# Core-collapse Supernovae in the Dark Energy Survey: Luminosity Functions and
Host Galaxy Demographics

M. Grayling,1 C. P. Gutiérrez,2,3,1 M. Sullivan,1 P. Wiseman,1 M. Vincenzi,4,1
L. Galbany,5,6 A. Möller,7 D. Brout,8 T. M. Davis,9 C. Frohmaier,4,1 O.
Graur,4 L. Kelsey,4,1 C. Lidman,10,11 B. Popovic,12 M. Smith,1 M. Toy,1 B. E.
Tucker,11 Z. Zontos,1 T. M. C. Abbott,13 M. Aguena,14 S. Allam,15 F. Andrade-
Oliveira,16 J. Annis,15 J. Asorey,17 D. Bacon,4 E. Bertin,18,19 S. Bocquet,20
D. Brooks,21 A. Carnero Rosell,22,14,23 D. Carollo,24 M. Carrasco Kind,25,26
J. Carretero,27 M. Costanzi,28,24,29 L. N. da Costa,14 M. E. S. Pereira,30 J.
De Vicente,17 S. Desai,31 H. T. Diehl,15 P. Doel,21 S. Everett,32 I.
Ferrero,33 D. Friedel,25 J. Frieman,15,34 J. García-Bellido,35 M. Gatti,36 D.
Gruen,20 J. Gschwend,14,37 G. Gutierrez,15 S. R. Hinton,9 D. L. Hollowood,38
K. Honscheid,39,40 D. J. James,8 K. Kuehn,41,42 N. Kuropatkin,15 G. F.
Lewis,43 U. Malik,11 M. March,36 F. Menanteau,25,26 R. Miquel,44,27 R.
Morgan,45 R. L. C. Ogando,37 A. Palmese,46 F. Paz-Chinchón,25,47 A.
Pieres,14,37 A. A. Plazas Malagón,48 M. Rodriguez-Monroy,49 A. K. Romer,50 A.
Roodman,51,52 E. Sanchez,17 V. Scarpine,15 I. Sevilla-Noarbe,17 E. Suchyta,53
G. Tarle,16 C. To,39 D. L. Tucker,15 and T. N. Varga54,55,56 (DES
Collaboration) Affiliations are listed at end of paper

(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

We present the luminosity functions and host galaxy properties of the Dark
Energy Survey (DES) core-collapse supernova (CCSN) sample, consisting of 69
Type II and 50 Type Ibc spectroscopically and photometrically-confirmed
supernovae over a redshift range $0.045<z<0.25$. We fit the observed DES
$griz$ CCSN light-curves and K-correct to produce rest-frame $R$-band light
curves. We compare the sample with lower-redshift CCSN samples from Zwicky
Transient Facility (ZTF) and Lick Observatory Supernova Search (LOSS).
Comparing luminosity functions, the DES and ZTF samples of SNe II are brighter
than that of LOSS with significances of 3.0$\sigma$ and 2.5$\sigma$
respectively. While this difference could be caused by redshift evolution in
the luminosity function, simpler explanations such as differing levels of host
extinction remain a possibility. We find that the host galaxies of SNe II in
DES are on average bluer than in ZTF, despite having consistent stellar mass
distributions. We consider a number of possibilities to explain this –
including galaxy evolution with redshift, selection biases in either the DES
or ZTF samples, and systematic differences due to the different photometric
bands available – but find that none can easily reconcile the differences in
host colour between the two samples and thus its cause remains uncertain.

###### keywords:

supernovae: general – surveys

††pubyear: 2022††pagerange: Core-collapse Supernovae in the Dark Energy
Survey: Luminosity Functions and Host Galaxy Demographics–D

##  1 Introduction

Core-collapse supernovae (CCSNe) are among the most complex and diverse
astrophysical events, demonstrating a wide range of spectroscopic and
photometric properties (Filippenko, 1997; Gal-Yam, 2017; Modjaz et al., 2019).
Type II SNe show hydrogen features in their spectra due to the outer hydrogen
envelope of the progenitor star, while stripped envelope SNe (i.e., SNe Ib,
Ic, IIb) present different features depending on the degree to which the outer
hydrogen and helium envelopes have been stripped away by processes such as
stellar winds (e.g., Woosley et al., 1993) and binary interaction (e.g.,
Nomoto et al., 1995). While it is generally understood that CCSNe result from
the cessation of fusion in the cores of massive stars after the formation of
iron leading to the core collapsing, there is a great deal that remains
uncertain about the exact mechanisms of this process. Studying the properties
of populations of CCSNe can help constrain our knowledge of the processes
involved in the explosion (Li et al., 2011; Richardson et al., 2014).

The most straightforward population diagnostic is the luminosity function, the
distribution of peak luminosities observed across the SN population. An
accurate knowledge of the luminosity function is important in simulating CCSN
explosions to ensure they exhibit the range of properties of the observed
population. Luminosity functions are also important when simulating sky
surveys to ensure simulated SNe are created with realistic properties.
Simulations of this nature are used for a number of applications, for example
calculating SN rates (e.g., Bazin et al., 2009; Graur et al., 2017a; Frohmaier
et al., 2021), optimising observing strategies and preparing for upcoming
surveys (e.g., Jones et al., 2017; Villar et al., 2018) and modelling the
contamination of CCSNe in cosmological samples of SNe Ia (e.g., Vincenzi et
al., 2019; Vincenzi et al., 2021b).

The host galaxies of SNe provide further demographic information about their
properties and sample the stellar populations from which the progenitor star
is drawn. CCSNe are generally found across a wide variety of star-forming host
galaxy environments (e.g. Anderson et al., 2010; Graur et al., 2017b), while
the most luminous transients (e.g., superluminous supernovae, broad line SNe
Ic, see e.g. Angus et al., 2016; Perley et al., 2016; Modjaz et al., 2020) and
rapidly-evolving transients (RETs, Wiseman et al., 2020b) are typically found
in low mass, low metallicity and/or strongly star-forming environments. SNe
Ib/c have also been shown to more closely trace underlying star formation in
their host galaxies than SNe II (e.g., Anderson & James, 2009; Galbany et al.,
2018).

Several studies have examined CCSNe luminosity functions, primarily in the
local universe. For example, Li et al. (2011, hereafter L11) produced
luminosity functions for all SNe in the Lick Observatory Supernova Search
(LOSS; Li, 2000), including 105 CCSNe, and Richardson et al. (2014) presented
luminosity functions based on data from the Asiago Supernova Catalogue (Barbon
et al., 1989) supplemented by further SNe from other studies. Here, we measure
luminosity functions based on SNe detected by the Dark Energy Survey (DES)
Supernova Program (DES-SN, Bernstein et al., 2012). DES-SN is a deep,
untargeted, five-season rolling SN search survey over 27 deg2 of sky. This
leads to a higher redshift sample than presented in previous work (Smith et
al., 2020b), providing an opportunity to study any redshift evolution in the
CCSN luminosity function and the effect of any evolution in the SN host galaxy
populations (e.g. Elbaz et al., 2011): such an evolution may in turn lead to
an evolution in the CCSN population. The Zwicky Transient Facility’s (ZTF)
Bright Transient Survey (Perley et al., 2020) provides an additional sample of
CCSNe which lie in a redshift range between that of DES and LOSS. We also
include this sample in our analysis to allow for further investigation of any
redshift evolution.

In this paper, we present luminosity functions and host galaxy properties of
CCSNe in DES, and compare these to samples from LOSS and ZTF. In Section 2, we
detail these three samples and describe the selection of objects suitable for
inclusion in a luminosity function. Section 3 describes the method used to
construct luminosity functions for both DES and ZTF, and presents the
luminosity functions themselves. In Section 4 we discuss the host galaxy
demographics of the samples and the correlations between different host
properties and peak SN luminosity. We discuss our results in Section 5 and
conclude in Section 6. Throughout, we assume a flat $\Lambda$CDM cosmology
with $\Omega_{M}=0.3$ and H${}_{0}=70$ km s-1 Mpc-1, and correct external
samples to this cosmological model as required. All photometry has been
corrected for the effects of Milky Way extinction using dust maps presented in
Schlegel et al. (1998) and re-calibrated in Schlafly & Finkbeiner (2011),
assuming $R_{V}=3.1$. All quoted magnitudes are in the AB system (Oke & Gunn,
1983).

##  2 Core-collapse Supernova Samples

We begin by presenting the different CCSN samples we have used for this
analysis, detailing the selection criteria applied to the DES and ZTF samples
and how we use the data presented in L11. We do not compare with the
luminosity functions presented in Richardson et al. (2014). This is because
the sample presented is from a wide variety of different instruments and
surveys which makes it very difficult to correct for Malmquist bias based on
the limiting magnitude of each survey as we do in this work (see Section 3.1).
Drout et al. (2011) presents a collection of 25 SNe Ibc and includes peak
absolute magnitudes, however, all of these objects are brighter than -17.5,
which suggests that only the most intrinsically luminous objects are included.
As a result, this sample is not included in our comparisons. A sample of CCSNe
is available from SDSS-II (e.g., Taylor et al., 2014), however, peak
luminosities and Malmquist bias corrections are not available for these
objects to allow for a comparison. Finally, Arcavi et al. (2012) and Kiewe et
al. (2012) present SNe II and IIn, respectively, from the Caltech Core-
Collapse Project (Gal-Yam et al., 2007, CCCP). However, these samples combined
contain relatively few objects with only 12 that have estimated peak absolute
magnitudes; a further 9 have absolute magnitudes but of the plateau phase of a
SN II rather than peak and 5 have only lower limits for the peak. As a result,
we do not compare to this sample in this analysis.

Throughout this analysis, we treat CCSNe as two general classes rather than
subdividing further. This is to ensure sufficient numbers of SNe in each
class, and to acknowledge the uncertainties in the photometric SN
classification that we use in the DES-SN sample. We refer to Type II SNe to
include all hydrogen-rich SNe and Type Ibc SNe to include all hydrogen-
poor/stripped-envelope SNe. Although SNe IIb, such as the very luminous SN IIb
DES14X2fna in the DES-SN sample (Grayling et al., 2021), do show hydrogen
features at early times, they also have a partially stripped outer hydrogen
envelope and are included with SNe Ibc for this analysis. Table 1 contains
summary information for each sample.

Table 1: Sample sizes for our luminosity functions after applying selection criteria. Survey | Total Sample Size | Sample After Quality Cuts | Sample After Redshift and Magnitude Cuts
---|---|---|---
| SNe II | SNe Ibc | SNe II | SNe Ibc | SNe II | SNe Ibc
DES (spectroscopically confirmed CCSNe) | 52 | 18 | 33 | 13 | 31 | 11
DES (photometric CCSNe with host spec-z) | – | – | 56 | 42 | 38 | 39
LOSS | 69 | 36 | – | – | 37 | 21
ZTF | 349 | 162 | 214 | 105 | 174 | 89

###  2.1 The DES core collapse supernova sample

The DES-SN CCSN sample contains three categories of objects: those with a
spectroscopic confirmation, those with a spectroscopic redshift of the host
galaxy, and those with photometric redshift information for the host galaxy.
We discuss each of these in turn.

####  2.1.1 Spectroscopically confirmed CCSNe

The DES-SN CCSN sample has 70 spectroscopically-confirmed CCSNe between
redshifts $0.045<z<0.33$. These were obtained over a variety of telescopes and
instruments during the course of the DES survey (Smith et al., 2020a).

We apply the following selection criteria to ensure that the light curve can
be analysed to measure the peak SN brightnesses required for the luminosity
function:

  * •

Each SN must have photometric coverage before and after maximum to ensure an
accurate interpolation of the peak luminosity.

  * •

Each object must have a well-constrained explosion epoch inferred either from
the date of last non-detection of the SN, or from spectral template matching
using the Supernova Identification code (snid; Blondin & Tonry, 2007)
following the prescriptions of Gutiérrez et al. (2017). An explosion epoch is
required to select an appropriate model spectral energy distribution (SED) at
each epoch for the K-correction of observed photometry to the rest-frame, as
the SED models we use are defined with respect to explosion. For objects with
pre-explosion non-detections, we assume an explosion epoch halfway between the
last non-detection and the first detection.

  * •

Each object must have at least 9 detections which are deemed real by a
supervised machined learning classifier (see Goldstein et al., 2015, for
details of the classifier). A limit of 9 is selected to maximise both the
sample size and photometric coverage, as overall this cut eliminates only 4
objects with the next object only having 6 detections deemed real by the
classifier.

This selection leaves 46 spectroscopically-confirmed CCSNe in DES. Of these,
33 are spectroscopically hydrogen-rich (Type II) and 13 hydrogen-
poor/stripped-envelope (Type Ibc).

####  2.1.2 Photometric CCSNe with host spec-z

DES also detected a much larger number of transients which have no
spectroscopic confirmation. We first investigate transients which have a
spectroscopic measurement of the SN host galaxy redshift (spec-z); for
example, from the OzDES survey (Yuan et al., 2015; Childress et al., 2017;
Lidman et al., 2020) or external redshift catalogues - Vincenzi et al. (2021b)
presents details of the different host galaxy redshift sources using galaxy
associations from Wiseman et al. (2020a). To this sample, we apply a number of
cuts to select objects that:

  * •

were detected in at least nine epochs based on the classifier detailed in
Goldstein et al. (2015) to ensure good photometric coverage, matching the cut
applied to spectroscopically confirmed sample in Section 2.1.1.

  * •

have an assigned host galaxy with a spectroscopic redshift less than 0.3 (as
an initial redshift cut).

  * •

were single-season transients, removing some obvious AGN.

These cuts gave a total of 1609 transients. We remove all spectroscopically-
confirmed objects that are not CCSNe (e.g., SNe Ia, AGN) and remove the
remaining AGN using the classifier discussed in Section 2.2.3 of Wiseman et
al. (2020b). We next remove SNe Ia from the sample employing the photometric
SN classifier SuperNNova (Möller & de Boissière, 2019), using the trained
model discussed in Vincenzi et al. (2021a). We follow Wiseman et al. (2021),
Möller et al. (2022) and Vincenzi et al. (2021a) in removing all objects with
a probability of being a SN Ia ($P_{\textrm{Ia}}$) of greater than 0.5111In
most cases, $P_{\textrm{Ia}}$ is close to 0 or 1 meaning that our results are
not sensitive to this threshold. For the remaining objects, we apply the same
quality cuts in terms of requiring coverage pre- and post-peak and a well-
constrained explosion date as in Section 2.1.1 and visually inspect the
photometry of the remaining objects to ensure they are consistent with a CCSN;
51 objects are excluded as they are active in several observing seasons and do
not resemble SNe, while 1 is excluded as it lacks any $r$-band data. We then
classify remaining objects as SNe II or SNe Ibc using the light curve template
fitter pSNid (Sako et al., 2011) – 8 SNe are excluded as they are either
classified as SNe Ia by pSNid or are consistent with both SNe II and Ibc. This
leaves a sample of 98 photometrically-confirmed CCSNe from DES, of which 56
are SNe II and 42 SNe Ibc; combined with spectroscopically-confirmed objects,
the total DES CCSN sample with spectroscopic redshifts has 89 SNe II and 55
SNe Ibc. The requirement for constraint of peak luminosity means that the
quality cuts we apply are relatively strict, making this sample smaller than
might be used for other purposes such as rate calculations.

To assess the suitability of pSNid for these purposes, we evaluate its
performance for the full sample of 70 spectroscopically-confirmed SNe in DES;
we obtain an estimated pSNid class for 69 of these. For SNe II, there are 8
misclassifications out of 51 - 6 are classified as SNe Ia and 2 as SNe Ibc by
pSNid. For SNe Ibc, there are 2 misclassifications out of 18 objects, with one
misclassified as a SN Ia and one as a SN II. This gives accuracy for these
classes of 84 and 89 per cent respectively and an overall accuracy of 86 per
cent. We opt to use SuperNNova to remove SNe Ia and then pSNid to separate SNe
II from SNe Ibc, rather than simply using pSNid for all SNe, as SuperNNova has
been shown to have very high performance upwards of 98 when separating SNe Ia
from non-SNe Ia (Möller & de Boissière, 2019; Vincenzi et al., 2021a). Out of
all DES transient candidates, of all objects classified as SNe Ia by
SuperNNova around 5 per cent are classified as CCSNe rather than SNe Ia by
pSNid. The good level of agreement between these classifiers is reassuring,
but we favour SuperNNova due to its high performance. It is not possible to
use SuperNNova for both tasks as a suitable SuperNNova model for multi-class
classification trained on DES-like light curves is not currently available.

It should be noted that we do not apply a confidence threshold based on chi-
squared ($\chi^{2}$) - for example, if the SN II template in pSNid is a much
better fit to a given SN than either the SN Ia or Ibc templates, it was
classified as a SN II regardless of the SN II template $\chi^{2}$ value when
fitting the light curve. Given all the checks and cuts we apply to remove SNe
Ia and other types of transients prior to using pSNid, we can be confident
that all remaining objects are CCSNe and are therefore justified in not using
a $\chi^{2}$ cut. Nevertheless, as a check, we analyse the properties of the
sample excluding the 20 objects with the worst $\chi^{2}$ values and find that
this does not impact the trends we observe.

####  2.1.3 Photometric CCSNe with host photo-z

Beyond the sample of CCSNe with spectroscopic host redshifts, there are a
number of CCSNe in DES with no spectroscopic information, for example because
the host was too faint to take a reliable spectrum. While we cannot directly
include these objects in our luminosity function, it is important to
understand any selection effects that arise from excluding these objects from
the sample.

Photometric redshifts (photo-zs) have been produced for three of the ten DES-
SN fields from coadded photometry as outlined in Hartley et al. (2022), based
on the photometric redshift fitting code EAzY (Brammer et al., 2008). To
produce a sample of DES CCSNe without spectroscopic host redshifts, we take
all real transient candidates in the full DES-SN sample, select those located
in hosts with photometric redshifts, and remove known AGN and other transient
types such as variable stars using existing catalogues. We use SuperNNova to
remove all likely SNe Ia using a model trained without spectroscopic redshift
information - this cut leaves 45 objects (again, using $P_{\textrm{Ia}}>0.5$).
Finally, we visually inspect each light curve to remove other types of
transient that are clearly not SN-like in nature (e.g., AGN) and apply the
same quality cuts as in Sections 2.1.1 and 2.1.2, leaving 25 CCSNe.

As we apply redshift cuts in this analysis and there are large uncertainties
in photometric redshifts, the exact size of this sample is not fixed but
typically varies between 3 and 8 – this is discussed in detail in Appendix A.
In brief, this analysis suggests that we obtain spectroscopic host redshifts
for $\sim 75-90$ per cent of CCSNe observed by DES. This sample of objects
with photometric redshifts is used only for selection efficiency checks.

###  2.2 The ZTF Bright Transient Survey

Perley et al. (2020) presents a public catalogue of transients from the ZTF
Bright Transient Survey with spectroscopic classifications. Excluding SNe Ia
and super-luminous SNe, this sample consists of 511 CCSNe. For all of these
objects, we gather publicly-available $g$ and $r$-band photometry from the
Lasair222Available at https://lasair.roe.ac.uk/ transient broker (Smith et
al., 2019). We apply the same cuts as for the DES sample, only including
objects with photometric coverage both pre and post-peak in both bands and
with a well-constrained explosion date. This leaves a sample of 319 CCSNe from
ZTF: 214 SNe II and 105 SNe Ibc, applying our broad classifications as
described earlier.

###  2.3 LOSS

The Lick Observatory Supernova Search (LOSS; Li, 2000) was a galaxy-targeted
SN survey that monitored approximately 5000 nearby galaxies for transients
using the Katman Automatic Imaging Telescope (KAIT). Luminosity functions of
different SN sub-types from LOSS were presented in L11, and we re-categorise
these into our broader classifications. Note that L11 includes SNe IIb with
SNe II rather than SNe Ibc, hence the LOSS luminosity functions presented here
will differ slightly from L11. With this classification scheme the LOSS sample
contains 105 CCSNe: 69 SNe II and 36 SNe Ibc. It should be noted that Shivvers
et al. (2017) revisits the classification of the LOSS sample, with some object
classes modified from L11. However, because we are using broad labels of SNe
II and Ibc, in all cases the new class falls into the same category as the
original. In Shivvers et al. (2017), there are a small number of SNe which
show hydrogen lines with only a single spectrum which are presented as having
an uncertain class of either SN II or SN IIb. We class these objects as SNe II
as robust classification of a SN IIb requires multiple spectra showing the
transition from hydrogen to helium, although changing this has little impact
on our results.

Note that for this analysis we correct the LOSS absolute magnitudes presented
in L11 from H${}_{0}=73$ km s-1 Mpc-1 to H${}_{0}=70$ km s-1 Mpc-1 and also
convert from Vega to AB magnitudes using conversions from Blanton & Roweis
(2007). This is done to ensure consistency with DES and ZTF.

###  2.4 Host galaxy properties

We also assign every SN across the three samples to a host galaxy, and
estimate the physical properties of those hosts. We use DES host galaxy
associations and $griz$ photometry from Wiseman et al. (2020a). We perform
galaxy SED fits based on the SED models produced by the spectral evolution
code PÉGASE.2 (Fioc & Rocca-Volmerange, 1997, 2019) following the procedure as
outlined in Smith et al. (2020b) and Kelsey et al. (2020), assuming a Chabrier
initial mass function (Chabrier, 2003). This provides us with host galaxy
stellar masses, star formation rates (SFRs) and rest-frame colours. These fits
require an input redshift: for the sample with photometric redshifts, the
larger redshift uncertainties must be accounted for. We calculate
distributions of host properties for this sample which factor in redshift
uncertainty using a method outlined in Appendix A.

For LOSS, several choices for stellar masses are available. Host galaxy
stellar masses are presented in L11 using $K$ and $B$-band mass-to-luminosity
ratios, and Graur et al. (2017a) presents stellar mass and SFR values for LOSS
hosts from Sloan Digital Sky Survey (SDSS, Blanton et al., 2017) spectroscopy.
In addition, the majority of LOSS hosts have stellar mass and SFRs calculated
in the literature using a variety of different methods, including using near-
infrared and far-ultraviolet flux (Leroy et al., 2019; Karachentseva et al.,
2020). However, for consistency across our samples, we obtain $ugriz$
photometry for the hosts from SDSS and follow the same SED-fitting procedure
described above. Each SN in LOSS is already matched to a host in Table 4 of
Leaman et al. (2011), which we match to a corresponding SDSS galaxy.

Out of 58 LOSS CCSNe in our sample, 30 fall in the SDSS footprint and we are
able to match to an SDSS host for 26 of these. To assess the quality of our
method, we compare the stellar mass and SFR values calculated from SED fits to
previously published literature values (see Appendix B). We find that our
inferred stellar masses from SED fitting are consistent with other methods
although there is unsurprising scatter in the SFR values derived from SED fits
that are known to be difficult to measure using only $ugriz$ data (Childress
et al., 2013). As rest-frame $U-R$ colour correlates with morphology and
traces star formation (Lintott et al., 2008; Trayford et al., 2016), we
instead use rest-frame $U-R$ colour as a proxy for star-formation. We opt to
use $U-R$ rather than SFR because it is more directly linked to the observed
photometry and is not dependent on the star formation history (SFH) model used
in the SED fits. By contrast, the SFR is estimated based on the average SFR
over the 250 Myr prior to the best-fit timestep in the SFH (see section 2.2.2
of Smith et al. 2020). It is thus dependent on the choice of that SFH (and
sensitive to other assumptions that we make) and is not directly linked to any
observable. The $U-R$ colour is directly linked to the observed colours,
modulo a $k$-correction (for which the best-fit SED is used). We also find
that $U-R$ correlates well with our inferred specific star formation rate
(sSFR) values.

For ZTF, we again search for host $ugriz$ photometry in SDSS, using a broad
search radius of 50″333This was set to a large value to ensure that large,
local galaxies were matched correctly radius around the SN position, matching
to the closest galaxy and then visually confirming the matches. Out of the 263
CCSNe in our ZTF sample, 212 objects lie within the SDSS footprint and we are
able to match 203 of these to an SDSS galaxy.

##  3 Core collapse SN luminosity functions

We construct luminosity functions for the DES and ZTF samples using the
following procedure:

  1. 1.

We interpolate the observed photometry to obtain simultaneous observations in
all photometric bands ($griz$ in the case of DES, $gr$ for ZTF) using Gaussian
Processes (GP; Rasmussen & Williams 2005). We use the python package george
(Ambikasaran et al., 2015), following the process outlined in Angus et al.
(2019). Each photometric band is interpolated separately.

  2. 2.

We K-correct this interpolated observed photometry to the rest-frame, using
SED models for SNe II from Dessart et al. (2013a)444Available at
https://www-n.oca.eu/supernova/home.html and for SNe Ibc from Levan et al.
(2005)555Available at https://c3.lbl.gov/nugent/nugent_templates.html. At each
epoch with observations, we interpolate the time series SED to obtain a model
SN SED. We then warp the model SED to colour-match it to our GP-interpolated
photometry in all bands and use this spectrum for the K-correction.

  3. 3.

This K-corrected rest-frame photometry is then again interpolated using GPs in
order to estimate the peak luminosity of each object as well as its
corresponding uncertainty.

We make an additional selection on peak absolute magnitude and redshift for
each survey to produce the luminosity functions. The absolute magnitude limit
of our combined sample is set by DES as it is the highest redshift survey and
thus shallowest in terms of absolute magnitude: we exclude objects with a peak
absolute magnitude fainter than $-16$ mag in $R$-band. We also exclude objects
brighter than $-19.5$ mag in $R$-band to ensure a like-for-like comparison
between the samples as these are only present in ZTF.

We make a redshift selection in the DES sample of $z<0.25$ to obtain a volume-
limited sample above our absolute magnitude limit, and similarly use a
redshift selection for the ZTF sample of $z<0.06$. This means that our ZTF
sample does not overlap in redshift with the DES sample. This leaves 69 SNe II
and 58 SNe Ibc in DES, 37 SNe II and 21 SNe Ibc in LOSS, and 177 SNe II and 89
SNe Ibc in ZTF, as detailed in Table 1.

Fig. 1 shows our final samples, including those objects removed by this
selection. As shown in Fig. 1, Malmquist bias (i.e. bias towards more luminous
objects at higher redshifts) is seen in our samples. The ZTF sample in
particular shows a strong trend towards more luminous supernovae at higher
redshift - the redshift cut at 0.25 and greater depth of DES means that it is
less affected by this, while the local nature of LOSS means that this sample
has good completeness over the absolute magnitude range we are studying.

Figure 1: Peak rest-frame CCSN $R$-band absolute magnitude, corrected for
Milky Way extinction, plotted against redshift for the DES, LOSS and ZTF CCSN
samples. Horizontal dashed lines indicate the median redshift of each SN
sample, while vertical dashed lines represent the bounds of the absolute
magnitude selection cut we apply. Closed symbols denote SNe included in the
final samples and open circles are SNe excluded by the cut.

###  3.1 Correcting for Malmquist Bias

We correct for Malmquist bias using a simple $V_{\text{max}}$ correction
(Schmidt, 1968). This weights fainter objects, which would not be detected (or
followed up) over the full survey volume, higher in the luminosity function
calculation. For a volume-limited sample with an upper redshift limit
$z_{\text{survey}}$, each SN has an upper redshift limit $z_{\text{max}}$,
beyond which the object would fall below the detection limit of the survey. We
calculate the weight $w$ each object makes to the luminosity function
according to

$w=\begin{cases}\Big{(}\frac{d_{c}(z_{\text{survey}})}{d_{c}(z_{\text{max}})}\Big{)}^{3}&\text{if
}z_{\text{max}}<z_{\text{survey}}\\\ 1&\text{otherwise}\end{cases}$ (1)

where $d_{c}$ is the comoving distance. Thus, an intrinsically luminous SN
that could have been detected over the full survey volume is given a weight of
1, while a fainter SN is assigned an increased weight.

This approach assumes that each survey has a magnitude limit above which it is
complete. For the DES sample we use limits of $m=23.5$ and $m=24.5$ for the
shallow and deep fields respectively (Kessler et al., 2015). In reality,
completeness in SN surveys is more complex than a simple cut-off, and thus
this assumption introduces some uncertainty in the analysis; however, we find
that altering these limits within $\pm 0.5$ mag has no significant effect on
the luminosity distributions.

For ZTF we consider the 97, 93 and 75 per cent spectroscopic completeness
limits of $18$, $18.5$ and $19$ mag respectively (Perley et al., 2020). In
brief, we find that we obtain consistent luminosity functions with a limit of
either 18.5 or 19 mag and that a limit of 18 mag causes the sample to miss
fainter supernovae (see Appendix C). As a result, we use 19 mag as the
magnitude limit in this analysis to maximise the sample size.

Finally, we experimented with using the $V_{\mathrm{max}}$ correction for the
LOSS sample. However, as expected we found that the sample is complete in the
absolute magnitude range we are studying.

We now form the luminosity functions for the three SN samples. We incorporate
the weighting (equation 1) into our cumulative distributions using

$C(M_{n})=\frac{\sum_{i=1}^{n}w_{i}}{\sum_{i=1}^{N}w_{i}},$ (2)

where $C(M_{n})$ is the cumulative density up to absolute magnitude $M$, $n$
is the index position of in the sorted distribution of $M$ values, $i$ is the
index of each supernova and $N$ is the total number of objects. In this
section we use the two-sample Kolmogorov–Smirnov (KS) test to compare
different luminosity functions – this weighted cumulative distribution is
incorporated into all KS tests in this section.

###  3.2 CCSN luminosity functions

The left two panels of Fig. 2 shows the SN II luminosity functions. Histogram
uncertainties in the upper panel represent the expected uncertainties from
Poisson statistics and are derived from confidence limits presented in Gehrels
(1986), while the cumulative density function (CDF) uncertainties in the lower
panel represent the statistical uncertainties in the individual measurement
and are estimated from a Monte Carlo (MC) approach described as follows:

  * •

The measured values of peak absolute magnitude and their uncertainties are
used as the mean and standard deviations of a Gaussian distribution.

  * •

1000 randomised CDFs are generated using the Gaussian distribution of each
data point.

  * •

The mean and standard deviation of the CDFs at each value are calculated –
these are the values and uncertainties plotted.

Uncertainties here will depend on both the uncertainty on the luminosity of
each SN and also on the sample size, as for a smaller sample changing an
individual measurement will have a larger effect on the CDF. As can be seen,
LOSS overall has a larger uncertainty in the CDF than DES in this figure. This
is because DES photometry has lower uncertainties than LOSS photometry,
meaning that the Gaussian distributions of each point are narrower.

Table 2 shows the results of two-sample KS tests between the different
samples, with the $p$-values converted to a significance in $\sigma$. The DES
sample overall appears brighter than both LOSS and ZTF, with significances of
3.0$\sigma$ and 1.8$\sigma$ respectively. The ZTF sample is also brighter than
LOSS at a significance level of 2.5$\sigma$.

Figure 2: SNe II and Ibc $R$-band luminosity functions for the DES, LOSS and ZTF samples. Each event has been weighted by equation 1 ($V_{\text{max}}$ correction). Histogram uncertainties are from the Poisson distribution confidence limits of Gehrels (1986), while CDF uncertainties are derived from a Monte Carlo approach based on the measurement uncertainty of each value. Table 2: Two-sample KS tests between the SN II and SN Ibc luminosity functions. Survey 1 | Survey 2 | KS test significance
---|---|---
|  | SNe II | SNe Ibc
DES | LOSS | 3.0$\sigma$ | 1.9$\sigma$
DES | ZTF | 1.8$\sigma$ | 1.1$\sigma$
LOSS | ZTF | 2.5$\sigma$ | 1.8$\sigma$

The right two panels of Fig. 2 shows the luminosity function of SNe Ibc in
DES, ZTF and LOSS. DES appears slightly more luminous than both LOSS and ZTF
although at low significance levels of 1.9$\sigma$ and 1.1$\sigma$.

###  3.3 Parameterised luminosity functions

In order to allow these luminosity functions to be used in simulations going
forward, we fit a number of different distributions to the histograms
presented in Section 3.2. We do this for our newly derived DES and ZTF
luminosity functions; LOSS luminosity functions are already presented in L11.
These fits are only possible where the distributions peak above $-16$ and
begin to decline again as otherwise we cannot constrain the location of the
peak of the luminosity function. For SNe II in ZTF, the distribution does not
obviously begin to decline above $-16$ which makes this difficult. As a
result, for this fit we include two extra SNe which have a peak $R$-band
magnitude below $-16$ which allows the peak of the distribution to be
constrained. This fit is included for completeness, but we emphasise that the
results for SNe II in ZTF should be considered with the strong caveat that
these two extra objects have significant weight in determining the location of
the peak of the distribution.

We consider both Gaussian and Lorentzian fits to the luminosity functions. We
also consider skewed Gaussian distributions but find we are not able to
constrain the skewness parameter $\gamma$ with the available data. The
parameter values for these fits are shown in Table 3. The exact parameter
values for the distributions are sensitive to the binning of the histogram.
The values shown in this table are based on the bin edges presented in Fig. 2,
from $-19.5$ to $-16$ in steps of 0.5 for SNe II and 0.7 for SNe Ibc. For
these fits, we use the mean of the absolute magnitudes in each bin for the
x-coordinates. In this table, we present the uncertainty in each parameter
when fitting to distributions with these bins (fit error). To take into
account the how varying the binning will affect the parameter values, we also
present a binning error; this is defined as the standard deviation of the
parameter values measured when considering all possible bin widths from 0.10
to 1.0 in steps of 0.01.

To assess the quality of each fit, we also present reduced $\chi^{2}$ values.
These are calculated assuming a $\sqrt{N}$ uncertainty in the histogram,
rather than the uncertainties based on Gehrels (1986) presented in Fig. 2, in
order to provide symmetric uncertainties for the fitting process. For SNe II
in DES, a Gaussian distribution provides a reasonable fit although is too
broad around the peak and underestimates the number of brighter SNe. A
Lorentzian distribution better fits the sharp peak and brighter tail of the
luminosity function. For SNe Ibc in DES, both distributions provide similar
fits although the Gaussian has a lower reduced $\chi^{2}$. In contrast, for
SNe II in ZTF a Gaussian better represents the luminosity function and a
Lorentzian overestimates the number of brighter SNe. As for SNe Ibc in ZTF, a
Gaussian has a lower reduced $\chi^{2}$ and better represents the luminosity
function around peak although again underestimates the number of SNe in the
brighter tail.

Table 3: Reduced $\chi^{2}$ values and parameter values for different model
fits to our calculated luminosity functions, including mean $\mu$ and width
$\sigma$. The fit error represents the uncertainty in the fit to the
distributions using the binning detailed in Section 3.3. The binning error
represents the uncertainty in the parameter values based on the binning of the
data and is defined as the standard deviation of the parameter values measured
when considering all possible bin widths from 0.10 to 1.0 in steps of 0.01.

Survey | SN Type | Model Type | Reduced $\chi^{2}$ | Parameter | Value | Fit Error | Binning Error
---|---|---|---|---|---|---|---
DES | II | Gaussian | 1.44 | $\mu$ | $-17.10$ | 0.13 | 0.07
|  |  |  | $\sigma$ | 0.70 | 0.13 | 0.06
|  | Lorentzian | 0.41 | $\mu$ | $-17.10$ | 0.05 | 0.05
|  |  |  | $\sigma$ | 0.53 | 0.08 | 0.11
DES | Ibc | Gaussian | 1.59 | $\mu$ | $-17.05$ | 0.19 | 0.18
|  |  |  | $\sigma$ | 0.72 | 0.16 | 0.28
|  | Lorentzian | 2.88 | $\mu$ | $-16.96$ | 0.25 | 0.18
|  |  |  | $\sigma$ | 0.72 | 0.29 | 0.38
ZTF∗ | II | Gaussian | 1.98 | $\mu$ | $-16.85$ | 0.09 | 0.15
|  |  |  | $\sigma$ | 1.02 | 0.07 | 0.11
|  | Lorentzian | 5.53 | $\mu$ | $-16.73$ | 0.13 | 0.12
|  |  |  | $\sigma$ | 0.84 | 0.15 | 0.14
ZTF | Ibc | Gaussian | 2.11 | $\mu$ | $-16.98$ | 0.14 | 0.12
|  |  |  | $\sigma$ | 0.78 | 0.11 | 0.10
|  | Lorentzian | 3.22 | $\mu$ | $-16.95$ | 0.10 | 0.10
|  |  |  | $\sigma$ | 0.60 | 0.11 | 0.11

  * •

∗Please note, for SNe II in ZTF two extra objects with a peak $R$-band
absolute magnitude below -16 were included in order to constrain the peak of
the distribution.

##  4 Host galaxy demographics

The host galaxy properties of a SN provide insight into the environment in
which the progenitor star exploded. In this section, we explore the
demographics of the host galaxies of our SNe in detail. When considering any
differences in the samples we perform both two-sample KS and Anderson-Darling
(AD) tests to assess.

###  4.1 Host stellar mass

Fig. 3 shows the distribution of host galaxy stellar masses across our three
samples for SNe II and SNe Ibc. DES and ZTF appear consistent with each other,
but show discrepancies with LOSS. This difference is expected: LOSS is a
galaxy targeted SN survey that monitored massive, luminous galaxies so low
mass galaxies will be underrepresented in the LOSS sample. We perform a two-
sample KS and AD tests for each combination of samples with results in Table 4
which reinforce our interpretations above.

Figure 3: Host galaxy stellar mass distributions and cumulative distributions
for SNe II and SNe Ibc for the DES, LOSS and ZTF samples. Histogram
uncertainties here (and throughout the paper) are estimated from the Poisson
distribution, while CDF uncertainties are estimated from the Monte Carlo
approach described in Section 3.2.

###  4.2 Host rest-frame colours

Fig. 4 shows the distribution of rest-frame $U-R$ colours for the host
galaxies in our three samples. For SNe II, we see differences between the
three samples: the high-redshift DES sample has the bluest host galaxies,
followed by the lower-redshift ZTF sample and then the local LOSS sample. Two-
sample KS and AD tests show that the differences between the samples have
significances in excess of 3$\sigma$ respectively. For the redshift range
considered here, $griz$ does not cover rest-frame $U$-band meaning some
extrapolation is involved in calculating $U-R$ for DES hosts. However, we see
similar results when using rest-frame $B-V$ which is covered by $griz$. For
SNe Ibc, the distributions visually suggest a similar finding however the
offset between DES and ZTF is reduced and significance levels are below
2$\sigma$ for $U-R$ and below 2.3$\sigma$ for $B-V$.

Figure 4: As Fig. 3, but for the host galaxy rest-frame $U-R$ colour in place
of stellar mass.

Fig. 5 shows host galaxy stellar mass plotted against host galaxy rest-frame
$U-R$ colour for each of the three samples, with the thicker symbols showing
the mean and standard error for each property across galaxies in bins of
$8.25<\log(M/M_{\odot})<9.25$, $9.25<\log(M/M_{\odot})<10.25$ and
$10.25<\log(M/M_{\odot})<11.25$ for each sample. Across DES, ZTF and LOSS we
see strong correlations between host stellar mass and host $U-R$ colour. This
plot also shows that the difference we see in rest-frame $U-R$ colour between
DES and ZTF is observed across the range of host galaxy masses in the DES
sample, i.e., at fixed stellar mass the DES host galaxy sample is bluer, with
this difference more pronounced for SNe II.

Figure 5: Host galaxy stellar mass plotted against host galaxy rest-frame $U-R$ colour for each sample along with correlation coefficients for each. Thick data points represent the mean and standard error for stellar mass and colour for each sample in stellar mass bins of $8.25<\log(M/M_{\odot})<9.25$, $9.25<\log(M/M_{\odot})<10.25$ and $10.25<\log(M/M_{\odot})<11.25$. The horizontal dashed lines mark these bin boundaries. Table 4: The results of two-sample KS and AD tests between the distributions of host galaxy stellar mass, rest-frame $U-R$ and $B-V$ colour. Also shown are metallicity values derived from stellar mass in Section 5.2.4 and $U-R$ (SFRcorr) and $U-R$ (Zcorr), the rest-frame $U-R$ colour corrected for SFR and metallicity evolution with redshift introduced in Section 5.3.3. Property | Survey 1 | Survey 2 | KS test significance | AD test significance
---|---|---|---|---
|  |  | SNe II | SNe Ibc | SNe II | SNe Ibc
Stellar mass | DES | LOSS | 3.2$\sigma$ | 2.0$\sigma$ | 3.4$\sigma$ | 1.9$\sigma$
| DES | ZTF | 0.4$\sigma$ | 0.5$\sigma$ | 0.8$\sigma$ | 0.3$\sigma$
| LOSS | ZTF | 3.4$\sigma$ | 1.5$\sigma$ | 2.9$\sigma$ | 1.4$\sigma$
$U-R$ | DES | LOSS | 4.7$\sigma$ | 1.9$\sigma$ | 4.8$\sigma$ | 2.6$\sigma$
| DES | ZTF | 3.4$\sigma$ | 1.6$\sigma$ | 3.8$\sigma$ | 1.8$\sigma$
| LOSS | ZTF | 2.7$\sigma$ | 1.4$\sigma$ | 3.7$\sigma$ | 2.0$\sigma$
$B-V$ | DES | LOSS | 4.3$\sigma$ | 2.1$\sigma$ | 4.8$\sigma$ | 2.7$\sigma$
| DES | ZTF | 3.6$\sigma$ | 1.9$\sigma$ | 3.9$\sigma$ | 2.3$\sigma$
| LOSS | ZTF | 2.6$\sigma$ | 1.5$\sigma$ | 3.7$\sigma$ | 2.2$\sigma$
Metallicity | DES | LOSS | 4.0$\sigma$ | 2.4$\sigma$ | 4.0$\sigma$ | 2.2$\sigma$
| DES | ZTF | 1.4$\sigma$ | 0.9$\sigma$ | 1.8$\sigma$ | 0.8$\sigma$
| LOSS | ZTF | 3.5$\sigma$ | 1.5$\sigma$ | 3.0$\sigma$ | 1.5$\sigma$
$U-R$ (SFRcorr) | DES | LOSS | 4.3$\sigma$ | 1.4$\sigma$ | 4.7$\sigma$ | 2.0$\sigma$
| DES | ZTF | 3.0$\sigma$ | 0.9$\sigma$ | 3.4$\sigma$ | 1.3$\sigma$
| LOSS | ZTF | 2.8$\sigma$ | 1.4$\sigma$ | 3.6$\sigma$ | 2.0$\sigma$
$U-R$ (Zcorr) | DES | LOSS | 3.8$\sigma$ | 1.6$\sigma$ | 4.5$\sigma$ | 2.4$\sigma$
| DES | ZTF | 2.4$\sigma$ | 0.9$\sigma$ | 2.8$\sigma$ | 1.0$\sigma$
| LOSS | ZTF | 2.7$\sigma$ | 1.4$\sigma$ | 3.6$\sigma$ | 2.1$\sigma$

###  4.3 Relations between SN and Host Properties

We next consider the relations between the properties of the SNe and the
properties of the host galaxies for the three samples.

####  4.3.1 SNe II/Ibc host properties comparison

Fig. 6 and Fig. 7 show the distributions of host stellar mass and $U-R$ colour
comparing the host galaxies of SNe II and Ibc, and Table 5 shows the results
of two-sample KS tests between these distributions. For host stellar mass, we
do not see any significant differences between the hosts of SNe II and SNe
Ibc. We also see no significant difference in host $U-R$ colour for LOSS and
ZTF, the latter consistent with the findings of Perley et al. (2020). For the
DES sample, the hosts of SNe Ibc appear slightly redder with a significances
of 2.0$\sigma$ from the KS and AD tests. Taking $U-R$ as a proxy for star
formation rate, this could indicate that SNe Ibc are exploding in galaxies
with less star formation than SNe II. SNe Ibc have been shown to trace galaxy
star formation more closely than SNe II (e.g., Anderson & James, 2009; Galbany
et al., 2018) which would makes this result surprising, albeit with the
caveats that these differences refer to local properties rather than the
global host properties we present here and that the significance level is not
high.

Figure 6: Host galaxy stellar mass distributions and cumulative distributions for each of the DES, LOSS and ZTF samples showing the properties of the hosts of SNe II and SNe Ibc for each sample. Figure 7: As Fig. 6, but for host galaxy rest-frame $U-R$ colour instead of stellar mass. Table 5: The results of two-sample KS tests between the SNe II and SNe Ibc host properties in each survey. Property | Survey | KS test significance | AD test significance
---|---|---|---
Stellar mass | DES | 1.1$\sigma$ | 1.4$\sigma$
| LOSS | 0.1$\sigma$ | 0.2$\sigma$
| ZTF | 1.2$\sigma$ | 1.3$\sigma$
$U-R$ | DES | 2.0$\sigma$ | 2.0$\sigma$
| LOSS | 0.1$\sigma$ | 0.2$\sigma$
| ZTF | 1.8$\sigma$ | 1.5$\sigma$

####  4.3.2 SN/host correlations

We also consider relations between the properties of the host galaxy and the
properties of the SN, looking at correlations between peak SN luminosity and
host stellar mass and rest-frame colour. Table 6 shows the Pearson correlation
coefficients ($r$) between these properties for each of the DES, LOSS and ZTF
samples.

For both SN II and SN Ibc samples, we see no obvious or significant trends
between the SN luminosities and the properties of the galaxies that host them
– the correlations seen for SNe Ibc in LOSS are not statistically significant
and correspond to only 7 galaxies. Gutiérrez et al. (2018) also finds no
relation between stellar mass and peak SN luminosity for SNe II, and Wiseman
et al. (2020b) finds a lack of strong evidence for a relation between peak
transient luminosity and host mass and sSFR for RETs.

Table 6: Correlation coefficients between peak SN $R$-band absolute magnitude and host galaxy stellar mass and rest-frame $U-R$ colour for SNe II and Ibc in DES, LOSS and ZTF. Property | Survey | SN Type | Correlation with SN peak
---|---|---|---
|  |  |  $R$-band absolute magnitude (r)
Stellar mass | DES | II | 0.09
|  | Ibc | 0.03
| LOSS | II | -0.28
|  | Ibc | 0.57
| ZTF | II | -0.12
|  | Ibc | 0.14
$U-R$ | DES | II | -0.06
|  | Ibc | 0.07
| LOSS | II | -0.12
|  | Ibc | 0.55
| ZTF | II | 0.07
|  | Ibc | 0.21

##  5 Discussion

In this section we explore our results, considering and assessing a number of
potential causes for some of the noteworthy trends that we observe.

###  5.1 Impact of photometric misclassification

We begin by discussing the potential impact of misclassification of the sample
of photometric CCSNe with host spec-z in DES. SNe Ia have been removed using
SuperNNova model presented in Vincenzi et al. (2021a), which has a high degree
of accuracy upward of 98 per cent. As discussed in Section 2.1.2, the pSNid
model used to split this sample into SNe II and Ibc has an accuracy of 86 per
cent on the sample of DES CCSNe with spectroscopic classifications, with
similar performance on each of the two classes. While this method works well,
it does leave open the possibility that a small proportion of SNe in this
sample are assigned to the wrong class.

To investigate what effect this may have on our analysis, we repeat the Monte
Carlo process for CDF uncertainty outlined in Section 3.2 but this time in
each iteration we flip 14 per cent of the classes, corresponding to the
expected error rate, for the photometrically classified DES CCSNe (SNe II are
changed to SNe Ibc and vice versa) to see what effect this has on the final
CDF. Fig. 8 shows the luminosity functions and host galaxy $U-R$ distributions
for the three samples, with the randomised class flipping applied to DES
photometrically classified SNe. Overall, these distributions appear very
similar to those in Fig. 2 and 4 and the incorporation of the class flipping
has little effect. We also try restricting the sample to only
spectroscopically confirmed supernovae from DES and see the same trends for
SNe II, although there are too few spectroscopically confirmed SNe Ibc to make
this comparison.

Considering the overall samples after quality cuts, but before redshift and
magnitude cuts, the final ZTF sample consists of 174 SNe II and 89 SNe Ibc, a
ratio of $\sim$2:1. In contrast, the photometrically confirmed sample with
hosts spectroscopic redshifts from DES consists of 56 SNe II and 42 SNe Ibc at
a ratio of $\sim$1.33:1. At first glance, this suggests that pSNid is
classifying too many objects as SNe Ibc. However, it is important also to
consider the spectroscopically confirmed sample from DES - as this sample is
based on targeted follow-up, it would not be expected to follow the same ratio
of classes as an untargeted sample such as ZTF. Combining both these DES
samples, there are 89 SNe II and 55 SNe Ibc, a ratio of $\sim$1.75:1 which is
much closer to ZTF. After redshift and magnitude cuts, this ratio shifts
further from 2:1, but the relatively small sample sizes compared with ZTF mean
this is not surprising. Overall, we consider the results presented in this
analysis robust to the potential misclassification of SNe II and Ibc.

Figure 8: CDFs for the luminosity functions and rest-frame host galaxy $U-R$
colours for each of the DES, LOSS and ZTF samples, incorporating a 14 per cent
misclassification rate for photometrically classified SNe in DES as outlined
in Section 5.1.

###  5.2 Difference in the luminosity function

As part of our analysis, we have carried out two-sample KS tests between the
luminosity functions of DES, LOSS and ZTF. For SNe II, DES is brighter than
LOSS at a significance level of 3.0$\sigma$ and appears brighter than ZTF
although only at a significance of 1.8$\sigma$. For SNe Ibc, DES also appears
brighter than both LOSS and ZTF although at a significances of only
1.9$\sigma$ and 1.1$\sigma$. Although the significance levels are not high,
these differences raise the possibility of underlying differences in the
luminosity functions of these samples. If there is a difference, one natural
explanation would be redshift evolution in the underlying stellar populations
and progenitor stars. However, we first consider other, simpler explanations.

####  5.2.1 Incompleteness

The most straight forward explanation for any difference between DES and LOSS
is a lack of completeness in the DES sample due to lower sensitivity to
fainter SNe. Fig. 1 shows the peak absolute magnitudes of all objects in our
samples plotted against redshift, prior to making any selection in absolute
magnitude. Fig. 2 shows the main differences between DES and LOSS for SNe II
are in the [$-16$, $-16.5$] luminosity bin and for SNe Ibc are in the [$-16$,
$-16.7$]. DES is not complete in this range and the distribution is affected
by the $V_{\text{max}}$ correction, whereas the LOSS sample is not affected by
this correction. The $V_{\text{max}}$ correction for DES gives a maximum
weighting of 2.9 but for ZTF this is much higher due to the lack of
completeness in the sample, up to a maximum of 14.5 although this is typically
around 2-3. We can mitigate for this with lower redshift cuts for DES and ZTF
to obtain more complete samples – doing so reduces the significances from the
KS test due to the smaller sample size but overall the trends that we see
appear unchanged. This suggests that incompleteness is not the cause of
potential differences between the samples.

####  5.2.2 Host Properties

An alternative possibility is that any difference in luminosity function could
be explained by a difference in host properties between the samples. For
example, the host galaxies of the LOSS sample are significantly more massive
and redder than that of DES, likely because of the galaxy-targeted nature of
LOSS (Section 4). However, as there are no significant correlations between
either host colour or mass and peak SN luminosity (Section 4.3) this is
unlikely to cause any differences in the luminosity function.

####  5.2.3 Host Extinction

The difference in luminosity function could also result from differing levels
of host galaxy extinction between the two samples. This could be due to both
global and local host properties; for example, on average we might expect a
higher level of host extinction in more massive, redder, dustier host galaxies
and SNe closer to the central dusty regions of the host.

The DES hosts are, on average, bluer than those of LOSS and ZTF, which could
indicate higher levels of host extinction in LOSS and ZTF that might explain
any differences we see. To explore this possibility, we compare the luminosity
functions of LOSS and ZTF with only SNe in DES that are in redder host
galaxies. When we make cuts at either $U-R>0.5$, $U-R>0.75$ or $U-R>1.0$,
while the KS test significances are reduced by the smaller sample size we find
that the same overall trends are observed as for the full sample. This would
indicate that differing levels of host extinction do not cause any differences
we see, though without measurements of the host extinction we cannot rule this
out as a possibility – local environment properties are likely to play a
significant part in the level of extinction. We also consider the possibility
of differing SN radial distributions across the three samples leading to
differing levels of extinction, but do not find any significant differences in
the physical separation between SN and host.

####  5.2.4 Metallicity

Differences in metallicity may also explain potential differences in the
luminosity functions; for example, DES SNe may occur in lower metallicity
environments than LOSS or ZTF. As previously mentioned, host metallicity
affects the supernova population as the most luminous classes of supernovae
preferentially occur in low-mass, low-metallicity environments. Metallicity
varies with stellar mass, star formation rate, redshift (e.g. Zahid et al.
2013; Yates et al. 2012; Curti et al. 2020) and also radially within a galaxy
(Parikh et al., 2021). There are a number of reasons why the DES hosts could
be expected to be lower metallicity than either the LOSS or ZTF hosts: the DES
hosts are lower stellar mass than those of LOSS, they are bluer and hence more
star forming than hosts in ZTF or LOSS (although the effect of increased SFR
on metallicity will vary depending on galaxy mass) and they are at higher
redshift. Metallicity differences are a possible cause of any differences in
luminosity function.

While we do not have metallicity values calculated from host galaxy
spectroscopy, we can get an indication of global host galaxy metallicity using
the relation between stellar mass and metallicity given in equation 4 of Zahid
et al. (2013). We calculate global galaxy metallicity using following
approach:

  * •

We fit a straight line to the relation between the redshifts and mass-
metallicity relation parameters quoted in Table 1 of Zahid et al. (2013). We
use only the samples from SDSS, the Smithsonian Hectospec Lensing Survey
(SHELS; Geller et al., 2014) and the DEEP2 survey (Newman et al., 2013) quoted
here as the higher redshift samples have very uncertain values for these
parameters.

  * •

For a galaxy at a given redshift, we use these linear fits to estimate the
mass-metallicity relation parameters at that redshift and then use the
relation at that redshift to convert our measured stellar mass from SED fits
to a metallicity.

The results of two-sample KS and AD tests between the global host
metallicities of each of our samples are shown in Table 4 \- as for stellar
mass, DES and ZTF are consistent while both show differences to LOSS. Of
course, in reality there will be a large degree of scatter around the mass-
metallicity relation. However, this indicates differing host metallicity could
explain differences between DES and ZTF but not between DES and LOSS.

We can also probe metallicity looking at the decline rates during the plateau
phase after maximum light of SNe II. Theoretical models suggest that the
metallicity of the progenitor star may affect the decline rate during the
‘plateau’ phase of the SN light curve (Dessart et al., 2013b), however,
observations do not show this dependence (Anderson et al., 2016). The absence
of correlations could be related to the lack of SNe II in low-luminosity
hosts. Nevertheless, some relations can be established when SNe II in faint
hosts are included. Gutiérrez et al. (2018) find that slow-decliner SNe II
(i.e. SNe with lower s2 values) occur preferentially in low-luminosity (and
therefore low-metallicity) hosts. For SNe II in DES and ZTF, we calculate the
decline rate of this phase of the light curve (corresponding to s2 in Anderson
et al., 2014) and find that the decline rates calculated are consistent across
the two samples. This suggests that there is not a significant metallicity
difference between the two samples, indicating that this is unlikely to
explain any differences between DES and ZTF.

####  5.2.5 Summary

The notion of a luminosity function which evolves with redshift is an
interesting one - the differences we see in the luminosity functions of SNe II
in DES and LOSS and SNe Ibc in DES, LOSS and ZTF raise this as a possibility,
with the caveat that the significances are not especially high. Any
differences could be explained by a lack of completeness in the DES sample,
however this will have been significantly mitigated for by the $V_{max}$
correction and making a lower redshift cut does not change the trends we see.
Greater dust extinction from redder host galaxies is another possible
explanation given that LOSS and ZTF hosts are bluer than DES, but selecting
only DES SNe in redder hosts or ZTF and LOSS SNe in bluer hosts does not
change the trends we see which suggests that this is not the case - despite
this, without measurements of the host extinction we cannot rule this out as a
possibility. Differing metallicity also does not seem to explain the
differences as we see consistent global host galaxy metallicities between DES
and ZTF using the mass-metallicity relation of Zahid et al. (2013) and
consistent decline rates after peak for SNe II.

###  5.3 Host galaxy colour discrepancy

Section 4.2 uncovered a puzzling trend: SNe II in DES on average occur in
bluer galaxies than those in ZTF and LOSS. A difference in host galaxy
properties between DES/ZTF and LOSS can be explained, at least in part, by the
differences in targeting between the surveys. However, the difference in host
rest-frame colour between DES and ZTF is not so easily understood. In this
section we explore possible explanations for this difference.

####  5.3.1 DES spectroscopic selection bias

The DES sample in Fig. 4 contains only CCSNe with a spectroscopic host
redshift, obtained from a variety of sources (Vincenzi et al., 2021b).
Typically, galaxy redshifts are measured through the presence of narrow
emission lines in their spectra, which will generally be stronger in bluer,
star-forming galaxies. This may lead to a bias towards bluer galaxies in the
DES sample, although Vincenzi et al. (2021b) finds that the difference in
spectroscopic selection efficiency in DES between red and blue galaxies is
small. By contrast, ZTF has an automated SN spectroscopic follow-up programme
which provides redshift information for 93 per cent of observed transients
with $m<18.5$ mag and 100 per cent with $m<17$ mag. As a result, any possible
bias affecting DES would not affect ZTF.

We compare the DES samples with host spec-zs (both the spectroscopically
confirmed and photometric with spec-z samples) and ZTF samples to the DES CCSN
sample with only photo-zs. (Fig. 9). However, rather than explaining the
difference in host colour, this photometric sample appears bluer than the DES
spec-z sample. Considering the host stellar mass distribution for this sample,
this is not unexpected: the DES hosts without spectroscopic redshifts are low
stellar mass galaxies which are typically bluer and more strongly star-forming
than higher mass galaxies. In summary, the difference in host rest-frame
colour cannot be easily explained by a simple spectroscopic selection bias in
DES.

Figure 9: Host galaxy rest-frame $U-R$ (top) and stellar mass (bottom)
cumulative distributions for all CCSNe in the the DES sample of objects
without spectroscopic host redshifts, compared with the DES sample with
spectroscopic host redshifts as well as the ZTF sample.

####  5.3.2 ZTF spectroscopic selection effects

An alternative explanation is some selection bias in ZTF which favours SNe in
redder hosts. The ZTF BTS sample has a very high level of spectroscopic
completeness; however, spectroscopy is not captured exclusively by the ZTF
spectroscopic instrument SEDMachine (SEDM) – in cases where the SEDM spectra
are unreliable other instruments may be used, and in cases where an object is
first classified by another survey ZTF do not take an additional spectrum. To
understand whether this may introduce selection effects, we examine the sub-
sample of ZTF SNe only with a classification reported by SEDM. However, we
find no significant difference in this population: a two-sample KS test
between the $U-R$ host colour distributions of DES and only ZTF SNe classified
by SEDM has a significance of 3.2$\sigma$ and 3.4$\sigma$ from KS and AD
tests, almost unchanged from the full ZTF sample.

####  5.3.3 Redshift evolution of the host galaxies

Another possible explanation is redshift evolution, with a period of $\sim$1–2
Gyr between most of the ZTF and DES SNe exploding. ZTF hosts are therefore on
average older and less strongly star-forming. The host galaxy SFRs can be
corrected for redshift evolution following the method of section 4.2 of
Taggart & Perley (2021), based on the star-forming sequences of thousands of
galaxies outlined in Salim et al. (2007) and Noeske et al. (2007), correcting
the SFR values to $z=0$. However, we do not measure SFR directly, and instead
measure $U-R$ colour. We adapt the Taggart & Perley (2021) method to $U-R$
using the following steps:

  * •

We calculate the SFR correction for each galaxy; this correction will be the
same for sSFR as well.

  * •

We fit a linear relationship between $U-R$ colour and sSFR for all host
galaxies in our sample.

  * •

We use the gradient of this line to convert the sSFR correction into a $U-R$
correction.

We can then compare the distributions of these corrected colours with those of
the ZTF hosts.

Fig. 10 shows the distributions of rest-frame $U-R$ colour corrected for the
evolution of SFR, hereafter $U-R$ (SFRcorr). The correction factor between
typical DES and ZTF redshifts is $\simeq 0.03$–$0.04$ mag and thus the effect
is small: the significance of the difference between DES and ZTF from the two-
sample KS test is reduced by only 0.4$\sigma$. Based on this, the difference
in host colour seems unlikely to be caused by redshift evolution of the
underlying galaxy populations.

Figure 10: Host galaxy rest-frame $U-R$ colour distributions and cumulative
distributions, corrected for the effects of SFR evolution with redshift, for
both SNe II and SNe Ibc for DES, LOSS and ZTF samples.

However, another redshift evolution we should consider is the evolution of the
mass-metallicity relation with redshift. The metallicity of the host galaxies
will have an effect on the emission lines produced, which will in turn affect
galaxy colour. To investigate this, we use the following process to correct
$U-R$ colour for the effects of metallicity evolution:

  * •

Fit a relation between our metallicity values inferred from Zahid et al.
(2013) discussed in Section 5.2.4 and our rest-frame host galaxy $U-R$
colours. Unlike the SFR correction, this relationship is not linear. Instead,
we fit an exponential relation with a linear term of the form
$y=mx+c+e^{A(x-x_{0})}$ where $m$, $c$, $A$ and $x_{0}$ are the fitting
parameters.

  * •

Compare the metallicity difference for each galaxy of a given mass between its
actual redshift and $z=0$.

  * •

Use the fitted relation between metallicity and $U-R$ to estimate how much
this change in metallicity would affect the rest-frame $U-R$ colour.

  * •

Modify our calculated $U-R$ colours by this correction factor to calculate the
rest-frame $U-R$ colour corrected for metallicity evolution, hereafter $U-R$
(Zcorr).

This correction involves the use of two relations which show a large degree of
scatter, the mass-metallicity relation from Zahid et al. (2013) and our
relation between $U-R$ colour and metallicity. However, this does give an
indication of the extent that evolving metallicity will have on $U-R$ colour.

Fig. 11 shows the distributions of $U-R$ (Zcorr). This correction factor is
larger than the previous correction for SFR evolution. For SNe II, this
correction reduces the gap between DES and ZTF and the significance of this
offset is reduced to 2.4$\sigma$ and 2.8$\sigma$ for both KS and AD tests,
however the offset in $U-R$ between DES and ZTF across different galaxy masses
as in Fig. 5 is still seen. Overall, metallicity evolution with redshift may
explain some but not all of the offset in rest-frame $U-R$ colour between host
galaxies in DES and ZTF.

Figure 11: Host galaxy rest-frame $U-R$ colour distributions and cumulative
distributions, corrected for the effects of metallicity evolution with
redshift, for both SNe II and SNe Ibc for DES, LOSS and ZTF samples.

####  5.3.4 Systematic differences in photometry used

An additional possibility is that the difference sources of host galaxy
photometry between DES and ZTF is causing some systematic offset between the
two samples. It may be that the inclusion of $u$-band data in the SED fits for
the ZTF hosts is causing a systematic difference compared with the $griz$-only
SED fits for the DES hosts (Section 2.4). We remove the $u$-band data from the
ZTF host photometry and repeat the SED fits using only $griz$, but find that
the ZTF rest-frame colours from $griz$ fits are consistent with those from
$ugriz$ fits and thus that the difference between DES and ZTF host colours
remains.

Alternatively, there may be differences between DES and SDSS photometry which
cause an offset when considering the same bands. To investigate this
possibility, we match DES supernovae to the SDSS host catalogue using a
5″search radius, finding 47 objects with SDSS host galaxies. We then repeat
the SED fits using SDSS photometry instead of DES. We find that rest-frame
$U-R$ colours from SDSS photometry are consistent with those from DES with no
systematic offset between the two. Overall, the difference in host colour does
not seem to be caused by systematic differences in the data used to calculate
host properties.

####  5.3.5 Summary

In summary, the difference in host galaxy rest-frame colour between the ZTF
and DES samples is not obviously caused by selection biases in the two samples
or systematic differences in the SED fitting for DES and ZTF, and metallicity
evolution with redshift can only partially explain this offset. We further
note that the difference in host colour is much more pronounced in the SN II
host sample: if there were some overall systematic bias, we would expect to
see the same effect in the SN Ibc sample as well. It remains unclear what may
be driving the difference in host colour, and more data is required to study
this in further detail.

##  6 Conclusions

DES provides a large sample of high-redshift spectroscopically and
photometrically-confirmed CCSNe. We derive rest-frame luminosity functions for
the DES sample using SED models to K-correct to the rest-frame and GP-
interpolations to estimate the peak luminosity. Using the deep $griz$ DES host
photometry from Wiseman et al. (2020a), we calculate the host properties of
the DES sample using SED fits. To examine any selection biases in the sample
and investigate the possible effect of redshift evolution on the luminosity
function and host properties, we also compare SN and host properties to a low
redshift CCSN sample from LOSS and an intermediate redshift sample from ZTF.
From this comparison, our main conclusions are as follows:

  1. 1.

We present luminosity functions of SNe II and SNe Ibc for DES, LOSS and ZTF,
incorporating a $V_{max}$ correction to mitigate for the effects of Malmquist
bias. Where we see a peak in the luminosity function, we fit Gaussian and
Lorentzian distributions and present the parameter values to allow these to be
used to simulate CCSN samples.

  2. 2.

We explore differences between the DES luminosity functions. The DES
luminosity functions appears brighter than those of LOSS and ZTF, with
differences of significance level 3.0$\sigma$ and 1.8$\sigma$ to each survey
for SNe II and 1.9$\sigma$ and 1.1$\sigma$ for SNe Ibc. This could result from
higher levels of host galaxy extinction in LOSS and ZTF, however selecting a
subset of DES SNe which explode in redder host galaxies does not change the
trends we see which suggests this is not the case. This raises the possibility
of a luminosity function which evolves with redshift, although at the
significance levels we calculate we cannot be sure that any differences are
real. Were these effects real, we also cannot rule out causes such as
differing host extinction without measurements of this.

  3. 3.

There are differences in the host galaxy properties of the LOSS CCSNe compared
to the DES and ZTF CCSNe, but these are expected given that LOSS is a galaxy-
targeted survey while DES and ZTF are untargeted.

  4. 4.

There are also differences in the host galaxy properties of DES CCSNe compared
with those in ZTF. The host galaxy stellar masses of both samples are
consistent across both SNe II and SNe Ibc. However for SNe II, DES host
galaxies are significantly bluer than the ZTF hosts with a significance levels
of 3.4$\sigma$ and 3.8$\sigma$ from two-sample KS and AD tests respectively.

  5. 5.

We explore correcting the host galaxy colours to account for redshift
evolution, and study the possibility that this difference is caused by
selection biases in the DES or ZTF samples or systematic differences in the
data used, but find that none of the are able to adequately explain the
differences.

  6. 6.

The host masses and rest-frame $U-R$ colours of SNe II compared to SNe Ibc are
generally consistent in both the LOSS and ZTF samples. In the DES sample,
hosts of SNe II appear bluer than those of SNe Ibc but only at a significance
level of 2.0$\sigma$.

  7. 7.

Overall, we observe little environmental dependence on SN peak magnitude
across the three samples.

## Acknowledgements

This work was supported by the Science and Technology Facilities Council
[grant number ST/P006760/1] through the DISCnet Centre for Doctoral Training.
M.S. acknowledges support from EU/FP7-ERC grant 615929. P.W. acknowledges
support from the Science and Technology Facilities Council (STFC) grant
ST/R000506/1.

Funding for the DES Projects has been provided by the U.S. Department of
Energy, the U.S. National Science Foundation, the Ministry of Science and
Education of Spain, the Science and Technology Facilities Council of the
United Kingdom, the Higher Education Funding Council for England, the National
Center for Supercomputing Applications at the University of Illinois at
Urbana-Champaign, the Kavli Institute of Cosmological Physics at the
University of Chicago, the Center for Cosmology and Astro-Particle Physics at
the Ohio State University, the Mitchell Institute for Fundamental Physics and
Astronomy at Texas A&M University, Financiadora de Estudos e Projetos,
Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro,
Conselho Nacional de Desenvolvimento Científico e Tecnológico and the
Ministério da Ciência, Tecnologia e Inovação, the Deutsche
Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy
Survey.

The Collaborating Institutions are Argonne National Laboratory, the University
of California at Santa Cruz, the University of Cambridge, Centro de
Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the
University of Chicago, University College London, the DES-Brazil Consortium,
the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH)
Zürich, Fermi National Accelerator Laboratory, the University of Illinois at
Urbana-Champaign, the Institut de Ciències de l’Espai (IEEC/CSIC), the
Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory,
the Ludwig-Maximilians Universität München and the associated Excellence
Cluster Universe, the University of Michigan, the National Optical Astronomy
Observatory, the University of Nottingham, The Ohio State University, the
University of Pennsylvania, the University of Portsmouth, SLAC National
Accelerator Laboratory, Stanford University, the University of Sussex, Texas
A&M University, and the OzDES Membership Consortium.

Based in part on observations at Cerro Tololo Inter-American Observatory,
National Optical Astronomy Observatory, which is operated by the Association
of Universities for Research in Astronomy (AURA) under a cooperative agreement
with the National Science Foundation.

The DES data management system is supported by the National Science Foundation
under Grant Numbers AST-1138766 and AST-1536171. The DES participants from
Spanish institutions are partially supported by MINECO under grants
AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and
MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE
is partially funded by the CERCA program of the Generalitat de Catalunya.
Research leading to these results has received funding from the European
Research Council under the European Union’s Seventh Framework Program
(FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We
acknowledge support from the Brazilian Instituto Nacional de Ciência e
Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2).

This manuscript has been authored by Fermi Research Alliance, LLC under
Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of
Science, Office of High Energy Physics.

Based in part on data acquired at the Anglo-Australian Telescope, under
program A/2013B/012. We acknowledge the traditional owners of the land on
which the AAT stands, the Gamilaraay people, and pay our respects to elders
past and present. Based on observations collected at the European Organisation
for Astronomical Research in the Southern Hemisphere under ESO programmes
093.A-0749(A), 094.A0310(B), 095.A-0316(A), 096.A-0536(A), 095.D-0797(A),
198.A-0915(A).

This manuscript has been authored by Fermi Research Alliance, LLC under
Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of
Science, Office of High Energy Physics. The United States Government retains
and the publisher, by accepting the article for publication, acknowledges that
the United States Government retains a non-exclusive, paid-up, irrevocable,
world-wide license to publish or reproduce the published form of this
manuscript, or allow others to do so, for United States Government purposes.

Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P.
Sloan Foundation, the U.S. Department of Energy Office of Science, and the
Participating Institutions.

SDSS-IV acknowledges support and resources from the Center for High
Performance Computing at the University of Utah. The SDSS website is
www.sdss.org.

SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, Center for Astrophysics | Harvard & Smithsonian, the Chilean Participation Group, the French Participation Group, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, the Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional / MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.

## Data Availability Statement

The data underlying this article, as well as the observed DES-SN photometry,
are available in the article and in its online supplementary material. The ZTF
photometry used can be accessed through the Lasair broker
(https://lasair.roe.ac.uk/).

## Affiliations

1 School of Physics and Astronomy, University of Southampton, Southampton,
SO17 1BJ, UK
2 Finnish Centre for Astronomy with ESO (FINCA), FI-20014 University of Turku,
Finland
3 Tuorla Observatory, Department of Physics and Astronomy, FI-20014 University
of Turku, Finland
4 Institute of Cosmology and Gravitation, University of Portsmouth,
Portsmouth, PO1 3FX, UK
5 Institut d’Estudis Espacials de Catalunya (IEEC), 08034 Barcelona, Spain
6 Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans,
s/n, 08193 Barcelona, Spain
7 Centre for Astrophysics & Supercomputing, Swinburne University of
Technology, Victoria 3122, Australia
8 Center for Astrophysics $|$ Harvard & Smithsonian, 60 Garden Street,
Cambridge, MA 02138, USA
9 School of Mathematics and Physics, University of Queensland, Brisbane, QLD
4072, Australia
10 Centre for Gravitational Astrophysics, College of Science, The Australian
National University, ACT 2601, Australia
11 The Research School of Astronomy and Astrophysics, Australian National
University, ACT 2601, Australia
12 Department of Physics, Duke University Durham, NC 27708, USA
13 Cerro Tololo Inter-American Observatory, NSF’s National Optical-Infrared
Astronomy Research Laboratory, Casilla 603, La Serena, Chile
14 Laboratório Interinstitucional de e-Astronomia - LIneA, Rua Gal. José
Cristino 77, Rio de Janeiro, RJ - 20921-400, Brazil
15 Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL 60510,
USA
16 Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
17 Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
(CIEMAT), Madrid, Spain
18 CNRS, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France
19 Sorbonne Universités, UPMC Univ Paris 06, UMR 7095, Institut
d’Astrophysique de Paris, F-75014, Paris, France
20 University Observatory, Faculty of Physics, Ludwig-Maximilians-Universität,
Scheinerstr. 1, 81679 Munich, Germany
21 Department of Physics & Astronomy, University College London, Gower Street,
London, WC1E 6BT, UK
22 Instituto de Astrofisica de Canarias, E-38205 La Laguna, Tenerife, Spain
23 Universidad de La Laguna, Dpto. Astrofísica, E-38206 La Laguna, Tenerife,
Spain
24 INAF-Osservatorio Astronomico di Trieste, via G. B. Tiepolo 11, I-34143
Trieste, Italy
25 Center for Astrophysical Surveys, National Center for Supercomputing
Applications, 1205 West Clark St., Urbana, IL 61801, USA
26 Department of Astronomy, University of Illinois at Urbana-Champaign, 1002
W. Green Street, Urbana, IL 61801, USA
27 Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of
Science and Technology, Campus UAB, 08193 Bellaterra (Barcelona) Spain
28 Astronomy Unit, Department of Physics, University of Trieste, via Tiepolo
11, I-34131 Trieste, Italy
29 Institute for Fundamental Physics of the Universe, Via Beirut 2, 34014
Trieste, Italy
30 Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112, 21029
Hamburg, Germany
31 Department of Physics, IIT Hyderabad, Kandi, Telangana 502285, India
32 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak
Grove Dr., Pasadena, CA 91109, USA
33 Institute of Theoretical Astrophysics, University of Oslo. P.O. Box 1029
Blindern, NO-0315 Oslo, Norway
34 Kavli Institute for Cosmological Physics, University of Chicago, Chicago,
IL 60637, USA
35 Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, 28049
Madrid, Spain
36 Department of Physics and Astronomy, University of Pennsylvania,
Philadelphia, PA 19104, USA
37 Observatório Nacional, Rua Gal. José Cristino 77, Rio de Janeiro, RJ -
20921-400, Brazil
38 Santa Cruz Institute for Particle Physics, Santa Cruz, CA 95064, USA
39 Center for Cosmology and Astro-Particle Physics, The Ohio State University,
Columbus, OH 43210, USA
40 Department of Physics, The Ohio State University, Columbus, OH 43210, USA
41 Australian Astronomical Optics, Macquarie University, North Ryde, NSW 2113,
Australia
42 Lowell Observatory, 1400 Mars Hill Rd, Flagstaff, AZ 86001, USA
43 Sydney Institute for Astronomy, School of Physics, A28, The University of
Sydney, NSW 2006, Australia
44 Institució Catalana de Recerca i Estudis Avançats, E-08010 Barcelona, Spain
45 Physics Department, 2320 Chamberlin Hall, University of Wisconsin-Madison,
1150 University Avenue Madison, WI 53706-1390
46 Department of Astronomy, University of California, Berkeley, 501 Campbell
Hall, Berkeley, CA 94720, USA
47 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge
CB3 0HA, UK
48 Department of Astrophysical Sciences, Princeton University, Peyton Hall,
Princeton, NJ 08544, USA
49 Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
(CIEMAT), Av. Complutense, 22, E- 28040 Madrid, Spain
50 Department of Physics and Astronomy, Pevensey Building, University of
Sussex, Brighton, BN1 9QH, UK
51 Kavli Institute for Particle Astrophysics & Cosmology, P. O. Box 2450,
Stanford University, Stanford, CA 94305, USA
52 SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
53 Computer Science and Mathematics Division, Oak Ridge National Laboratory,
Oak Ridge, TN 37831
54 Excellence Cluster Origins, Boltzmannstr. 2, 85748 Garching, Germany
55 Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse,
85748 Garching, Germany
56 Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians
Universität München, Scheinerstr. 1, 81679 München, Germany

## References

  * Ambikasaran et al. (2015) Ambikasaran S., Foreman-Mackey D., Greengard L., Hogg D. W., O’Neil M., 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 252
  * Anderson & James (2009) Anderson J. P., James P. A., 2009, MNRAS, 399, 559
  * Anderson et al. (2010) Anderson J. P., Covarrubias R. A., James P. A., Hamuy M., Habergham S. M., 2010, MNRAS, 407, 2660
  * Anderson et al. (2014) Anderson J. P., et al., 2014, ApJ, 786, 67
  * Anderson et al. (2016) Anderson J. P., et al., 2016, A&A, 589, A110
  * Angus et al. (2016) Angus C. R., Levan A. J., Perley D. A., Tanvir N. R., Lyman J. D., Stanway E. R., Fruchter A. S., 2016, MNRAS, 458, 84
  * Angus et al. (2019) Angus C. R., et al., 2019, MNRAS, 487, 2215
  * Arcavi et al. (2012) Arcavi I., et al., 2012, ApJ, 756, L30
  * Barbon et al. (1989) Barbon R., Cappellaro E., Turatto M., 1989, A&AS, 81, 421
  * Bazin et al. (2009) Bazin G., et al., 2009, A&A, 499, 653
  * Bernstein et al. (2012) Bernstein J. P., et al., 2012, ApJ, 753, 152
  * Blanton & Roweis (2007) Blanton M. R., Roweis S., 2007, AJ, 133, 734
  * Blanton et al. (2017) Blanton M. R., et al., 2017, AJ, 154, 28
  * Blondin & Tonry (2007) Blondin S., Tonry J. L., 2007, ApJ, 666, 1024
  * Brammer et al. (2008) Brammer G. B., van Dokkum P. G., Coppi P., 2008, The Astrophysical Journal, 686, 1503
  * Chabrier (2003) Chabrier G., 2003, PASP, 115, 763
  * Childress et al. (2013) Childress M., et al., 2013, ApJ, 770, 107
  * Childress et al. (2017) Childress M. J., et al., 2017, MNRAS, 472, 273
  * Curti et al. (2020) Curti M., Mannucci F., Cresci G., Maiolino R., 2020, MNRAS, 491, 944
  * Dessart et al. (2013a) Dessart L., Hillier D. J., Waldman R., Livne E., 2013a, MNRAS, 433, 1745
  * Dessart et al. (2013b) Dessart L., Hillier D. J., Waldman R., Livne E., 2013b, MNRAS, 433, 1745
  * Drout et al. (2011) Drout M. R., et al., 2011, ApJ, 741, 97
  * Elbaz et al. (2011) Elbaz D., et al., 2011, A&A, 533
  * Filippenko (1997) Filippenko A. V., 1997, ARA&A, 35, 309
  * Fioc & Rocca-Volmerange (1997) Fioc M., Rocca-Volmerange B., 1997, A&A, 500, 507
  * Fioc & Rocca-Volmerange (2019) Fioc M., Rocca-Volmerange B., 2019, A&A, 623, A143
  * Frohmaier et al. (2021) Frohmaier C., et al., 2021, MNRAS, 500, 5142
  * Gal-Yam (2017) Gal-Yam A., 2017, Observational and Physical Classification of Supernovae. Springer, p. 195, doi:10.1007/978-3-319-21846-5_35
  * Gal-Yam et al. (2007) Gal-Yam A., Cenko S. B., Fox D. B., Leonard D. C., Moon D. S., Sand D. J., Soderberg A. M., 2007, in di Salvo T., Israel G. L., Piersant L., Burderi L., Matt G., Tornambe A., Menna M. T., eds, American Institute of Physics Conference Series Vol. 924, The Multicolored Landscape of Compact Objects and Their Explosive Origins. pp 297–303 (arXiv:astro-ph/0611919), doi:10.1063/1.2774873
  * Galbany et al. (2018) Galbany L., et al., 2018, ApJ, 855, 107
  * Gehrels (1986) Gehrels N., 1986, ApJ, 303, 336
  * Geller et al. (2014) Geller M. J., Hwang H. S., Fabricant D. G., Kurtz M. J., Dell’Antonio I. P., Zahid H. J., 2014, ApJS, 213, 35
  * Goldstein et al. (2015) Goldstein D. A., et al., 2015, AJ, 150, 82
  * Graur et al. (2017a) Graur O., Bianco F. B., Huang S., Modjaz M., Shivvers I., Filippenko A. V., Li W., Eldridge J. J., 2017a, ApJ, 837, 120
  * Graur et al. (2017b) Graur O., Bianco F. B., Modjaz M., Shivvers I., Filippenko A. V., Li W., Smith N., 2017b, ApJ, 837, 121
  * Grayling et al. (2021) Grayling M., et al., 2021, MNRAS, 505, 3950
  * Gutiérrez et al. (2017) Gutiérrez C. P., et al., 2017, ApJ, 850, 89
  * Gutiérrez et al. (2018) Gutiérrez C. P., et al., 2018, MNRAS, 479, 3232
  * Hartley et al. (2022) Hartley W. G., et al., 2022, MNRAS, 509, 3547
  * Jones et al. (2017) Jones D. O., et al., 2017, ApJ, 843, 6
  * Karachentseva et al. (2020) Karachentseva V. E., Karachentsev I. D., Kashibadze O. G., 2020, Astrophysics, 63, 151
  * Kelsey et al. (2020) Kelsey L., et al., 2020, MNRAS, 501, 4861
  * Kessler et al. (2015) Kessler R., et al., 2015, AJ, 150, 172
  * Kiewe et al. (2012) Kiewe M., et al., 2012, ApJ, 744, 10
  * Leaman et al. (2011) Leaman J., Li W., Chornock R., Filippenko A. V., 2011, MNRAS, 412, 1419
  * Leroy et al. (2019) Leroy A. K., et al., 2019, ApJS, 244, 24
  * Levan et al. (2005) Levan A., et al., 2005, ApJ, 624, 880
  * Li (2000) Li W. D., 2000, AIP Conference Proceedings
  * Li et al. (2011) Li W., et al., 2011, MNRAS, 412, 1441
  * Lidman et al. (2020) Lidman C., et al., 2020, MNRAS, 496, 19
  * Lintott et al. (2008) Lintott C. J., et al., 2008, MNRAS, 389, 1179
  * Modjaz et al. (2019) Modjaz M., Gutiérrez C. P., Arcavi I., 2019, Nature Astronomy, 3, 717
  * Modjaz et al. (2020) Modjaz M., et al., 2020, The Astrophysical Journal, 892, 153
  * Möller et al. (2022) Möller A., et al., 2022, arXiv e-prints, p. arXiv:2201.11142
  * Möller & de Boissière (2019) Möller A., de Boissière T., 2019, MNRAS, 491, 4277–4293
  * Newman et al. (2013) Newman J. A., et al., 2013, ApJS, 208, 5
  * Noeske et al. (2007) Noeske K. G., et al., 2007, The Astrophysical Journal, 660, L43
  * Nomoto et al. (1995) Nomoto K. I., Iwamoto K., Suzuki T., 1995, Phys. Rep., 256, 173
  * Oke & Gunn (1983) Oke J. B., Gunn J. E., 1983, ApJ, 266, 713
  * Parikh et al. (2021) Parikh T., Thomas D., Maraston C., Westfall K. B., Andrews B. H., Boardman N. F., Drory N., Oyarzun G., 2021, MNRAS, 502, 5508
  * Perley et al. (2016) Perley D. A., et al., 2016, ApJ, 830, 13
  * Perley et al. (2020) Perley D. A., et al., 2020, The Astrophysical Journal, 904, 35
  * Rasmussen & Williams (2005) Rasmussen C. E., Williams C. K. I., 2005, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press
  * Richardson et al. (2014) Richardson D., III R. L. J., Wright J., Maddox L., 2014, The Astronomical Journal, 147, 118
  * Sako et al. (2011) Sako M., et al., 2011, ApJ, 738, 162
  * Salim et al. (2007) Salim S., et al., 2007, ApJS, 173, 267
  * Schlafly & Finkbeiner (2011) Schlafly E. F., Finkbeiner D. P., 2011, ApJ, 737, 103
  * Schlegel et al. (1998) Schlegel D. J., Finkbeiner D. P., Davis M., 1998, ApJ, 500, 525
  * Schmidt (1968) Schmidt M., 1968, ApJ, 151, 393
  * Shivvers et al. (2017) Shivvers I., et al., 2017, PASP, 129, 054201
  * Smith et al. (2019) Smith K. W., et al., 2019, Research Notes of the AAS, 3, 26
  * Smith et al. (2020a) Smith M., et al., 2020a, AJ, 160, 267
  * Smith et al. (2020b) Smith M., et al., 2020b, MNRAS, 494, 4426
  * Taggart & Perley (2021) Taggart K., Perley D. A., 2021, MNRAS, 503, 3931
  * Taylor et al. (2014) Taylor M., et al., 2014, ApJ, 792, 135
  * Trayford et al. (2016) Trayford J. W., Theuns T., Bower R. G., Crain R. A., Lagos C. d. P., Schaller M., Schaye J., 2016, MNRAS, 460, 3925
  * Villar et al. (2018) Villar V. A., Nicholl M., Berger E., 2018, ApJ, 869, 166
  * Vincenzi et al. (2019) Vincenzi M., Sullivan M., Firth R. E., Gutiérrez C. P., Frohmaier C., Smith M., Angus C., Nichol R. C., 2019, MNRAS, 489, 5802
  * Vincenzi et al. (2021a) Vincenzi M., et al., 2021a, arXiv e-prints, p. arXiv:2111.10382
  * Vincenzi et al. (2021b) Vincenzi M., et al., 2021b, MNRAS, 505, 2819
  * Wiseman et al. (2020a) Wiseman P., et al., 2020a, MNRAS, 495, 4040
  * Wiseman et al. (2020b) Wiseman P., et al., 2020b, MNRAS, 498, 2575
  * Wiseman et al. (2021) Wiseman P., et al., 2021, MNRAS, 506, 3330
  * Woosley et al. (1993) Woosley S. E., Langer N., Weaver T. A., 1993, ApJ, 411, 823
  * Yates et al. (2012) Yates R. M., Kauffmann G., Guo Q., 2012, MNRAS, 422, 215
  * Yuan et al. (2015) Yuan F., et al., 2015, MNRAS, 452, 3047
  * Zahid et al. (2013) Zahid H. J., Geller M. J., Kewley L. J., Hwang H. S., Fabricant D. G., Kurtz M. J., 2013, ApJ, 771, L19

##  Appendix A Estimating host properties using photometric redshifts

For the DES-SN sample of CCSNe with only photometric host redshifts, we
estimate host galaxy properties using the following Monte Carlo (MC) process:

  * •

For each object, we have 0.5th, 2.5th, 16th, 84th, 97.5th and 99.5th
percentiles of the photometric redshift distribution. Studying the cumulative
distributions of these values shows the distribution to be approximately
Gaussian - as such, we model the redshift distribution of each host as a
Gaussian, estimating the mean and standard deviation of this Gaussian by
fitting a generalised error function to our cumulative distribution.

  * •

We use the $griz$ photometry of each host from the DES deep coadded host
images to estimate the properties of the host, following the same SED fitting
process as for objects with spectroscopic redshift information. We do this for
every redshift between the 0.5 and 99.5 percentiles in the redshift
distribution of each host, in intervals of 0.001. This gives the properties
the host galaxy would have were it located at each redshift in this
distribution.

  * •

For each host galaxy, we draw a random redshift from a Gaussian distribution
using our estimates of mean and standard deviation. We then select the
properties of each host galaxy at these redshifts and use these to produce a
CDF for each host property for this randomised sample. (If a randomised
redshift lies outside the redshift range of our spectroscopic sample, it is
excluded from the sample.)

  * •

We repeat this 10,000 times in an Monte Carlo process, examining the spread of
the CDFs over all iterations to obtain a final CDF with an associated error.
This allows us to include the large uncertainties in host redshift into a
comparison of the host galaxy properties for different samples.

We find that only including SNe with a randomised redshift of less than 0.25
in each iteration gives a sample size that varies between 3 and 8, and
typically 5 or 6. Photometric redshifts are currently only available for three
out of the ten DES fields – extrapolating, we would expect there to be between
10–27 CCSNe for which we have DES photometry but no spectroscopic host
redshift, and which would otherwise be included in our luminosity functions.
Our luminosity function sample contains 98 DES SNe with spectroscopic host
redshifts, thus we have spectroscopic host redshifts for $\sim 75-90$ per cent
of the CCSNe which should be included in our sample suggesting that these
‘missing’ SNe should not have a significant effect on the luminosity function.

##  Appendix B SED-fitting analysis for LOSS host galaxies

Fig. 12 shows comparisons with our host properties from SED fitting for the
$B$/$K$-band stellar masses and host stellar masses and SFRs from Leroy et al.
(2019) and Karachentseva et al. (2020). These were performed with the full
LOSS SN sample, without the selection of events used for the SNe in the
luminosity functions – this gives 56 host galaxies with properties derived
from SED fits to SDSS photometry, 96 host galaxies with $B$/$K$-band masses
and 71 with previously published literature values for stellar mass and SFR.

Our stellar masses from SED-fitting are consistent with the $B$/$K$-band
masses. The Pearson correlation coefficient ($r$) for these two sets of masses
is 0.75 indicating a strong correlation, with a dispersion of 0.39 dex.
Comparing our stellar masses from others in the literature gives $r=0.65$ and
a dispersion of 0.65 dex. Overall, our masses seem broadly consistent with
those derived from other methods. However, the correlation between our SFR
values derived from SED fits and those from literature is only $r=0.30$ with a
dispersion of 2.17 dex, demonstrating the uncertainties in estimating SFRs
from SED fitting which have a stronger dependence on star-formation history.
We do not use SFR in our analysis, and instead use rest-frame $U-R$, which is
well-constrained by the observed data.

Figure 12: Upper panels: Host galaxy stellar masses and SFRs for LOSS from our
SED fits to SDSS $ugriz$ photometry compared with stellar masses derived from
the $K$-band mass-to-luminosity ratio presented in L11 and with stellar masses
and SFRs presented in Leroy et al. (2019) and Karachentseva et al. (2020).
Correlation coefficients are shown, and the dashed line shows a perfect
agreement. Lower panels: Residuals from the perfect agreement in the upper
panels.

##  Appendix C ZTF Magnitude Limit for Malmquist Bias Correction

As mentioned in Section 3.1, we consider the 97, 93 and 75 per cent
spectroscopic completeness limits of ZTF, $18$, $18.5$ and $19$ mag
respectively. The luminosity functions for ZTF with these three limits are
shown in figure 13. Above -17 mag, these appear consistent with each other,
however these luminosity functions diverge between -16 and -17. A limit of 18
mag omits some SNe in this region and appears to bias the sample in favour of
brighter objects. The luminosity functions for a limit of 18.5 and 19 appear
consistent with each other - as a result, we settle on a limit of 19 mag to
maximise the sample size.

Figure 13: As Fig. 2, but for samples only from ZTF with $V_{max}$ corrections
calculated for magnitude limits of 18, 18.5 and 19 mag.

##  Appendix D Data Tables

Table LABEL:long_table present the peak $R$-band absolute magnitudes and host
galaxy properties used for all analysis in this work, for the three different
samples. Table 8 presents DECam photometry of the DES sample presented in this
paper.

Table 7: Supernova peak $R$-band absolute magnitudes (M${}_{\text{max}}$) and host galaxy properties for the DES, LOSS and ZTF samples presented in this work, along with their associated errors. Please note, objects denoted with ∗ correspond to two SNe II in ZTF with M${}_{\text{max}}$ less than -16 which have used only for the fits presented in Section 3.3, to help constrain the peak of the luminosity function, and not in the rest of the analysis. SN | Survey | Class | Source${}^{\text{a}}$ | z | M${}_{\text{max}}^{\text{b}}$ | err | weight${}^{\text{c}}$ | $M^{\text{d}}$ | err | $U-R^{\text{e}}$ | err | $B-V^{\text{f}}$ | err
---|---|---|---|---|---|---|---|---|---|---|---|---|---
DES13C2jtx | DES | II | spec | 0.223 | -18.61 | 0.03 | 1.00 | 10.63 | 0.02 | 1.32 | 0.01 | 0.72 | 0.01
DES13X3fca | DES | II | spec | 0.096 | -16.92 | 0.02 | 1.00 | 10.02 | 0.01 | 1.48 | 0.01 | 0.69 | 0.01
DES14C3aol | DES | II | spec | 0.076 | -16.21 | 0.02 | 1.00 | 10.83 | 0.02 | 1.71 | 0.02 | 0.89 | 0.02
DES14X2cy | DES | II | spec | 0.232 | -18.99 | 0.01 | 1.00 | 9.22 | 0.02 | 0.43 | 0.02 | 0.38 | 0.01
DES14X2nzt | DES | II | spec | 0.195 | -18.31 | 0.02 | 1.00 | – | – | – | – | – | –
DES15C2eaz | DES | II | spec | 0.062 | -17.49 | 0.03 | 1.00 | 8.10 | 0.01 | 0.78 | 0.03 | 0.39 | 0.02
DES15C2lna | DES | II | spec | 0.069 | -16.72 | 0.02 | 1.39 | 10.02 | 0.03 | 1.07 | 0.04 | 0.62 | 0.02
DES15C2lpp | DES | II | spec | 0.181 | -17.12 | 0.04 | 1.00 | 10.47 | 0.01 | 1.21 | 0.02 | 0.65 | 0.01
DES15C2npz | DES | II | spec | 0.123 | -17.18 | 0.02 | 1.00 | 9.13 | 0.01 | 0.73 | 0.02 | 0.46 | 0.01
DES15E1iuh | DES | II | spec | 0.105 | -17.14 | 0.02 | 1.00 | 7.44 | 0.08 | 0.68 | 0.14 | 0.43 | 0.06
DES15S1by | DES | II | spec | 0.129 | -17.13 | 0.03 | 1.00 | 9.33 | 0.04 | 0.60 | 0.06 | 0.40 | 0.01
DES15S1cj | DES | II | spec | 0.167 | -18.16 | 0.02 | 1.00 | 10.17 | 0.01 | 1.33 | 0.02 | 0.71 | 0.01
DES15S1lrp | DES | II | spec | 0.223 | -18.44 | 0.02 | 1.00 | 8.36 | 0.05 | 0.71 | 0.10 | 0.41 | 0.05
DES15S2eaq | DES | II | spec | 0.068 | -16.37 | 0.03 | 2.09 | 9.22 | 0.01 | 0.69 | 0.02 | 0.44 | 0.01
DES15X2mku | DES | II | spec | 0.09 | -16.67 | 0.02 | 1.47 | 8.73 | 0.04 | – | – | 0.36 | 0.02
DES15X3mpq | DES | II | spec | 0.188 | -17.55 | 0.02 | 1.00 | 10.29 | 0.01 | 1.00 | 0.02 | 0.56 | 0.01
DES15X3nad | DES | II | spec | 0.10 | -18.15 | 0.03 | 1.00 | – | – | – | – | – | –
DES16C1cbg | DES | II | spec | 0.111 | -16.99 | 0.02 | 1.02 | 9.07 | 0.01 | 0.85 | 0.03 | 0.51 | 0.01
DES16C2cbv | DES | II | spec | 0.109 | -17.37 | 0.03 | 1.00 | 8.08 | 0.02 | 0.10 | 0.02 | 0.25 | 0.01
DES16X2bkr | DES | II | spec | 0.159 | -17.42 | 0.02 | 1.00 | 8.20 | 0.02 | – | – | 0.31 | 0.02
DES16X3cpl | DES | II | spec | 0.205 | -17.29 | 0.01 | 1.00 | 9.07 | 0.03 | – | – | 0.59 | 0.03
DES17C2pf | DES | II | spec | 0.135 | -17.22 | 0.02 | 1.00 | 7.15 | 0.14 | 0.39 | 0.23 | 0.34 | 0.09
DES17C3aye | DES | II | spec | 0.157 | -18.07 | 0.02 | 1.00 | 9.83 | 0.01 | 0.89 | 0.02 | 0.51 | 0.01
DES17C3bei | DES | II | spec | 0.103 | -17.15 | 0.03 | 1.00 | 10.46 | 0.01 | 1.12 | 0.02 | 0.61 | 0.01
DES17E2bhj | DES | II | spec | 0.186 | -17.09 | 0.03 | 1.00 | 10.44 | 0.01 | 1.03 | 0.03 | 0.57 | 0.01
DES17S2oo | DES | II | spec | 0.23 | -17.82 | 0.03 | 1.00 | 7.95 | 0.16 | 0.53 | 0.22 | 0.39 | 0.11
DES17X1aow | DES | II | spec | 0.139 | -17.65 | 0.04 | 1.00 | 10.56 | 0.06 | – | – | 0.91 | 0.01
DES17X1axb | DES | II | spec | 0.139 | -16.77 | 0.02 | 1.30 | 9.44 | 0.02 | 0.39 | 0.03 | 0.35 | 0.01
DES17X1gd | DES | II | spec | 0.189 | -17.59 | 0.03 | 1.00 | 9.02 | 0.03 | 0.31 | 0.03 | 0.31 | 0.01
DES17X2dql | DES | II | spec | 0.143 | -16.66 | 0.03 | 1.48 | – | – | – | – | – | –
DES17X3dub | DES | II | spec | 0.123 | -16.11 | 0.02 | 1.00 | 8.53 | 0.06 | 0.45 | 0.09 | 0.36 | 0.02
DES13C1ffj | DES | II | phot | 0.218 | -17.36 | 0.02 | 1.00 | 9.32 | 0.04 | – | – | 0.36 | 0.02
DES13C1woj | DES | II | phot | 0.232 | -17.06 | 0.04 | 1.00 | 9.04 | 0.02 | 0.47 | 0.03 | 0.38 | 0.02
DES13C3abtm | DES | II | phot | 0.195 | -16.75 | 0.08 | 1.00 | 9.43 | 0.02 | 0.23 | 0.02 | 0.27 | 0.01
DES13C3avns | DES | II | phot | 0.242 | -17.28 | 0.02 | 1.00 | 9.85 | 0.02 | 0.48 | 0.03 | 0.36 | 0.01
DES13E1ackb | DES | II | phot | 0.216 | -17.55 | 0.03 | 1.00 | 9.04 | 0.03 | – | – | 0.37 | 0.02
DES13E1pat | DES | II | phot | 0.223 | -18.54 | 0.03 | 1.00 | 10.80 | 0.06 | 1.86 | 0.02 | 0.95 | 0.01
DES13X1hxq | DES | II | phot | 0.24 | -17.22 | 0.05 | 1.00 | 9.31 | 0.01 | 0.56 | 0.02 | 0.40 | 0.01
DES14C1lnl | DES | II | phot | 0.196 | -17.67 | 0.01 | 1.00 | 8.46 | 0.03 | 0.38 | 0.04 | 0.29 | 0.02
DES14C2rqo | DES | II | phot | 0.216 | -18.17 | 0.02 | 1.00 | 7.88 | 0.06 | 0.41 | 0.09 | 0.34 | 0.05
DES14C2rso | DES | II | phot | 0.223 | -17.41 | 0.02 | 1.00 | 10.04 | 0.02 | 0.79 | 0.02 | 0.47 | 0.01
DES14C3asy | DES | II | phot | 0.174 | -16.20 | 0.02 | 1.00 | 9.05 | 0.01 | 0.57 | 0.02 | 0.40 | 0.01
DES14C3kzd | DES | II | phot | 0.078 | -17.33 | 0.03 | 1.00 | 10.59 | 0.01 | 1.18 | 0.02 | 0.64 | 0.01
DES14E1bdh | DES | II | phot | 0.238 | -16.89 | 0.02 | 1.13 | 9.11 | 0.02 | 0.49 | 0.02 | 0.38 | 0.01
DES14E2fmi | DES | II | phot | 0.21 | -17.56 | 0.03 | 1.00 | 9.50 | 0.01 | 0.73 | 0.02 | 0.44 | 0.01
DES14E2gvo | DES | II | phot | 0.126 | -16.44 | 0.03 | 1.91 | 10.06 | 0.01 | – | – | 0.79 | 0.01
DES14S1mkj | DES | II | phot | 0.189 | -17.23 | 0.03 | 1.00 | 9.93 | 0.01 | – | – | 0.71 | 0.01
DES14S2dbe | DES | II | phot | 0.201 | -17.81 | 0.03 | 1.00 | 10.67 | 0.01 | 0.91 | 0.02 | 0.52 | 0.01
DES14S2obu | DES | II | phot | 0.18 | -17.49 | 0.03 | 1.00 | 9.59 | 0.01 | – | – | 0.45 | 0.01
DES14X2mqu | DES | II | phot | 0.092 | -16.93 | 0.03 | 1.09 | 9.85 | 0.01 | 0.91 | 0.02 | 0.53 | 0.01
DES14X3ddy | DES | II | phot | 0.205 | -17.21 | 0.02 | 1.00 | 9.86 | 0.02 | 0.56 | 0.02 | 0.41 | 0.01
DES14X3ili | DES | II | phot | 0.142 | -17.24 | 0.16 | 1.00 | 9.05 | 0.02 | 0.56 | 0.03 | 0.41 | 0.01
DES15C1ats | DES | II | phot | 0.244 | -17.16 | 0.05 | 1.00 | 9.32 | 0.02 | 0.54 | 0.02 | 0.39 | 0.01
DES15C1lwn | DES | II | phot | 0.206 | -16.84 | 0.02 | 1.20 | 9.03 | 0.02 | 0.28 | 0.02 | 0.31 | 0.01
DES15C2lpm | DES | II | phot | 0.19 | -17.00 | 0.04 | 1.00 | 10.10 | 0.01 | 0.87 | 0.02 | 0.50 | 0.01
DES15X2aso | DES | II | phot | 0.207 | -17.44 | 0.04 | 1.00 | 9.59 | 0.02 | 0.65 | 0.02 | 0.43 | 0.01
DES16C1ftn | DES | II | phot | 0.205 | -17.27 | 0.03 | 1.00 | 9.62 | 0.01 | 1.02 | 0.02 | 0.57 | 0.01
DES16C3ecv | DES | II | phot | 0.244 | -17.69 | 0.02 | 1.00 | – | – | – | – | – | –
DES16C3fuk | DES | II | phot | 0.25 | -17.05 | 0.04 | 1.00 | – | – | – | – | – | –
DES16E1eab | DES | II | phot | 0.179 | -16.88 | 0.02 | 1.15 | 8.64 | 0.03 | 0.34 | 0.03 | 0.34 | 0.01
DES16E1eke | DES | II | phot | 0.081 | -17.65 | 0.09 | 1.00 | 8.24 | 0.01 | 0.68 | 0.03 | 0.43 | 0.01
DES16S2eam | DES | II | phot | 0.181 | -16.39 | 0.02 | 2.03 | 10.36 | 0.01 | 1.00 | 0.04 | 0.55 | 0.01
DES16X3qx | DES | II | phot | 0.187 | -16.46 | 0.02 | 1.00 | 10.81 | 0.02 | 1.28 | 0.02 | 0.70 | 0.01
DES17C2eoa | DES | II | phot | 0.213 | -16.64 | 0.02 | 1.51 | 10.06 | 0.01 | 0.69 | 0.02 | 0.43 | 0.01
DES17E1dtv | DES | II | phot | 0.199 | -17.25 | 0.02 | 1.00 | 10.11 | 0.01 | 1.21 | 0.02 | 0.65 | 0.01
DES17E2brh | DES | II | phot | 0.222 | -16.97 | 0.03 | 1.04 | 10.00 | 0.01 | – | – | 0.48 | 0.01
DES17X1gco | DES | II | phot | 0.203 | -17.59 | 0.02 | 1.00 | 8.84 | 0.01 | 0.38 | 0.02 | 0.35 | 0.01
DES17X2ayo | DES | II | phot | 0.122 | -17.02 | 0.04 | 1.00 | 8.83 | 0.02 | – | – | 0.39 | 0.01
DES17X2bxs | DES | II | phot | 0.177 | -16.57 | 0.06 | 1.65 | 10.40 | 0.01 | – | – | 0.64 | 0.01
DES13C1feu | DES | Ibc | spec | 0.059 | -17.68 | 0.03 | 1.00 | 10.42 | 0.04 | 1.29 | 0.04 | 0.70 | 0.01
DES14X2fna | DES | Ibc | spec | 0.045 | -19.37 | 0.04 | 1.00 | 8.17 | 0.01 | 0.83 | 0.02 | 0.43 | 0.02
DES15C1mat | DES | Ibc | spec | 0.12 | -17.48 | 0.03 | 1.00 | 10.54 | 0.01 | 0.87 | 0.02 | 0.50 | 0.01
DES15E2mhi | DES | Ibc | spec | 0.208 | -17.51 | 0.04 | 1.00 | 8.93 | 0.01 | 0.64 | 0.02 | 0.43 | 0.01
DES16C1zb | DES | Ibc | spec | 0.13 | -17.18 | 0.02 | 1.00 | 10.70 | 0.01 | 1.18 | 0.02 | 0.64 | 0.01
DES16S1kt | DES | Ibc | spec | 0.068 | -17.31 | 0.04 | 1.00 | 9.90 | 0.04 | 1.02 | 0.05 | 0.58 | 0.02
DES16X2bvf | DES | Ibc | spec | 0.135 | -17.97 | 0.02 | 1.00 | 8.57 | 0.02 | 0.32 | 0.03 | 0.32 | 0.02
DES16X2dqz | DES | Ibc | spec | 0.204 | -17.55 | 0.03 | 1.00 | 8.75 | 0.02 | 0.94 | 0.06 | 0.52 | 0.03
DES16X3bdb | DES | Ibc | spec | 0.229 | -18.08 | 0.02 | 1.00 | 9.68 | 0.01 | – | – | 0.50 | 0.01
DES17C1bzd | DES | Ibc | spec | 0.12 | -17.91 | 0.02 | 1.00 | 11.67 | 0.02 | 1.28 | 0.02 | 0.70 | 0.01
DES17C1ffz | DES | Ibc | spec | 0.09 | -17.85 | 0.02 | 1.00 | 9.50 | 0.01 | – | – | 0.54 | 0.01
DES13C1anve | DES | Ibc | phot | 0.214 | -17.34 | 0.03 | 1.00 | 9.44 | 0.02 | 0.57 | 0.03 | 0.40 | 0.01
DES13C1hxh | DES | Ibc | phot | 0.161 | -17.18 | 0.03 | 1.00 | 9.98 | 0.01 | 0.87 | 0.04 | 0.50 | 0.01
DES13C2rgr | DES | Ibc | phot | 0.244 | -18.20 | 0.03 | 1.00 | 9.44 | 0.02 | – | – | 0.24 | 0.01
DES13C3absw | DES | Ibc | phot | 0.069 | -18.55 | 0.03 | 1.00 | 9.08 | 0.04 | – | – | 0.66 | 0.02
DES13C3aeiv | DES | Ibc | phot | 0.174 | -17.76 | 0.02 | 1.00 | 8.36 | 0.03 | – | – | 0.28 | 0.01
DES13C3lex | DES | Ibc | phot | 0.109 | -16.88 | 0.02 | 1.00 | 8.48 | 0.01 | – | – | 0.20 | 0.01
DES13C3tqe | DES | Ibc | phot | 0.217 | -17.15 | 0.03 | 1.00 | 9.11 | 0.02 | 0.31 | 0.02 | 0.33 | 0.01
DES13S1rww | DES | Ibc | phot | 0.201 | -17.10 | 0.03 | 1.00 | 9.06 | 0.04 | 0.42 | 0.04 | 0.37 | 0.02
DES13S2hxn | DES | Ibc | phot | 0.143 | -16.82 | 0.04 | 1.23 | 9.53 | 0.02 | – | – | 0.39 | 0.01
DES13X1atuq | DES | Ibc | phot | 0.084 | -16.28 | 0.06 | 2.31 | 9.98 | 0.03 | 1.16 | 0.04 | 0.65 | 0.02
DES14C2bcu | DES | Ibc | phot | 0.213 | -17.47 | 0.02 | 1.00 | 10.86 | 0.01 | – | – | 0.94 | 0.01
DES14C3dzo | DES | Ibc | phot | 0.247 | -17.62 | 0.01 | 1.00 | 9.54 | 0.01 | – | – | 0.44 | 0.01
DES14C3guv | DES | Ibc | phot | 0.215 | -17.25 | 0.02 | 1.00 | 9.86 | 0.01 | – | – | 0.44 | 0.01
DES14C3ouq | DES | Ibc | phot | 0.107 | -16.74 | 0.03 | 1.00 | 10.03 | 0.01 | 1.10 | 0.02 | 0.60 | 0.01
DES14C3qby | DES | Ibc | phot | 0.219 | -17.65 | 0.07 | 1.00 | 10.50 | 0.01 | 1.28 | 0.01 | 0.69 | 0.01
DES14C3sle | DES | Ibc | phot | 0.144 | -17.25 | 0.02 | 1.00 | 9.48 | 0.01 | – | – | 0.58 | 0.01
DES14E1nyv | DES | Ibc | phot | 0.118 | -16.65 | 0.03 | 1.49 | 10.39 | 0.04 | 1.61 | 0.03 | 0.84 | 0.01
DES14X1mhi | DES | Ibc | phot | 0.129 | -17.28 | 0.05 | 1.00 | 9.78 | 0.01 | 1.50 | 0.02 | 0.79 | 0.01
DES14X1qwn | DES | Ibc | phot | 0.151 | -18.28 | 0.02 | 1.00 | 10.74 | 0.02 | – | – | 0.91 | 0.01
DES14X1tae | DES | Ibc | phot | 0.046 | -16.70 | 0.07 | 1.42 | 10.77 | 0.01 | 2.02 | 0.02 | 1.02 | 0.02
DES14X3fok | DES | Ibc | phot | 0.094 | -16.66 | 0.03 | 1.00 | 10.18 | 0.01 | 1.08 | 0.02 | 0.59 | 0.02
DES15C3hbo | DES | Ibc | phot | 0.16 | -16.87 | 0.02 | 1.00 | 9.72 | 0.01 | 0.79 | 0.03 | 0.47 | 0.01
DES15C3lrw | DES | Ibc | phot | 0.129 | -17.46 | 0.01 | 1.00 | 9.40 | 0.01 | – | – | 0.61 | 0.01
DES15C3mbe | DES | Ibc | phot | 0.181 | -16.41 | 0.02 | 1.00 | 8.94 | 0.02 | 0.52 | 0.03 | 0.37 | 0.01
DES15C3mud | DES | Ibc | phot | 0.182 | -17.85 | 0.06 | 1.00 | 10.02 | 0.01 | 1.10 | 0.01 | 0.60 | 0.01
DES15C3nqt | DES | Ibc | phot | 0.139 | -17.44 | 0.12 | 1.00 | 10.78 | 0.01 | 1.25 | 0.02 | 0.68 | 0.01
DES15X2kzu | DES | Ibc | phot | 0.136 | -17.14 | 0.04 | 1.00 | 9.67 | 0.01 | 0.87 | 0.02 | 0.51 | 0.02
DES15X3nso | DES | Ibc | phot | 0.238 | -17.15 | 0.02 | 1.00 | 9.32 | 0.01 | 0.72 | 0.02 | 0.44 | 0.01
DES16C3aky | DES | Ibc | phot | 0.239 | -16.10 | 0.04 | 1.00 | 8.77 | 0.02 | – | – | 0.38 | 0.02
DES16C3byu | DES | Ibc | phot | 0.143 | -18.09 | 0.02 | 1.00 | 10.96 | 0.01 | – | – | 0.94 | 0.01
DES16E1bkh | DES | Ibc | phot | 0.115 | -17.19 | 0.02 | 1.00 | 10.13 | 0.01 | – | – | 0.75 | 0.01
DES16S1bnj | DES | Ibc | phot | 0.183 | -17.46 | 0.02 | 1.00 | 7.83 | 0.09 | 0.23 | 0.13 | 0.28 | 0.05
DES16S1ku | DES | Ibc | phot | 0.064 | -16.19 | 0.03 | 2.59 | 10.20 | 0.04 | 1.33 | 0.04 | 0.72 | 0.02
DES16X1bax | DES | Ibc | phot | 0.213 | -17.76 | 0.02 | 1.00 | 10.14 | 0.01 | 0.94 | 0.02 | 0.53 | 0.01
DES16X1fmd | DES | Ibc | phot | 0.155 | -16.09 | 0.03 | 2.92 | 9.22 | 0.03 | 0.54 | 0.05 | 0.39 | 0.01
DES16X2eaw | DES | Ibc | phot | 0.151 | -17.52 | 0.05 | 1.00 | 10.74 | 0.02 | 1.44 | 0.03 | 0.77 | 0.01
DES16X3cpc | DES | Ibc | phot | 0.147 | -17.51 | 0.01 | 1.00 | 9.58 | 0.01 | – | – | 0.66 | 0.01
DES17C3pt | DES | Ibc | phot | 0.187 | -16.38 | 0.04 | 1.00 | 9.51 | 0.02 | 0.63 | 0.03 | 0.41 | 0.01
DES17X3mr | DES | Ibc | phot | 0.166 | -16.99 | 0.02 | 1.00 | 9.30 | 0.02 | – | – | 0.56 | 0.01
SN 1999D | LOSS | II | spec | 0.01 | -16.47 | 0.16 | 1.00 | 10.04 | 0.01 | 0.89 | 0.03 | 0.51 | 0.01
SN 1999an | LOSS | II | spec | 0.005 | -16.09 | 0.32 | 1.00 | 8.83 | 0.01 | 0.90 | 0.02 | 0.47 | 0.02
SN 1999el | LOSS | II | spec | 0.005 | -18.00 | 0.26 | 1.00 | – | – | – | – | – | –
SN 1999em | LOSS | II | spec | 0.002 | -16.02 | 0.62 | 1.00 | – | – | – | – | – | –
SN 1999go | LOSS | II | spec | 0.013 | -18.32 | 0.18 | 1.00 | – | – | – | – | – | –
SN 2000cb | LOSS | II | spec | 0.006 | -16.07 | 0.23 | 1.00 | 9.74 | 0.07 | 1.56 | 0.01 | 0.83 | 0.01
SN 2000dc | LOSS | II | spec | 0.01 | -16.99 | 0.15 | 1.00 | – | – | – | – | – | –
SN 2000eo | LOSS | II | spec | 0.01 | -18.16 | 0.24 | 1.00 | – | – | – | – | – | –
SN 2001K | LOSS | II | spec | 0.011 | -16.43 | 0.20 | 1.00 | 9.89 | 0.03 | 1.52 | 0.01 | 0.76 | 0.01
SN 2001bq | LOSS | II | spec | 0.008 | -17.11 | 0.22 | 1.00 | – | – | – | – | – | –
SN 2001bq | LOSS | II | spec | 0.008 | -17.11 | 0.22 | 1.00 | – | – | – | – | – | –
SN 2001cm | LOSS | II | spec | 0.012 | -17.10 | 0.19 | 1.00 | 11.23 | 0.01 | – | 0.01 | 1.17 | 0.01
SN 2001do | LOSS | II | spec | 0.011 | -17.46 | 0.14 | 1.00 | – | – | – | – | – | –
SN 2001hf | LOSS | II | spec | 0.014 | -16.96 | 0.32 | 1.00 | – | – | – | – | – | –
SN 2002an | LOSS | II | spec | 0.012 | -17.65 | 0.23 | 1.00 | – | – | – | – | – | –
SN 2002ds | LOSS | II | spec | 0.007 | -16.73 | 0.24 | 1.00 | – | – | – | – | – | –
SN 2002gw | LOSS | II | spec | 0.009 | -16.25 | 0.21 | 1.00 | – | – | – | – | – | –
SN 2003G | LOSS | II | spec | 0.011 | -18.42 | 0.23 | 1.00 | 10.08 | 0.03 | 1.60 | 0.01 | 0.85 | 0.01
SN 2003dv | LOSS | II | spec | 0.008 | -16.45 | 0.19 | 1.00 | 8.91 | 0.01 | 0.87 | 0.02 | 0.47 | 0.02
SN 2003ef | LOSS | II | spec | 0.013 | -16.55 | 0.32 | 1.00 | – | – | – | – | – | –
SN 2003hg | LOSS | II | spec | 0.014 | -17.06 | 0.14 | 1.00 | 10.83 | 0.01 | 1.58 | 0.01 | 0.82 | 0.01
SN 2003hl | LOSS | II | spec | 0.008 | -16.42 | 0.18 | 1.00 | 11.11 | 0.00 | 2.14 | 0.01 | 1.06 | 0.01
SN 2003iq | LOSS | II | spec | 0.008 | -17.02 | 0.18 | 1.00 | 11.11 | 0.00 | 2.14 | 0.01 | 1.06 | 0.01
SN 2003ld | LOSS | II | spec | 0.013 | -16.42 | 0.41 | 1.00 | 10.11 | 0.01 | 1.20 | 0.01 | 0.67 | 0.01
SN 2004al | LOSS | II | spec | 0.013 | -16.55 | 0.22 | 1.00 | – | – | – | – | – | –
SN 2004ci | LOSS | II | spec | 0.013 | -16.23 | 0.18 | 1.00 | 10.61 | 0.13 | 1.86 | 0.03 | 0.94 | 0.02
SN 2004dd | LOSS | II | spec | 0.013 | -16.16 | 0.18 | 1.00 | 9.96 | 0.03 | 1.29 | 0.02 | 0.71 | 0.02
SN 2004er | LOSS | II | spec | 0.014 | -16.82 | 0.18 | 1.00 | 10.14 | 0.01 | 1.03 | 0.01 | 0.60 | 0.01
SN 2004et | LOSS | II | spec | 0.001 | -16.39 | 1.06 | 1.00 | – | – | – | – | – | –
SN 2005H | LOSS | II | spec | 0.012 | -17.31 | 0.23 | 1.00 | 10.12 | 0.02 | 1.02 | 0.03 | 0.59 | 0.02
SN 2005J | LOSS | II | spec | 0.013 | -16.94 | 0.18 | 1.00 | 10.20 | 0.14 | 1.82 | 0.03 | 0.89 | 0.03
SN 2005an | LOSS | II | spec | 0.01 | -16.62 | 0.24 | 1.00 | – | – | – | – | – | –
SN 2005aq | LOSS | II | spec | 0.012 | -16.53 | 0.51 | 1.00 | – | – | – | – | – | –
SN 2005mg | LOSS | II | spec | 0.013 | -17.07 | 0.32 | 1.00 | 10.70 | 0.03 | 2.16 | 0.02 | 1.07 | 0.01
SN 2006be | LOSS | II | spec | 0.007 | -16.40 | 0.27 | 1.00 | 9.96 | 0.01 | 1.98 | 0.02 | 1.00 | 0.01
SN 2006bp | LOSS | II | spec | 0.004 | -16.10 | 0.33 | 1.00 | 10.69 | 0.01 | 2.32 | 0.01 | 1.13 | 0.01
SN 2006ca | LOSS | II | spec | 0.009 | -17.25 | 0.18 | 1.00 | – | – | – | – | – | –
SN 1998dt | LOSS | Ibc | spec | 0.014 | -16.84 | 0.51 | 1.00 | – | – | – | – | – | –
SN 1999bu | LOSS | Ibc | spec | 0.009 | -16.22 | 0.52 | 1.00 | 10.68 | 0.03 | 2.02 | 0.01 | 1.01 | 0.01
SN 1999cd | LOSS | Ibc | spec | 0.014 | -16.13 | 0.18 | 1.00 | – | – | – | – | – | –
SN 1999dn | LOSS | Ibc | spec | 0.009 | -16.94 | 0.16 | 1.00 | – | – | – | – | – | –
SN 2000C | LOSS | Ibc | spec | 0.012 | -17.64 | 0.19 | 1.00 | 10.03 | 0.01 | 0.61 | 0.01 | 0.42 | 0.01
SN 2000H | LOSS | Ibc | spec | 0.012 | -17.18 | 0.23 | 1.00 | – | – | – | – | – | –
SN 2000N | LOSS | Ibc | spec | 0.013 | -16.63 | 0.23 | 1.00 | – | – | – | – | – | –
SN 2001is | LOSS | Ibc | spec | 0.013 | -16.07 | 0.32 | 1.00 | – | – | – | – | – | –
SN 2002J | LOSS | Ibc | spec | 0.012 | -16.31 | 0.15 | 1.00 | 10.80 | 0.02 | 2.02 | 0.01 | 1.01 | 0.01
SN 2002ap | LOSS | Ibc | spec | 0.002 | -17.43 | 0.56 | 1.00 | – | – | – | – | – | –
SN 2002jj | LOSS | Ibc | spec | 0.013 | -17.38 | 0.23 | 1.00 | 10.04 | 0.05 | 1.51 | 0.02 | 0.80 | 0.02
SN 2002jz | LOSS | Ibc | spec | 0.005 | -16.20 | 0.33 | 1.00 | – | – | – | – | – | –
SN 2003aa | LOSS | Ibc | spec | 0.01 | -16.91 | 0.17 | 1.00 | – | – | – | – | – | –
SN 2004al | LOSS | Ibc | spec | 0.013 | -16.85 | 0.22 | 1.00 | – | – | – | – | – | –
SN 2004be | LOSS | Ibc | spec | 0.007 | -16.97 | 0.36 | 1.00 | – | – | – | – | – | –
SN 2004dk | LOSS | Ibc | spec | 0.005 | -17.23 | 0.25 | 1.00 | 10.23 | 0.00 | 2.25 | 0.00 | 1.10 | 0.01
SN 2004gq | LOSS | Ibc | spec | 0.006 | -16.80 | 0.24 | 1.00 | – | – | – | – | – | –
SN 2005U | LOSS | Ibc | spec | 0.011 | -17.76 | 0.24 | 1.00 | 10.28 | 0.01 | 1.29 | 0.01 | 0.71 | 0.01
SN 2005az | LOSS | Ibc | spec | 0.009 | -16.87 | 0.18 | 1.00 | 9.65 | 0.02 | 0.90 | 0.01 | 0.54 | 0.01
SN 2006F | LOSS | Ibc | spec | 0.013 | -16.44 | 0.41 | 1.00 | – | – | – | – | – | –
SN 2006T | LOSS | Ibc | spec | 0.007 | -17.34 | 0.24 | 1.00 | – | – | – | – | – | –
ZTF18aaaibml | ZTF | II | spec | 0.035 | -17.76 | 0.04 | 1.66 | 9.25 | 0.01 | 0.41 | 0.01 | 0.36 | 0.01
ZTF18aavqdyq | ZTF | II | spec | 0.026 | -16.61 | 0.06 | 7.66 | 8.92 | 0.02 | 0.81 | 0.04 | 0.42 | 0.02
ZTF18aawyjjq | ZTF | II | spec | 0.04 | -17.55 | 0.04 | 2.20 | 10.65 | 0.02 | 1.84 | 0.02 | 0.90 | 0.01
ZTF18abceakp | ZTF | II | spec | 0.03 | -17.50 | 0.11 | 2.34 | 7.52 | 0.07 | 0.63 | 0.15 | 0.36 | 0.08
ZTF18abcezmh | ZTF | II | spec | 0.057 | -18.13 | 0.05 | 1.02 | 9.62 | 0.02 | 0.57 | 0.02 | 0.41 | 0.01
ZTF18abcpmwh | ZTF | II | spec | 0.015 | -17.51 | 0.10 | 2.31 | 10.81 | 0.0 | 1.74 | 0.0 | 0.85 | 0.01
ZTF18abcptmt | ZTF | II | spec | 0.05 | -18.10 | 0.04 | 1.06 | 9.15 | 0.02 | 0.73 | 0.03 | 0.45 | 0.01
ZTF18abltfho | ZTF | II | spec | 0.055 | -18.81 | 0.03 | 1.0 | 10.42 | 0.01 | 0.72 | 0.01 | 0.45 | 0.01
ZTF18abokyfk | ZTF | II | spec | 0.017 | -17.11 | 0.09 | 3.95 | – | – | – | – | – | –
ZTF18abqyvzy | ZTF | II | spec | 0.015 | -17.90 | 0.10 | 1.38 | 8.96 | 0.02 | 0.41 | 0.02 | 0.34 | 0.02
ZTF18abrlljc | ZTF | II | spec | 0.05 | -18.57 | 0.03 | 1.0 | – | – | – | – | – | –
ZTF18abvmlow | ZTF | II | spec | 0.007 | -16.05 | 0.21 | 16.22 | 8.98 | 0.02 | 1.17 | 0.02 | 0.65 | 0.01
ZTF18abvvmdf | ZTF | II | spec | 0.03 | -17.08 | 0.07 | 4.11 | 10.34 | 0.04 | 1.30 | 0.02 | 0.71 | 0.01
ZTF18abzrgim | ZTF | II | spec | 0.021 | -17.37 | 0.08 | 2.77 | 10.61 | 0.05 | 1.79 | 0.02 | 0.91 | 0.01
ZTF18acebssa | ZTF | II | spec | 0.03 | -18.35 | 0.05 | 1.0 | 10.20 | 0.02 | 1.68 | 0.02 | 0.87 | 0.01
ZTF18acefuhk | ZTF | II | spec | 0.057 | -18.68 | 0.03 | 1.0 | 9.10 | 0.02 | 0.25 | 0.03 | 0.30 | 0.01
ZTF18achtnvk | ZTF | II | spec | 0.04 | -17.73 | 0.04 | 1.72 | 9.97 | 0.02 | 1.58 | 0.04 | 0.82 | 0.03
ZTF18acqwdla | ZTF | II | spec | 0.028 | -18.72 | 0.06 | 1.0 | 8.76 | 0.03 | 0.64 | 0.04 | 0.42 | 0.02
ZTF18acrtvmm | ZTF | II | spec | 0.023 | -17.57 | 0.07 | 2.12 | 10.90 | 0.05 | 2.0 | 0.02 | 0.99 | 0.01
ZTF18acszaiy | ZTF | II | spec | 0.03 | -17.84 | 0.05 | 1.49 | 10.02 | 0.06 | 1.90 | 0.04 | 0.96 | 0.02
ZTF18acuqskr | ZTF | II | spec | 0.045 | -18.35 | 0.05 | 1.0 | 9.86 | 0.02 | 1.42 | 0.02 | 0.75 | 0.01
ZTF18acvwdkk | ZTF | II | spec | 0.023 | -16.11 | 0.07 | 15.01 | – | – | – | – | – | –
ZTF18adazblo | ZTF | II | spec | 0.027 | -16.60 | 0.06 | 7.76 | 9.88 | 0.02 | 0.96 | 0.01 | 0.57 | 0.01
ZTF18adbacau | ZTF | II | spec | 0.037 | -17.36 | 0.04 | 2.81 | 10.57 | 0.04 | 1.42 | 0.02 | 0.77 | 0.01
ZTF18adbmrug | ZTF | II | spec | 0.024 | -17.97 | 0.08 | 1.25 | – | – | – | – | – | –
ZTF19aadnxog | ZTF | II | spec | 0.02 | -18.13 | 0.08 | 1.02 | 10.03 | 0.02 | 1.23 | 0.02 | 0.66 | 0.02
ZTF19aailepg | ZTF | II | spec | 0.03 | -17.43 | 0.05 | 2.55 | 8.98 | 0.01 | 0.48 | 0.01 | 0.39 | 0.01
ZTF19aakjcxs | ZTF | II | spec | 0.038 | -19.11 | 0.05 | 1.0 | 9.63 | 0.01 | 0.91 | 0.02 | 0.52 | 0.01
ZTF19aaloqmd | ZTF | II | spec | 0.034 | -17.58 | 0.05 | 2.09 | 9.12 | 0.02 | 1.0 | 0.02 | 0.58 | 0.01
ZTF19aamhgwm | ZTF | II | spec | 0.034 | -17.84 | 0.05 | 1.50 | 10.13 | 0.02 | 1.10 | 0.02 | 0.62 | 0.01
ZTF19aamhmsx | ZTF | II | spec | 0.047 | -18.18 | 0.04 | 1.0 | 9.10 | 0.03 | 0.99 | 0.05 | 0.47 | 0.03
ZTF19aamkmxv | ZTF | II | spec | 0.014 | -17.54 | 0.11 | 2.21 | 9.29 | 0.01 | 1.14 | 0.02 | 0.64 | 0.01
ZTF19aamvape | ZTF | II | spec | 0.03 | -17.96 | 0.05 | 1.28 | 7.16 | 0.19 | 1.31 | 0.30 | 0.68 | 0.21
ZTF19aanfnvl | ZTF | II | spec | 0.032 | -17.72 | 0.05 | 1.74 | 11.24 | 0.0 | 2.36 | 0.0 | 1.14 | 0.01
ZTF19aanfqug | ZTF | II | spec | 0.046 | -17.79 | 0.04 | 1.59 | 6.77 | 0.19 | -0.19 | 0.25 | 0.13 | 0.15
ZTF19aanhhal | ZTF | II | spec | 0.026 | -17.15 | 0.06 | 3.72 | 10.96 | 0.04 | 1.87 | 0.01 | 0.95 | 0.01
ZTF19aaniore | ZTF | II | spec | 0.03 | -17.23 | 0.05 | 3.35 | 10.85 | 0.01 | 2.03 | 0.01 | 1.01 | 0.01
ZTF19aanpcep | ZTF | II | spec | 0.031 | -17.74 | 0.05 | 1.71 | 8.99 | 0.02 | 0.94 | 0.05 | 0.46 | 0.02
ZTF19aanqzhm | ZTF | II | spec | 0.049 | -18.33 | 0.05 | 1.0 | – | – | – | – | – | –
ZTF19aanrrqu | ZTF | II | spec | 0.024 | -18.01 | 0.06 | 1.19 | 8.80 | 0.02 | 0.11 | 0.02 | 0.25 | 0.03
ZTF19aapafit | ZTF | II | spec | 0.019 | -16.92 | 0.08 | 5.05 | 9.23 | 0.02 | 0.90 | 0.05 | 0.50 | 0.02
ZTF19aapafqd | ZTF | II | spec | 0.032 | -17.56 | 0.05 | 2.17 | 10.73 | 0.02 | 1.90 | 0.01 | 0.93 | 0.01
ZTF19aapbfot | ZTF | II | spec | 0.03 | -16.97 | 0.05 | 4.75 | 8.59 | 0.03 | 0.74 | 0.07 | 0.40 | 0.02
ZTF19aapzbjr | ZTF | II | spec | 0.03 | -17.77 | 0.05 | 1.64 | 9.78 | 0.02 | 0.98 | 0.02 | 0.57 | 0.02
ZTF19aaqdkrm | ZTF | II | spec | 0.034 | -17.83 | 0.05 | 1.52 | 10.13 | 0.02 | 1.10 | 0.02 | 0.62 | 0.01
ZTF19aaqxosb | ZTF | II | spec | 0.019 | -16.56 | 0.08 | 8.23 | 10.48 | 0.05 | 1.47 | 0.01 | 0.79 | 0.01
ZTF19aathllr | ZTF | II | spec | 0.055 | -18.77 | 0.04 | 1.0 | – | – | – | – | – | –
ZTF19aatqzim | ZTF | II | spec | 0.05 | -18.22 | 0.04 | 1.0 | 10.65 | 0.01 | 1.72 | 0.02 | 0.89 | 0.01
ZTF19aaugaam | ZTF | II | spec | 0.05 | -18.84 | 0.03 | 1.0 | 10.13 | 0.01 | 1.06 | 0.01 | 0.61 | 0.01
ZTF19aauisdr | ZTF | II | spec | 0.043 | -17.82 | 0.05 | 1.53 | 9.38 | 0.02 | 0.66 | 0.03 | 0.43 | 0.01
ZTF19aauishy | ZTF | II | spec | 0.023 | -16.40 | 0.07 | 10.21 | 9.17 | 0.02 | 0.73 | 0.04 | 0.46 | 0.02
ZTF19aauxxgk | ZTF | II | spec | 0.026 | -18.01 | 0.06 | 1.19 | – | – | – | – | – | –
ZTF19aavbjfp | ZTF | II | spec | 0.028 | -17.06 | 0.05 | 4.20 | 8.60 | 0.03 | 0.69 | 0.05 | 0.45 | 0.02
ZTF19aavbkly | ZTF | II | spec | 0.041 | -17.40 | 0.04 | 2.67 | 10.37 | 0.01 | 1.24 | 0.02 | 0.65 | 0.02
ZTF19aavhblr | ZTF | II | spec | 0.05 | -18.37 | 0.06 | 1.0 | 9.0 | 0.02 | 0.93 | 0.04 | 0.53 | 0.02
ZTF19aavjukt | ZTF | II | spec | 0.036 | -18.43 | 0.05 | 1.0 | 9.10 | 0.01 | 0.29 | 0.01 | 0.34 | 0.01
ZTF19aavkptg | ZTF | II | spec | 0.038 | -17.26 | 0.04 | 3.20 | 9.73 | 0.05 | 1.74 | 0.06 | 0.87 | 0.01
ZTF19aawgxdn | ZTF | II | spec | 0.031 | -17.29 | 0.05 | 3.07 | 10.82 | 0.02 | 1.65 | 0.02 | 0.82 | 0.02
ZTF19aazfvhh | ZTF | II | spec | 0.034 | -17.20 | 0.05 | 3.50 | – | – | – | – | – | –
ZTF19aazudta | ZTF | II | spec | 0.024 | -16.88 | 0.07 | 5.35 | – | – | – | – | – | –
ZTF19aazyvub | ZTF | II | spec | 0.023 | -17.09 | 0.07 | 4.04 | 9.37 | 0.02 | 1.25 | 0.02 | 0.62 | 0.02
ZTF19abacxod | ZTF | II | spec | 0.018 | -16.85 | 0.09 | 5.57 | 9.81 | 0.01 | 0.74 | 0.02 | 0.46 | 0.01
ZTF19abajxet | ZTF | II | spec | 0.015 | -17.68 | 0.10 | 1.84 | 10.25 | 0.02 | 1.05 | 0.01 | 0.61 | 0.01
ZTF19abbnamr | ZTF | II | spec | 0.014 | -17.30 | 0.10 | 3.06 | 10.88 | 0.02 | 1.63 | 0.01 | 0.85 | 0.01
ZTF19abbwfgp | ZTF | II | spec | 0.026 | -18.10 | 0.06 | 1.06 | 10.71 | 0.03 | 1.82 | 0.01 | 0.90 | 0.01
ZTF19abbxykm | ZTF | II | spec | 0.047 | -18.96 | 0.03 | 1.0 | 9.30 | 0.02 | 1.15 | 0.03 | 0.64 | 0.02
ZTF19abcneik | ZTF | II | spec | 0.035 | -17.35 | 0.06 | 2.85 | 9.25 | 0.03 | 1.10 | 0.05 | 0.60 | 0.02
ZTF19abctxhf | ZTF | II | spec | 0.05 | -18.16 | 0.04 | 1.0 | 9.76 | 0.02 | 1.03 | 0.02 | 0.59 | 0.01
ZTF19abddsvk | ZTF | II | spec | 0.058 | -18.54 | 0.05 | 1.0 | 9.93 | 0.02 | 0.86 | 0.04 | 0.51 | 0.02
ZTF19abdviwl | ZTF | II | spec | 0.03 | -17.50 | 0.05 | 2.35 | 7.30 | 0.14 | 0.44 | 0.20 | 0.35 | 0.07
ZTF19abecaca | ZTF | II | spec | 0.032 | -19.45 | 0.05 | 1.0 | 9.68 | 0.01 | 0.78 | 0.02 | 0.49 | 0.01
ZTF19abegizf | ZTF | II | spec | 0.037 | -17.48 | 0.05 | 2.39 | – | – | – | – | – | –
ZTF19abfloxk | ZTF | II | spec | 0.016 | -16.84 | 0.11 | 5.61 | – | – | – | – | – | –
ZTF19abgndlf | ZTF | II | spec | 0.03 | -17.65 | 0.05 | 1.91 | – | – | – | – | – | –
ZTF19abgrmfu | ZTF | II | spec | 0.035 | -18.46 | 0.05 | 1.0 | 8.73 | 0.02 | 0.94 | 0.03 | 0.56 | 0.02
ZTF19abiqfxi | ZTF | II | spec | 0.054 | -19.44 | 0.03 | 1.0 | 9.48 | 0.02 | 0.68 | 0.04 | 0.43 | 0.01
ZTF19abiszoe | ZTF | II | spec | 0.043 | -18.22 | 0.10 | 1.0 | 10.72 | 0.02 | 1.90 | 0.01 | 0.93 | 0.01
ZTF19abjpntj | ZTF | II | spec | 0.055 | -18.14 | 0.03 | 1.01 | 10.67 | 0.02 | 1.23 | 0.02 | 0.67 | 0.02
ZTF19abjsmmv | ZTF | II | spec | 0.02 | -17.19 | 0.08 | 3.53 | 10.47 | 0.08 | 1.75 | 0.02 | 0.90 | 0.02
ZTF19abkfqqp | ZTF | II | spec | 0.03 | -19.32 | 0.05 | 1.0 | 11.25 | 0.02 | 1.42 | 0.02 | 0.75 | 0.01
ZTF19ablfdwt | ZTF | II | spec | 0.026 | -17.31 | 0.06 | 3.03 | 9.15 | 0.03 | 1.04 | 0.04 | 0.58 | 0.02
ZTF19ablojrw | ZTF | II | spec | 0.049 | -18.53 | 0.05 | 1.0 | – | – | – | – | – | –
ZTF19abovstj | ZTF | II | spec | 0.045 | -18.22 | 0.06 | 1.0 | 9.49 | 0.01 | 0.90 | 0.02 | 0.53 | 0.02
ZTF19abpidqn | ZTF | II | spec | 0.015 | -16.29 | 0.10 | 11.72 | – | – | – | – | – | –
ZTF19abpxiff | ZTF | II | spec | 0.055 | -18.19 | 0.05 | 1.0 | – | – | – | – | – | –
ZTF19abpyqog | ZTF | II | spec | 0.031 | -17.12 | 0.05 | 3.85 | – | – | – | – | – | –
ZTF19abqgtqo | ZTF | II | spec | 0.036 | -17.44 | 0.04 | 2.54 | 9.08 | 0.03 | 0.68 | 0.05 | 0.44 | 0.02
ZTF19abqhobb | ZTF | II | spec | 0.018 | -17.49 | 0.08 | 2.38 | 9.07 | 0.02 | 0.71 | 0.04 | 0.45 | 0.01
ZTF19abqrhvt | ZTF | II | spec | 0.021 | -18.09 | 0.07 | 1.08 | 9.29 | 0.01 | 0.87 | 0.03 | 0.42 | 0.02
ZTF19abqrhvy | ZTF | II | spec | 0.032 | -18.13 | 0.05 | 1.02 | 11.19 | 0.01 | 1.58 | 0.01 | 0.82 | 0.01
ZTF19abrbmvt | ZTF | II | spec | 0.039 | -17.81 | 0.04 | 1.56 | 11.09 | 0.08 | 2.23 | 0.03 | 1.09 | 0.01
ZTF19abueupg | ZTF | II | spec | 0.02 | -16.05 | 0.07 | 16.29 | 9.19 | 0.06 | 1.25 | 0.04 | 0.70 | 0.02
ZTF19abukakm | ZTF | II | spec | 0.057 | -18.65 | 0.03 | 1.0 | – | – | – | – | – | –
ZTF19abuzinv | ZTF | II | spec | 0.02 | -16.45 | 0.07 | 9.46 | – | – | – | – | – | –
ZTF19abwsagv | ZTF | II | spec | 0.038 | -18.18 | 0.04 | 1.0 | – | – | – | – | – | –
ZTF19abyuzch | ZTF | II | spec | 0.024 | -18.20 | 0.07 | 1.0 | – | – | – | – | – | –
ZTF19abzqwpr | ZTF | II | spec | 0.037 | -18.24 | 0.08 | 1.0 | 9.36 | 0.02 | 0.85 | 0.03 | 0.50 | 0.01
ZTF19acanzwg | ZTF | II | spec | 0.047 | -18.29 | 0.04 | 1.0 | – | – | – | – | – | –
ZTF19acbhvgi | ZTF | II | spec | 0.031 | -17.36 | 0.05 | 2.81 | 7.79 | 0.07 | 1.06 | 0.13 | 0.60 | 0.08
ZTF19acbmxky | ZTF | II | spec | 0.048 | -18.34 | 0.04 | 1.0 | 9.78 | 0.02 | 0.87 | 0.03 | 0.52 | 0.01
ZTF19acbrzzr | ZTF | II | spec | 0.03 | -17.17 | 0.05 | 3.61 | 7.40 | 0.06 | 0.16 | 0.10 | 0.20 | 0.04
ZTF19acbwejj | ZTF | II | spec | 0.014 | -16.80 | 0.11 | 5.95 | 8.63 | 0.02 | 0.39 | 0.02 | 0.37 | 0.01
ZTF19acbwouf | ZTF | II | spec | 0.03 | -17.03 | 0.05 | 4.39 | 7.14 | 0.22 | -0.04 | 0.31 | 0.16 | 0.15
ZTF19accbeju | ZTF | II | spec | 0.055 | -18.61 | 0.05 | 1.0 | 10.58 | 0.01 | 1.61 | 0.02 | 0.84 | 0.01
ZTF19acchaza | ZTF | II | spec | 0.035 | -17.79 | 0.05 | 1.60 | 8.16 | 0.04 | 0.78 | 0.06 | 0.48 | 0.03
ZTF19acctwpz | ZTF | II | spec | 0.017 | -16.39 | 0.09 | 10.29 | 7.75 | 0.10 | 0.96 | 0.18 | 0.56 | 0.08
ZTF19aceshib | ZTF | II | spec | 0.048 | -18.67 | 0.04 | 1.0 | 9.75 | 0.01 | 0.96 | 0.02 | 0.56 | 0.02
ZTF19acfejbj | ZTF | II | spec | 0.011 | -17.21 | 0.14 | 3.44 | 9.72 | 0.01 | 0.92 | 0.01 | 0.55 | 0.01
ZTF19acftude | ZTF | II | spec | 0.04 | -17.98 | 0.04 | 1.24 | 9.89 | 0.01 | 1.12 | 0.02 | 0.62 | 0.01
ZTF19acgbkzr | ZTF | II | spec | 0.026 | -16.64 | 0.06 | 7.34 | 10.16 | 0.02 | 1.20 | 0.02 | 0.68 | 0.01
ZTF19achjqbk | ZTF | II | spec | 0.05 | -17.93 | 0.04 | 1.32 | – | – | – | – | – | –
ZTF19acignlo | ZTF | II | spec | 0.048 | -18.87 | 0.03 | 1.0 | 9.85 | 0.01 | 0.98 | 0.03 | 0.55 | 0.01
ZTF19acjwdnu | ZTF | II | spec | 0.053 | -18.76 | 0.03 | 1.0 | 7.75 | 0.17 | 0.85 | 0.30 | 0.47 | 0.15
ZTF19aclobbu | ZTF | II | spec | 0.012 | -16.68 | 0.12 | 7.01 | 8.29 | 0.02 | 0.98 | 0.02 | 0.57 | 0.01
ZTF19acrcxri | ZTF | II | spec | 0.027 | -18.55 | 0.06 | 1.0 | 8.76 | 0.01 | 0.24 | 0.01 | 0.32 | 0.01
ZTF19acryurj | ZTF | II | spec | 0.022 | -18.08 | 0.07 | 1.08 | 10.53 | 0.04 | 1.71 | 0.02 | 0.87 | 0.02
ZTF19acwrrvg | ZTF | II | spec | 0.027 | -18.59 | 0.06 | 1.0 | – | – | – | – | – | –
ZTF19acxowrr | ZTF | II | spec | 0.051 | -18.54 | 0.03 | 1.0 | 10.94 | 0.02 | 1.65 | 0.02 | 0.85 | 0.01
ZTF19acyjviz | ZTF | II | spec | 0.022 | -17.34 | 0.07 | 2.88 | 9.40 | 0.02 | 1.01 | 0.01 | 0.59 | 0.01
ZTF19aczlldp | ZTF | II | spec | 0.028 | -18.0 | 0.06 | 1.21 | 7.96 | 0.05 | 0.15 | 0.05 | 0.25 | 0.02
ZTF19adannbl | ZTF | II | spec | 0.048 | -18.93 | 0.05 | 1.0 | – | – | – | – | – | –
ZTF19adavzew | ZTF | II | spec | 0.04 | -17.85 | 0.04 | 1.47 | 9.16 | 0.01 | 1.03 | 0.02 | 0.57 | 0.01
ZTF19adccrca | ZTF | II | spec | 0.045 | -17.82 | 0.03 | 1.53 | 9.20 | 0.01 | 0.99 | 0.02 | 0.56 | 0.01
ZTF20aabconi | ZTF | II | spec | 0.043 | -18.07 | 0.04 | 1.11 | – | – | – | – | – | –