating neutron star (magnetar, Kasen & Bildsten,
2010; Woosley, 2010). Alternatively, the interaction between the SN ejecta and
dense circumstellar material (CSM) can efficiently convert kinetic energy into
radiation (Chevalier & Irwin, 2011; Chatzopoulos et al., 2013). Finally, some
SLSNe could be powered by massive amounts of 56Ni synthesised in a pair-
instability supernova explosion (PISN) of low-metallicity stars with masses
$>140\,M_{\odot}$ (Woosley et al., 2007; Kasen et al., 2011). It is commonly
accepted that most SLSNe-II are analogous to Type IIn SNe (Schlegel, 1990;
Filippenko, 1997), primarily powered by ejecta interactions with dense CSM
(Ofek et al., 2007; Miller et al., 2010; Chevalier & Irwin, 2011), while a
small fraction show broad H$\alpha$ features with no signs of strong
interaction in spectra, e.g. SN 2008es, SN 2013hx, PS15br (Miller et al.,
2009; Gezari et al., 2009; Inserra et al., 2018b), but interactions are still
likely required in these events (Kangas et al., 2022).

Between 2005 and 2009, a handful of SLSNe were discovered by several
untargeted transient surveys which were not specifically targeting bright
nearby galaxies. This small number of luminous events sparked a flurry of
studies in both theory and observation of SLSNe. The next big advance in this
field came between 2009 and 2016 when large area, untargeted transient surveys
started operating. For example, the Palomar Transient Factory (PTF, Law et
al., 2009), the Pan-STARRS1 Medium Deep Survey (PS1 MDS, Chambers et al.,
2016), the Catalina Real-time Transient Survey (CRTS, Drake et al., 2009), the
All-Sky Automated Survey for SuperNovae (ASAS-SN, Shappee, 2014), the Dark
Energy Survey (DES, The Dark Energy Survey Collaboration, 2005) and the Gaia
Photometric Science Alerts (Gaia, Hodgkin et al., 2021) made major
contributions to the discoveries of several dozen SLSNe at both low ($z\sim
0.2$) and high redshift ($z\sim 1$) (Nicholl et al., 2015; De Cia et al.,
2018; Quimby et al., 2018; Lunnan et al., 2018; Angus et al., 2019). However,
with over $\sim 90$ discovered SLSNe-I by the end of 2017, many questions
regarding their physical nature still remained unclear. For example, the SLSN
volumetric rates are poorly constrained, with only estimates from small SLSN-I
samples (Quimby et al., 2013; McCrum et al., 2015; Prajs et al., 2017;
Frohmaier et al., 2021). Attempts to examine the statistical distributions,
such as luminosity functions, were also quite limited due to small number
statistics.

Assembling a large sample of low-$z$ SLSNe with a well-defined survey volume
and cadence is one of the goals of the Zwicky Transient Facility (ZTF, Graham
et al., 2019; Bellm et al., 2019a; Masci et al., 2019). ZTF utilizes a
600-megapixel camera mounted on the Palomar Samuel Oschin 48 inch Schmidt
telescope to reach a 47 deg2 field of view (Dekany et al., 2020). ZTF can
cover the full Northern Sky in 3 days down to a $5\sigma$ limiting magnitude
of $20.5-20.8$ magnitude, which is about 3.5 and 0.5 magnitudes deeper than
that of ASAS-SN (Shappee, 2014), and the Asteroid Terrestrial-impact Last
Alert System (ATLAS, Tonry et al., 2018), respectively. The ZTF survey offers
several advantages for discovering rare transient events such as SLSNe. It is
an untargeted, all sky, and moderately high cadence survey, probing large
volumes with its large area coverage and deep sensitivity limits. Its well-
defined survey strategy – area coverage and cadence – also makes it possible
to quantify the survey efficiency.

ZTF conducted several surveys with different cadences (ranging from minutes to
days) and area coverage during the phase-I operation (Bellm et al., 2019b).
Among them, a particularly important one for extragalactic transient studies
is the Northern Sky Public Survey. ZTF covered roughly the entire northern sky
accessible from Palomar, corresponding to a total sky area of $\sim 23,675$
deg2. In every 3 days, each field was observed once in $g$ band and once in
$r$ band, with an interval of at least 30 minutes between observations.

Between March 17, 2018 and October 31, 2020, ZTF Phase-I 111The ZTF public
survey transitioned to a 2-day cadence on October 1, 2020, however the
official start of the ZTF Phase II is December 1, 2020. We picked our date
range for the convenience of the sources analysed in our sample. discovered
and spectroscopically confirmed 85 SLSNe-I, 6 SLSNe-I.5 (classified as SLSNe-I
but showing H lines after the peak) and 61 SLSNe-II (defined as SNe II with
peak magnitudes brighter than $-20.0$ mag). The number of SLSNe discovered by
ZTF from 2018 to 2020 (about 60 per year) is roughly 5 – 7 times higher than
that detected in any previous year. The SLSN-I sample will be the focus of a
series of three papers. Paper I (this paper) presents the observational data
and analysis of the overall observational properties. Paper II (Chen et al.,
2022) discusses the LC modeling and analysis of the LC morphology. Paper III
(Yan et al. in preparation) will present the derived SLSN-I volumetric rates
and luminosity functions at $z\leq 0.7$. Several additional papers based on
some individual SLSNe discovered during the ZTF Phase-I operation have been
recently published. Lunnan et al. (2020) showcased the first four SLSNe-I
discovered by ZTF during its science commissioning phase. Yan et al. (2020)
presented the discovery of six He-rich SLSNe-I (SLSNe-Ib), revealing
additional constraints on the progenitor mass-loss history.

This paper is organized as follows. Section 2 introduces the selection and
classification of this sample. Section 3 presents the photometry from ZTF and
other facilities. Section 4 discusses our methodology with various photometric
corrections and the calculations of peak absolute magnitudes. The measurements
of time scales, colors, black-body temperatures and bolometric luminosities
are presented in Section 5. Section 6 summarizes the conclusions. Throughout
the paper, all magnitudes are in the AB system unless explicitly noted
otherwise. We adopt a $\Lambda$CDM cosmology with H${}_{0}=70.0$ km s-1 Mpc-1,
$\Omega_{M}=0.3$ and $\Omega_{\Lambda}=0.7$.

##  2 The SLSN-I sample from ZTF-I

During the phase-I survey, ZTF discovered 85 SLSNe-I. This paper focuses on 78
of these 85 events whose LCs have turned over from the peak by October 31,
2020, enabling better LC modeling. Of these 78 SLSNe-I, seven can be
classified as He-rich SLSNe-Ib, including six published by Yan et al. (2020)
and one by Terreran et al. (2020). For completeness, this sample paper also
includes the four sources published in Lunnan et al. (2020). In addition,
Schulze et al. (in preparation) will focus on an extremely slow and peculiar
SLSN-I, ZTF18acenqto (SN 2018ibb), and provided the derived parameters to
include in our catalog.

Table A1 compiles the metadata for each of the targets, including the internal
ZTF name, IAU name, right ascension (RA), declination (DEC), redshift,
Galactic extinction $E(B-V)$, discovery group, and additional information on
spectral classification. Our sample covers the redshift of $z\sim 0.06-0.67$
with a median of $z_{\rm med}=0.265^{+0.143}_{-0.135}$. All redshifts in our
sample are determined using the narrow emission lines from the host galaxy,
except for nine events without host lines. The redshifts for these nine events
are estimated from template matching by running superfit (Howell et al., 2006)
over a range of $z$ values. These redshifts are less accurate and marked with
$\star$ in Table A1. There are two additional events, ZTF19abcvwrz (SN
2019aamx) and ZTF19aawsqsc (SN 2019hno), which also have less accurate
redshifts because of the low signal-to-noise ratios of Mg II
$\lambda\lambda\,2796,2803$ absorption lines in their host-galaxy spectra.
Figure 1 displays the redshift distribution of the full sample, including
those derived from template fitting. To avoid possible biases caused by the
choice of histogram grids, we apply kernel density estimation on all the
histograms in this paper using a Gaussian kernel offered by the machine
learning package Scikit-learn (Pedregosa et al., 2011), as shown in Figure 1.

Several SLSN-I samples from different surveys, including PS1, DES and PTF, as
well as samples collected from the literature by Nicholl et al. (2015) and
Inserra et al. (2018a), have revealed many important properties of SLSNe-I.
Compared with these previous samples, our sample size is significantly larger
and the observing cadence is also better, as shown in Table
LABEL:tab:previous. These two key features allow us to investigate the LC
properties of SLSNe with much better statistics.

Figure 1: Distribution of redshifts for the sample of 78 SLSNe-I presented in this paper. Other SLSN-I samples are plotted as hollow bars for comparison. The dashed line and the shaded area mark the median value and $1\sigma$ error (16% and 84% percentiles) of ZTF sample, $z_{\rm med}=0.265^{+0.143}_{-0.135}$. The black solid line shows the kernel density estimation of the distribution. Table 1: SLSN samples Source | Candidates | Redshift range | Observing cadencea | Reference
---|---|---|---|---
(rest-frame days)
Literature | 25b(14) | 0.10 - 1.19 | - | Nicholl et al. (2015); Inserra et al. (2018a)
PS1 | 17 | 0.32 - 1.57 | 2.30 | Lunnan et al. (2018)
DES | 22 | 0.22 - 2.00 | 3.65 | Angus et al. (2019)
PTF | 26 | 0.06 - 0.74 | 2.26 | De Cia et al. (2018)
ZTF | 78 | 0.06 - 0.67 | 1.45 | This paper
aThe median value of the observing cadence in the rest frame.
bIncluding 11 SLSNe-I from PTF and PS1 samples and 14 independent SLSNe-I.

###  2.1 Photometric Selection

Here we briefly describe the photometric selection system of our SLSN-I
candidates. Daily ZTF alerts are ingested into a dynamic science portal called
the GROWTH Marshal (Kasliwal et al., 2019). A filter is implemented within the
Marshal to select SLSN candidates. The candidates passing the filter do not
get automatically saved; instead, each week, a human scanner has to visually
examine the LC of each candidate selected by the filter and make a decision if
it is worth being saved. The subsequent spectral follow up is based on the
human-saved candidates. This filter adopts several cutoff conditions,
including: [1] Not moving; the same alert is detected in two consecutive
epochs with $\Delta t>0.02$ days. [2] Not a star, based on the SDSS star-
galaxy scores (Tachibana & Miller, 2018). [3] Excluding bogus alerts based on
scores constructed by Duev et al. (2019). [4] Not in the galactic plane, with
Galactic latitude $\rm|b|>7^{\circ}$. [5] Variability was not detected at this
location more than a year prior to the alert.

In addition, we assign numerical scores to several properties, including: [1]
slowly rising events; [2] faint blue hosts; [3] spatial location of the
transient relative to the center of the host (against nuclear transient); [4]
brightness of the candidate relative to the host brightness (against faint
transients); [5] the time interval between the first detection and the last
one (against very long-lived transients like active galactic nucleus). This
candidate filter gives high scores (thus preferences) to slowly rising events
with faint blue hosts. For example, a candidate rising at least 20 - 25 days
with a faint host galaxy will have a high score and pass the filter and be
also saved by the human scanner.

We note that the ZTF collaboration has multiple transient groups which also
perform daily alert stream scanning, and most transient candidates can be
saved by multiple groups, for example, the ZTF Bright Transient Survey (BTS,
Fremling et al., 2020), the ZTF Census of the Local Universe (CLU, De et al.,
2020), the fast transient group, the stripped envelop SN group and the ZTF
Nuclear Transient group. Therefore, the classification of a specific SLSN-I
candidate can also be drawn from other classification efforts, most noticeably
the BTS. A few classifications are taken from the Transient Name Server (TNS)
reported by external groups. But almost all of these externally-classified
sources were also identified as candidates by our filter. The filtering method
will be described in detail in the forthcoming work.

With the above selection criteria, on the order of 50 candidates per week are
saved and get another round of vetting when spectral classification
observations are planned. Any candidates brighter than 18.5 mag are classified
by the ZTF BTS. Their classification efficiency at $\leq 18.5$ mag is very
high, close to $95\%$ (Fremling et al., 2020; Perley et al., 2020). We
anticipate that our catalog is almost entirely complete to SLSNe within the
ZTF footprint peaking at magnitudes brighter than 18.5 mag, and highly
complete to 19.0 mag. For magnitudes fainter than 19.0 (44 out of 78 events in
total), there may be biases primarily related to the nature of the host and/or
the rising phase of the light curve that will be examined in future work. The
detailed structure of the light curve, e.g. the presence of bumps, was not a
crucial factor in selection or triggering follow up.

Our effort for spectroscopic classification is primarily focused on SLSN
candidates fainter than 18.5 mag, using the Spectral Energy Distribution
Machine (SEDM, Blagorodnova et al., 2018) and the Double Beam Spectrograph
(DBSP, Oke & Gunn, 1983) mounted on the Palomar 60 inch (P60) and 200 inch
(P200) telescope, respectively. Additional facilities include the Low
Resolution Imaging Spectrometer (LRIS, Oke et al., 1995) on the Keck I
telescope, the Alhambra Faint Object Spectrograph and Camera (ALFOSC) on the
2.56m Nordic Optical Telescope (NOT), SPectrograph for the Rapid Acquisition
of Transients (SPRAT) on the 2m Liverpool Telescope (LT), and the
Intermediate-dispersion Spectrograph and Imaging System (ISIS) on the 4.2 m
William Herschel Telescope (WHT). The basic information of classification
spectra is listed in Table A2. The spectral reduction is performed using
various standard reduction pipelines. This includes the SEDM automated
pipeline (Rigault et al., 2019), the pyraf-dbsp package (Bellm & Sesar, 2016)
and DBSP_DRP (Roberson et al., 2022) pipelines for the DBSP data, and the
LPipe package (Perley et al., 2019) for the LRIS data.

Finally, we note that the SLSN project has at least 0.5 to 1 night of DBSP
time on P200 per month for spectral classification (PI: Yan). More
quantitative discussions on the completeness of spectral classification will
be included in Paper III.

###  2.2 Spectral Classification

Every event in our sample has at least one spectrum and most have multi-epoch
spectra. The hallmark spectral features for SLSNe-I are the five O II
absorption features in the wavelength range of $3737-4650$ Å in pre-peak
and/or near-peak optical spectra. These were identified and discussed in
Quimby et al. (2011, 2018). We utilize the large SLSN-I spectral template
library assembled by Quimby et al. (2018), and update the library by adding
the missing phase information to some of the templates. Our classification
relies on matching with the spectral templates using SNID (Blondin & Tonry,
2007) and superfit.

To determine the best matched spectral templates, we run both superfit and
SNID on the smoothed spectra with host-galaxy emission lines removed and with
redshifts fixed for most sources. If the spectrum of a SLSN-I has a good match
with that of a SN Ic but its $g$-band peak luminosity is higher than $-20.0$
mag, we classify this candidate as a SLSN-I since many SLSNe-I develop spectra
similar to those of SNe Ic after the peak (Pastorello et al., 2010; Quimby et
al., 2018; Gal-Yam, 2019).

In Appendix A, Figures A7 – A9 present the best-fit spectral template for each
event of our sample, with the information of event name, phase and template
information labeled after the spectra. The phases are measured relative to the
rest-frame $g$-band peak in this paper. We note that the phase differences
between the observed spectra of our SLSNe-I and the templates in the library
are not zero, but generally within 30 days (rest frame). This is not
surprising since SLSNe-I can have similar photospheric spectra but different
LC evolution time scales (see Kangas et al., 2017; Quimby et al., 2018).

The above classification procedure can sometimes give ambiguous results for a
small number of events, i.e. two events for our sample. In these cases, we
rely on additional LC information, such as rise time and peak luminosity to
break the degeneracy. For example, ZTF19aacxrab (SN 2019J) and ZTF19aaqrime
(SN 2019kwt) are almost equally well-matched with the spectral templates of SN
Ia and SLSN-I. In the case of SN 2019J, although the overall spectral features
broadly match with those of SNe Ia, its spectrum lacks S
II$\,\lambda\lambda\,5433,5606$ commonly seen in SNe Ia. In addition, because
of their slow rising LCs and high peak luminosities (i.e. SN 2019kwt has
$M_{g}\sim-22.8$ mag), we adopt the SLSN-I classifications, corresponding to
the template spectra of SN 2007bi and PTF09cnd, respectively.

In summary, the 78 sources listed in Table A1 can be classified as SLSNe-I
according to their features of spectra and light curves. The classification
spectra are made available to the public as part of electronic data at the
Journal website and will be uploaded to the Weizmann Interactive Supernova
Data Repository (WISeREP, Yaron & Gal-Yam, 2012)222https://www.wiserep.org/.

##  3 Observations and Data

###  3.1 ZTF data and forced photometry

The bulk of the photometric data comes from ZTF, including the data from the
public survey with a 3-day cadence, the ZTF partnership and Caltech surveys
with a faster cadence ($\leq 2$ days) over smaller areas (Bellm et al.,
2019b).

The IPAC ZTF pipeline produces reference-subtracted images using the ZOGY
algorithm (Zackay et al., 2016) and aperture photometry for all transients
detected at $\geq 5\sigma$. However, there are two issues with the LCs
produced by this pipeline. One is that the reference images built in 2018 may
contain signals from transients that exploded during the same period, for
which the photometric offset needs to be corrected. The second issue is that
the upper limits prior to the first detection are aperture photometry and
based on the image noise measured over the entire quadrant. This can
significantly underestimate transient signals.

To fix these two issues, we perform forced PSF photometry using the software
provided by IPAC333
http://web.ipac.caltech.edu/staff/fmasci/ztf/forcedphot.pdf. With a code from
Yao et al. (2019), we refine the astrometric position of each event by using
only the images around the peak phase. The very early and late-time forced
photometry without transient signals allow us to compute baseline offsets to
the LCs. In addition, we reject bad-quality data if: [1] the image processing
and instrumental calibration fail to meet predefined quality criteria; [2] the
robust estimate of $1\sigma$ value of the spatial noise per pixel in the image
is over 25; [3] the seeing is larger than $5^{{}^{\prime\prime}}$, similar to
what was used in Yao et al. (2019). The photometry of the same transient
observed with different CCD quadrants can have a systematic offset. This
problem gets fixed by our photometric reprocessing as well. For the final
photometric collections, a detection is defined as $4\sigma$ ($3\sigma$ for
Swift data) above the background and an upper limit is computed at $3\sigma$.

The ZTF astrometric and photometric systems are calibrated using the Gaia DR1
data and the Pan-STARRS 1 catalogs, respectively (for details see Masci et
al., 2019). The output magnitudes are in the AB system. The airmass and color
term corrections are included when the photometry is calibrated to the PS1
system. The color information for color corrections ($g-r$ for $g$, $r$ bands
and $r-i$ for $i$ band) is obtained from the $g$, $r$, $i$ photometry taken at
the same epoch or neighboring observations.

###  3.2 Supplemental photometry

####  3.2.1 P60, LT and P200 photometry

When ZTF is not able to take observations due to scheduling conflicts, bad
weather, or when the transient is fainter than the ZTF detection limits,
additional data are taken with SEDM on the P60 (Blagorodnova et al., 2018),
the Optical imager of the Infrared-Optical suite of instruments (IO:O) on the
LT (Steele et al., 2004) or the Wafer-Scale camera for Prime
(WASP)444https://sites.astro.caltech.edu/palomar/observer/200inchResources/waspmanual.html
on the P200. All of the P60, LT and P200 photometric results are obtained via
PSF fitting, which are calibrated with the PS1 and SDSS (for $u$-band and part
of P60 data only) standard stars. For the P60 data, the systematic errors
between PS1 and SDSS systems may introduce an additional uncertainty of 0.01
mag in r and i bands and a larger error (up to 0.25 mag) in g band. The
airmass and color-term corrections are included in all the P60/LT/P200 data.
The P60 data are processed using the software Fpipe (Fremling et al., 2016)
and image subtraction uses the SDSS references. While LT photometric data are
processed with the software specifically built for IO:O (Steele et al., 2004;
Fremling et al., 2016, Taggart et al. 2022 in prep.). The IO:O collects data
in SDSS $u$, $g$, $r$, $i$ and $z$ bands and image subtraction is performed
using reference images from the PS1 or the SDSS (for $u$-band only). The P200
data are processed using the software AutoPhOT (Brennan & Fraser, 2022) and
the PS1 reference images are used for image subtractions before performing
photometry.

####  3.2.2 Swift data

27 of 78 events in our sample have observations from the UV/Optical Telescope
(UVOT, Roming et al., 2005) aboard the Neil Gehrels Swift Observatory (Gehrels
et al., 2004). We retrieved the UVOT data from the NASA Swift Data
Archive555https://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/swift.pl, and used
the standard UVOT data analysis software distributed with HEASoft version
6.19666https://heasarc.gsfc.nasa.gov/lheasoft/download.html (Nasa High Energy
Astrophysics Science Archive Research Center, 2014), along with the standard
calibration data. We manually define sky background apertures devoid of any
sources. Four events in our sample, namely ZTF18aavrmcg (SN 2018bgv),
ZTF18acslpji (SN 2018hti), ZTF19aawfbtg (SN 2019hge) and ZTF19abpbopt (SN
2019neq), have bright host galaxies. We therefore requested host-galaxy images
at very late phases when the SNe had faded. The photometry for these four
objects are obtained by subtracting out the host galaxy fluxes. For most of
the other events, their host galaxies are very faint and contribute no more
than $10\%$ of the SN fluxes in the UV-bands, so no host-galaxy subtractions
are applied.

All the photometry data are listed in Table A3, and are available to the
public in electronic format at the journal website.

##  4 The Observed Light Curves

The observed LCs for the full sample are presented in Figures A1 \- A6, where
the left Y-axis is apparent magnitude and the X-axis is the rest-frame days
relative to the $g$-band peak phase. For a small number of events without a
$g$-band peak phase, we use the $r$-band peak phase as the reference. Table A3
contains the complete upper limits over much wider time ranges (early and late
times) than those shown in the plots. For better display, we plot only a small
number of photometric upper limits.

One striking feature apparent in Figures A1 \- A6 is that a large fraction of
the LCs in our sample show significant undulations, and some have multiple
peaks. In Paper II, we find $18-44\%$ of the well-sampled SLSNe-I have LC
undulations. Among a small sub-set of SLSNe-I with early phase coverage,
$6-44\%$ show early double peak LCs. The quantitative analysis of the LCs and
the discussions of these features are included in Paper II.

###  4.1 Magnitude corrections

####  4.1.1 Extinction corrections

The Galactic reddening $E(B-V)$ is taken from the Schlafly & Finkbeiner (2011)
dust map using the NASA/IPAC InfraRed Science Archive (IRSA) database. The
extinction corrections at different wavelengths are computed using the
empirical dust extinction laws of Fitzpatrick & Massa (2007) with $R_{V}=3.1$.

Previous studies have shown that SLSN-I hosts are mostly low-mass, metal-poor
dwarf galaxies (Lunnan et al., 2014, 2015; Leloudas et al., 2015; Perley et
al., 2016; Angus et al., 2016; Chen et al., 2017a; Schulze et al., 2018). This
is the case for most of the events in our sample. Because their LCs do not
show particularly red colors at pre-peak phases, we do not make any host-
galaxy reddening corrections to these events. However, a small fraction of
SLSN-I hosts are massive ($10^{9-10}M_{\odot}$, Perley et al., 2016; Chen et
al., 2017b), and the host-galaxy reddening in such cases can be large. We find
that seven events in our sample (Table LABEL:tab:hostextinction) have non-
negligible host-galaxy reddening. They either reside in bright hosts
($M_{r}\lesssim-18.5$ mag) or have redder $(g-r)$ colors at peak than average
(see Figure 8), which is presumably due to host-galaxy reddening.

Table 2: Host-galaxy Reddening Name |  $E(B-V)_{\rm host}$ (mag) | Method
---|---|---
SN 2018don | 0.4a | spec.temp
SN 2018kyt | 0.11 | line ratio
SN 2019kwt | 0.22 | line ratio
SN 2019aamx | 0.07 | spec.temp
SN 2019aamu | 0.23 | spec.temp
SN 2019stc | 0.18 | line ratio
SN 2020xkv | 0.24 | spec.temp
aEstimated in Lunnan et al. (2020).

The host-galaxy reddening is difficult to measure accurately, but can be
roughly estimated using two different methods. The first one uses Balmer line
ratio measured from the host-galaxy spectra. Without dust attenuation, the
predicted line ratio between H$\alpha$ and H$\beta$ remains constant in a wide
range of temperature and electron density in the nebular regions (e.g. ${\rm
H\alpha/H\beta}\approx 2.86$, Osterbrock & Ferland, 2006). The attenuation
caused by dust scattering is wavelength-dependent and stronger at shorter
wavelengths. This method is affected by both stellar and supernova absorption
features. We remove the stellar Balmer absorption using FIREFLY (Wilkinson et
al., 2017) before we measure the line flux, but ignore the influence of
supernova features. This method is applied to the host spectra of ZTF18acyxnyw
(SN 2018kyt) and ZTF19acbonaa (SN 2019stc). Note that the SN location may not
coincide with the H II region responsible for the Balmer lines, so the real
extinction at the SN site could be different from the inferred value. But such
an estimation is at least an indicator of whether the galaxy has significant
dust.

The second method is to infer the host-galaxy reddening by matching the
observed spectrum (including spectral continuum slope and absorption features)
with a SLSN-I template with negligible extinction and at a similar phase. For
this method, the assumption is that the observed red spectral color is due to
the host-galaxy reddening. The results from this method are very uncertain,
but offer crude extinction estimates for those events without host emission
lines. This method is applied to ZTF18aajqcue (SN 2018don), SN 2019aamx and
ZTF19acvxquk (SN 2019aamu), whose spectra lack distinct Balmer emission lines
from host galaxies. For example, to match the near-peak spectrum of SN
2018don, the $t\sim+54$ day template spectrum of SN 2007bi is required to be
reddened by $E(B-V)\sim 0.4$ mag (Lunnan et al., 2020). Similarly, the $+9$
day spectrum of SN 2019aamu matches well with the $-7$ day template spectrum
of PTF12gty after considering a host-galaxy reddening of $E(B-V)=0.23$ mag.
And the $+13$ day spectrum of SN 2019aamx matches well with the $+22$ day
template spectrum of PTF09cwl with $E(B-V)=0.07$ mag (shown in Figures A7 –
A9). Note that both PTF12gty and PTF09cwl have a faint, dwarf host, so their
host-galaxy reddenings are assumed to be negligible (Quimby et al., 2018;
Perley et al., 2016).

For SN 2019kwt and ZTF20abzaacf (SN 2020xkv), we applied both of the above
methods, since their spectra contain strong supernova signals and we are not
able to remove the stellar absorption nor supernova features. The derived
$E(B-V)$ for SN 2019kwt is $0.22$ mag from the host-galaxy spectrum and $0.18$
mag from template matching, which appear to be consistent. In the case of SN
2020xkv, the $E(B-V)$ derived from the spectral matching ranges from
$0.22-0.26$ mag, in contrast to the lower value ($0.17$ mag) inferred from the
Balmer decrement. Its spectrum has very low signal-to-noise ratio at H$\beta$,
and with both highly uncertain measurements, we adopt the average value of
$0.24$ mag from the template matching method.

####  4.1.2 The K-corrections

One of the key parameters of SLSNe-I is the absolute $g$-band peak magnitudes.
Proper estimate of this value requires proper K-corrections to derive reliable
rest-frame values, either in $g$-band for sources at $z\leq 0.17$ or in
$r$-band for sources at $z>0.17$. This switch is chosen because the observed
$r$-band LC at $z>0.17$ is closer to the rest-frame $g$-band in wavelength.

Most events in our sample have at least one spectrum around the LC peak (i.e.
$\lesssim 20$ days in rest frame), which allow us to compute the K-corrections
using the formula described in Hogg et al. (2002). For those without spectra
near the peak phases, we apply a constant K-correction of $-2.5\times{\rm
log}(1+z)$. This approximation is not too far from the spectral estimates, as
shown below.

Figure 2 compares the calculated K-corrections based on observed spectra (blue
dots) and an assumed $10^{4}$ K blackbody spectral energy distribution (SED)
(green line) with the constant correction (black line). The constant value of
$-2.5\times{\rm log}(1+z)$ appears to capture most of the corrections within a
range of $\pm 0.1$ mag (shaded region) for SLSNe-I at $0.06<z<0.67$. We list
the K-corrections for each event in Table A4.

Figure 2: K-corrections from the observed band to the rest-frame $g$ band for
SLSNe-I around the peak. Blue points represent the corrections computed from
the observed spectra. The black line shows the constant correction of
$-2.5\times{\rm log}(1+z)$ with an uncertainty of 0.1 mag shown by the gray
shaded area. The green line shows the correction calculated assuming a
blackbody spectrum with a temperature of $10^{4}$ K. The break at $z=0.17$ is
caused by the change of observed band (from $g$ to $r$ band).

####  4.1.3 The S-corrections

As the $g$-, $r$\- and $i$-band photometry of SLSNe-I presented in this work
are obtained from four different telescopes, the errors caused by filter
differences need to be evaluated. Since most of the data are from the ZTF it
self, we choose to correct the LT/P200/P60 photometry to that of the ZTF
filters. Following the method of Stritzinger et al. (2002, 2005), Pignata et
al. (2008) and Wang et al. (2009), the correction between different
instruments (known as the S-correction) can be computed using

$Sc_{\rm\lambda 1}=M_{\rm\lambda 1}-m_{\rm\lambda 1}-CT_{\rm\lambda
1}\left(m_{\rm\lambda 1}-m_{\rm\lambda 2}\right)-ZP_{\rm\lambda 1},$ (1)

where, $M_{\rm\lambda 1}$ is the SN synthetic magnitude computed with the
response functions of ZTF $\lambda 1$ filter, and $m_{\rm\lambda 1}$ and
$m_{\rm\lambda 2}$ are the SN synthetic magnitudes computed with the
LT/P200/P60 filters. $CT_{\rm\lambda 1}$ is the color term and $ZP_{\rm\lambda
1}$ is a zero point, which are measured by convolving the ZTF filters with a
large sample of spectrophotometric Landolt standard stars from Stritzinger et
al. (2005). The response function above includes filter transmission, detector
quantum efficiency and atmospheric transmission.

As S-corrections are spectrum-dependent and accurate measurements at any given
epoch require far more SLSN-I spectra than we have in this work. We use the
S-correction values computed from the spectra as an additional $1\sigma$ error
in photometry, and add them to the existing errors of LT/P200/P60 photometry
in quadrature. The additional $1\sigma$ errors due to the S-corrections are
listed in Table LABEL:tab:Sc.

Table 3: The estimated error caused by the S-correction. Telescopes | Filters
---|---
$g$ (mag) |  $r$ (mag) |  $i$ (mag)
LT | 0.018 | 0.044 | 0.030
P60 | 0.042 | 0.030 | 0.026
P200 | 0.023 | 0.030 | 0.025

###  4.2 Empirical LC Fitting Method

The LC analysis requires estimates of various parameters such as peak
magnitude, rise time and rise rate, which all involve numerical interpolation
and fitting. We adopt a machine learning algorithm, Gaussian Process (GP)
regression which has various kernel functions. The GP interpolation can reduce
the influence of outliers and gives robust error estimates. We use a composite
kernel which is the sum of Matérn kernel with a white noise kernel. We tested
Matérn kernels with $\nu$ parameter of 3/2 (Matérn 3/2) (Inserra et al.,
2018a; Angus et al., 2019; Lunnan et al., 2020) as well as $\nu=5/2$ (Matérn
5/2). When we compute peak magnitudes and phases, GP fit with Matérn 3/2 is
performed in the flux space. The Python package george (Ambikasaran et al.,
2016) and the Scikit-learn give comparable results.

###  4.3 LCs in Absolute g-band magnitudes

To set an approximate luminosity scale, we also plot the absolute magnitude on
the right Y-axis of Figures A1 \- A6. This is calculated by assuming a
constant K-correction of $-2.5\times{\rm log}(1+z)$. In these figures, the LCs
have been corrected for the Galactic and host-galaxy reddening whenever
possible.

To calculate the apparent peak magnitude and phase, we run the GP to
interpolate the LCs in flux space. The errors are shown as $1\sigma$
uncertainty. The $g$-band absolute peak magnitudes are derived using
$M_{g}=m_{g}-\mu-KC$, where $\mu$ is the distance modulus, $KC$ is the
K-correction, and $m_{g}$ is the apparent $g$-band magnitudes corrected for
both the host (in rest frame) and the Galactic extinction (in observed frame).
The rest-frame $g$-band absolute peak magnitudes and the peak dates are
tabulated in Table A4. The peaks of some events are not well constrained due
to the poor sampling around the peak. Figure 3 shows the distribution of the
$g$-band absolute peak magnitudes ($M_{g}$) for the ZTF sample, ranging from
$-19.8$ mag to $-22.8$ mag with a median and $1\sigma$ error of $M_{g,\rm
med}=-21.48^{+1.13}_{-0.61}$ mag. Both our median value and dispersion are
consistent with those of previous samples.

The weak bimodal distribution in the peak absolute magnitudes should not be
over-interpreted because this is the raw distribution function without
applying corrections for various selection biases (e.g. Malmquist bias,
preference of our selection system for slowly-rising events). Selection biases
could also have a strong impact on the distribution of the timescales (e.g.
Figure 5), peak bolometric luminosities (e.g. Figure 16) and other photometric
properties. The correct interpretation of these distributions requires a well-
defined selection function of our sample, the detailed simulation of which,
however, is well beyond the scope of this paper. Further studies (e.g. Paper
III) are required to confirm these distributions.

Figure 3: The distribution of the rest-frame $g$-band absolute peak
magnitudes. The dashed line and shaded region mark the median value and the
$1\sigma$ dispersion, $-21.48^{+1.13}_{-0.61}$ mag. The black solid line shows
the kernel density estimation of the distribution. Other SLSN-I samples are
plotted in different hatching patterns for comparison. DES and PS1 samples are
measured at 400 nm, which is bluer than the $g$ band (472 nm).

##  5 Light Curve Parameters

###  5.1 Rise/decay time scales – fast and slow SLSN-I events

Traditional rise times, defined as the interval between the explosion date and
the peak, are notoriously difficult to measure, especially for distant
transients such as SLSNe-I. Although the ZTF detection can go down to
$20.5-21.0$ magnitude, at the median redshift of our sample, it only probes an
absolute magnitude of $\sim-20$ mag. The upper limits before the first
detection usually can not place strong constraints on the LC evolution in very
early phases and only about one-third of our sample have high-quality early
time data. In addition, Anderson et al. (2018) shows that a SLSN-I (SN
2018bsz) can have a long, slowly rising “plateau” before a steeper, faster
rise to the peak, which makes it harder to speculate explosion dates without
enough deep, early data.

Instead of using the traditional rise time, we define $t_{\rm rise/decay,x}$
as the time interval between the peak and the flux when it is at a fraction
$x$ of the peak value. Here the $x$ factor can be $10$% ($\Delta$mag $=2.5$),
or $1/e$ ($\Delta$mag $\approx 1.09$). Time dilation is corrected for all time
scale measurements. The derived $t_{\rm rise,10\%}$ and $t_{\rm
rise/decay,1/e}$ are listed in Table A4. The poorly constrained time scales
are not included in the following analysis.

Figure 4 shows a direct proportionality between $t_{\rm rise,1/e}$ and $t_{\rm
rise,10\%}$ in the rest-frame $g$ band. Linear fit gives $t_{\rm
rise,1/e}=0.80~{}t_{\rm rise,10\%}-1.73$ days with $1\sigma$ uncertainty as
small as $\sim 2.84$ days. The small scatter implies that the LC rise rates at
the very early phases do not have significant differences in our sample. The
rise times $t_{\rm rise,10\%}$ cover a wide distribution, ranging from $10-90$
days, with the mean (median) value and a standard deviation of
$\overline{t_{\rm rise,10\%}}=41.9(38.3)\pm 17.8$ days.

Figure 4: Rise time measured at 10% of the peak flux $t_{\rm rise,10\%}$
versus that measured at $1/e$ of the peak flux $t_{\rm rise,1/e}$. The
vertical solid gray line marks the mean value of $t_{\rm rise,10\%}$, the
dashed and dotted lines represent the $1\sigma$ and $2\sigma$ uncertainties,
respectively. The black solid line and shaded area show the linear fit to the
measured time scales ($t_{\rm rise,1/e}=0.80~{}t_{\rm rise,10\%}-1.73$ days)
and the $1\sigma$ dispersion. The events with extreme time scales are
highlighted in different colors.

Of the total 56 SLSNe-I with $t_{\rm rise,10\%}$ measurements, the two slowest
rising events SN 2018ibb and ZTF20aapaecd (SN 2020fyq) stand out in Figure 4,
with time scales longer than $\overline{t_{\rm rise,10\%}}+2\sigma=78$ days.

For another slowly-evolving event ZTF20aadzbcf (SN 2020fvm), its $t_{\rm
rise,1/e}$ reaches 91 days, suggestive of an unusually long rise time,
although $t_{\rm rise,10\%}$ is not well constrained. SN 2020fvm has two LC
peaks and we set the second, also the brighter one as its main peak. If the
first one is set as the main peak, its $t_{\rm rise,1/e}$ is around 48 days.
The fastest event in our sample is SN 2018bgv (Lunnan et al., 2020), which
rises to the peak in less than 10 days. Such a fast and luminous event is very
rare and it may be associated with the Fast Blue Optical Transients (FBOT,
Drout et al., 2014; Ho et al., 2021). Compared with previous SLSN-I samples,
our sample contains slightly more fast-evolving events. For instance, 9 out of
56 events (9-26%, calculated at a confidence level of 95% based on Gehrels,
1986) are found to have $t_{\rm rise,1/e}\lesssim 15$ days, while only 5 in 55
events (4-18%) were reported in the previous samples.

Outside the $\pm 1\sigma$ range, we have 10 fast-evolving events with $t_{\rm
rise,10\%}\leq\overline{t_{\rm rise,10\%}}-1\sigma=24$ days and 10 slow events
with $t_{\rm rise,10\%}\geq\overline{t_{\rm rise,10\%}}+1\sigma=60$ days. The
$t_{\rm rise,10\%}$ shows a continuous distribution and can not be divided
into two separate fast and slow subclasses, as indicated by previous studies
(Nicholl et al., 2015; De Cia et al., 2018).

Figure 5 shows comparison of the rise and decay time scales measured at $1/e$
of the peak flux for 48 events. Both rise/decay time scales show a
distribution centered at $\sim 25-30$ days with an extended tail. For the
whole sample, the rise and decay time scales show a strong positive
correlation with a Spearman correlation coefficient of $\rho=0.73$ and null
probability $p<10^{-8}$, i.e. slowly-rising events tend to decay slowly.
Applying a linear fit, we find $t_{\rm decay,1/e}=(1.47\pm 0.07)~{}t_{\rm
rise,1/e}+(0.35\pm 2.50)$ days, which is similar to what was found in previous
studies (Nicholl et al., 2015; De Cia et al., 2018).

It is interesting to note that four of the five SLSNe-Ib in the Yan et al.
(2020) sample, namely SN 2018kyt, SN 2019hge, ZTF19acgjpgh (SN 2019unb) and
ZTF20ablkuio (SN 2020qef), have rise time scales which are 9 – 32 days longer
than that of the rise-decay time correlation. The last SLSN-Ib, ZTF19aamhhiz
(SN 2019kws), in the Yan et al. (2020) sample has a short rise time but much
longer decay time. As noted in Yan et al. (2020), these SLSNe-Ib may have He-
rich CSM and CSM interaction may affect the LC evolution before or after the
peak. Detailed modeling and discussions of their LCs are given in Paper II.

Figure 5:  Time scales measured at $1/e$ of the peak flux for the rising and
declining portions of LCs. Normal SLSNe-I are shown in blue circles while He-
rich SLSNe-Ib are highlighted in red. The black dashed line and the shaded
area show the best linear fit and 1-sigma error, $t_{\rm decay}=1.47~{}t_{\rm
rise}+0.35$ days. The histograms along the horizontal and vertical axes show
the distributions of the rise and decay time scales, respectively. The black
solid lines show the kernel density estimation of the distributions. The time
scales from other SLSN-I samples are plotted with smaller gray points for
comparison. The data from PTF are measured at 1 mag below peak magnitude and
is slightly shorter than the $1/e$-maximum (1.09 mag) time scales. And those
from the literature (Nicholl et al., 2015) and the PS1 are measured from
bolometric LCs and may have biases due to redshift and the SED.

Although SNe Ic and SLSNe-I have similar post-peak spectra, they have very
different peak luminosity and time scales. Figure 6 compares the peak $M_{g}$
and rise times between normal SNe Ic, broad-lined SNe Ic (SNe Ic-BL) and
SLSNe-I. The SLSNe-I are from our sample, while the normal SN Ic sample and
the SN Ic-BL sample are taken from Barbarino et al. (2021) and Taddia et al.
(2019), respectively. We exclude two bright SNe Ic from literature, iPTF12gty
and iPTF15eov, which can be better classified as SLSNe-I as suggested by their
authors. Both SN Ic samples are obtained from the (intermediate) PTF, which
has similar observation depth to ZTF. The overall distribution in Figure 6 is
similar to that shown in De Cia et al. (2018). It should be noted that
correction for observational selection biases and volumetric correction are
required to interpret the distribution correctly. The peak $M_{g}$ of SLSNe-I
is about 4 and 3 magnitude brighter than those of normal SNe Ic and SNe Ic-BL,
respectively. All SLSNe-Ib have low luminosities and moderate rise times
compared with normal SLSNe-I. SLSNe-I have significantly longer rise times and
wider dispersion compared to those of SNe Ic. First, this is physically
related to the fact that they have larger ejecta masses and more massive
progenitor stars (see Paper II). Second, SNe Ic are primarily powered by
radioactive decay which has a constant time scale of energy injection. In the
magnetar model proposed for SLSNe-I, the time scale of energy injection
depends on magnetic field $B$ ($\propto B^{-2}$) and spin period $P$ ($\propto
P^{2}$). The LC width expands with the decrease of $B$ (see Figure 2 of Kasen
& Bildsten, 2010). The large spread in their rise times could suggest that the
magnetic field strengths of the neutron stars can vary over a wide range as
well.

Figure 6:  Rise times $t_{\rm rise,10\%}$ versus $g$-band absolute peak
magnitudes $M_{g}$ for SLSNe-I (this paper), normal SNe Ic (from Barbarino et
al., 2021) and broad-lined SNe Ic (SNe Ic-BL, from Taddia et al., 2019). The
$M_{g}$ of the SN Ic and SN Ic-BL samples is computed from the $r$-band
magnitudes using a color correction of $\sim 0.36$ mag (Taddia et al., 2015;
Prentice et al., 2016). The rise times of SNe Ic and SNe Ic-BL are measured
from the explosion date in $r$ band, which are slightly longer than $t_{\rm
rise,10\%}$. The SLSNe-Ib in our sample are highlighted.

###  5.2 Color and blackbody temperature

Understanding the variation and uniformity of the SLSN-I SEDs as a function of
time should shed light on the physical nature of this population of stellar
explosions. Although the complete characterization of the SLSN-I SEDs is
beyond the scope of this paper, we can infer some basic properties by
examining the distributions of $(g-r)$ colors and blackbody temperatures since
both parameters are determined by the transient SEDs.

Figure 7 shows the observed color evolution as a function of time. All of the
colors have been corrected for reddening but not for K-corrections, since that
requires better spectral coverage than we have. The overall $(g-r)$ color
trend evolves from blue (i.e. $\sim-0.3$ mag, hotter temperatures) at early
phases to red and reaches $(g-r)\sim+0.8$ mag at about 2 months after the
peak. In our sample, four events have peculiar color (temperature) evolution,
and do not follow the general trend. We discuss these outliers below. We
further examine the distribution of the observed $(g-r)$ color obtained at the
maximum light in Figure 8. At the peak phases, the median observed color is
close to zero, $(g-r)_{\rm med}=-0.03^{+0.12}_{-0.11}$ mag.

Figure 7: The observed $(g-r)$ color evolution tracks with time. We highlight
four events whose $(g-r)$ color tracks do not follow the general trend shown
in gray. Figure 8: The distribution of the $(g-r)$ colors at peak. _Top
panel:_ The red dotted line represents the original colors of the events with
host-galaxy reddening and the red area shows their corrected observed color.
The blue area represents the observed colors of the events with only Galactic
extinction correction. After host-galaxy reddening correction, the observed
$(g-r)$ color at peak has a median value with the $1\sigma$ dispersion of
$-0.03^{+0.12}_{-0.11}$ mag, marked by the blue dashed line. The blue solid
line shows the kernel density estimation of the distribution. _Bottom panel:_
The green area represents the rest-frame colors of the events with
K-correction. The rest-frame $(g-r)$ color at peak has a median value with the
$1\sigma$ dispersion of $-0.21^{+0.19}_{-0.12}$ mag, which is represented by
the green dashed line. The green and blue solid lines show the kernel density
estimations of the rest-frame colors and the observed colors (normalized to
the number of the rest-frame colors), respectively.

Figures 7 and 8 show large scatters. The observed colors are not easy to
interpret because they sample different parts of the SEDs, depending on the
transient redshifts. The proper measurements are the rest-frame color tracks,
however, reliable results would require many more spectra than our sample has.
Here we compute only the rest-frame $(g-r)$ colors at the peak phase using the
color corrections computed from the near-peak spectra. The rest-frame ($g-r$)
colors are corrected from the observed ($g-r$) for the events at $z\leq 0.17$
and ($i-r$) for $z>0.17$, if photometry data and spectra are available. All
the peak colors are listed in Table A5. The green histogram in Figure 8 shows
that the median rest-frame $(g-r)_{\rm rest,med}$ is $-0.21^{+0.19}_{-0.12}$
mag, which is consistent with $-0.27$ mag calculated from the PTF SLSN-I
sample (De Cia et al., 2018). The large scatter in the peak rest-frame $(g-r)$
indicates that the SEDs of SLSNe-I may show a diverse shape and hence a wide
range of blackbody temperatures.

Figure 9 illustrates a moderate correlation ($\rho=0.52,~{}p<10^{-3}$) between
the peak rest-frame $(g-r)$ colors and the $g$-band absolute peak magnitudes
$M_{g,\rm peak}$, i.e. brighter SLSNe-I tend to have bluer color, which was
also previously found by Inserra & Smartt (2014) and De Cia et al. (2018) in
smaller samples. If combined with the PTF sample from De Cia et al. (2018),
the correlation becomes stronger ($\rho=0.56,~{}p\approx 2\times 10^{-5}$),
and can be described by a linear function, $M_{g,\rm peak}=(11.5\pm
2.6)\times(g-r)-(18.9\pm 0.5)$ mag, with $1\sigma$ error of 1.7 mag. This
correlation can be used in cosmological searches of SLSNe, e.g. Inserra et al.
(2021). However, this is beyond the scope of this paper.

Figure 9: The correlation between the $g$-band absolute peak magnitudes
$M_{g,\rm peak}$ and the rest-frame ($g-r$) colors. The linear fit and
$1\sigma$ error are shown by the black line and the shaded area. The SLSN-I
sample from this paper is marked in blue while that from PTF (De Cia et al.,
2018) is in orange.

To fit the blackbody temperature, we adopt a modified blackbody function,
defined by $f_{\lambda}={\rm
max}[~{}0,~{}1-A\times(1.0-\lambda/3000.0)~{}]\times B_{\lambda}$, with
$B_{\lambda}$ being the Planck function, and $A$ being the scaling factor. The
modified blackbody function aims to quantitatively capture the variations in
the UV spectral suppression for different events, as shown by the HST UV
spectra of SLSNe-I (Yan et al., 2017, 2018). The scaling factor $A$ is derived
from the fitting at $\lambda\leq 3000$ Å , set to zero at $\lambda\geq 3000$ Å
and fit in a range of 0 – 3. Larger scaling factors represent stronger
suppression in UV and $A=1$ represents the SED function used in Nicholl et al.
(2017). The error associated with the temperature is estimated using the
Markov Chain Monte Carlo (MCMC) method.

Of the 78 events in our sample, only 15 have at least 3 epochs of Swift UV
photometry for properly computing the blackbody temperatures. In Figure 10,
the left panel shows the temperature evolution tracks for 12 events, and the
right panel shows the rest three with peculiar color/temperature evolution.
Although the statistics is not large, Figure 10 indicates the large
temperature spread at any given phase, especially at pre-peak and peak phases.
In the left panel, some SLSNe-I have high temperatures at $\sim 15000$ K at
$t\sim-10$ days, and cool down to $\sim 9000$ K at $+20$ days. Interestingly,
there are also two cooler SLSNe-I (i.e. SN 2019hge and SN 2019unb) with the
peak temperatures less than $\sim 10000$ K. Consistently, neither of them
shows clear O II absorption lines in their classification spectra, which
require a high ionization temperature (i.e. $T\sim 15000$ K, Quimby et al.,
2018). We perform a polynomial fit to the temperature tracks and derive
$T=0.090201\,t^{3}-3.4306\,t^{2}-180.30\,t+13395$ K and
$T=-0.052720\,t^{3}+0.3319\,t^{2}-15.84\,t+9007$ K for the high and low
temperature tracks, respectively. These are shown as blue and red shaded
regions with the $\pm 1\sigma$ uncertainty in Figure 10. Consistent with the
temperature evolution, the ($g-r$) colors of low-temperature events are $\sim
0.2-0.3$ mag redder than those of high-temperature ones at the peak but become
indistinguishable from that of the full sample after $+15$ days after the
peak.

Moreover, the color distribution plots suggest that there are more low-
temperature SLSNe-I. For instance, 10 out of 35 (29%) events are found to have
redder peak rest-frame colors than the two low-temperature events ($\sim 0.10$
mag). However these 10 events have no UV photometry available, thus no
temperature measurements. It is possible that these 10 red events could also
have low peak temperatures.

Figure 10: Blackbody temperature evolution as a function of time. For
comparison, we plot the temperature measurements from the PS1 sample in light
gray. _Left panel:_ We include 12 of 15 events with at least 3 epochs of UV
data. We apply third-order polynomial fits to the temperature evolution of two
low-temperature events, SN 2019hge and SN 2019unb, and show the result with
the $1\sigma$ error in the red shaded area. Similarly, the fit of 10 high-
temperature events is shown by the blue shaded area. _Right panel:_ We
highlight the three extraordinary events and plot the 12 normal events in dark
gray for comparison.

Combining the temperature measurements from the PS1 sample, we do see more
low-temperature events. To better quantify the temperature distribution of
SLSNe-I, we choose three typical epochs (i.e. $t\sim-10$ days, 0 days and
$+20$ days relative to the rest-frame $g$-band peak) when sufficient data are
available from the ZTF and PS1 sample. We show the temperature distribution at
these epochs in Figure 11. At $t\sim-10$ days, the temperature of SLSNe-I
ranges from 7000 to 23000 K for different events, while this range is
$6000-20000$ K around the peak and $6000-12000$ K (excluding one special event
SN 2019szu) at $t\sim+20$ days. As SLSNe-I evolve, both the median value and
the spread of temperature become smaller. This conclusion will still hold true
if we expand the temporal range from $-20$ to $+40$ days, according to the
temperature trend shown in Figure 10. Furthermore, the temperatures at any
epochs in Figure 11 are found to show flat and unimodal distributions, which
indicates that SLSNe-I may have a continuous temperature distribution.

Figure 11: The temperature distributions (including both ZTF and PS1 samples)
at $-10$ days, 0 days and $+20$ days relative to the rest-frame $g$-band peak.
The dashed lines mark the median temperature at different epochs, i.e. $T\sim
12600$ K at $-10$ days, $T\sim 12000$ K at peak and $T\sim 9200$ K at $+20$
days. The solid lines show the kernel density estimation of the distribution.

We also measure the blackbody radius of the photosphere. As shown in the top
panel of Figure 12, most events show a linearly expanding photosphere from
explosion to $10-20$ days around the peak. This is due to that the recession
of the photosphere is negligible during this phase. We apply a linear fit to
measure the photosphere velocity, $V_{\rm phot}$. In paper II, we measured the
velocity from the Fe II and O II absorption lines in spectra. The velocity
implied from the species $V_{\rm ion}$ are expected to be higher than $V_{\rm
phot}$ since only the line features formed at higher velocity and lying
outside the photosphere can be observed. As shown in the bottom panel of
Figure 12, the distribution of the $\Delta V=V_{\rm phot}-V_{\rm ion}$ proves
that the most events have a negative $\Delta V$ and their $V_{\rm phot}$ is on
average lower than $V_{\rm ion}$ by about $2000-3000{\rm km\,s^{-1}}$ . Two
outliers, SN 2018bgv and SN 2018kyt, are found to have significantly higher
$V_{\rm phot}$ than $V_{\rm ion}$. SN 2018bgv is the fastest-evolving event in
our sample, while SN 2018kyt also exhibits relatively fast-evolving behaviors.
Thus the inconsistency between $V_{\rm phot}$ and $V_{\rm ion}$ may be due to
that $V_{\rm phot}$ is the average velocity measured in a period of $10-20$
days while $V_{\rm ion}$ is fast-evolving and measured at a single epoch. All
of the measured temperature and radius values are listed in Table A6.

Figure 12: _Top panel:_ The photosphere radius evolution as a function of
time. Our measurements are plotted as open points while those for PS1 sample
(Lunnan et al., 2018) are shown in grey lines for comparison. For the events
showing a linearly expanding photosphere before or at the peak, we apply a
linear fit for them and plot the results with solid lines. _Bottom panel:_ The
distribution of the velocity difference between those derived from photosphere
radius ($V_{\rm phot}$) and those measured from the absorption lines in
spectra ($V_{\rm ion}$). Blue and orange bars represent the value measured
from Fe II and O II, respectively. The solid lines show the kernel density
estimation of the distributions.

In summary, our measurements show that the SLSN-I have a wide range of
temperatures, especially at early phases. While most SLSNe-I from ZTF have
temperatures over 11000 K at the peak and cool down rapidly, there are also
many lower-temperature and slowly-evolving events. This indicates that the
SEDs of SLSNe-I may have diverse shapes and different evolutionary tracks.

Finally, we discuss four peculiar events that have extraordinary color and
temperature evolution as shown in Figures 7 and 10.

SN 2018hti is well sampled in UV and shows notably higher temperatures and
much bluer $(g-r)$ colors compared with other events. Its LC can be well
reproduced by a magnetar model (Lin et al., 2020). This event may represent
some of the population with higher temperatures and bluer SEDs. However, we
caution that this event has a very high Galactic extinction $E(B-V)=0.4$ mag.
The dust extinction corrections in the UV-bands are highly uncertain.

SN 2020fvm has two almost equally bright LC peaks (at phase $\sim-60$ and $0$
days, respectively) and the longest $1/e$-maxima rise time scale of 91 days.
Both its color and temperature evolutions are peculiar. The $(g-r)$ color
around two peaks shows similar evolutionary trends, i.e. initially evolving
from red to blue before the peak and then turning red after the peak. During
the phase from $-75$ days to $+35$ days, its temperature remains almost
constant of $\sim 10000$ K, which could be due to the absence of sampling.
Such a long time scale and double-peak evolution are unexpected for a simple
radiative cooling or magnetar model. Dessart (2019) examined the color
evolution for various configurations of magnetar models, and found that almost
all have fairly blue colors in the early phase and none shows the color change
of $\sim 0.7$ mag from red to blue. The ejecta interaction with an extended
H-poor CSM could be a possible explanation.

ZTF19aanesgt (SN 2019cdt) has much redder color at +20 days after the peak and
it evolves much faster in comparison with other objects of our sample. No
temperature evolution is computed due to the lack of UV data. This event is
somewhat similar to SN 2018bgv, both belonging to the fast-evolving and CSM-
model-favored events (see LC modeling in Paper II), though it has redder
$(g-r)$ color. Such a red color and fast LC evolution could also be consistent
with a magnetar model with high kinetic energy, as shown in Figure 13 of
Dessart (2019). Nevertheless, the Fe II velocity and the kinetic energy of the
ejecta derived for SN 2019cdt are $\sim 14400\,{\rm km\,s^{-1}}$ and
$7.6\times 10^{51}$ erg, respectively, which are both higher than the average
value (see Paper II).

Finally, unlike any other SLSN-I, ZTF19acfwynw (SN 2019szu) shows unusually
high temperatures which are rising as the luminosity declines. At the early
phases, its colors are blue, consistent with having high temperatures.
Although the black-body temperatures at late time have large errors due to
contamination of increasingly strong nebular emission lines, its spectral
sequence has revealed a clear excess of UV continuum emission well past the
peak phase. One possible explanation is CSM interaction, which could offer an
additional heating source, boosting the emission at shorter wavelength.
Another possibility is that the ejecta could be ionized by the ionizing flux
from a long-lived central source (e.g. a magnetar, Margutti et al., 2017).
Ionization can increase the optical opacity dominated by the electron
scattering and decrease the UV opacity dominated by line transitions of
metals, which leads to a shift of the peak of the SED from optical to UV
frequencies.

It is worth noting that SN 2020fvm and SN 2019cdt are poorly fit by simple
magnetar models. It is possible that CSM interaction plays significant roles
in these two systems. Additional modeling and analysis are presented in Paper
II.

###  5.3 Bolometric correction and bolometric LC

Bolometric light curve is an important indicator of the total radiative energy
of a transient and sets constraints on the possible explosion mechanisms. Due
to the high photospheric temperatures of SLSNe-I, UV radiation contributes to
a large fraction of their emission (Yan et al., 2017, 2018). Since only a
small portion of the sample has UV data, we need to first derive an empirical
bolometric correction (BC) relation, which can tie $g$\- and $r$-band
photometry to the bolometric luminosity, with ${\rm log}L_{\rm bol}={\rm
log}L_{gr}+\rm BC$ 777Another definition of bolometric correction is $M_{\rm
bolo}=M_{K}+{\rm BC}_{K}$ for $K$-band absolute magnitude $M_{K}$, where
$M_{\rm bolo}$ is bolometric magnitude. Our correction is applied to two bands
($g$ and $r$), and we use the definition introduced in §5.3.. Here $L_{gr}$ is
the sum of the $g$\- and $r$-band luminosities.

Lyman et al. (2013) derived bolometric corrections for a sample of core-
collapse SNe at low redshifts using their well-observed SEDs. We adopt a
similar method and apply it to our sample. The basic concept is following.
Bolometric correction is strongly influenced by the redshift and the
temperature (slope of the spectra) at each epoch. As the observed $(g-r)$
color is also largely determined by the same two parameters, it is expected to
see a correlation between the BC and the $(g-r)$ color. This correlation can
serve as the basis of constructing bolometric LCs with only optical data.

We first compute the bolometric luminosity at the epochs when both UV and
optical data are available, using the modified blackbody SED fit described in
§5.2. The UV scaling factor $A$ is derived by fitting the UV to optical SEDs.
For the epochs without UV data, we set $A=1.55$, which is the median value of
the epochs with UV data. The bolometric luminosities are shown in Figure 14
(only 15 events with at least three-epoch UV photometry) and listed in Table
A6.

Figure 13 shows the observed $(g-r)$ color versus the bolometric correction,
i.e. log$L_{\rm bol}/L_{gr}$, the luminosity ratio. We apply a third-order
polynomial fit by optimizing a Gaussian likelihood function and derive an
empirical relation as $\log(L_{\rm
bolo}/L_{gr})=-1.093\,x^{3}+1.244\,x^{2}-0.261\,x+0.410$, where $x=(g-r)$.
Each point is weighted by the errors of both color and luminosity ratio. This
equation can be used to compute the BC for low-$z$ SLSNe-I with similar
$(g-r)$ color evolution as our sample. Note that our fit is applicable for
$-0.4<(g-r)<+0.9$ mag, the phase range of $-74$ days $<$ phase $<+173$ days
and $0.06<z<0.57$.

Figure 13: Bolometric corrections (ratio between bolometric luminosity and the
$gr$-band luminosity) derived from the events with both UV and $gr$-band
photometry. X axis represents the $(g-r)$ color in the observed frame, where
the Galactic and host-galaxy reddening have been corrected. The color bar on
the right indicates the redshift of each data point. The black solid line
shows the result of third-polynomial fit and the gray area shows $1\sigma$
uncertainty.

As inferred from the residuals, the systematic error for the derived
bolometric luminosity is about 19%. The BC uncertainty (shown as the shaded
area in Figure 13) is the combined errors from MCMC estimates and the
systematic error. This error is the dominant one compared with other error
sources like host-galaxy reddening and redshifts. In Figure 14, the bolometric
luminosities constructed with both UV and optical photometry are consistent
with the ones derived from $g$\- and $r$-band photometry. This illustrates the
reliability of our method.

Figure 14: Bolometric LCs for the events with at least three-epoch UV
photometry. The black points present the bolometric luminosities constructed
with both UV and optical photometry. The bolometric LCs derived from $g$\- and
$r$-band photometry are shown in solid lines with $1\sigma$ error marked by
the shaded area. The breaks in LCs are due to the lack of data (e.g. SN
2019szu) or that the ($g-r$) color is 0.1 mag beyond the range where the
bolometric correction is reliable (e.g. SN 2018bgv).

The bolometric LCs derived from $g$\- and $r$-band photometry are shown in
Figure 15. The errors of both photometry and bolometric correction are
combined together in quadrature to represent the error of the bolometric LCs.
ZTF19aauiref (SN 2019fiy) and SN 2019aamu are excluded in this figure due to
the bad photometry quality. Figure 15 illustrates a diversity in both peak
luminosities and LC widths. Bumpy LCs are commonly seen and we perform
detailed analysis in Paper II.

Figure 15: The derived bolometric LCs from $g$\- and $r$-band photometry for
our whole sample. Due to the LC quality, SN 2019fiy and SN 2019aamu are
excluded. The LCs are plotted in different colors, line styles and widths.

The peak bolometric luminosities are tabulated in Table A4 and the
distribution is shown in Figure 16. The peak bolometric luminosities show a
similar distribution as the absolute peak magnitudes. These are the raw
distribution functions without any corrections for selection biases. Further
studies are required to confirm such bimodal distributions. The peak
bolometric luminosity spans from $3.22\times 10^{43}$ to $7.80\times
10^{44}\,{\rm erg\,s^{-1}}$, with a median value and the $1\sigma$ dispersion
of $L_{\rm bolo,med}=2.00^{+1.97}_{-1.44}\times 10^{44}\,{\rm erg\,s^{-1}}$.
Our measurements are consistent with the results from the PTF and PS1 sample.
Note that the DES sample tends to have lower luminosities, which could be due
to that they are pseudo bolometric luminosities constructed from trapezoidal
integration of photometry. Compared to the median peak luminosity of SNe Ib/c
($\sim 2\times 10^{42}\,{\rm erg\,s^{-1}}$, Prentice et al., 2016), SLSNe-I
are about 100 times more luminous, indicating different energy sources and/or
explosion mechanisms from normal ccSNe.

Figure 16: The distribution of the peak bolometric luminosities. The black
dashed line and the gray region show the median value and the $1\sigma$
dispersion of $2.00^{+1.97}_{-1.44}\times 10^{44}\,{\rm erg\,s^{-1}}$. The
black solid line shows the kernel density estimation of the distribution.
Other SLSN-I samples are plotted in different hatching patterns for
comparison.

##  6 Conclusions

The large sky coverage, high sensitivity and uniform cadence have made ZTF a
very efficient discovery machine for superluminous supernovae. During the
phase-I operation of ZTF survey, a total of 85 SLSNe-I were discovered. This
paper presents 78 SLSNe-I whose LCs cover both pre- and post-peak phases. The
other 7 SLSNe-I were still rising before October 30, 2021, and are not
included in current analysis.

This sample represents the largest sample of SLSNe-I at $z<1$ discovered from
a single survey. Compared with the previous SLSN-I samples, our sample also
has a better observing cadence on average. The large sample size and relative
good observing cadence make it a good SLSN-I sample for statistical and
detailed studies on SLSN-I light curves (e.g. precursor peak and post-peak
bump features investigated in Paper II).

Based on this sample, we derive a bolometric correction relation for SLSNe-I,
which allows a simple conversion between the optical $g$\- and $r$-band
photometry to the bolometric luminosity for low-$z$ SLSNe-I. The bolometric
correction follows, $\log(L_{\rm
bolo}/L_{gr})=-1.093\,x^{3}+1.244\,x^{2}-0.261\,x+0.410$, where $x$ is the
observed color $(g-r)$.

Our other findings are summarized as follows.

  1. 1.

The rise time $t_{\rm rise,10\%}$ of our SLSN-I events has a mean value of
$41.9\pm 17.8$ days. Compared with SNe Ic, SLSNe-I have significantly longer
rise times and wider dispersion (Nicholl et al., 2015; De Cia et al., 2018).
In our sample, there is one fast-evolving SLSN-I, SN 2018gbv, with $t_{\rm
rise,10\%}$ shorter than 10 days; while there are only three slowly-evolving
events with $t_{\rm rise,10\%}>$ 78 days, which is about 5% in our sample.
Compared with previous samples, the observed ratio of fast-rising ($t_{\rm
rise,1/e}\lesssim 15$ days) events in our sample is slightly higher, despite
the target selection criteria disfavor the capture of fast-rising SNe.

We confirm that the $t_{\rm rise,10\%}$ shows a continuous distribution and
can not be divided into two clearly detached subclasses (De Cia et al., 2018).
And as proven by many studies (Nicholl et al., 2015, 2017; De Cia et al.,
2018), the $1/e$-maxima rise and decay time scales show a positive
correlation, i.e. slowly-rising events tend to decay slowly, and the decay
time scales are about $1.4-1.6$ times the rise time scales.

  2. 2.

The observed $(g-r)$ color at peak has a median value of
$-0.03^{+0.12}_{-0.11}$ mag and the rest-frame $(g-r)$ color
$-0.21^{+0.19}_{-0.12}$ mag. The majority of our SLSNe-I follows a wide color
trend that evolves from blue ($(g-r)\sim-0.3$ mag) at early phases to red, and
reaches $(g-r)\sim+0.8$ mag at about 2 months after the peak. As Inserra &
Smartt (2014) and De Cia et al. (2018) have proposed, we confirm that the peak
rest-frame $(g-r)$ color is moderately correlated with the $g$-band absolute
peak magnitudes, i.e. brighter SLSNe-I tend to have bluer color.

  3. 3.

SLSNe-I have a wide range of temperatures at any given phase from $t\sim-20$
to $+40$ days relative to the peak, especially at early phases, and the spread
becomes smaller as SLSNe-I evolve. Based on the measurements from our sample
and the PS1 sample, we suggest that the temperature of SLSNe-I at a given
phase can have a continuous distribution.

  4. 4.

We find four peculiar events that have extraordinary temperature and $(g-r)$
color evolution. SN 2018hti shows a notably high temperature and blue color.
SN 2020fvm remains almost at a constant temperature of $10000\,K$ for over 100
days and has a double-peak color evolution like its LC. SN 2019szu shows an
unusual temperature evolution, which slowly rises from $13000$ K at $-20$ day
to $20000$ K at $+170$ day. The color of SN 2019cdt turns red much faster than
that of any other event.

  5. 5.

The absolute peak magnitudes of our SLSN-I sample are $-22.8\,{\rm mag}\leq
M_{g}\leq-19.8$ mag, with a median value and the $1\sigma$ error of
$-21.48^{+1.13}_{-0.61}$ mag. On average, the peak $M_{g}$ of SLSNe-I is
around 4 mag and 3 mag brighter than normal SNe Ic and SNe Ic-BL,
respectively. The peak bolometric luminosities of our sample distribute from
$3.22\times 10^{43}$ to $7.80\times 10^{44}\,{\rm erg\,s^{-1}}$ and have a
median value with the $1\sigma$ dispersion of $2.00^{+1.97}_{-1.44}\times
10^{44}\,{\rm erg\,s^{-1}}$.

We acknowledge very helpful discussions about data processing of P200
photometry with Shengyu Yan from Tsinghua University. 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 Grant No. AST-1440341
and a collaboration including Caltech, IPAC, the Weizmann Institute for
Science, the Oskar Klein Center at Stockholm University, the University of
Maryland, the University of Washington, Deutsches Elektronen-Synchrotron and
Humboldt University, Los Alamos National Laboratories, the TANGO Consortium of
Taiwan, the University of Wisconsin at Milwaukee, and Lawrence Berkeley
National Laboratories. Operations are conducted by COO, IPAC, and UW. 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 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 Nordic Optical Telescope is
owned in collaboration by the University of Turku and Aarhus University, and
operated jointly by Aarhus University, the University of Turku and the
University of Oslo, representing Denmark, Finland and Norway, the University
of Iceland and Stockholm University at the Observatorio del Roque de los
Muchachos, La Palma, Spain, of the Instituto de Astrofisica de Canarias. This
research has made use of data obtained through the High Energy Astrophysics
Science Archive Research Center Online Service, provided by the NASA/Goddard
Space Flight Center. This work was supported by the GROWTH project funded by
the National Science Foundation under Grant No. 1545949. Z. Chen acknowledges
support from the China Scholarship Council. T. K. acknowledges support from
the Swedish National Space Agency and the Swedish Research Council. S. Schulze
acknowledges support from the G.R.E.A.T research environment, funded by
Vetenskapsrådet, the Swedish Research Council, project number 2016-06012. R.
L. acknowledges support from a Marie Skłodowska-Curie Individual Fellowship
within the Horizon 2020 European Union (EU) Framework Programme for Research
and Innovation (H2020-MSCA-IF-2017-794467). A. Gal-Yam acknowledges support
from the EU via ERC grant No. 725161, the ISF GW excellence center, an IMOS
space infrastructure grant and BSF/Transformative and GIF grants, as well as
the André Deloro Institute for Advanced Research in Space and Optics, the
Schwartz/Reisman Collaborative Science Program and the Norman E Alexander
Family M Foundation ULTRASAT Data Center Fund, Minerva and Yeda-Sela. The work
of X. Wang is supported by the National Natural Science Foundation of China
(NSFC grants 12288102, 12033003 and 11633002), the Major State Basic Research
Development Program (grant 2016YFA0400803), the Scholar Program of Beijing
Academy of Science and Technology (DZ:BS202002), and the Tencent XPLORER
Prize.

##  Appendix A Information on the ZTF SLSN-I sample

Table A1: The ZTF SLSN-I Sample

ZTF Name | IAU Name | RAa | DECa | Redshift | $E(B-V)^{b}$ | Discovery | Template | Spectrum | Template | Template
---|---|---|---|---|---|---|---|---|---|---
(mag) | group | Phasec | Phasec | Type
ZTF18aaisyyp | SN 2018avk | 13:11:27.72 | +65:38:16.7 | 0.132 | 0.011 | Gaia | PTF12gty | -13.5 | -7 | SLSN-I
ZTF18aajqcue | SN 2018don | 13:55:08.65 | +58:29:42.0 | 0.0735 | 0.0089 | PS1 | SN2007gr | -2.1 | -7 | Ic
ZTF18aapgrxo | SN 2018bym | 18:43:13.42 | +45:12:28.2 | 0.2744 | 0.0517 | ATLAS | PTF13ajg | +5.4 | +8 | SLSN-I
ZTF18aavrmcg | SN 2018bgv | 11:02:30.29 | +55:35:55.8 | 0.0795 | 0.0074 | Gaia | SN2011ke | +17.8 | +20 | SLSN-I
ZTF18aazgrfl | SN 2018lzv | 12:44:02.32 | +56:01:44.5 | 0.434 | 0.0081 | ZTF | PTF09atu | -19.1 | -20 | SLSN-I
ZTF18abjwagv | SN 2018gbw | 15:55:38.02 | +28:21:38.0 | 0.3454 | 0.0386 | PS1 | PTF13ajg | -4.1 | -4 | SLSN-I
ZTF18abmasep | SN 2018fcg | 21:09:36.78 | +33:28:59.6 | 0.1011 | 0.1435 | ZTF | PTF12dam | -4.4 | -22 | SLSN-I
ZTF18abrzcbp | SN 2018lzw | 07:39:32.76 | +27:44:02.7 | 0.3198 | 0.0373 | ZTF | PTF10uhf | +27.6 | +15 | SLSN-I
ZTF18abshezu | SN 2018gft | 23:57:17.95 | -15:37:53.3 | 0.2320 | 0.0256 | ZTF | PTF09cnd | -38.4 | -14 | SLSN-I
ZTF18abszecm | SN 2018lzx | 22:29:27.24 | +13:10:39.8 | 0.4373 | 0.0529 | ZTF | PTF09cnd | +19.2 | +37 | SLSN-I
ZTF18abvgjyl | SN 2018gkz | 07:58:11.54 | +19:31:07.9 | 0.2405 | 0.036 | ZTF | SN2007bi | +10.6 | +50 | SLSN-I
ZTF18acapyww | SN 2018hpq | 18:28:41.24 | +75:48:47.3 | 0.124 | 0.0868 | Gaia | PTF10nmn | +25.0 | max | SLSN-I
ZTF18acenqto | SN 2018ibb | 04:38:56.93 | -20:39:44.2 | 0.166 | 0.0275 | ATLAS | PTF12dam | -4.0 | +7 | SLSN-I
ZTF18acqyvag | SN 2018lfe | 09:33:29.56 | +00:03:08.4 | 0.3505 | 0.0286 | PS1 | SN2011ke | +59.6 | +53 | SLSN-I
ZTF18acslpji | SN 2018hti | 03:40:53.77 | +11:46:37.9 | 0.0613 | 0.3983 | ATLAS | PTF12dam | +21.2 | +7 | SLSN-I
ZTF18acxgqxq | SN 2018lfd | 23:14:59.32 | +48:45:27.6 | 0.2686 | 0.1506 | ZTF | PTF12dam | -7.5 | -22 | SLSN-I
ZTF18acyxnyw | SN 2018kyt | 12:27:56.23 | +56:23:35.6 | 0.1080 | 0.0091 | ZTF | PTF10hgi | +45.5 | +47 | SLSN-I
ZTF19aacxrab | SN 2019J | 10:03:46.78 | +06:46:24.7 | 0.1346 | 0.0229 | PS1 | PTF10gvb | +8.8 | -6 | SLSN-I
ZTF19aajwogx | SN 2019cca | 12:02:50.91 | -16:39:53.6 | 0.4103 | 0.0462 | ZTF | PTF10uhf | +28.5 | +15 | SLSN-I
ZTF19aaknqmp | SN 2019bgu | 09:57:15.34 | +32:00:05.6 | 0.1480 | 0.0123 | ATLAS | PTF12gty | +16.6 | -7 | SLSN-I
ZTF19aalbrph | SN 2019kwq | 17:07:58.84 | +58:42:03.9 | 0.49$\star$ | 0.0257 | ZTF | PTF09cnd | +68.9 | +37 | SLSN-I
ZTF19aamhast | SN 2019dgr | 09:45:32.68 | +04:56:02.2 | 0.3815 | 0.0348 | ATLAS | PTF13ajg | +13.2 | +8 | SLSN-I
ZTF19aamhhiz | SN 2019kws | 14:15:04.46 | +50:39:06.8 | 0.1977 | 0.0142 | ZTF | PTF12gty | +21.7 | -7 | SLSN-I
ZTF19aanesgt | SN 2019cdt | 08:17:53.90 | +65:28:46.7 | 0.153 | 0.0441 | ZTF | PTF10nmn | +11.6 | +29 | SLSN-I
ZTF19aantokv | SN 2019aamp | 14:37:49.27 | +20:18:16.6 | 0.4040 | 0.0237 | ZTF | PTF13ajg | +8.4 | +8 | SLSN-I
ZTF19aaohuwc | SN 2019dlr | 11:17:34.18 | +00:30:02.6 | 0.26$\star$ | 0.0309 | ZTF | SN2004aw | +57.7 | +11 | Ic
ZTF19aapaeye | SN 2019cwu | 14:51:37.29 | +48:59:13.7 | 0.32$\star$ | 0.0186 | ZTF | SN2011ke | +27.6 | +20 | SLSN-I
ZTF19aaqrime | SN 2019kwt | 19:39:22.59 | +78:45:43.8 | 0.3562 | 0.0775 | ZTF | SN2007bi | +11.3 | +50 | SLSN-I
ZTF19aarphwc | SN 2019eot | 18:00:29.95 | +50:17:43.3 | 0.3057 | 0.0366 | ZTF | PTF13ajg | -13.8 | -4 | SLSN-I
ZTF19aaruixj | SN 2019kwu | 13:57:39.77 | +64:21:18.6 | 0.60$\star$ | 0.0138 | ZTF | PTF10vqv | +28.4 | +11 | SLSN-I
ZTF19aasdvfr | SN 2019gqi | 14:21:11.98 | +28:54:05.9 | 0.3642 | 0.0129 | ATLAS | PTF11rks | +21.3 | +7 | SLSN-I
ZTF19aauiref | SN 2019fiy | 14:05:46.73 | +33:27:38.3 | 0.67$\star$ | 0.0142 | PS1 | PTF09atu | +15.6 | +28 | SLSN-I
ZTF19aauvzyh | SN 2019gam | 10:19:18.32 | +17:12:42.6 | 0.1235 | 0.0272 | ATLAS | SN2011ke | +17.6 | +29 | SLSN-I
ZTF19aavouyw | SN 2019gfm | 15:35:46.59 | +24:03:45.0 | 0.1816 | 0.0465 | PS1 | PTF11rks | +16.8 | +18 | SLSN-I
ZTF19aawfbtg | SN 2019hge | 22:24:21.20 | +24:47:17.1 | 0.0866 | 0.058 | ZTF | PTF12dam | -25.7 | +2 | SLSN-I
ZTF19aawsqsc | SN 2019hno | 19:39:12.95 | +62:43:41.0 | 0.26 | 0.0583 | ATLAS | PTF09cnd | +1.6 | +9 | SLSN-I
ZTF19aayclnm | SN 2019aamq | 20:55:36.14 | -08:40:31.4 | 0.386 | 0.0636 | ZTF | PTF10bjp | +3.8 | +8 | SLSN-I
ZTF19abaeyqw | SN 2019kcy | 14:08:19.78 | +08:58:01.0 | 0.399 | 0.0227 | ZTF | PTF13ajg | -19.3 | -4 | SLSN-I
ZTF19abcvwrz | SN 2019aamx | 15:57:48.27 | +27:28:03.5 | 0.41 | 0.0362 | ZTF | PTF09cwl | +13.0 | +22 | SLSN-I
ZTF19abdlzyq | SN 2019aamr | 15:29:23.55 | +38:06:12.6 | 0.42$\star$ | 0.0118 | ZTF | PTF09cwl | +8.0 | +22 | SLSN-I
ZTF19abfvnns | SN 2019lsq | 00:04:40.58 | +42:52:11.3 | 0.1295 | 0.0829 | ATLAS | PTF12dam | -3.3 | -22 | SLSN-I
ZTF19abkfshj | SN 2019otl | 02:52:21.63 | -17:48:12.4 | 0.500 | 0.0241 | ZTF | PTF13ajg | +11.2 | -4 | SLSN-I
ZTF19abnacvf | SN 2019nhs | 00:52:01.44 | +07:36:59.7 | 0.189 | 0.0514 | PS1 | PTF13ajg | -8.6 | -4 | SLSN-I
ZTF19abnqqdp | SN 2019aams | 23:43:36.16 | +12:29:01.0 | 0.6360 | 0.0448 | ZTF | PTF13ajg | +0.8 | -4 | SLSN-I
ZTF19abpbopt | SN 2019neq | 17:54:26.76 | +47:15:40.6 | 0.1060 | 0.0334 | ZTF | PTF11rks | +16.1 | +7 | SLSN-I
ZTF19abrbsvm | SN 2019obk | 22:33:54.08 | -02:09:42.3 | 0.1656 | 0.048 | PS1 | PTF12gty | +12.8 | -7 | SLSN-I
ZTF19abuolvj | SN 2019qgk | 22:29:57.55 | -04:06:02.2 | 0.3468 | 0.054 | ZTF | SN2010gx | +47.2 | +29 | SLSN-I
ZTF19abuyuwa | SN 2019sgg | 01:01:11.77 | +14:01:35.4 | 0.5726 | 0.0373 | ZTF | PTF13ajg | -18.6 | -4 | SLSN-I
ZTF19abzoyeg | SN 2019aamt | 21:15:08.00 | +32:43:01.3 | 0.2138 | 0.126 | ZTF | PTF12dam | +15.2 | +7 | SLSN-I
ZTF19abzqmau | SN 2019sgh | 01:12:39.42 | +36:28:24.8 | 0.3436 | 0.0599 | ZTF | PTF10uhf | +39.8 | +15 | SLSN-I
ZTF19acbonaa | SN 2019stc | 06:54:23.10 | +17:29:31.4 | 0.1178 | 0.072 | ZTF | PTF10hgi | +25.6 | +47 | SLSN-I
ZTF19acfwynw | SN 2019szu | 00:10:13.14 | -19:41:32.4 | 0.2120 | 0.018 | ATLAS | PTF12dam | -15.2 | +2 | SLSN-I
ZTF19acgjpgh | SN 2019unb | 09:47:57.02 | +00:49:36.0 | 0.0635 | 0.1045 | ZTF | PTF10hgi | -7.5 | +15 | SLSN-I
ZTF19ackjrru | SN 2019ujb | 09:03:15.18 | +40:14:32.6 | 0.2008 | 0.0165 | ZTF | PTF11rks | +25.7 | +18 | SLSN-I
ZTF19acsajxn | SN 2019xdy | 08:24:51.33 | +22:10:46.0 | 0.2206 | 0.0425 | ZTF | PTF10vwg | +31.6 | +22 | SLSN-I
ZTF19acucxij | SN 2019vvc | 09:13:30.13 | +44:46:26.2 | 0.3314 | 0.0115 | ATLAS | PTF09cwl | +16.0 | +22 | SLSN-I
ZTF19acvxquk | SN 2019aamu | 02:55:08.89 | +11:27:22.4 | 0.2590 | 0.1925 | ZTF | PTF12gty | +8.6 | -7 | SLSN-I
ZTF19adaivcf | SN 2019zbv | 10:15:01.10 | +43:24:53.6 | 0.3785 | 0.0119 | ZTF | PTF13ajg | +10.4 | +8 | SLSN-I
ZTF20aahbfmf | SN 2020ank | 08:16:14.65 | +04:19:26.9 | 0.2485 | 0.0193 | ZTF | PTF13ajg | -0.3 | -4 | SLSN-I
ZTF20aaifybu | SN 2020auv | 16:34:12.51 | +13:05:51.9 | 0.280 | 0.0565 | ZTF | PTF13ajg | -2.4 | +8 | SLSN-I
ZTF19acujvsi | SN 2019aamw | 23:48:54.54 | +24:59:59.8 | 0.22$\star$ | 0.0526 | ZTF | PTF10vwg | +137.6 | +147 | SLSN-I
ZTF20aadzbcf | SN 2020fvm | 14:12:45.93 | +34:44:16.2 | 0.2428 | 0.0129 | ZTF | PTF10uhf | -1.4 | +15 | SLSN-I
ZTF20aagikvv | SN 2019aamv | 12:45:01.65 | +33:33:14.1 | 0.3996 | 0.0127 | ZTF | PTF13ajg | -11.4 | +8 | SLSN-I
ZTF20aahrxgw | SN 2020aup | 13:09:44.44 | +12:29:13.4 | 0.31$\star$ | 0.0208 | ZTF | PTF09cwl | +16.2 | +22 | SLSN-I
ZTF20aaoqwpo | SN 2020dlb | 08:08:34.14 | +34:44:12.9 | 0.398 | 0.0327 | ZTF | SN2005ap | +0.2 | +4 | SLSN-I
ZTF20aapaecd | SN 2020fyq | 14:46:10.44 | +23:48:02.0 | 0.1765 | 0.0325 | PS1 | SN2011ke | +27.5 | +53 | SLSN-I
ZTF20aattyuz | SN 2020exj | 14:42:40.01 | +30:14:39.1 | 0.1216 | 0.0096 | ATLAS | PTF10hgi | +44.2 | +47 | SLSN-I
ZTF20aauoudz | SN 2020htd | 17:44:17.28 | +38:55:30.4 | 0.3515 | 0.026 | PS1 | PTF09cnd | +7.4 | +13 | SLSN-I
ZTF20aavfbqz | SN 2020iyj | 09:15:36.64 | +53:27:32.0 | 0.3690 | 0.0155 | ZTF | PTF09cwl | +1.9 | +1 | SLSN-I
ZTF20aavqrzc | SN 2020kox | 11:06:04.97 | +26:17:28.7 | 0.456 | 0.0166 | ATLAS | PTF13ajg | +2.6 | -4 | SLSN-I
ZTF20aawfxlt | SN 2020jii | 15:34:55.31 | +02:51:11.5 | 0.396 | 0.0427 | PS1 | PTF13ajg | -1.8 | -4 | SLSN-I
ZTF20aawkgxa | SN 2020afah | 10:20:18.32 | +53:19:21.4 | 0.3754 | 0.0086 | ZTF | PTF09cnd | -41.8 | -16 | SLSN-I
ZTF20abisijg | SN 2020afag | 00:15:46.25 | +47:00:08.5 | 0.3815 | 0.0898 | ZTF | PTF09cwl | +6.7 | +22 | SLSN-I
ZTF20abjwjrx | SN 2020onb | 14:23:00.61 | +49:10:40.7 | 0.16$\star$ | 0.0208 | ZTF | SN2004aw | +24.8 | +4 | Ic
ZTF20ablkuio | SN 2020qef | 22:56:10.53 | +28:45:53.3 | 0.1831 | 0.0459 | ATLAS | PTF12gty | +24.4 | -7 | SLSN-I
ZTF20abpuwxl | SN 2020rmv | 00:40:00.19 | -14:35:25.1 | 0.2621 | 0.0185 | ATLAS | PTF10uhf | +13.2 | +15 | SLSN-I
ZTF20abzaacf | SN 2020xkv | 22:37:46.00 | +23:31:37.4 | 0.2410 | 0.0303 | ATLAS | SN2011ke | -8.7 | +20 | SLSN-I
ZTF20aceqspy | SN 2020xgd | 00:19:45.83 | +05:08:18.7 | 0.455 | 0.0145 | PS1 | PTF13ajg | -4.4 | -4 | SLSN-I

aIn J2000.
bFrom Schlafly & Finkbeiner (2011).
cIn rest-frame days.
$\star$ means the redshift is fit by superfit.

Table A2: Classification spectra

Name | Observing date | Telescope + instrument | Exposure timea (s)
---|---|---|---
SN 2018avk | 2018-05-04 | NOT+ALFOSC | $1800$
SN 2018don | 2018-06-09 | P200+DBSP | $2\times 600/2\times 600$
SN 2018bym | 2018-06-12 | P200+DBSP | $600/600$
SN 2018bgv | 2018-06-04 | NOT+ALFOSC | $2\times 2400$
SN 2018lzv | 2018-07-14 | WHT+ISIS | $600$
SN 2018gbw | 2018-08-13 | P200+DBSP | $600/600$
SN 2018fcg | 2018-08-21 | P200+DBSP | $600/600$
SN 2018lzw | 2018-10-06 | P200+DBSP | $610/400$

aSlash-separated values indicate the exposure time of blue and red sides,
respectively.
(This table is available in its entirety in machine-readable form.)

Table A3: Photometry data

Name | MJD | Filter | Mag(AB) | Errormag | Labela | Flux${}_{\rm ratio}\,^{b}$ | Tel.+Ins.c
---|---|---|---|---|---|---|---
(days) | (mag) | (mag) | ($10^{-9}$)
SN 2018avk | 58202.30 | g | 20.48 | 0.21 | F | 6.40 | P48+ZTF
SN 2018avk | 58202.32 | g | 20.64 | 0.18 | F | 5.53 | P48+ZTF
SN 2018avk | 58202.32 | g | 20.74 | 0.16 | F | 5.04 | P48+ZTF
SN 2018avk | 58202.34 | g | 20.78 | 0.19 | F | 4.86 | P48+ZTF
SN 2018avk | 58202.36 | g | 20.92 | 0.14 | F | 4.27 | P48+ZTF
SN 2018avk | 58202.36 | g | 20.80 | 0.21 | F | 4.77 | P48+ZTF
SN 2018avk | 58203.35 | g | 20.54 | 0.15 | F | 6.06 | P48+ZTF
SN 2018avk | 58204.39 | g | 20.10 | 0.19 | F | 9.09 | P48+ZTF

aF means real detection and T means upper limit.
bThe ratio of observed flux and flux zero point.
cTelescope+Instrument.
(This table is available in its entirety in machine-readable form.)

Table A4: LC properties

Name | K-correctiona | $M_{\rm peak}$ | $L_{\rm peak}$ | $t_{\rm peak}$ | $t_{\rm rise,1/e}$ | $t_{\rm decay,1/e}$ | $t_{\rm rise,10\%}$
---|---|---|---|---|---|---|---
(mag) | (mag) | ($10^{43}\,{\rm erg\,s^{-1}}$) | (MJD) | (days) | (days) | (days)
SN 2018avk | -0.00 | $-20.27\pm 0.03$ | $5.5\pm 1.0$ | $58257.29^{+5.45}_{-4.35}$ | $41.94^{+4.84}_{-3.88}$ | $76.83^{+4.70}_{-5.44}$ | $50.27^{+4.99}_{-3.91}$
SN 2018don | 0.09 | $-20.05\pm 0.04$ | $3.4\pm 0.6$ | $58280.21^{+3.68}_{-3.49}$ | $48.70^{+3.55}_{-3.29}$ | $49.04^{+3.91}_{-4.31}$ | -
SN 2018bym | -0.22 | $-22.03\pm 0.03$ | $33.2\pm 5.9$ | $58274.14^{+1.82}_{-1.87}$ | $24.05^{+2.33}_{-1.81}$ | $43.10^{+1.50}_{-1.46}$ | -
SN 2018bgv | -0.09 | $-20.61\pm 0.10$ | ($14.9\pm 3.6$)b | $58253.74^{+1.30}_{-1.76}$ | $8.40^{+1.20}_{-1.63}$ | $19.39^{+1.66}_{-1.22}$ | $9.99^{+1.20}_{-1.63}$
SN 2018lzv | -0.39 | $-22.23\pm 0.10$ | $52.6\pm 16.1$ | $58340.35^{+12.83}_{-9.63}$ | $40.47^{+8.97}_{-6.75}$ | $67.81^{+7.07}_{-9.56}$ | $47.35^{+8.98}_{-6.75}$
SN 2018gbw | -0.33 | $-22.10\pm 0.07$ | ($41.1\pm 7.6$) | $58348.56^{+4.55}_{-3.23}$ | $22.58^{+3.53}_{-2.62}$ | - | $32.78^{+3.60}_{-2.77}$
SN 2018fcg | -0.13 | $-20.35\pm 0.06$ | ($6.0\pm 1.1$) | $58355.83^{+2.93}_{-2.54}$ | $13.33^{+2.67}_{-2.32}$ | $18.00^{+2.33}_{-2.68}$ | $17.75^{+2.76}_{-2.35}$
SN 2018lzw | -0.30$\star$ | ($-21.68\pm 0.10$) | ($22.7\pm 4.6$) | ($58360.51^{+3.57}_{-0}$) | - | ($56.57^{+1.40}_{-2.99}$) | -
SN 2018gft | -0.18 | $-22.22\pm 0.02$ | $40.1\pm 7.2$ | $58420.27^{+7.66}_{-5.97}$ | $37.70^{+6.22}_{-4.85}$ | - | $49.95^{+6.26}_{-4.88}$
SN 2018lzx | -0.44 | $-22.01\pm 0.04$ | $32.6\pm 5.9$ | $58437.41^{+11.79}_{-9.64}$ | $60.45^{+8.24}_{-6.75}$ | $108.79^{+10.04}_{-13.22}$ | $76.50^{+8.55}_{-7.05}$
SN 2018gkz | -0.24 | ($-21.81\pm 0.03$) | ($21.3\pm 4.3$) | ($58383.86^{+9.92}_{-9.14}$) | - | ($86.65^{+8.11}_{-8.60}$) | -
SN 2018hpq | -0.13$\star$ | $-20.10\pm 0.03$ | $4.8\pm 0.9$ | $58427.85^{+0.93}_{-1.31}$ | $20.37^{+1.88}_{-3.47}$ | - | $31.92^{+1.41}_{-1.99}$
SN 2018lfe | -0.33$\star$ | $-21.43\pm 0.09$ | $27.4\pm 9.1$ | $58465.45^{+3.90}_{-6.95}$ | $17.12^{+3.21}_{-5.34}$ | $26.03^{+6.25}_{-5.42}$ | -
SN 2018hti | -0.09 | $-22.09\pm 0.07$ | $55.1\pm 14.9$ | $58460.47^{+8.51}_{-5.88}$ | $28.11^{+8.02}_{-5.55}$ | - | $35.73^{+8.03}_{-5.55}$
SN 2018lfd | -0.19 | $-21.98\pm 0.11$ | $38.6\pm 8.5$ | $58496.49^{+23.40}_{-9.15}$ | $35.45^{+18.61}_{-7.50}$ | - | -
SN 2018kyt | -0.13 | $-20.51\pm 0.08$ | $9.2\pm 0.4\star\star$ | $58506.63^{+4.10}_{-6.14}$ | $29.07^{+3.75}_{-5.63}$ | $26.42^{+5.69}_{-4.76}$ | $33.35^{+3.70}_{-5.54}$
SN 2019J | -0.05 | $-19.86\pm 0.13$ | $3.7\pm 0.9$ | $58546.97^{+9.61}_{-18.26}$ | $44.98^{+8.70}_{-16.29}$ | $30.58^{+16.26}_{-8.77}$ | $54.62^{+8.60}_{-16.20}$
SN 2019cca | -0.37$\star$ | $-21.99\pm 0.10$ | ($33.3\pm 7.8$) | $58559.82^{+6.71}_{-10.28}$ | $35.36^{+10.88}_{-9.31}$ | $39.88^{+9.58}_{-5.97}$ | -
SN 2019bgu | -0.14 | $-20.27\pm 0.15$ | $5.5\pm 1.4$ | $58559.92^{+5.53}_{-11.12}$ | - | $24.74^{+9.98}_{-5.20}$ | -
SN 2019kwq | -0.43$\star$ | $-21.99\pm 0.05$ | $32.2\pm 5.8$ | $58563.33^{+5.87}_{-8.38}$ | $45.37^{+8.32}_{-8.52}$ | $63.96^{+6.57}_{-5.02}$ | $63.10^{+12.81}_{-10.97}$
SN 2019dgr | -0.37 | $-21.89\pm 0.10$ | $33.0\pm 9.8$ | $58581.83^{+5.11}_{-7.84}$ | $25.06^{+3.99}_{-5.84}$ | - | $36.56^{+3.87}_{-6.08}$
SN 2019kws | -0.31 | $-20.05\pm 0.04$ | $5.1\pm 0.9$ | $58573.97^{+4.64}_{-3.92}$ | $22.70^{+3.97}_{-3.40}$ | $53.26^{+3.36}_{-3.99}$ | -
SN 2019cdt | -0.04 | $-20.85\pm 0.05$ | $7.6\pm 1.4$ | $58583.63^{+2.01}_{-6.10}$ | $17.59^{+1.89}_{-5.34}$ | $14.24^{+5.29}_{-1.77}$ | $26.66^{+5.70}_{-5.86}$
SN 2019aamp | -0.40 | $-21.92\pm 0.04$ | $30.5\pm 5.7$ | $58588.27^{+6.54}_{-6.84}$ | - | $43.93^{+5.98}_{-5.34}$ | -
SN 2019dlr | -0.24 | $-21.11\pm 0.07$ | $18.1\pm 3.7$ | $58593.30^{+6.51}_{-7.17}$ | $28.54^{+5.67}_{-6.04}$ | $42.00^{+9.39}_{-6.08}$ | $38.58^{+8.43}_{-6.61}$
SN 2019cwu | -0.30 | $-21.27\pm 0.04$ | $17.8\pm 3.4$ | $58599.56^{+4.14}_{-3.73}$ | $17.43^{+3.15}_{-2.84}$ | $48.64^{+3.14}_{-3.32}$ | $20.92^{+3.20}_{-2.86}$
SN 2019kwt | -0.34 | $-22.74\pm 0.05$ | $65.1\pm 11.9$ | $58650.73^{+5.03}_{-4.48}$ | $51.21^{+4.08}_{-3.67}$ | $49.09^{+4.94}_{-4.72}$ | $70.50^{+8.91}_{-4.56}$
SN 2019eot | -0.28 | $-22.13\pm 0.03$ | $40.1\pm 7.1$ | $58644.97^{+2.46}_{-2.33}$ | $31.28^{+1.90}_{-1.80}$ | $37.61^{+2.04}_{-2.11}$ | $40.87^{+2.04}_{-1.90}$
SN 2019kwu | -0.51$\star$ | $-22.32\pm 0.11$ | $50.8\pm 9.4$ | $58620.64^{+5.06}_{-3.19}$ | $14.54^{+3.18}_{-2.02}$ | $27.67^{+2.62}_{-3.50}$ | $20.29^{+3.27}_{-2.19}$
SN 2019gqi | -0.34$\star$ | $-21.43\pm 0.08$ | $19.3\pm 4.8$ | $58636.92^{+4.27}_{-5.71}$ | $26.80^{+3.32}_{-4.30}$ | - | $34.99^{+3.61}_{-4.53}$
SN 2019fiy | -0.69 | $-22.71\pm 0.03$ | - | $58640.96^{+5.24}_{-5.82}$ | $23.49^{+3.21}_{-3.54}$ | - | $32.24^{+4.75}_{-4.20}$
SN 2019gam | 0.32 | ($-19.88\pm 0.12$) | ($2.6\pm 0.7$) | ($58648.17^{+0}_{-4.25}$) | ($26.13^{+1.91}_{-4.49}$) | - | ($38.28^{+1.07}_{-3.92}$)
SN 2019gfm | -0.02 | $-20.99\pm 0.05$ | $13.7\pm 2.7$ | $58645.17^{+3.58}_{-2.24}$ | $13.80^{+3.19}_{-2.03}$ | $26.18^{+2.32}_{-3.23}$ | $20.53^{+3.51}_{-2.68}$
SN 2019hge | -0.11 | $-19.82\pm 0.05$ | $3.6\pm 0.2\star\star$ | $58693.95^{+3.00}_{-7.76}$ | $46.63^{+2.79}_{-7.15}$ | $39.35^{+7.26}_{-2.92}$ | $57.21^{+14.29}_{-7.24}$
SN 2019hno | -0.21 | $-21.05\pm 0.04$ | $13.4\pm 2.5$ | $58663.92^{+7.75}_{-4.02}$ | $22.89^{+6.17}_{-3.24}$ | $29.89^{+3.51}_{-6.25}$ | $29.45^{+6.30}_{-3.30}$
SN 2019aamq | -0.36 | $-21.76\pm 0.03$ | $32.9\pm 11.8$ | $58690.78^{+11.60}_{-10.21}$ | $50.47^{+9.01}_{-7.92}$ | - | -
SN 2019kcy | -0.41 | $-22.12\pm 0.09$ | $46.6\pm 11.8$ | $58693.95^{+16.31}_{-11.69}$ | $33.01^{+11.69}_{-8.40}$ | - | $42.58^{+11.82}_{-8.45}$
SN 2019aamx | -0.33 | $-21.80\pm 0.07$ | $21.7\pm 4.8$ | $58709.67^{+9.10}_{-12.60}$ | $42.97^{+6.58}_{-9.03}$ | $45.91^{+10.48}_{-8.31}$ | $50.92^{+6.51}_{-8.98}$
SN 2019aamr | -0.41 | $-21.36\pm 0.10$ | $20.5\pm 4.3$ | $58684.58^{+6.37}_{-3.66}$ | $14.04^{+4.50}_{-2.62}$ | $27.09^{+3.89}_{-5.38}$ | $17.70^{+4.50}_{-2.60}$
SN 2019lsq | -0.22 | $-20.87\pm 0.04$ | $12.5\pm 2.3$ | $58707.77^{+1.48}_{-1.44}$ | $21.29^{+1.36}_{-1.33}$ | $43.02^{+1.50}_{-1.80}$ | $31.31^{+1.65}_{-1.46}$
SN 2019otl | -0.46 | $-22.41\pm 0.06$ | ($61.2\pm 14.4$) | $58715.21^{+12.61}_{-13.82}$ | - | - | -
SN 2019nhs | -0.05 | $-21.44\pm 0.06$ | $18.6\pm 0.9\star\star$ | $58732.19^{+3.96}_{-4.79}$ | $22.77^{+3.36}_{-4.06}$ | $27.57^{+4.16}_{-3.42}$ | $30.59^{+3.70}_{-4.18}$
SN 2019aams | -0.60 | $-22.11\pm 0.08$ | ($47.2\pm 13.6$) | $58730.76^{+8.27}_{-4.61}$ | $25.18^{+6.30}_{-3.55}$ | $23.86^{+3.23}_{-5.82}$ | $33.92^{+7.83}_{-5.12}$
SN 2019neq | -0.24 | $-21.30\pm 0.03$ | $20.1\pm 0.4\star\star$ | $58730.16^{+0.80}_{-0.83}$ | $14.69^{+0.73}_{-0.76}$ | $19.97^{+0.88}_{-0.83}$ | $21.97^{+0.74}_{-0.76}$
SN 2019obk | -0.10 | $-19.80\pm 0.11$ | $3.8\pm 1.1$ | $58735.13^{+10.43}_{-10.59}$ | $37.49^{+10.91}_{-10.12}$ | - | $56.10^{+10.52}_{-12.16}$
SN 2019qgk | -0.32$\star$ | ($-21.55\pm 0.11$) | ($21.2\pm 5.5$) | ($58744.36^{+9.70}_{-0}$) | - | ($43.96^{+3.91}_{-9.03}$) | -
SN 2019sgg | -0.42 | ($-22.32\pm 0.07$) | $56.6\pm 12.0$ | ($58791.33^{+0}_{-8.53}$) | ($42.77^{+0.95}_{-5.51}$) | - | ($51.83^{+1.60}_{-5.61}$)
SN 2019aamt | -0.24 | $-20.74\pm 0.05$ | $17.0\pm 7.1$ | $58773.51^{+6.45}_{-7.02}$ | $24.61^{+5.32}_{-5.79}$ | - | $33.31^{+5.37}_{-5.82}$
SN 2019sgh | -0.34 | $-21.32\pm 0.05$ | $19.3\pm 3.5$ | $58758.54^{+7.89}_{-3.86}$ | $13.96^{+5.89}_{-2.90}$ | $32.03^{+6.33}_{-10.59}$ | $18.16^{+6.34}_{-3.15}$
SN 2019stc | -0.02 | $-20.55\pm 0.06$ | $5.8\pm 1.0$ | $58783.42^{+12.67}_{-5.51}$ | $26.13^{+12.49}_{-5.24}$ | $38.22^{+6.25}_{-11.77}$ | -
SN 2019szu | -0.06 | $-21.19\pm 0.06$ | $22.3\pm 5.2$ | $58829.39^{+6.34}_{-11.76}$ | $54.57^{+11.65}_{-11.07}$ | - | -
SN 2019unb | -0.03 | $-20.13\pm 0.07$ | $4.1\pm 0.1\star\star$ | $58843.01^{+4.50}_{-4.87}$ | - | $20.80^{+4.64}_{-4.30}$ | -
SN 2019ujb | -0.05 | $-21.13\pm 0.08$ | $12.2\pm 2.6$ | $58818.10^{+5.57}_{-7.89}$ | $26.06^{+4.67}_{-6.58}$ | $50.81^{+6.58}_{-4.66}$ | $35.07^{+8.06}_{-6.93}$
SN 2019xdy | -0.22$\star$ | $-20.72\pm 0.06$ | $7.9\pm 4.2$ | $58833.43^{+3.14}_{-5.67}$ | $22.84^{+6.08}_{-8.07}$ | $32.42^{+4.65}_{-2.59}$ | $37.94^{+3.82}_{-6.22}$
SN 2019vvc | -0.31$\star$ | $-22.06\pm 0.02$ | $44.7\pm 18.1$ | $58859.72^{+3.61}_{-3.91}$ | $40.06^{+2.86}_{-3.09}$ | - | $53.55^{+5.18}_{-4.85}$
SN 2019aamu | -0.25$\star$ | ($-21.64\pm 0.07$) | - | ($58861.15^{+0}_{-7.55}$) | ($45.20^{+1.81}_{-6.33}$) | - | ($65.18^{+1.02}_{-6.05}$)
SN 2019zbv | -0.39 | $-21.98\pm 0.04$ | $33.4\pm 6.0$ | $58866.72^{+3.55}_{-3.40}$ | $26.50^{+2.77}_{-2.60}$ | $54.00^{+3.13}_{-3.16}$ | -
SN 2020ank | -0.22 | $-21.75\pm 0.06$ | $24.6\pm 4.5$ | $58893.40^{+2.91}_{-2.06}$ | $15.56^{+2.37}_{-1.69}$ | $32.76^{+2.06}_{-3.09}$ | $22.81^{+3.48}_{-2.41}$
SN 2020auv | -0.21 | $-21.85\pm 0.09$ | $31.2\pm 6.7$ | $58884.04^{+5.22}_{-6.59}$ | - | $26.77^{+5.23}_{-4.16}$ | -
SN 2019aamw | -0.22$\star$ | $-20.49\pm 0.07$ | $6.4\pm 1.3$ | $58854.17^{+8.75}_{-11.51}$ | $43.66^{+7.27}_{-9.51}$ | $57.18^{+18.25}_{-11.62}$ | $63.85^{+7.30}_{-9.62}$
SN 2020fvm | -0.19 | $-21.39\pm 0.05$ | $15.1\pm 2.7$ | $58999.69^{+4.88}_{-2.82}$ |  $91.12^{+4.02}_{-2.41}$c | - | -
SN 2019aamv | -0.38 | $-21.37\pm 0.06$ | $19.6\pm 3.9$ | $58919.96^{+6.07}_{-3.72}$ | $51.73^{+5.17}_{-3.39}$ | $58.20^{+4.38}_{-5.00}$ | $66.80^{+19.86}_{-6.96}$
SN 2020aup | -0.28 | $-21.48\pm 0.12$ | $24.6\pm 4.8$ | $58882.76^{+5.31}_{-4.11}$ | $14.87^{+4.18}_{-3.27}$ | $28.44^{+4.82}_{-4.58}$ | $20.82^{+4.66}_{-3.70}$
SN 2020dlb | -0.36$\star$ | $-22.61\pm 0.07$ | $78.0\pm 23.9$ | $58948.76^{+3.86}_{-3.88}$ | $30.31^{+2.78}_{-2.80}$ | $32.26^{+3.08}_{-3.00}$ | $41.57^{+3.21}_{-2.93}$
SN 2020fyq | -0.18$\star$ | $-19.89\pm 0.06$ | $3.2\pm 0.6$ | $58965.65^{+4.40}_{-33.19}$ | $70.06^{+4.04}_{-28.25}$ | $62.83^{+28.28}_{-4.70}$ | $86.77^{+4.71}_{-28.37}$
SN 2020exj | -0.22 | $-20.08\pm 0.07$ | $5.2\pm 1.0$ | $58946.37^{+3.63}_{-2.43}$ | $19.70^{+3.51}_{-2.53}$ | $21.99^{+2.18}_{-3.24}$ | $26.57^{+3.44}_{-2.61}$
SN 2020htd | -0.33$\star$ | $-21.22\pm 0.05$ | $14.3\pm 2.6$ | $58987.98^{+4.85}_{-4.94}$ | $50.90^{+4.35}_{-4.93}$ | $47.26^{+5.44}_{-3.99}$ | $65.69^{+4.72}_{-4.30}$
SN 2020iyj | -0.35 | $-21.73\pm 0.03$ | $23.3\pm 4.3$ | $58995.41^{+6.73}_{-7.58}$ | $30.48^{+4.96}_{-5.57}$ | $68.36^{+17.54}_{-27.95}$ | $39.22^{+5.07}_{-5.65}$
SN 2020kox | -0.36 | $-21.78\pm 0.11$ | ($44.6\pm 12.7$) | $58994.23^{+9.02}_{-8.89}$ | $26.52^{+6.30}_{-6.20}$ | - | $34.57^{+6.46}_{-6.33}$
SN 2020jii | -0.40 | $-21.86\pm 0.07$ | $44.2\pm 13.9$ | $59000.44^{+4.02}_{-3.77}$ | $30.36^{+3.27}_{-3.00}$ | $32.84^{+2.83}_{-3.01}$ | $39.44^{+3.27}_{-3.22}$
SN 2020afah | -0.35$\star$ | $-21.84\pm 0.05$ | $25.0\pm 5.3$ | $59055.47^{+19.11}_{-7.13}$ | $61.40^{+13.90}_{-5.20}$ | $54.31^{+6.56}_{-17.50}$ | $69.96^{+13.91}_{-5.21}$
SN 2020afag | -0.37 | $-21.67\pm 0.04$ | $23.6\pm 4.3$ | $59065.68^{+3.48}_{-2.84}$ | $36.29^{+3.23}_{-2.59}$ | $49.05^{+3.39}_{-4.45}$ | $49.71^{+6.04}_{-4.63}$
SN 2020onb | -0.11 | $-21.07\pm 0.12$ | $9.8\pm 1.8$ | $59078.29^{+2.54}_{-5.48}$ | $28.91^{+2.25}_{-4.75}$ | - | $40.04^{+2.32}_{-4.78}$
SN 2020qef | -0.26 | $-20.03\pm 0.05$ | $4.3\pm 0.8$ | $59083.09^{+2.01}_{-2.18}$ | $36.52^{+1.99}_{-2.06}$ | $31.05^{+9.62}_{-2.30}$ | $47.82^{+2.28}_{-2.37}$
SN 2020rmv | -0.18 | $-21.60\pm 0.05$ | $22.1\pm 3.9$ | $59117.41^{+7.57}_{-6.28}$ | $39.81^{+6.23}_{-5.49}$ | - | $54.13^{+7.81}_{-5.25}$
SN 2020xkv | -0.36 | $-21.02\pm 0.02$ | $10.7\pm 2.0$ | $59144.80^{+9.81}_{-8.07}$ | $46.48^{+7.94}_{-6.55}$ | - | $71.62^{+8.08}_{-6.72}$
SN 2020xgd | -0.45 | $-21.83\pm 0.09$ | $34.7\pm 6.7$ | $59149.39^{+4.78}_{-3.70}$ | $23.67^{+3.31}_{-2.58}$ | $21.92^{+4.10}_{-3.77}$ | $29.58^{+3.85}_{-2.78}$

$M_{\rm peak}$, $t_{\rm peak}$, $t_{\rm rise,1/e}$, $t_{\rm decay,1/e}$ and
$t_{\rm rise,10\%}$ are measured in the rest-frame $g$-band LCs.
aK-correction to rest-frame $g$-band at the peak. It equals $K_{g\rightarrow
g}$ for $z\leq 0.17$ and $K_{r\rightarrow g}$ for $z>0.17$.
bThe value in parentheses means they are not well-constrained due to the lack
of data.
cSN 2020fvm has two LC peaks and its $t_{\rm rise,1/e}$ is
$47.52^{+6.66}_{-6.88}$ days if the first (and the fainter) one is set as the
main peak.
$\star$ means the K-correction is calculated by $-2.5\times{\rm log}(1+z)$.
$\star\star$ means the $L_{\rm peak}$ is calculated from multi-band data
instead of corrected from $g$\- and $r$-band flux.

Table A5: ($g-r$) colors at peak

Name | Observed | Rest frame
---|---|---
(mag) | (mag)
SN 2018avk | $-0.03\pm 0.03$ | $-0.21\pm 0.03$
SN 2018don | $0.15\pm 0.02$ | $-0.01\pm 0.02$
SN 2018bym | $-0.11\pm 0.01$ | $-0.32\pm 0.06$
SN 2018bgv | $-0.23\pm 0.03$ | $-0.25\pm 0.03$
SN 2018lzv | $-0.03\pm 0.07$ | -
SN 2018gbw | $-0.12\pm 0.04$ | -
SN 2018fcg | $-0.16\pm 0.04$ | $-0.18\pm 0.04$
SN 2018gft | $-0.14\pm 0.03$ | $-0.21\pm 0.05$
SN 2018lzx | $0.02\pm 0.04$ | -
SN 2018hpq | $-0.11\pm 0.02$ | -

(This table is available in its entirety in machine-readable form.)

Table A6: Temperatures and bolometric luminosities

Name | Phase | Temperature | $L_{\rm bolo}$ | Radius
---|---|---|---|---
(days) | ($10^{4}$ K) | ($10^{43}\,{\rm erg\,s^{-1}}$) | ($10^{10}$ km)
SN 2018avk | $-4.23$ | $1.05^{+0.15}_{-0.11}$ | $5.14^{+0.58}_{-0.56}$ | $2.46^{+0.51}_{-0.71}$
SN 2018avk | $16.97$ | $0.74^{+0.05}_{-0.04}$ | $4.24^{+0.43}_{-0.40}$ | $4.39^{+0.54}_{-0.63}$
SN 2018bym | $1.06$ | $1.21^{+0.07}_{-0.06}$ | $29.67^{+1.13}_{-1.24}$ | $4.43^{+0.42}_{-0.50}$
SN 2018bym | $9.70$ | $1.14^{+0.13}_{-0.10}$ | $25.89^{+2.09}_{-2.29}$ | $4.66^{+0.81}_{-1.08}$
SN 2018bym | $33.24$ | $0.86^{+0.03}_{-0.03}$ | $12.23^{+0.58}_{-0.57}$ | $5.64^{+0.44}_{-0.46}$
SN 2018bgv | $6.26$ | $1.39^{+0.32}_{-0.19}$ | $7.44^{+0.68}_{-0.64}$ | $1.67^{+0.46}_{-0.77}$
SN 2018bgv | $9.04$ | $1.17^{+0.10}_{-0.09}$ | $5.98^{+0.34}_{-0.34}$ | $2.12^{+0.32}_{-0.37}$
SN 2018bgv | $11.82$ | $0.94^{+0.12}_{-0.09}$ | $4.47^{+0.50}_{-0.51}$ | $2.83^{+0.55}_{-0.73}$
SN 2018bgv | $17.38$ | $0.73^{+0.57}_{-0.09}$ | $3.43^{+0.84}_{-0.70}$ | $4.07^{+1.15}_{-6.33}$
SN 2018bgv | $20.16$ | $0.64^{+0.05}_{-0.04}$ | $2.64^{+0.34}_{-0.31}$ | $4.63^{+0.68}_{-0.82}$
SN 2018bgv | $22.94$ | $0.61^{+0.18}_{-0.05}$ | $2.29^{+0.42}_{-0.49}$ | $4.87^{+0.85}_{-2.98}$

(This table is available in its entirety in machine-readable form.)

Figure A1: The LCs of our sample. All magnitudes are in the AB system and have
been corrected for Milky Way extinction. The absolute magnitude y-axis on the
right side is calculated by assuming a constant K-correction of
$-2.5\times{\rm log}(1+z)$. The rings mark the data measured from the combined
image of multiple nearby images. The symbol ‘S’ at the top of each panel shows
the epoch of the closest spectrum to the peak, which is used to calculate
accurate K-corrections and color corrections. X-axis shows the rest-frame days
relative to the observed $g$-band peak. SN 2018gbw, SN 2019cca, SN 2019fiy, SN
2019xdy, SN 2019vvc, SN 2019aamu and SN 2020ank are relative to $r$-band peak.
Figure A2: Similar to Figure A1. Figure A3: Similar to Figure A1. Figure A4:
Similar to Figure A1. Figure A5: Similar to Figure A1. Figure A6: Similar to
Figure A1. Figure A7: Spectral classification. This figure shows the
comparison between our spectrum and their best-matched templates (shown in
gray) from superfit. Event name, the phase of our spectrum (rest-frame days),
the best-matched templates, the template phase and the template type are
labeled after each spectrum in the same color. Figure A8: Similar to Figure A7
Figure A9: Similar to Figure A7

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