# Collapsars as Sites of _r_ -process Nucleosynthesis: Systematic Near-
Infrared Follow-up of Type Ic-BL Supernovae

Shreya Anand Cahill Center for Astrophysics, California Institute of
Technology, Pasadena CA 91125, USA  Jennifer Barnes Kavli Institute for
Theoretical Physics, Kohn Hall, University of California, Santa Barbara, CA
93106, USA  Sheng Yang  The Oskar Klein Centre, Department of Astronomy,
Stockholm University, AlbaNova, SE-10691, Stockholm, Sweden  Mansi M. Kasliwal
Cahill Center for Astrophysics, California Institute of Technology, Pasadena
CA 91125, USA  Michael W. Coughlin School of Physics and Astronomy, University
of Minnesota, Minneapolis, Minnesota 55455, USA  Jesper Sollerman The Oskar
Klein Centre, Department of Astronomy, Stockholm University, AlbaNova,
SE-10691, Stockholm, Sweden  Kishalay De MIT-Kavli Institute for Astrophysics
and Space Research, 77 Massachusetts Ave., Cambridge, MA 02139, USA
Christoffer Fremling Cahill Center for Astrophysics, California Institute of
Technology, Pasadena CA 91125, USA  Alessandra Corsi Department of Physics and
Astronomy, Texas Tech University, Box 1051, Lubbock, TX 79409-1051, USA  Anna
Y. Q. Ho Department of Astronomy, Cornell University, Ithaca, NY 14850, USA
Arvind Balasubramanian Department of Physics and Astronomy, Texas Tech
University, Box 1051, Lubbock, TX 79409-1051, USA  Conor Omand The Oskar Klein
Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691,
Stockholm, Sweden  Gokul P. Srinivasaragavan  Department of Astronomy,
University of Maryland, College Park, MD 20742, USA  S. Bradley Cenko
Astrophysics Science Division, NASA Goddard Space Flight Center, MC 661,
Greenbelt, MD 20771, USA  Joint Space-Science Institute, University of
Maryland, College Park, MD 20742, USA  Tomás Ahumada Cahill Center for
Astrophysics, California Institute of Technology, Pasadena CA 91125, USA  Igor
Andreoni Joint Space-Science Institute, University of Maryland, College Park,
MD 20742, USA  Astrophysics Science Division, NASA Goddard Space Flight
Center, MC 661, Greenbelt, MD 20771, USA  Department of Astronomy, University
of Maryland, College Park, MD 20742, USA  Aishwarya Dahiwale  Cahill Center
for Astrophysics, California Institute of Technology, Pasadena CA 91125, USA
Kaustav Kashyap Das Cahill Center for Astrophysics, California Institute of
Technology, Pasadena CA 91125, USA  Jacob Jencson Department of Physics and
Astronomy, The Johns Hopkins University, Baltimore, MD 21218, USA  Viraj
Karambelkar Cahill Center for Astrophysics, California Institute of
Technology, Pasadena CA 91125, USA  Harsh Kumar Department of Physics, Indian
Institute of Technology Bombay, Powai, Mumbai 400076, India  LSSTC Data
Science Fellow 2018  Brian D. Metzger Department of Physics and Columbia
Astrophysics Laboratory, Columbia University, Pupin Hall, New York, NY 10027,
USA  Center for Computational Astrophysics, Flatiron Institute, 162 5th Ave,
New York, NY 10010, USA  Daniel Perley Astrophysics Research Institute,
Liverpool John Moores University, IC2, Liverpool Science Park, 146 Brownlow
Hill, Liverpool L3 5RF, UK  Nikhil Sarin Nordita, Stockholm University and KTH
Royal Institute of Technology Hannes Alfvéns väg 12, SE-106 91 Stockholm,
Sweden  The Oskar Klein Centre, Department of Physics, Stockholm University,
AlbaNova, SE-106 91 Stockholm, Sweden  Tassilo Schweyer  The Oskar Klein
Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691,
Stockholm, Sweden  Steve Schulze The Oskar Klein Centre, Department of
Physics, Stockholm University, AlbaNova, SE-106 91 Stockholm, Sweden  Yashvi
Sharma Cahill Center for Astrophysics, California Institute of Technology,
Pasadena CA 91125, USA  Tawny Sit  Department of Astronomy, The Ohio State
University, Columbus, OH 43210, USA  Robert Stein Cahill Center for
Astrophysics, California Institute of Technology, Pasadena CA 91125, USA
Leonardo Tartaglia Istituto Nazionale di Astrofisica, Osservatorio Astronomico
di Padova, Vicolo dell’Osservatorio 5, 35122 Padova, Italy  Samaporn Tinyanont
Department of Astronomy and Astrophysics, University of California, Santa
Cruz, CA 95064, USA  Anastasios Tzanidakis Department of Astronomy, University
of Washington, Seattle, WA 98195, USA  Jan van Roestel Cahill Center for
Astrophysics, California Institute of Technology, Pasadena CA 91125, USA
Yuhan Yao Cahill Center for Astrophysics, California Institute of Technology,
Pasadena CA 91125, USA  Joshua S. Bloom Department of Astronomy, University of
California, Berkeley, CA 94720-3411, USA; Physics, Lawrence Berkeley National
Laboratory, 1 Cyclotron Road, MS50B-4206, Berkeley, CA 94720, USA  David O.
Cook IPAC, California Institute of Technology, 1200 E. California Blvd,
Pasadena, CA 91125, USA  Richard Dekany Caltech Optical Observatories,
California Institute of Technology, Pasadena, CA 91125  Matthew J. Graham
Cahill Center for Astrophysics, California Institute of Technology, Pasadena
CA 91125, USA  Steven L. Groom IPAC, California Institute of Technology, 1200
E. California Blvd, Pasadena, CA 91125, USA  David L. Kaplan Center for
Gravitation, Cosmology, and Astrophysics, Department of Physics, University of
Wisconsin-Milwaukee, PO Box 413, Milwaukee, WI, 53201, USA  Frank J. Masci
IPAC, California Institute of Technology, 1200 E. California Blvd, Pasadena,
CA 91125, USA  Michael S. Medford Department of Astronomy, University of
California, Berkeley, Berkeley, CA 94720  Lawrence Berkeley National
Laboratory, 1 Cyclotron Rd., Berkeley, CA 94720  Reed Riddle Caltech Optical
Observatories, California Institute of Technology, Pasadena, CA 91125  Chaoran
Zhang Center for Gravitation, Cosmology, and Astrophysics, Department of
Physics, University of Wisconsin-Milwaukee, PO Box 413, Milwaukee, WI, 53201,
USA

###### Abstract

One of the open questions following the discovery of GW170817 is whether
neutron star mergers are the only astrophysical sites capable of producing _r_
-process elements. Simulations have shown that 0.01-0.1M⊙ of _r_ -process
material could be generated in the outflows originating from the accretion
disk surrounding the rapidly rotating black hole that forms as a remnant to
both neutron star mergers and collapsing massive stars associated with long-
duration gamma-ray bursts (collapsars). The hallmark signature of _r_ -process
nucleosynthesis in the binary neutron star merger GW170817 was its long-
lasting near-infrared emission, thus motivating a systematic photometric study
of the light curves of broadlined stripped-envelope (Ic-BL) supernovae (SNe)
associated with collapsars. We present the first systematic study of 25 SNe
Ic-BL—discovered with the Zwicky Transient Facility and from the literature—in
the optical/near-infrared bands to determine what quantity of _r_ -process
material, if any, is synthesized in these explosions. Using semi-analytic
models designed to account for _r_ -process production in SNe Ic-BL, we
perform light curve fitting to derive constraints on the _r_ -process mass for
these SNe. We also perform independent light curve fits to models without _r_
-process. We find that the _r_ -process-free models are a better fit to the
light curves of the objects in our sample. Thus we conclude that there is no
compelling evidence of _r_ -process enrichment in any of our objects. Further
high-cadence infrared photometric studies and nebular spectroscopic analysis
would be sensitive to smaller quantities of _r_ -process ejecta mass or
indicate whether all collapsars are completely devoid of _r_ -process
nucleosynthesis.

##  1 Introduction

The dominant process responsible for producing elements heavier than iron is
the rapid neutron capture process, known as the _r_ -process (Burbidge et al.,
1957; Cameron, 1957), which only has a few plausible astrophysical sites.
While standard core-collapse supernovae (SNe) were previously considered as
candidate sites for _r_ -process nucleosynthesis (Woosley et al., 1994;
Takahashi et al., 1994; Qian & Woosley, 1996), they have since been disfavored
because simulations of neutrino-driven winds in core-collapse SNe fail to
create conducive conditions for _r_ -process production (Thompson et al.,
2001; Roberts et al., 2010; Martínez-Pinedo et al., 2012; Hotokezaka et al.,
2018). On the other hand, before 2017, many studies (Lattimer & Schramm, 1974,
1976; Symbalisty & Schramm, 1982) predicted that mergers of two neutron stars
(NSs) or neutron stars with black holes were capable of generating _r_
-process elements during the decompression of cold, neutron-rich matter
ensuing from the tidal disruption of the neutron stars. Li & Paczyński (1998)
first suggested that the signature of such _r_ -process nucleosynthesis would
be detectable in an ultraviolet, optical and near-infrared (NIR) transient
powered by the radioactive decay of neutron-rich nuclei, termed as a
“kilonova” for its brightness, which was predicted to be 1000$\times$ that of
a classical nova (Metzger et al., 2010). Other studies proposed that _r_
-process elements could be synthesized in a rare SN subtype known as a
hypernova (e.g. Fujimoto et al., 2007). In this scenario, the SN explosion
produces a rapidly rotating central BH surrounded by an accretion disk.
Accretion onto the BH is thought to power a relativistic jet, while material
in the disk may neutronize, allowing the _r_ -process to occur when the newly
neutron-rich material is unbound as a disk wind.

Galactic archaeological studies (Ji et al., 2016a, b), geochemical studies
(Wallner et al., 2021), and studies of the early solar system (Tissot et al.,
2016) offer unique insights into which astrophysical sites could plausibly
explain observed _r_ -process elemental abundances. A recent study of _r_
-process abundances in the Magellanic Clouds indicate that the astrophysical
_r_ -process site has a time-delay longer than for core-collapse SNe (Reggiani
et al., 2021). Second- and third-peak abundance patterns inferred from metal-
poor Galactic halo stars show consistency with the solar _r_ -process
abundance pattern at high atomic number, but scatter at low atomic number that
could be attributed to enrichment from multiple sources, including magneto-
rotational hypernovae (Yong et al., 2021). Measurements of excess $[\rm
Ba/Fe]$ and $[\rm Eu/Fe]$ abundances in the dwarf galaxy Reticulum II argue
for not only a rare and prolific event, but one capable of enriching the
galaxy early in its history (Ji et al., 2016a; Tarumi et al., 2020), pointing
towards a potential rare SN subtype whose _r_ -process production would follow
star formation (Côté et al., 2019; Siegel et al., 2019). Further evidence of
_heavy_ _r_ -process enrichment in the disrupted dwarf galaxy Gaia Sausage
Enceladus ($\sim$3.6 Gyr star formation duration) but not in the disrupted
dwarf galaxy Kraken (with $\approx$2 Gyr star formation duration) points
towards multiple _r_ -process enrichment sites operating on different
timescales (Naidu et al., 2021).

Overall, geological studies and studies of the early solar system and Galactic
chemical evolution exemplify the need for rare and prolific astrophysical
sites to explain observed abundances, and insinuate that the solar _r_
-process abundance pattern could be universal. While NS mergers are compatible
with many facets of the above findings (Hotokezaka et al., 2018; Côté et al.,
2018; Metzger, 2019), assuming that mergers are the sole producers of _r_
-process material presents some potential hurdles. For example, the time delay
between formation and merger of NS systems must be short enough to enrich old,
ultra-faint dwarf galaxies with heavy elements (Roederer et al., 2016; Ji et
al., 2016a; Côté et al., 2019). Furthermore, natal merger kicks present a
challenge for low-mass galaxies to retain pre-merger compact binaries (Komiya
& Shigeyama, 2016). The question of whether NS mergers alone can explain the
relative abundances of _r_ -process elements (e.g. $[\rm Eu/Fe]$ vs. $[\rm
Fe/H]$) in the solar neighborhood remains unanswered (Beniamini et al., 2016;
Bonetti et al., 2019).

The multi-messenger detection of the binary neutron star merger GW170817
(Abbott et al., 2017a), an associated short burst of gamma-rays GRB170817
(Abbott et al., 2017b) and the kilonova AT2017gfo (Andreoni et al., 2017;
Chornock et al., 2017; Coulter et al., 2017; Cowperthwaite et al., 2017; Drout
et al., 2017; Evans et al., 2017; Kasliwal et al., 2017, 2022; Kilpatrick et
al., 2017; Lipunov et al., 2017; McCully et al., 2017; Nicholl et al., 2017;
Shappee et al., 2017; Soares-Santos et al., 2017; Pian et al., 2017; Smartt et
al., 2017; Tanvir et al., 2017; Villar et al., 2017; Utsumi et al., 2017)
relayed the first direct evidence that NS mergers are an astrophysical site of
_r_ -process nucleosynthesis and short GRB progenitors. Multi-band photometry
and optical/NIR spectroscopy of AT2017gfo indicated that the KN ejecta was
enriched with _r_ -process elements (Drout et al., 2017; Chornock et al.,
2017; Cowperthwaite et al., 2017; Kasliwal et al., 2017; Kilpatrick et al.,
2017; Pian et al., 2017; Smartt et al., 2017; Tanvir et al., 2017; Watson et
al., 2019) including heavier species occupying the second- and third-peak
(Tanvir et al., 2017; Watson et al., 2019; Kasliwal et al., 2019; Gillanders
et al., 2021).

Although GW170817 confirmed NS mergers as _r_ -process nucleosynthesis sites,
some fundamental open questions on the nature of _r_ -process production still
remain. Namely, can the rates of and expected yields from NS mergers explain
the total amount of _r_ -process production measured in the Universe? Or, do
the direct and indirect clues about _r_ -process production in the Universe
point towards an alternative _r_ -process site, such as rare core-collapse
SNe?

The discovery of the broadlined Type Ic supernova SN 1998bw at 40 Mpc (Galama
et al., 1998), following the long GRB 980425 was a watershed event that
provided the first direct evidence of the long GRB–SN connection (Kulkarni et
al., 1998; Galama et al., 1999). The spectra of these SNe exhibit broad
features due to high photospheric velocities (${\gtrsim}20,000$ km s-1). They
have higher inferred energies than typical SNe (at $\sim$1052 erg), and are
stripped of both hydrogen and helium (Modjaz et al., 2016; Sun & Gal-Yam,
2017). Since SN 1998bw, several other SNe Ic-BL have been discovered in
conjunction with long GRBs (e.g. Kocevski et al. 2007; Olivares E. et al.
2012; Cano et al. 2017), prompting the need for a robust theoretical model
that could motivate this established association. Thus, collapsar theory
(MacFadyen & Woosley, 1999; MacFadyen et al., 2001) was constructed to explain
long GRBs and their associated SN counterparts. The term collapsar refers to a
rapidly-rotating, massive star that collapses into a black hole, forming an
accretion disk around the central black hole. Collapsars are distinct from the
magnetar-powered explosions (referred to as “magnetohydrodynamic (MHD) SNe”)
also proposed to be related to SNe Ic-BL (Metzger et al., 2011; Kashiyama et
al., 2016). However, recent discoveries of kilonovae associated with long GRBs
(e.g. Rastinejad et al., 2022) have shifted the paradigm from the traditional
conception that all long GRBs have a collapsar or magnetar origin. Thus some
fraction of long-duration GRBs may also originate from compact binary mergers.

Several works (Fujimoto et al., 2007; Ono et al., 2012; Nakamura et al., 2015;
Soker & Gilkis, 2017) have since hypothesized that the explosions that give
rise to SNe Ic-BL and (in some cases) to their accompanying long GRBs (i.e.
collapsars) are capable of producing 0.01-0.1M⊙ of _r_ -process material per
event. Simulations suggest that in the case of a NS merger, an accretion disk
forms surrounding the merger’s newly-born central black hole (Shibata &
Taniguchi, 2006) and _r_ -process elements originate in the associated disk
outflows (Metzger et al., 2008, 2009). Such accretion flows are not only
central to the short GRBs associated with NS mergers, but also with the long
classes of GRBs associated with collapsars. However, predictions about _r_
-process-production in the collapsar context are sensitive to assumptions
about the magnetic field, the disk viscosity model, and the treatment of
neutrinos, among other factors. Surman et al. (2006) argued that only light
_r_ -process elements can be synthesized in collapsar accretion disks due to
neutrino-driven winds. More recently, Siegel et al. (2019) conducted 3D
general-relativistic, magnetohydrodynamic simulations demonstrating sufficient
_r_ -process yields to explain the observed abundances in the Universe. Siegel
et al. (2019) found that the disk material becomes neutron-rich through weak
interactions, enabling the production of even 2nd and 3rd peak _r_ -process
elements in disk-wind outflows. Other works in the literature (Miller et al.,
2020; Just et al., 2022; Fujibayashi et al., 2022) have argued that collapsars
are inefficient producers of _r_ -process elements based on studies of the
full radiation transport and $\alpha$-viscosity in collapsar disks. Whether or
not collapsars are sites of _r_ -process nucleosynthesis is still an active
area of investigation, motivating detailed studies of the photometric
evolution of _r_ -process enriched SNe.

Recently, Barnes & Metzger (2022), motivated by Siegel et al. (2019), created
semi-analytic models of the light curves of SNe from collapsars producing _r_
-process elements, yielding concrete predictions for the photometric evolution
of _r_ -process-enriched SNe Ic-BL. Our work is focused on observationally
testing the models from Barnes & Metzger (2022).

In this work, we report our findings from an extensive observational campaign
to determine whether collapsars powering SNe Ic-BL are capable of synthesizing
_r_ -process elements. We present optical and near-infrared photometric
observations and compare both color evolution and absolute light curves
against the predictions from Barnes & Metzger (2022). Our paper is structured
as follows: First, we detail our sample selection criteria in Sec. 2, then
Sec. 3 describes our optical and NIR observations, followed by Sec. 4 which
provides the discovery details about each candidate. Sec. 5 introduces the
objects from the literature used in our study, and in Sec. 6 we introduce the
latest collapsar _r_ -process models. In Sec. 7, we show how we derive
explosion properties. The results of our light curve model fits are presented
in Sec. 8, and finally we discuss our conclusions and future work in Sec. 9.

##  2 Sample Selection

To test the hypothesis that SNe Ic-BL generate _r_ -process elements, we
require a statistically robust sample size of SNe with contemporaneous NIR and
optical light curves. To obtain optical light curves, we use data from the
Zwicky Transient Facility (ZTF; Bellm et al. 2019; Masci et al. 2019; Graham
et al. 2019), a 47 sq. deg. field-of-view mosaic camera with a pixel scale of
1$\arcsec$/pixel (Dekany et al., 2020) installed on the Palomar 48 in.
telescope. ZTF images the entire Northern sky every $\sim$2 nights in g- and
$r-$bands, attaining a median 5$\sigma$ detection depth of 20.5 mAB. Amongst
the systematic efforts aimed at SN detection with ZTF, our SNe draw from two
surveys in particular: “Bright Transient Survey” (BTS; Fremling et al. 2020)
and the ZTF “Census of the Local Universe” survey (CLU; De et al. 2020) which
are conducted as a part of ZTF’s nightly operations. BTS is a magnitude-
limited survey aimed at spectroscopically classifying all SNe $<18.5$ mag at
peak brightness (Perley et al., 2020). CLU, in contrast, is a volume-limited
survey aimed at classifying all SNe within 150 Mpc whose hosts belong to the
CLU galaxy catalog (Cook et al., 2019). The CLU galaxy catalog is designed to
provide spectroscopic redshifts of all galaxies within 200 Mpc, and is 90%
complete (for an H$\alpha$ line flux of 4$\times 10^{-14}$ erg cm2 s-1). Hence
the two surveys provide complementary methods for SN identification. Our
sample consists of 18 spectroscopically-confirmed ZTF SNe Ic-BL within
$z\lesssim 0.05$. Due to our low redshift cut, we assume that the photometric
K-corrections are negligible (Taddia et al., 2018). The details of the
instruments and configurations used to take our classification spectra are
described in Sec. 3 (see also Fig. 1). Where available, we use the
spectroscopic redshift from the SDSS galaxy host (especially for sources
falling in the CLU sample) and otherwise determine the SN redshift from
spectral fitting to the narrow galaxy H$\alpha$ feature. For each spectrum, we
use the Supernova Identification code (SNID; Blondin & Tonry 2007) to
determine the best match template (also plotted in Fig. 1), fixing the
redshift to the value determined using the methods described above. We
overplot the characteristic spectroscopic lines for SNe Ic-BL including O I,
Fe II, and Si II in dashed lines, along with Na I D, an indicator of the
amount of supernova host galaxy extinction (Stritzinger et al., 2018a). For
all of the ZTF SNe, we assume zero host attenuation. Higher host attenuation
results in redder observed SN colors.

We impose a redshift cut to eliminate distant SNe that might fade rapidly
below ZTF detection limits within 60 days post-peak. ZTF yields an average
rate of SNe Ic-BL discovery of $\sim$1/month, but due to visibility and
weather losses, we followed-up $\sim$10 SNe per year. Amongst our sample, we
include one LLGRB (GRB190829A), a published SN with fast and luminous emission
(Ho et al., 2019), and another published SN with a mildly-relativistic ejecta
(Ho et al., 2020a).

In the analyses in subsequent sections, we assume the following cosmological
parameters: H${}_{0}=63.7\rm km\,s^{-1}\,Mpc^{-1}$, $\Omega_{m}=0.307$.

Figure 1: Classification spectra for the SNe Ic-BL in our sample, along with
their SNID best-match templates, labeled by name, supernova phase relative to
the peak light, and corresponding template name, and template phase from SNID.
GRB190829A only has a host spectrum, which we do not display here. The spectra
for SN 2018gep and SN 2020bvc are published in Ho et al. (2019) and Ho et al.
(2020a) so we do not show them here. The spectra show broad Fe II, Si II and O
I lines. The Na I D absorption line, an indicator of host extinction, is
plotted for reference – none of the SNe appear to have strong Na I D features.

##  3 Observations

Here we describe the photometric and spectroscopic observations obtained by
various facilities in our follow-up campaign.

###  3.1 Photometry

####  3.1.1 ZTF

We use the ZTF camera on the Palomar 48-in telescope for supernova discovery
and initial follow-up. ZTF’s default observing mode consists of 30 s
exposures. Alerts (5$\sigma$ changes in brightness relative to the reference
image) are disseminated in avro format (Patterson et al., 2019) and filtered
based on machine-learning real-bogus classifiers (Mahabal et al., 2019), star-
galaxy classifiers (Tachibana & Miller, 2018), and light curve properties.
Cross-matches with solar-system objects serve to reject asteroids. ZTF’s
survey observations automatically obtain $r-$, $g-$ and sometimes $i-$ band
imaging lasting $\approx$60 days after peak, while the supernova is brighter
than 20.5 mag. Masci et al. (2019) provides more information about the data
processing and image subtraction pipelines. More details about specific
surveys used to obtain these data are provided in Sec 2.

####  3.1.2 LCOGT

We performed photometric follow-up of our SNe with the Sinistro and Spectral
cameras on the Las Cumbres Observatory Global Telescope (LCOGT; Brown et al.
2013) Network’s 1-m and 2-m telescopes respectively. The Sinistro (Spectral)
camera has a field of view of 26.5 (10.5)$\arcmin$ x 26.5 (10.5)$\arcmin$ and
a pixel scale of 0.389 (0.304)$\arcsec$/pixel. The observations relied on two
separate LCO programs: one aimed at supplementing ZTF light curves of Bright
Transient Survey objects and the other intending to acquire late-time $r-$ and
$i-$ band follow-up of stripped-envelope SNe fainter than 21 mag. The exposure
times and number of images requested varied based on filter and desired depth,
ranging from 160 s to 300 s and from 1 to 5 images. The data are automatically
flat-fielded and bias-subtracted. Though both programs use different data
reduction pipelines, the methodology is nearly the same. Both pipelines
extract sources using the Source Extractor package (Bertin & Arnouts, 2010)
and calibrate magnitudes against Pan-STARRS1 (PS1) (Chambers et al., 2016;
Flewelling, 2018) objects in the vicinity. The BTS-targeted program uses the
High Order Transform of Psf ANd Template Subtraction code (HOTPANTS; Becker
2015) to subtract a PSF scaled Pan-STARRS1 template previously aligned using
SCAMP (Bertin, 2006). For the late-time LCOGT follow-up program, our pipeline
performed image subtraction with pyzogy (Guevel & Hosseinzadeh, 2017), based
on the ZOGY algorithm (Zackay et al., 2016). Both pipelines stack multiple
images to to increase depth.

####  3.1.3 WASP

We performed deep imaging with the WAfer-scale imager for Prime (WASP),
mounted on the Palomar 200-in. prime focus with a 18.5$\arcmin$ x
18.5$\arcmin$ field of view and a plate scale of 0.18$\arcsec$/pixel. We
obtained data from WASP for the transients at late times in the $g^{\prime}-$,
$r^{\prime}-$ and $i^{\prime}-$ filters. The data were reduced using a python
based pipeline that applied standard optical reduction techniques (as
described in De et al. 2020), and the photometric calibration was obtained
against PS1 sources in the field. Image subtraction was performed with
HOTPANTS with references from PS1 and SDSS.

####  3.1.4 Spectral Energy Distribution Machine

We obtained additional photometric follow-up with the Spectral Energy
Distribution Machine (SEDM; Blagorodnova et al. 2018; Rigault et al. 2019; Kim
et al. 2022) on the Palomar 60-inch telescope which has a field of view of
13$\arcmin$ x 13$\arcmin$ and a plate scale of 0.378$\arcsec$/pixel. The
processing is automated, and can be triggered using the Fritz marshal
(Kasliwal et al., 2019; Duev et al., 2019; van der Walt et al., 2019).
Standard imaging requests involve $g-$, $r-$, and $i-$ band 300s exposures
with the Rainbow Camera on SEDM. The data are later reduced using a python-
based pipeline that applies standard reduction techniques and applies a
customized version of FPipe (Fremling Automated Pipeline; Fremling et al.
2016) for image subtraction.

####  3.1.5 Liverpool Telescope

We acquired late-time, multi-band imaging with the Liverpool Telescope (Steele
et al., 2004) using the IO:O camera with the Sloan $griz$ filter set. The IO:O
camera has a 10$\arcmin$x10$\arcmin$ field of view with a plate scale of
0.15$\arcsec$/pixel. An automatic pipeline reduces the images, performing bias
subtraction, trimming of the overscan regions, and flat fielding. Once a PS1
template is aligned, the image subtraction takes place, and the final
photometry comes from the analysis of the subtracted image.

####  3.1.6 GROWTH-India Telescope

We obtained photometric follow-up of our SNe with the 0.7m robotic GROWTH-
India Telescope (GIT; Kumar et al. 2022) equipped with a 4096×4108 pixel back-
illuminated Andor camera. GIT has a circular field of view of 0.86$\deg$ x
0.86 $\deg$ (corresponding to 51.6$\arcmin$ x 51.6$\arcmin$) and has a pixel
scale of 0.676$\arcsec$/pixel. GIT is located at the IAO (Hanle, Ladakh).
Targeted observations were conducted in SDSS
$r^{\prime},\text{and}\,i^{\prime}$ filters with varying exposure times. All
data were downloaded in real time and processed with the automated GIT
pipeline. Zero points for photometry were calculated using the PanSTARRS
catalogue (Flewelling, 2018), downloaded from Vizier (Ochsenbein et al.,
2000). We performed image subtraction with pyzogy and PSF photometry with
PSFEx (Bertin, 2011).

####  3.1.7 WIRC

We obtained near-infrared follow-up imaging of candidates with the Wide-field
Infrared Camera (WIRC; Wilson et al. 2003), on the Palomar 200-inch telescope
in $J-$, $H-$ and $K$-short ($Ks-$) bands. WIRC’s field of view is
8.7$\arcmin$ x 8.7$\arcmin$ with a pixel scale of 0.2487$\arcsec$/pixel. The
WIRC data was reduced using the same pipeline as described above for WASP, but
it was additionally stacked using Swarp (Bertin, 2010) while the calibration
was done using the 2MASS point source catalog (Skrutskie et al., 2006). We
obtained the WIRC data during classical observing runs on a $\sim$monthly
cadence between January 2019 and December 2021. Due to the fact that the 2MASS
Catalog is far shallower ($J=15.8,H=15.1,Ks=14.3$ mAB; Skrutskie et al. 2006)
compared to WIRC’s limiting magnitudes ($J=22.6,H=22.0,Ks=21.5$, in AB mag),
we obtained reference images with WIRC after the SNe had faded in order to
perform reference image subtraction. We perform image subtraction using the
HOTPANTS algorithm and obtain aperture photometry using photutils (Bradley et
al., 2020).

###  3.2 Spectroscopy

####  3.2.1 SEDM

We also used the SEDM’s low-dispersion (R$\sim$100) integral field
spectrograph (IFU) to obtain classification spectra for several of our
objects. The field of view is 28$\arcsec$ x 28$\arcsec$ with a pixel scale of
0.125$\arcsec$/pixel. The SEDM is fully roboticized from the request
submission to data acquisition to image reduction and uploading. The IFU
images are reduced using the custom SEDM IFU data reduction pipeline
(Blagorodnova et al., 2018; Rigault et al., 2019), which relies on the steps
flat-fielding, wavelength calibration, extraction, flux calibration, and
telluric correction.

####  3.2.2 DBSP

We obtained low to medium resolution (R$\sim$1000-10000) classification
spectra of many of the SNe in our sample with Double Spectrograph (DBSP; Oke &
Gunn 1982) on the Palomar 200-in telescope. Its plate scale is
0.293$\arcsec$/pixel (red side) and 0.389 $\arcsec$/pixel (blue side) and
field of view is 120$\arcsec$ x 70$\arcsec$. The setup included a red grating
of 316/7500, a blue grating of 600/400, a D55 dichroic, and slitmasks of
1$\arcsec$, 1.5$\arcsec$, and 2$\arcsec$. Some of our data was reduced using a
custom PyRAF DBSP reduction pipeline (Bellm & Sesar, 2016) while the rest were
reduced using a custom DBSP Data Reduction pipeline relying on Pypeit
(Prochaska et al., 2019; Roberson et al., 2022).

####  3.2.3 LRIS

Some of the SNe in our sample also have spectra from the Low Resolution
Imaging Spectrometer (LRIS; Oke et al. 1995) mounted on the 10 m Keck I
telescope. LRIS has a 6$\arcmin$ x 7.8$\arcmin$ field of view and a pixel
scale of 0.135$\arcsec$/pixel. We used the 400/3400 grism on the blue arm and
the 400/8500 grating on the red arm, with a central wavelength of 7830
$\rm\AA$ to cover the bandpass from 3,200-10,000 $\rm\AA$. We used longslit
masks of 1.0$\arcsec$ and 1.5$\arcsec$ width. We typically used an exposure
time of 600 s to obtain our classification spectra. The spectra were reduced
using LPipe (Perley, 2019).

####  3.2.4 NOT

We obtained low-resolution spectra with the Alhambra Faint Object Spectrograph
and Camera (ALFOSC)111http://www.not.iac.es/instruments/alfosc on the 2.56 m
Nordic Optical Telescope (NOT) at the Observatorio del Roque de los Muchachos
on La Palma (Spain). The ALFOSC has a field of view of 6.4$\arcmin$ x
6.4$\arcmin$ and a pixel scale of 0.2138$\arcsec$/pixel. The spectra were
obtained with a 1$\farcs$0 wide slit and grism #4. The data were reduced with
IRAF and PypeIt. The spectra were calibrated with spectrophotometric standard
stars observed during the same night and the same instrument setup.

##  4 Description of ZTF candidates

In the section below we include descriptions of all of the 18 candidates with
ZTF data that were analyzed in this paper, including details about its
discovery, coincident radio and X-ray data and any other notable
characteristics about the objects. Our literature sample is described in Sec.
5. Some of these candidates are part of a companion study (Corsi et al., 2022)
focusing on radio properties of SNe Ic-BL; the full ZTF sample of SNe Ic-BL
will be presented in Srinivasaragavan et al., in prep. The objects described
here range from M${}_{r}=-16.58$ to M${}_{r}=-20.60$ mag and from $z=0.017$ to
$z=0.056$ (excluding the LLGRB, at $z=0.077$). All of the transients included
below are ZTF SNe, but we hereafter refer to them by their IAU names. We
performed forced photometry (using the MCMC method) for all of the candidates
using ForcePhotZTF222https://github.com/yaoyuhan/ForcePhotZTF (Yao et al.,
2019).

We found no coincident Fermi and Swift GRB triggers or serendipitous Chandra
X-ray coverage for these SNe based on their derived explosion dates. Though
several candidate counterparts were found in temporal coincidence with KONUS
instrument on the Wind satellite, the imprecise explosion constraints hinders
our ability to make any firm association. These objects are summarized in
Tables 1, and their classification spectra are shown in Figure 1.

###  4.1 SN 2021ywf

Our first ZTF photometry of SN 2021ywf (ZTF21acbnfos) was obtained on 2021
September 12 ($\mathrm{MJD}=59469.47$) with the P48. This first detection was
in the $r-$ band, with a host-subtracted magnitude of $20.03\pm 0.20$, at
$\alpha=05^{h}14^{m}11.00^{s}$, $\delta=+01\arcdeg 52\arcmin 52.3\arcsec$
(J2000.0). The discovery was reported to TNS on September 14 (Nordin et al.,
2021), with a note saying that the latest non-detection from ZTF was just 1
day prior to discovery ($r>20.2$). The high cadence around discovery allows
for a well constrained explosion date. With power-law fits to the early $g-$
and $r-$ band data, we estimate the explosion date as
$\mathrm{MJD_{explosion}^{SN2021ywf}}=59467.70\pm 0.2$ (see below).

We classified the transient as a Type Ic-BL using a spectrum from P200 DBSP
obtained on September 27 (Chu et al., 2021). The first spectrum was actually
obtained using the SEDM. However, the quality of that spectrum was not good
enough to warrant a classification. SN 2020ywf exploded in the outskirts of
the spiral galaxy CGCG 395-022 with a well established redshift of
$z=0.028249$. This redshift is confirmed with narrow host lines in our
classification spectrum.

SN 2021ywf was detected with the Swift XRT with $5.3_{-3.3}^{+4.9}\times
10^{-14}\rm\,erg\,cm^{-2}\,s^{-1}$ (0.3-10 keV) in a 7.2 ks observation on
MJD=59487.60. On MJD=59487.57, the transient was detected with the VLA at
$83\pm 10\,\mu$Jy at 5.0 GHz (see Corsi et al. 2022 for details).

###  4.2 SN 2021xv

Our first ZTF photometry of SN 2021xv (ZTF21aadatfg) was obtained on 2021
January 10 ($\mathrm{MJD}=59224.52$) with the P48. The transient was
discovered in the public ZTF alert stream and reported by ALeRCE (Forster,
2021). This first detection was in the $r-$ band, with a host-subtracted
magnitude of 19.93, at $\alpha=16^{h}07^{m}32.82^{s}$, $\delta=+36\arcdeg
46\arcmin 46.07\arcsec$ (J2000.0). The discovery was reported to TNS (Forster
et al., 2021), with a note saying that the last non-detection was 3 days
before discovery (on 2021 January 07 at $r>$19.52). We classified the
transient as a Type Ic-BL using a spectrum from the NOT ALFOSC obtained on
2021 Jan 25 (Schulze & Sollerman, 2021). The transient appears to be
associated with the galaxy host SDSS J160732.83+364646.1. We measure a
redshift of $z=0.041$ from the narrow host lines in the NOT spectrum. SN
2021xv was marginally detected with the VLA on MJD=59353.11 at
F${}_{\nu}=34.3\pm 8.1\,\mu$Jy at 5.2 GHz, but the detection is consistent
with host galaxy emission (see Corsi et al. 2022 for details).

###  4.3 SN 2021too

SN 2021too (ZTF21abmjgwf) was discovered by the PS1 Young Supernova Experiment
on 2021 July 17 ($\mathrm{MJD}=59412.60$) with the internal name PS21iap, but
the first ZTF alerts are from $\mathrm{MJD}=59411.33$. This first detection
was in the $i-$ band, with a host-subtracted magnitude of 19.5, at
$\alpha=21^{h}40^{m}54.28^{s}$, $\delta=+10\arcdeg 19\arcmin 30.3\arcsec$
(J2000.0). The discovery was reported to TNS (Jones et al., 2021), with a note
saying that the last non-detection was archivally recorded in DSS. The
transient was classified as a Type Ic-BL using a spectrum from EFOSC2-NTT
obtained on 2021 August 02 by ePESSTO (Pessi et al., 2021). The object was
positioned in the starforming galaxy SDSS J214054.29+101930.5. We measure a
redshift of 0.035 from the narrow host lines in its P200 DBSP spectrum taken
on 2021 Aug 07.

###  4.4 SN 2021bmf

SN 2021bmf (ZTF21aagtpro) was discovered by ATLAS on 2021 January 30
($\mathrm{MJD}=59244.0$) with the internal name ATLAS 21djt, and later by ZTF
($\mathrm{MJD}=2459248.50$). This first detection was in the $o$ band, with a
host-subtracted magnitude of 18.12, at $\alpha=16^{h}33^{m}29.41^{s}$,
$\delta=-06\arcdeg 22\arcmin 49.53\arcsec$ (J2000.0). The discovery was
reported to TNS (Tonry et al., 2021), with a note saying that the last non-
detection was on 2021 January 16 at $c>18.4$ mag. The transient was classified
as a Type Ic-BL using a spectrum from ePESSTO obtained on 2021 February 03
(Magee et al., 2021). SN 2021bmf was found in the faint host galaxy SDSS
J163329.48-062249.9, which was determined to be at $z=0.0175$ based on narrow
host lines in the Keck I LRIS spectrum taken on 2021 July 09.

###  4.5 SN 2020tkx

Our first ZTF photometry of SN 2020tkx (ZTF20abzoeiw) was obtained on 2020
September 16 ($\mathrm{MJD}=59108.26$) with the P48. This first detection was
in the $g-$ band, with a host-subtracted magnitude of $18.09\pm 0.08$, at
$\alpha=18^{h}40^{m}09.01^{s}$, $\delta=+34\arcdeg 06\arcmin 59.5\arcsec$
(J2000.0). The discovery was done by Gaia two days earlier (Hodgkin et al.,
2020). The last ZTF non-detection is from September 7th, a full week before
discovery, and the constraints on the explosion date are therefore imprecise.

The transient was classified as a Type Ic-BL by Srivastav et al. (2020) based
on a spectrum obtained on September 18. Our sequence of P60 spectra confirm
this classification.

SN 2020tkx exploded in a faint host galaxy without a known redshift. Using the
spectral template fitting SNID for our best NOT spectrum, the redshift can be
constrained to $z\sim 0.02-0.03$, and our adopted redshift of $z=0.027$ is
based on a weak, tentative H$\alpha$ line from the host galaxy in the
spectrum.

The object has a upper limit of $<3.3\times 10^{-14}\rm erg\,cm^{-2}\,s^{-1}$
with the Swift XRT. SN 2020tkx was detected with the VLA at $286\pm 15\,\mu$Jy
(10 GHz) on MJD=59117.89 (see Corsi et al. 2022 for more details).

###  4.6 SN 2020rph

Our first ZTF photometry of SN 2020rph (ZTF20abswdbg) was obtained on 2020
August 11 ($\mathrm{MJD}=59072.49$) with the P48. The transient was discovered
in the public ZTF alert stream and reported by ALeRCE. This first detection
was in the r band, with a host-subtracted magnitude of 20.36, at
$\alpha=03^{h}15^{m}17.82^{s}$, $\delta=+37\arcdeg 00\arcmin 50.57\arcsec$
(J2000.0). The discovery was reported to TNS (Forster et al., 2020a), with the
last non-detection just 1 hour before discovery at $r>$19.88 mag. We
classified the transient as a Type Ic-BL using a spectrum from the P60 SEDM
obtained on 2020 August 24 (Dahiwale & Fremling, 2020a). The supernova was
found offset from the galaxy WISEA J031517.67+370055.3. We measure a redshift
of $z=0.042$ based on a Keck I LRIS spectrum taken on 2020 October 19. SN
2020rph has a Swift XRT upper limit of f$<3.6\times 10^{-14}\text{ erg
cm}^{-2}\text{ s}^{-1}$ on MJD=59088.89. It is detected with the VLA at
$42.7\pm 7.4\mu$Jy (5.5 GHz) at MJD=59089.59, but the detection is consistent
with host galaxy emission (see Corsi et al. 2022 for details).

###  4.7 SN 2020lao

Our first ZTF photometry of SN 2020lao (ZTF20abbplei) was obtained on 2020 May
25 ($\mathrm{MJD}=58994.41$) with the P48. This first detection was in the
$g-$ band, with a host-subtracted magnitude of $19.69\pm 0.10$, at
$\alpha=17^{h}06^{m}54.61^{s}$, $\delta=+30\arcdeg 16\arcmin 17.3\arcsec$
(J2000.0). The discovery was reported to TNS on the same day (Forster et al.,
2020b). The field was well covered both before and after this first detection,
and the P60 telescope was immediately triggered to provide $ugr$ photometry
1.4 hours after first detection. The high cadence around discovery allows for
a well constrained explosion date. With power-law fits to the early $g-$ and
$r-$ data, we estimate the explosion date as
$\mathrm{MJD_{explosion}^{SN2020lao}}=58993.07\pm 0.75$.

SN 2020lao was also reported in a paper by the Transient Exoplanet Satellite
Survey (TESS; Vallely et al. 2021) with high cadence photometry. The TESS
paper finds a slightly different rise time (13.5 $\pm$ 0.22 days) relative to
our ZTF observations; however this can be attributed to their broad peak and
bandpass that may also contain NIR flux. On the other hand, we find that our
narrow $i-$ band peak is consistent with our estimated $r-$band peak.

Our first spectrum of this event was obtained with the SEDM on May 26. It was
mainly blue and featureless and did not warrant any classification. We
obtained several more inconclusive spectra the following days, and the
transient was finally classified as a Type Ic-BL by the Global SN Project on
June 2 (Burke et al., 2020). Our subsequent SEDM and Nordic Optical Telescope
ALFOSC spectra confirmed this classification based on its broad Fe II
features.

SN 2020lao exploded in the face on spiral galaxy CGCG 169-041 with a well
established redshift of $z=0.030814$. This redshift is confirmed with narrow
host lines in our later spectra.

###  4.8 SN 2020dgd

Our first ZTF photometry of SN 2020dgd (ZTF20aapcbmc) was obtained on 2020
February 19 ($\mathrm{MJD}=58898.52$) with the P48. This first detection was
in the $r-$ band, with a host-subtracted magnitude of 18.99, at
$\alpha=15^{h}45^{m}35.57^{s}$, $\delta=+29\arcdeg 18\arcmin 38.4\arcsec$
(J2000.0). The discovery was reported to TNS (Nordin et al., 2020), with a
note saying that the last non-detection was 5 days before discovery (on 2020
February 14 at $r\,>\,20.03$ mag). We classified the transient as a Type Ic-BL
using a spectrum from the P60 SEDM obtained on 2020 March 05 (Dahiwale &
Fremling, 2020b). The transient appears to be separated by 14$\arcsec$ from
any visible host galaxy in the vicinity; however with a Keck I LRIS spectrum
taken on 2020 June 23 in the nebular phase (not shown in Figure 1), we measure
weak host lines at a redshift of $z=0.032$. In addition, that LRIS spectrum of
the SN exhibits strong Ca II emission features.

###  4.9 SN 2020bvc

Our first ZTF photometry of SN 2020bvc (ZTF20aalxlis) was obtained on 2020
February 04 ($\mathrm{MJD}=58883.0$) with the P48. This first detection was in
the $i-$ band, with a host-subtracted magnitude of 17.48, at
$\alpha=14^{h}33^{m}57.01^{s}$, $\delta=+40\arcdeg 14\arcmin 37.5\arcsec$
(J2000.0). SN 2020bvc, reported originally in Ho et al. (2020a), shows very
similar optical, X-ray and radio properties to SN 2006aj, which was associated
with the low-luminosity GRB 060218. See Ho et al. (2020a) for more details
about this object.

###  4.10 SN 2019xcc

Our first ZTF photometry of SN 2019xcc (ZTF19adaiomg) was obtained on 2019
December 19 ($\mathrm{MJD}=58836.48$) with the P48. This first detection was
in the $r-$ band, with a host-subtracted magnitude of $19.40\pm 0.13$, at
$\alpha=11^{h}01^{m}12.39^{s}$, $\delta=+16\arcdeg 43\arcmin 29.1\arcsec$
(J2000.0). The discovery was reported to TNS on the same day (Forster et al.,
2019), with a note saying that the latest non-detection from ZTF was five days
prior to discovery ($r>19.3$). This transient has very sparse light curves
with only four data points from P48 in the alert stream, all in the $r-$ band,
but forced photometry also retrieved detections in the $g-$ band.

The transient was classified as a Type Ic-BL by Prentice et al. (2019), based
on a spectrum from SPRAT on the Liverpool Telescope obtained on December 20.
We could confirm this classification with a spectrum from the Keck telescope a
few days later, using the LRIS instrument.

SN 2019xcc exploded close to the centre of the face on grand spiral CGCG
095-091 with a well established redshift of $z=0.028738$. This redshift is
confirmed with narrow host H$\alpha$ in our Keck spectrum.

###  4.11 SN 2019qfi

SN 2019qfi (ZTF19abzwaen) was discovered by ATLAS on 2019 September 07
($\mathrm{MJD}=58743.29$) with the internal name ATLAS2019vdc, with the first
ZTF alerts around the same time ($\mathrm{MJD}=58743.38$). This first
detection was in the $o$ band, with a host-subtracted magnitude of 18.81, at
$\alpha=21^{h}51^{m}07.90^{s}$, $\delta=+12\arcdeg 25\arcmin 38.5\arcsec$
(J2000.0). The discovery was reported to TNS (Tonry et al., 2019a), with a
note saying that the last non-detection was 6 days before the discovery at
$o>18.69$ mag. We classified the transient as a Type Ic-BL using a spectrum
from the P60 SEDM obtained on 2019 Sep 21 (Fremling et al., 2019a). SN 2019qfi
was identified in the starforming galaxy SDSS J215107.99+122542.5 with a known
spectroscopic redshift of $z=0.028$.

###  4.12 SN 2019moc

SN 2019moc (ZTF19ablesob) was discovered by ATLAS on 2019 August 04
($\mathrm{MJD}=58699.47)$) with the internal name ATLAS2019rgu. This first
detection was in the $c$ band, with a host-subtracted magnitude of 18.54, at
$\alpha=23^{h}55^{m}45.95^{s}$, $\delta=+21\arcdeg 57\arcmin 19.67\arcsec$
(J2000.0). Its first ZTF detection was on $\mathrm{MJD}=58695.41$. The
discovery was reported to TNS (Tonry et al., 2019b), with a note saying that
the last non-detection was 6 days before discovery at $c>19.44$ mag. We
classified the transient as a Type Ic-BL using a spectrum from the P200 DBSP
obtained on 2019 Aug 10 (Dahiwale et al., 2019). The SN was found in the
galaxy SDSS J235545.94+215719.7 with a known spectroscopic redshift of 0.055.

###  4.13 SN 2019gwc

Our first ZTF photometry of SN 2019gwc (ZTF19aaxfcpq) was obtained on 2019
June 04 ($\mathrm{MJD}=58638.28$) with the P48. This first detection was in
the $r-$ band, with a host-subtracted magnitude of 19.73, at
$\alpha=16^{h}03^{m}26.88^{s}$, $\delta=+38\arcdeg 11\arcmin 02.6\arcsec$
(J2000.0). The discovery was reported to TNS (Nordin et al., 2019), with a
note saying that the last non-detection was three days before discovery (2019
Jun 01 at $r>20.98$ mag). We classified the transient as a Type Ic-BL using a
spectrum from the P60 SEDM obtained on 2019 Jun 16 (Fremling et al., 2019b).
The transient was identified in the starforming host galaxy SDSS
J160326.65+381057.1 at a known spectroscopic redshift of $z=0.038$.

###  4.14 SN 2019hsx

Our first ZTF photometry of SN 2019hsx (ZTF19aawqcgy) was obtained on 2019
June 02 ($\mathrm{MJD}=58636.31$) with the P48. This first detection was in
the $r-$ band, with a host-subtracted magnitude of $18.62\pm 0.08$, at
$\alpha=18^{h}142^{m}56.22^{s}$, $\delta=+68\arcdeg 21\arcmin 45.2\arcsec$
(J2000.0). The discovery was reported to TNS (Fremling, 2019), with a note
saying that the latest non-detection from ZTF was 3 days prior to discovery
(May 30; $g>20.3$). We classified the transient as a Type Ic-BL using a
spectrum from P60 SEDM obtained on June 14 (Fremling et al., 2019c). SN
2019hsx exploded fairly close to the center of NGC 6621 with redshift
$z=0.020652$.

###  4.15 SN 2018kva

Our first ZTF photometry of SN 2018kva (ZTF18aczqzrj) was obtained on 2018
December 23 ($\mathrm{MJD}=58475.51$) with the P48. This first detection was
in the $r-$ band, with a host-subtracted magnitude of 19.08, at
$\alpha=08^{h}35^{m}16.21^{s}$, $\delta=+48\arcdeg 19\arcmin 03.4\arcsec$
(J2000.0). The discovery was reported to TNS (Fremling, 2018), with a note
saying that the latest non-detection was 3 days before discovery, at $g>20.33$
mag. We classified the transient as a Type Ic-BL using a spectrum from the P60
SEDM obtained on 2019 Jan 03 (Fremling et al., 2019d). The object was
identified in the host galaxy WISEA J083516.34+481901.2 at a known redshift of
$z=0.043$.

###  4.16 SN 2018jaw

Our first ZTF photometry of SN 2018jaw (ZTF18acqphpd) was obtained on 2018
November 20 ($\mathrm{MJD}=58442.51$) with the P48. This first detection was
in the $g-$ band, with a host-subtracted magnitude of 18.39, at
$\alpha=12^{h}54^{m}04.10^{s}$, $\delta=+13\arcdeg 32\arcmin 47.9\arcsec$
(J2000.0). The discovery was reported to TNS (Nordin et al., 2018), with a
note that the object was missing ZTF non-detection limits. We classified the
transient as a Type Ic-BL using a spectrum from the P60 SEDM obtained on 2018
Dec 12 (Fremling et al., 2018), and tentatively estimated its redshift to be
$z=0.037$. However, the narrow host lines in the Keck I-LRIS spectrum taken on
2019 Apr 06 indicate that the object is at a redshift of $z=0.047$. SN 2018jaw
was identified in the galaxy host WISE J125404.15+133244.9.

###  4.17 SN 2018gep

Our first ZTF photometry of SN 2018gep (ZTF18abukavn) was obtained on 2018
September 09 ($\mathrm{MJD}=58370.16$) with the P48. This first detection was
in the $r-$ band, with a host-subtracted magnitude of 20.5, at
$\alpha=16^{h}43^{m}48.22^{s}$, $\delta=+41\arcdeg 02\arcmin 43.4\arcsec$
(J2000.0).

SN 2018gep belongs to the class of Fast Blue Optical Transients (FBOTs) with
its rapid rise time, high peak luminosity, and blue colors at peak (Pritchard
et al., 2021). It was classified as a Ic-BL supernova whose early multi-
wavelength data can be explained by late-stage eruptive mass loss. The
transient is detected with the VLA over three epochs (9, 9.7 and 14 GHz), but
the emission is likely galaxy-dominated. See Ho et al. (2019) for more details
on the discovery of this supernova.

IAU name | ZTF name | type | RA | Dec | z | F${}_{\nu}^{a}$ | $\rm F_{0.3-10\rm keV}^{b}$
---|---|---|---|---|---|---|---
|  |  |  |  |  | ($\mu$Jy) | (10-14 erg cm-2 s-1)
2018gep | ZTF18abukavn | FBOT* | 16:43:48.21 | +41:02:43.29 | 0.032 |  $<34\pm 4$ (9.7 GHz) | $<9.9$
2018jaw | ZTF18acqphpd | Ic-BL | 12:54:04.10 | +13:32:47.9 | 0.047 | - | -
2018kva | ZTF18aczqzrj | Ic/Ic-BL | 08:35:16.21 | +48:19:03.4 | 0.043 | - | -
2019gwc | ZTF19aaxfcpq | Ic-BL | 16:03:26.88 | +38:11:02.6 | 0.038 | - | -
2019hsx | ZTF19aawqcgy | Ic-BL | 18:12:56.22 | +68:21:45.2 | 0.021 |  $\lesssim 19$ (6.2 GHz) | 6.2${}^{+2.3}_{-1.8}$
2019moc | ZTF19ablesob | Ic-BL | 23:55:45.95 | +21:57:19.67 | 0.056 | - | -
2019qfi | ZTF19abzwaen | Ic-BL | 21:51:07.90 | +12:25:38.5 | 0.029 | - | -
2019xcc | ZTF19adaiomg | Ic-BL | 11:01:12.39 | +16:43:29.30 | 0.029 |  $<62.7\pm 8.7$ (6.3 GHz) | -
GRB190829A | - | LLGRB | 2:58:10.580 | -8:57:29.82 | 0.077 | - | -
2020bvc | ZTF20aalxlis | Ic-BL** | 14:33:57.01 | +40:14:37.5 | 0.025 |  $63\pm 6$ (10 GHz) | 9.3${}^{+10.6}_{-6.1}$
2020dgd | ZTF20aapcbmc | Ic-BL | 15:45:35.57 | +29:18:38.4 | 0.03 | - | -
2020lao | ZTF20abbplei | Ic-BL | 17:06:54.61 | +30:16:17.3 | 0.031 |  $\lesssim 33$ (5.2 GHz) | $<2.9$
2020rph | ZTF20abswdbg | Ic-BL | 03:15:17.82 | +37:00:50.57 | 0.042 |  $<42.7\pm 7.4$ (5.5 GHz) | $<3.6$
2020tkx | ZTF20abzoeiw | Ic-BL | 18:40:09.01 | +34:06:59.5 | 0.027 |  $272\pm 16$ (10 GHz) | $<3.3$
2021bmf | ZTF21aagtpro | Ic-BL | 16:33:29.41 | -06:22:49.53 | 0.017 | - | -
2021xv | ZTF21aadatfg | Ic-BL | 16:07:32.82 | +36:46:46.07 | 0.041 |  $<34.3\pm 8.1$ (5.2 GHz) | -
2021ywf | ZTF21acbnfos | Ic-BL | 05:14:11.00 | +01:52:52.28 | 0.028 |  $83\pm 10$ (5.0 GHz) | 5.3${}^{+4.9}_{-3.3}$
2021too | ZTF21abmjgwf | Ic-BL | 21:40:54.28 | +10:19:30.33 | 0.035 | - | -
Table 1: Sample summary table of Ic-BL supernova properties, estimated _r_
-process ejecta mass and mixing fraction along with their 1$\sigma$
uncertainties, and first radio/X-ray detection. In the absence of any
X-ray/radio detections we quote an upper limit; if the source was not observed
we mark the cell with a dash. a) Flux density in $\mu$Jy with the VLA. We list
only the first VLA observation at $\lesssim$50 days from the first ZTF
detection as reported in Corsi et al. (2022). b) Swift XRT flux in units of
$10^{-14}$erg cm-2 s-1, taken from Corsi et al. (2022) *This SN Ic-BL is also
categorized as a fast blue optical transient (FBOT), and was published in Ho
et al. (2019). The quoted radio detection with the VLA could be galaxy-
dominated. **This Ic-BL had a double-peaked light curve from shock-cooling;
X-ray and radio measurements taken from Ho et al. (2020a).

##  5 Literature sample

In addition to the ZTF SNe in our sample we examine the Open Supernova
Catalog333https://github.com/astrocatalogs/supernovae for historical low-
redshift SNe Ic-BL with $\gtrsim$3 epochs of multi-band NIR photometry
concurrent with the optical coverage. We impose a redshift cut of $z=0.1$ for
the SNe from the literature, and exclude those objects with only NIR
observations of the afterglow and early ($\lesssim 10$ days from explosion) SN
light curve, in the case of a GRB association. We find that SN 1998bw (Patat
et al., 2001; Clocchiatti et al., 2011), SN 2002ap (Yoshii et al., 2003;
Tomita et al., 2006), SN 2010bh (Olivares E. et al., 2012), and SN 2016coi
(Terreran et al., 2019) match our criteria. We also find that SN 2016jca has
extensive optical and NIR follow-up (Cano et al., 2017; Ashall et al., 2019)
but exclude it from further study due to its redshift being much higher than
the objects in our sample (at $z=0.146$), and the fact that the reported NIR
photometry is neither host- nor afterglow-subtracted.

SN 2016coi uniquely shows a huge 4.5 $\mu$m excess in the mid-infrared with
archival WISE coverage in its late-time light curve. This object also has
detections in the H-band past 300 days post-peak which coincide with the mid-
IR detections. Given that it also has a bright radio counterpart, the mid-IR
excess could be attributed to CO formation in the ejecta (Liljegren et al.,
2022), or dust formation due to adiabatic cooling (Omand et al., 2019), or
metal cooling in highly mixed SN ejecta (Omand & Jerkstrand, 2022). Though we
lack model predictions in the mid-IR bands, we test whether the long-lived NIR
emission could also be attributed to _r_ -process production.

The Open SN Catalog pointed us to two systematic samples of stripped envelope
SNe. Bianco et al. (2014) collected optical and NIR photometry for a set of 61
stripped envelope SNe that also satisfy our low-redshift cut after conducting
template-based subtraction in order to subtract host galaxy emission (for most
SNe). Amongst the SNe in that sample classified as Type Ic-BL, only two SNe
have observations in the $J$, $H$, or $K_{s}$ bands: SN 2007I and SN 2007ce.
Similar to the case of our ZTF SNe, during the earlier epochs ($<$ 60 days
post-peak) these two SNe have well-sampled optical photometry, while later in
time there is only NIR coverage. The second study, Stritzinger et al. (2018b),
acquired optical light curves for 34 stripped-envelope SNe, 26 of which have
NIR follow-up in the $YJH$ bands as a part of the Carnegie Supernova Project.
Explosion and bolometric light curve properties for some of these SNe were
released in a companion paper (Taddia et al., 2018). Of the 26 SNe, only one
(SN 2009bb) has adequate coverage at late times in the NIR.

Li et al. (2022) perform detailed blackbody fits to several SNe from the Open
SN Catalog that have optical and NIR coverage to search for SNe that show NIR
excesses in their SEDs that could be attributed to dust formation. Amongst the
sample they consider, the authors find SN 2007I and SN 2009bb to be consistent
with blackbody emission with a slight NIR excess that evolves from a
photospheric temperature of $\sim$5000 ($\sim$7000) to 4300 K over the course
of 51 (33) days in the case of SN 2007I (SN 2009bb). The same authors find
that the SED of SN 2007ce is inconsistent with a blackbody, though they use
only the early-time measurements of the object (at 1.9 days). Furthermore, Li
et al. (2022) find no evidence for intrinsic dust formation or significant
host extinction in order to explain their SEDs. In contrast to their study, we
note that our analysis includes photometry for these SNe over a much longer
baseline taken from Bianco et al. (2014) and Stritzinger et al. (2018b).

For each of the above-mentioned SNe, we correct for Galactic extinction where
extinction has not been accounted for, and convert from Vega to AB magnitudes.
We also correct the light curves for host attenuation for all of these SNe
except for SN 1998bw (light curve already corrected for Galactic and host
extinction) and SN 2007ce (lacks host galaxy extinction information); the
assumed host E(B-V) values are listed in Table 3. We include host extinction
here as it is significant for the literature SNe. The measurements of total
ejecta mass, kinetic energy, and nickel mass for each object are recorded in
Table 3, along with the appropriate reference we took these estimates from. We
include these seven SNe in our analysis, described in Sec. 8.

##  6 Collapsar light curve models

We model the evolution of the emission from _r_ -process-enriched collapsars
using a semi-analytic model of Barnes & Metzger (2022). While the details of
our method are described there, we present an outline here.

The models comprise a series of concentric shells whose densities ($\rho(v)$)
follow a broken power law in velocity space:

$\rho(v)\propto\begin{cases}v^{-n},\>\>v\leq v_{\rm t},\\\
v^{-\delta}\>\>v>v_{\rm t},\end{cases}$ (1)

where we set the power-law index $n$ ($\delta$) equal to 1 (10). In Eq. 1,
$v_{\rm t}$ is a transition velocity chosen to produce the desired total mass
$M_{\rm ej}$ and kinetic energy $E_{\rm kin}$, which is parameterized via the
average velocity $v_{\rm ej}=\sqrt{2E_{\rm kin}/M_{\rm ej}}$. In addition to
$M_{\rm ej}$ and $v_{\rm ej}$, each model is characterized by its mass of
${}^{56}\text{Ni}$, $M_{56}$, which we assume is uniformly distributed
throughout the ejecta.

We assume that some amount $M_{r\mathrm{p}}$ of the ejecta is composed of pure
_r_ -process material, and that this material is mixed evenly into the ejecta
interior to a velocity $v_{\rm mix}$, which we define such that

$\int\limits_{0}^{v_{\rm mix}}\rho(v)\>\mathrm{d}\;V=\psi_{\rm mix}M_{\rm
ej}{},$

with $\psi_{\rm mix}$ a parameter of the model. (In other words, $\psi_{\rm
mix}$ is the fraction of the total ejecta mass for which the _r_ -process mass
fraction is non-zero.) By distributing the _r_ -process mass within a core of
mass ${>}M_{r\mathrm{p}}{}$, we account for hydrodynamic (e.g., Kelvin-
Helmholtz) instabilities at the wind-ejecta boundary, which may mix the _r_
-process-rich disk outflow out into the initially _r_ -process-free ejecta.

The _r_ -process elements serve as a source of radioactive energy beyond
${}^{56}\text{Ni/Co}$. More importantly (especially at early times—see Siegel
et al. 2019), they impart to the enriched layers the high opacity (Kasen et
al., 2013; Tanaka & Hotokezaka, 2013) known to be a unique feature of _r_
-process compositions. This high opacity affects local diffusion times and the
evolution of the photosphere, thereby altering SN emission relative to the _r_
-process-free case.

We model the spectral energy distribution (SED) from the photospheric ejecta
layers ($r\leq R_{\rm ph}$) as a black body, and integrate it to get the
bolometric luminosity, given by

$L=4\pi R^{2}_{\rm ph}\sigma_{\rm SB}T_{\rm ph}^{4}$ (2)

with $\sigma_{\rm SB}$ the Stefan-Boltzman constant. The opacity in our model
is gray and defined for every zone, allowing a straightforward determination
of the photospheric radius $R_{\rm ph}$. The photospheric temperature $T_{\rm
ph}$ is then chosen so the RHS of Eq. 2 is equal to the luminosity emerging
from behind the photosphere, which is an output of our calculation.

Since we are equally interested in SN signals beyond the photospheric phase,
we also track emission from optically thin regions of the ejecta. These are
assumed to have an SED determined by their composition. The _r_ -process free
layers conform to expectations set by observed SNe (e.g. Hunter et al., 2009).
For enriched layers, we rely on theoretical studies of nebular-phase _r_
-process transients (Hotokezaka et al., 2021). The radioactive heating,
opacity, and photospheric and nebular SEDs of each model are thus fully
determined, allowing us to predict light curves and colors as a function of
time.

##  7 Analysis

In the sections below, for the analysis and fitting of our light curves, we
assume the central wavelengths for the optical and NIR bandpasses listed in
Table 2, ignoring any small differences due to non-standard filters.

filter | central wavelength ($\mathrm{\AA}$)
---|---
$g$ | 4770
$r$ | 6231
$i$ | 7625
$U$ | 3600
$B$ | 4380
$V$ | 5450
$R$ | 6410
$I$ | 7980
$J$ | 1235
$H$ | 1662
$K_{s}$ | 2159
$K$ | 2190
Table 2: Central wavelengths for the optical and NIR filters assumed during
the analysis and fitting of our light curves.

###  7.1 Estimation of explosion properties

The combination of using both a volume-limited and a magnitude-limited survey
for SN Ic-BL discovery has yielded SNe with a diverse range of absolute
magnitudes. In Table 1 we summarize the SNe in our sample, which have
redshifts ranging from 0.01 to 0.05 and peak $r-$band absolute magnitudes from
M${}_{\rm r}\sim-17$ mag to M${}_{\rm r}\sim-19$ mag. Here we summarize our
process for deriving explosion parameters (i.e. total ejecta mass, kinetic
energy, and nickel mass) from these SN light curves.

The details of the methodology behind our analysis of the bolometric light
curves in this sample are described at length in a companion paper, Corsi et
al. (2022), though only a subset of our sample is included in the companion
paper. This analysis is done with the open-source code
HAFFET444https://github.com/saberyoung/HAFFET (Yang & Sollerman, 2023). First,
we correct the light curves for Milky Way extinction, and then derive
bolometric light curves from the $g-$ and $r-$ band photometry after
calculating bolometric corrections from the empirical relations given in Lyman
et al. (2014, 2016). We estimate the explosion epoch with power law fits
unless the early-time SN data are limited, in which case the explosion times
are set as the midpoint between the last non-detection before discovery and
the first ZTF detection. We then fit the bolometric light curves to Arnett
models (Arnett, 1982) between $-20$ and 60 days from the peak to obtain the
${}^{56}\text{Ni}$ mass, $M_{\rm 56}$ and the characteristic timescale
$\tau_{m}$. The early-time optical light curves of typical SNe Ic-BL are well-
approximated by the Arnett model which describes the 56Ni-powered light curve
during the supernova’s photospheric phase.

For each of the SNe we estimate the photospheric velocity ($v_{\rm ph}$) using
the earliest high-quality spectrum taken of the object. We use the IDL routine
WOMBAT to remove host galaxy lines and tellurics, and then smooth the spectrum
using SNspecFFTsmooth (Liu et al., 2016). The broad Fe II feature at 5169
$\rm\AA$ is considered to be a proxy for the photospheric velocity of a Type
Ic-BL SN (Modjaz et al., 2016). Thus we use the open source code
SESNspectraLib555https://github.com/nyusngroup/SESNspectraLib (Liu et al.,
2016; Modjaz et al., 2016) to fit for the Fe II velocity by convolving with SN
Ic templates. The velocities were measured at different phases for each SN, as
shown in Figure 2.

We then estimate the kinetic energy, $E_{\rm k}$, and the total ejecta mass
$M_{\rm ej}$ of the explosion using our derived values for $\tau_{\mathrm{m}}$
and $v_{\rm ph}$ and the empirical relations from Lyman et al. (2016). In some
cases where $v_{\rm ph}$ was only measured $>$15 days after the peak, we could
only quote lower limits on the kinetic energy and ejecta mass of the
explosion.

The explosion properties we derive are given in Table 4.

Figure 2: SN velocities measured from the Fe II 5169$\rm\AA$ line as a
function of the spectroscopic phase for each supernova in our sample (black
points) plotted along with the measured velocities of SNe Ic-BL from the
literature and from PTF (Taddia et al., 2018). The velocities we measure here
are broadly consistent with both the literature and PTF sample.

###  7.2 Comparing color-color predictions to observations

Optical-NIR colors are a useful diagnostic to determine whether SNe Ic-BL
could be potential sites of _r_ -process production. The high opacity of _r_
-process elements causes emission from the enriched regions to shift to redder
wavelengths.

In Figure 3, we plot colors with respect to the $r-$band as $r-X$
($X=J,H,K_{s}$) for _r_ -process enriched models corresponding to the
following parameters: “high mass, high velocity”: $M_{\rm
ej}$$=7.93\rm\,M_{\odot}$, $\beta_{\rm ej}$$=0.25c$, $M_{\rm 56}$$=0.85\,\rm
M_{\odot}$ (solid line), “medium mass, medium velocity”: $M_{\rm
ej}$$=2.62\,\rm M_{\odot}$, $\beta_{\rm ej}$$=0.038c$, $M_{\rm
56}$$=0.39\rm\,M_{\odot}$ (dotted line), and “low mass, low velocity”: $M_{\rm
ej}$$=1.00\rm\,M_{\odot}$, $\beta_{\rm ej}$$=0.033c$, $M_{\rm
56}$$=0.07\rm\,M_{\odot}$ (dashed line). This set of models illustrates how
different combinations of assumed parameters affect the color curves. These
model grids were used to fit the light curves of three objects in our sample
and represent the broad range of explosion parameters derived for our SNe.

We use these color evolution predictions from the models to compare against
the optical-NIR colors of our SNe. Our $r-X$ color measurements rely on two
different methods: if there is an optical data point within three days of the
NIR data point, we compute the color difference directly (filled circles);
otherwise, we estimate the color by subtracting the NIR photometry from a
scaled and shifted optical template (open circles). We construct this template
from the light curve of SN 2020bvc, one of the SNe with the most well-sampled
light curves, and then compute the shift and scale factors needed for the
template to fit the data. For the cases in which the optical model does not
fit the optical light curve perfectly, there can be a systematic offset
between the open and closed circles. For example, the estimated $r-K_{s}$
color of SN 2019xcc (Figure 3, bottom panel) is $>1$ mag, but this is likely
attributed to the fact that there is no concurrent optical photometry along
with the $Ks-$band data point, and the optical light curve fades much faster
than that of SN 2020bvc.

The predicted $r-J$ colors for _r_ -process collapsar light-curve models range
from $r-J\sim-$0.5 mag to $r-J\sim$1.5 mag. In the lefthand-side panels of
Figure 3, we fix the mixing fraction to a moderate value of $\psi_{\rm
mix}$$=0.3$ and vary the amount of _r_ -process ejecta mass. On the righthand-
side panels, we fix the _r_ -process ejecta mass to 0.01M⊙ and vary the mixing
fraction. The amount of reddening in the model light curves is more strongly
affected by the amount of mixing assumed; even for the lowest value of
$M_{r\mathrm{p}}$, we find prolific reddening predictions for high mixing
fractions relative to models with moderate mixing fractions and high _r_
-process yield.

However, _r_ -process enrichment is not the only factor affecting colors;
unenriched SN models also have a range of colors, depending on their masses,
velocities, and nickel production. Even amongst different models with
identical _r_ -process composition, color evolution can be sensitive to the
explosion properties assumed. Here, the “high mass, high velocity” model set
also shows the most dramatic reddening predictions for models that have
extreme mixing; in general, higher mass models tend to show larger $r-X$
colors.

When comparing our color measurements against _r_ -process models, we find
that several of our objects show colors similar to the _r_ -process models
with minimal mixing. However, after 50 days post-peak, our detections and
upper limits altogether strongly suggest that our SNe are brighter in the
optical compared to the NIR. In particular, as many of our SNe are detected in
the $J-$band over a wide range of phases, we can constrain the $r-J$ color to
$<\,-0.5$ after 50 days post-peak. On the other hand, only one object shows
$r-J$/$H$/$K_{s}$ colors $\sim$ 0.5 mag: SN 2007I. In particular, SN 2007I
exhibits an increase in its $r-J$ color until about 60 days.

While these empirical color comparisons can be useful for identifying any
obvious reddening signature that could be a smoking gun for _r_ -process
enrichment, more detailed fitting is required to establish whether or not
these SNe are _r_ -process enriched. Hence, in the next section, we describe
our detailed model fitting aimed at determining whether there is room for an
_r_ -process contribution to their light curves.

Figure 3: $r-J$, $r-H$, and $r-K_{s}$ color evolution plots for the _r_
-process enriched models for a representative set of model parameters,
compared to color measurements for the SNe in our sample. Each model is shown
in a separate linestyle: “solid”: $M_{\rm ej}$$=7.93\rm M_{\odot}$,
$\beta_{\rm ej}$$=0.25c$, $M_{\rm 56}$$=0.85\rm M_{\odot}$, “dotted”: $M_{\rm
ej}$$=2.62\rm M_{\odot}$, $\beta_{\rm ej}$$=0.038c$, $M_{\rm 56}$$=0.39\rm
M_{\odot}$, and “dashed”: $M_{\rm ej}$$=1.00\rm M_{\odot}$, $\beta_{\rm
ej}$$=0.033c$, $M_{\rm 56}$$=0.07\rm M_{\odot}$. When possible, the $r-X$
color of observed SNe was estimated using either concurrent $r-$band
photometry or the closest optical photometry within 3 days of a given NIR
datapoint (filled markers). Otherwise, the $r-$band magnitude is extrapolated
from a stretched and scaled light curve of SN 2020bvc (unfilled markers).
Left: Fixing the mixing fraction to a moderate value of 0.3, we vary _r_
-process ejecta masses [0.01, 0.03, 0.08, 0.13] M⊙. Right: Fixing the _r_
-process mass to a conservative value of 0.01 M⊙, we vary the mixing
coordinate from 0.1 to 0.9. In general, the objects in our sample appear to
have bluer colors relative to the models (with the exception of SN 2007I).

##  8 Results of light curve Model Fitting

To quantitatively determine whether _r_ -process contribution is required to
explain the light curves of SNe Ic-BL, we perform nested sampling fits over
multi-dimensional parameter space spanned by the _r_ -process enriched models.
However, in order to perform the fitting, we need a distribution over
functions with a _continuous_ domain. Since these _r_ -process models are
discretely parameterized, we invoke gaussian process regression (GPR) to
predict light curves from the training set (which are the _r_ -process
enriched models, in this case) for each linear combination over the continuous
ranges of parameters.

We first considered the full grid of _r_ -process enriched models from Barnes
& Metzger (2022). For objects for which it was possible to estimate the total
ejecta mass and kinetic energy, we select grids where the parameters fall
within the following bounds: $M_{\rm ej}{}\in(M_{\rm ej,0}-3\sigma,M_{\rm
ej,0}+3\sigma)$, $\beta_{\rm ej}{}\in(\beta_{\rm ej,0}-3\sigma,\beta_{\rm
ej,0}+3\sigma)$, and $M_{56}\in(M_{56}-3\sigma,M_{56}+10\sigma)$, where
$M_{\rm ej,0}$, $\beta_{\rm ej,0}$, and $M_{56}$ are the independently derived
explosion properties for the supernovae (see Table 4). We changed the upper
bound on $M_{\rm ej}$ ($\beta_{\rm ej}$) to $M_{\rm ej,0}+10\sigma$
($\beta_{\rm ej,0}+10\sigma$) for those SNe for which only a lower limit on
those quantities was derived. We use the entire range of parameters in the
grid for $M_{r\mathrm{p}}$ and $\psi_{\rm mix}$.

We then perform singular value decomposition on each light curve in the model
grid tailored to each supernova and interpolate between model parameters using
scikit-learn’s GPR package, sampling between $-5$ and 200 days relative to the
supernova peak in a similar fashion to Coughlin et al. (2019) and Pang et al.
(2022). We allow GPR to interpolate the range of _r_ -process ejecta masses
and mixing fractions between $M_{r\mathrm{p}}$$=$ 0.01 M⊙, $\psi_{\rm mix}$$=$
0.1 (which are technically the lowest values in the _r_ -process enriched
grid) and $M_{r\mathrm{p}}$$=0.00$, $\psi_{\rm mix}$$=0.0$, though we do not
allow it to exceed the maximum values for these quantities (i.e.
$M_{r\mathrm{p}}$$\leq$ 0.15 M⊙ and $\psi_{\rm mix}$$\leq$ 0.9). We limit
interpolation of the remaining explosion parameters within the maximum and
minimum bounds of the original grid. For a given set of explosion parameters
($M_{\rm ej}$, $\beta_{\rm ej}$, $M_{\rm 56}$), each grid also contains an _r_
-process-free model.

We compute a likelihood function based on the interpolated light curve models
and our multi-band ZTF forced photometry, follow-up photometry, and WIRC
photometry. Since the errors from GPR are small (i.e. they well-approximate
the original model grid), we assume a systematic fitting uncertainty of 0.5
mag in the NIR bands and a fitting uncertainty of 1.0 mag in the optical. We
converged upon a 1.0 mag systematic uncertainty in the optical after
evaluating how different assumed errors affect the fit quality. The difference
in the systematic errors is motivated by the fact that the NIR bands, rather
than the optical bands, are a stronger determinant of whether there is
evidence for _r_ -process production. Furthermore, these assumptions on the
systematic error compensate for the finer sampling in the optical bands
relative to the NIR. In the likelihood calculation, we also impose a condition
that rejects samples with a linear least squares fitting error worse than 1.0
mag. For the _r_ -process enriched model fits, our prior also restricts the
inference of parameters within the ranges of the grid (0.0 $\leq\rm x_{\rm
mix}\leq$ 0.90; 0.0 M${}_{\odot}\,\leq\rm M_{r\mathrm{p}}\leq\,0.15\,\rm
M_{\odot}$) and within physical constraints (i.e. $M_{r\mathrm{p}}$$<$
$\psi_{\rm mix}$($M_{\rm ej}$ \- $M_{\rm 56}$)). We impose this upper limit on
Mrp to satisfy the requirement that the _r_ -process enriched core also
contains ${}^{56}\text{Ni}$ (see Figure 8 of Barnes & Metzger 2022). Finally,
we employ PyMultinest’s (Buchner et al., 2014) nested sampling algorithm to
maximize the likelihood and converge on the best fit parameters and their
uncertainties.

_Most of the SNe in our sample show no compelling evidence for r-process
production._ In our model fits, the general trend we observe is that the best
fit consistently under-predicts the peak of the optical light curve, while
performing better at predicting the NIR flux. In some cases, the under-
prediction is egregious, while in other cases it is more modest. In general
under-prediction indicates that the optical-NIR color of the SN is actually
_bluer_ than predicted by the models, providing stronger evidence towards
favoring _r_ -process-free models over the enriched models. As mentioned
earlier, as Mrp increases, the NIR light curve gets brighter; as $\psi_{\rm
mix}$ increases, the optical light curve peak diminishes and the optical flux
is suppressed more at later times.

To quantitatively assess the fit quality, we compute $\chi^{2}$ values between
the best-fit model and the data points. We adopt the convention that if
$\chi^{2}>\chi^{2}_{crit}$ (at the $>5$% level), we can reject our hypothesis
that these SNe are well-described by the best-fit _r_ -process enriched model.
Therefore, given that our fits have 4 degrees of freedom, a $\chi^{2}>9.49$ is
indicative that the _r_ -process enriched models are poor fits to the data.
Applying this criteria suggests that SN 2018gep, SN 2019xcc and SN 2020rph are
very unlikely to harbor _r_ -process material in their ejecta.

Similarly, we select the subset of objects for which
$\chi^{2}<\chi^{2}_{crit}$ for a p-value of 0.90 ($\chi^{2}_{crit}=1.06$, for
4 degrees of freedom). Based on their $\chi^{2}$ values, SN 1998bw, SN 2007ce,
SN 2018kva, SN 2019gwc, SN 2020lao, SN 2020tkx, SN 2021xv and SN 2021bmf show
the most convincing fits to the _r_ -process enriched models. Upon visual
inspection of the remainder of the light curve fits, we find that none of the
other objects are well-described by the _r_ -process model predictions. We
display the corner plots showing the posterior probability distributions on
the derived parameters in Fig. 4 for the objects passing our $\chi^{2}$ cut,
along with the best-fit light curves in Fig. 5.

Figure 4: Corner plots showing the posterior probability distributions for
each of the parameters in the _r_ -process enriched models for the subset of
objects satisfying our $\chi^{2}$ cut, ordered by the amount of _r_ -process
mass inferred. The posterior probability plots are more well-constrained for
the objects with low inferred $M_{r\mathrm{p}}$; in the remaining cases, the
posterior distributions are poorly constrained. $M_{\rm ej}$ and $\beta_{\rm
ej}$ inferred here are generally in agreement with HAFFET, but discrepancies
exist in the amount of nickel mass inferred.

Figure 5: Plots of light curve models with best-fit parameters (red dotted
line) and corresponding 1$\sigma$ uncertainties (black) with photometric data
overplotted, for both ZTF candidates and candidates from the literature shown
in Figure 4 which pass our $\chi^{2}$ cut, ordered by inferred
$M_{r\mathrm{p}}$. The objects whose optical and NIR photometry are both well-
described by the models are consistent with $M_{r\mathrm{p}}$$\lesssim 0.01\rm
M_{\odot}$. The remaining objects do not show convincing fits to the _r_
-process enriched models.

###  8.1 _r_ -process Candidate ZTF SNe

Only two of the SNe in this subset have well-constrained parameters derived
from the corner plots: SN 2020lao and SN 2021xv. The remainder of the objects
have nearly flat posteriors on $M_{\rm 56}$ and $\beta_{\rm ej}$. For SN
2019gwc the peaks of the posterior probability distributions for both Mrp and
$\psi_{\rm mix}$ are consistent with zero. This is supported by the fact that
while both the $r-$band and $i-$band light curves are slightly under-predicted
by the models, the $J-$band flux is also over-predicted; the observed colors
are bluer than a best-fit model with negligible _r_ -process. SN 2020lao and
SN 2021xv, in turn, have a best fit value of M${}_{r\mathrm{p}}=0.01\,\rm
M_{\odot}$ and $\psi_{\rm mix}$ $<\,0.1$. In the case of SN 2020lao, the
optical flux is under-predicted by the models, and there are no NIR
detections. On the other hand, for SN 2021xv, the optical models provide a
decent fit to the optical data, but the NIR flux is still slightly over-
predicted by the models.

SN 2018kva, SN 2019moc, SN 2020tkx and SN 2021bmf show posterior support for
higher _r_ -process enrichment. SN 2020tkx and SN 2018kva have inferred values
of $M_{r\mathrm{p}}$$\sim 0.03\,\rm M_{\odot}$ and $\psi_{\rm mix}$$\lesssim
0.1$. For these two objects, the model under-predicts the peak of the optical
light curve, though for SN 2018kva the $J-$band models fit the corresponding
photometry. SN 2020tkx has two NIR detections in each of $J$, $H$, and $K_{s}$
filters which are well below the NIR model prediction, demonstrating that its
light curve is inconsistent with the _r_ -process enriched model. Finally, SN
2019moc and SN 2021bmf have parameter fits consistent with
M${}_{r\mathrm{p}}\gtrsim 0.03$ and $\psi_{\rm mix}$$\gtrsim 0.1$. Similar to
other cases, the best fit model for SN 2019moc under-predicts its optical
light curve. While the model is consistent with the $K_{s}-$band upper limit,
it still over-predicts the $J-$band flux. SN 2021bmf has one of the best-
sampled optical light curves in our sample, and the model provides a beautiful
fit to the optical bands. However, the NIR photometry is still vastly over-
predicted by the same model.

###  8.2 _r_ -process Candidate Literature SNe

Similarly, the two objects with $\chi^{2}$ fits that pass our criteria are SN
1998bw and SN 2007ce. In this category we also include SN 2007I because it
shows more significant photometric reddening relative to the other objects in
the sample, even though it does not pass our nominal cuts.

The corner plots for these three objects show posterior distributions that are
not well-constrained. However, all three objects have high predicted values
for both $M_{r\mathrm{p}}$ as well as $\psi_{\rm mix}$. The light curve fits
show the same phenomenon that we identify for the ZTF SN light curve fits: the
peak of the optical light curve is under-predicted, while the NIR data shows
better agreement with the models. In the case of SN 1998bw, the low $\chi^{2}$
is likely attributed to the fact that the optical data are extremely well-
sampled, and the model provides a decent fit to its late-time light curve (in
the $B$, $V$, and $R-$ bands), but the same model does not describe the decay
in the NIR flux accurately. The best-fit model for SN 2007ce matches the NIR
bands but again under-predicts the optical. For SN 2007I, the $riJ$-band
fluxes are wholly underestimated, and in the $HK$-bands, the light curve
appears to be declining much slower than predicted by the models.

As emphasized by Barnes & Metzger (2022), color evolution can be a more
powerful metric in comparison to absolute magnitude comparisons between the
model light curves and data in determining whether a SN Ic-BL harbors _r_
-process material. We thus plot color evolution ($r-J/H/K_{s}$) as a function
of time for our two reddest objects, SN 2007I and SN 2007ce. In Figure 6 we
show their photometric colors along with their best fit _r_ -process-free and
_r_ -process enriched models. In the shaded regions we include the $\pm
1\sigma$ uncertainty on the model parameters from our fits. SN 2007ce’s colors
appear too blue in comparison with its best-fit _r_ -process model. We note
that the color measurements for this object are secure because of several
contemporaneous optical-NIR epochs. Given that it only attains a maximum $r-X$
color of $\sim$0.1 mag 50 days post-peak, we conclude that SN 2007ce is most
likely not an _r_ -process collapsar. SN 2007I is completely inconsistent with
the color evolution of its best-fit _r_ -process model, even within the
parameter uncertainties. However, one challenge arises from the fact that in
the late-time ($\gtrsim$50 days post-peak) SN 2007I lacks any optical
photometry. Based on our extrapolation of the $r-$band light curve of SN 2007I
we see evidence for further reddening which starts to become consistent with
the _r_ -process enriched model predictions in the late-times. Thus, we are
unable to rule out the possibility of _r_ -process production in SN 2007I
based on the _r_ -process fits and the color evolution comparison alone.

Figure 6: Color evolution as a function of time for both SN 2007ce (left) and
SN 2007I (right). Similar to Figure 3, the filled circles with errobars
represent the $r-J/H/K_{s}$ color estimated directly from the data, while the
unfilled circles correspond to a stretched and scaled $r-$band model of SN
2020bvc used as a proxy to estimate the color at each NIR photometric epoch,
in the absence of $r-$band photometry. The dashed line represents the color
evolution of the best-fit _r_ -process enriched model, and the shaded regions
encompass the $\pm 1\sigma$ uncertainty on the model parameters from our fits.
Using the same convention as in Figure 3, magenta is $r-J$, brown is $r-H$ and
cyan is $r-K_{s}$. As shown here, the color evolution of both SN 2007I and SN
2007ce appear to be inconsistent with their best-fitting _r_ -process enriched
model colors and associated 1$\sigma$ uncertainties.

Figure 7: Light curve fits to the literature supernova events from HAFFET. The
dashed cyan line is the best fit bolometric light curve, while the remaining
dashed lines show the fits to each of the broadband light curves. Broadband
light curves are calculated by fitting bolometric corrections in each band,
and using these corrections to rescale the Arnett fitted bolometric light
curves. The circles are the photometry for each object in $griJHK_{s}$ bands.
In the bolometric light curve plot, the points correspond to bolometric
luminosity estimated from both $g-$ and $r-$ bands, or from a single band, and
using GPR to estimate the flux in the other band. For SNe with photometry in
the Johnson filter system, we convert the photometry to SDSS assuming
photometric conversions from Jordi et al. (2006). We find that the HAFFET
models are good fits to the photometric data from our sample.

Figure 8: Light curve fits to ZTF SNe from HAFFET, similar to Fig. 7.

###  8.3 Independent Arnett Fits

To supplement our fits to _r_ -process enriched models we use HAFFET to
construct a bolometric light curve from our optical data and fit to the
standard Arnett model (as described in more detail in Sec. 7). We then
calculate broadband light curve models by fitting bolometric corrections in
each band, and using these corrections to rescale the Arnett fitted bolometric
light curve models. Our fits are shown in Figures 7 and 8.

In order to compare the two models, we compute $\chi^{2}$ for each of the
bolometric light curves. Two of the objects have approximately the same
$\chi^{2}$ values ($\delta\chi^{2}\lesssim 0.1$) for both models: SN 2018kva
and SN 2019gwc. Six objects have better $\chi^{2}$ fits to the _r_ -process
enriched models compared to the _r_ -process-free models: SN 2019moc, SN
2020lao, SN 2020tkx, SN 2021bmf, SN 2021xv and SN 2021too. We note that
amongst the subset of objects that are equally fit both models or are better
described by the _r_ -process-enriched models, only SN 2021too did not make
our $\chi^{2}$ cut in Sec. 8.1. The remainder of the objects have stronger
fits to the _r_ -process-free models.

We examine the bolometric and broadband light curves for each of the objects
listed above. For all of the objects, the light curve peak is well-described
by the Arnett model fits, in contrast with the fits to _r_ -process enriched
models that tend to underestimate the light curve peak. However, these light
curves tend to decay slower in the optical bands compared to the Arnett
prediction, which is a known limitation of the Arnett model (Afsariardchi et
al., 2021). On the other hand, we find stronger agreement between the models
and our data in the NIR bands compared to the _r_ -process enriched scenario.
Upon visual inspection, we find convincing fits to both the early optical
light curves and the NIR light curves for these objects for the _r_ -process-
free models.

Furthermore, we derive blackbody effective temperatures for the closest epoch
to 30 days post-peak where both optical and NIR photometry are available. The
effective temperatures range from $4000-15000$ K; the SED colors are well-
described by a single-component blackbody at this phase. Based on the quality
of our Arnett fits, and the fact that the SED for these SNe in the
photospheric phase is well-described by a blackbody, we conclude that no _r_
-process contribution is needed to explain the color evolution of the objects
in our sample, including SN 2007I.

Thus, we find no compelling evidence of _r_ -process enrichment in any of the
SNe in our sample.

##  9 Discussion and Outlook

From our systematic study in optical and NIR of the SNe Ic-BL associated with
collapsars discovered by ZTF and reported in the literature, we do not find
any evidence of _r_ -process enrichment based on theoretical models which
predict observable NIR excesses in the SN light curves. After constructing GPR
models from the _r_ -process-enriched model grid and performing fitting, the
SNe that pass our nominal $\chi^{2}$ cuts still do not show convincing fits in
both the optical and NIR to the _r_ -process enriched broadband light curve
predictions. On the other hand, for the _r_ -process-free models, when
computing broadband light curves from the bolometric corrections, we get
compelling fits in both optical and NIR for each SN. Our single-component
blackbody fits at $\sim$1 month after peak (see Table 4) further suggest that
no additional _r_ -process enrichment is required to explain the SN SED
colors.

Our use of two models, one for _r_ -process-free SNe and another for _r_
-process enriched cases, complicates our efforts to derive global constraints
on _r_ -process production in SNe. To estimate the level of enrichment our
analysis is sensitive to, we take the reddest object in our sample that is
consistent with the _r_ -process enriched models, and compare the color
measurements with the predicted color evolution from the models. To derive
these global constraints, we focus on SN 2007ce. Amongst our sample, SN 2007ce
has the highest inferred _r_ -process ejecta mass of 0.07M⊙ while passing the
$\chi^{2}$ cut (we ignore SN 1998bw, whose extremely well-sampled light curve
could be influencing the final $\chi^{2}$ value). Though SN 2007I is redder
than SN 2007ce, it shows color evolution that is completely inconsistent with
the models (see Fig. 6) making it unsuitable for deriving _r_ -process
constraints. In Fig. 6 we display the predicted color evolution of the best
fit model bounded by its 1$\sigma$ uncertainties on the parameters, where the
lower bound corresponds to a model with $M_{r\mathrm{p}}$$=0.02\rm M_{\odot}$
and the upper bound corresponds to a model with $M_{r\mathrm{p}}$$=0.12\rm
M_{\odot}$. SN 2007ce’s color measurements exhibit a similar shape to the
model color evolution, but show a significant offset with bluer colors
compared to the best-fit models. As shown in Figure 3, a model with a higher
$M_{\rm ej}$ can yield a slightly bluer color evolution for the same _r_
-process mass, so it is difficult to confidently exclude the possibility that
$M_{r\mathrm{p}}$$=0.02\rm M_{\odot}$ (the lower bound on the parameter
inference) was synthesized in SN 2007ce. In addition, relaxing the assumptions
on the SED underlying the models could also alter the color evolution of the
model. Thus, based on the the upper bound of these color curves, which
corresponds to an _r_ -process mass of 0.12M⊙, we conservatively argue here
that no more than 0.12M⊙ of _r_ -process material was generated in SN 2007ce.
Furthermore, since SN 2007ce has the highest inferred _r_ -process mass
amongst the objects passing our $\chi^{2}$ cut, we suggest that
$M_{r\mathrm{p}}$$\lesssim 0.12\rm M_{\odot}$ represents a reasonable global
_r_ -process constraint on all of the models in our sample, based on the
observations. Future improvements in the models as well as more systematic
observations will allow for tighter and more robust constraints on the _r_
-process nucleosynthesis in SNe Ic-BL.

We also examine any other associated relativistic emission to check whether
that may introduce a bias. Only three objects in our full sample are
counterparts to GRBs: GRB980425 (SN 1998bw), GRB100316D (SN 2010bh) and
GRB190829A. Amongst these three, GRB980425 and GRB190829A are considered to be
LLGRBs based on their peak $\gamma$-ray luminosities (Galama et al., 1998; Ho
et al., 2020b; Dichiara et al., 2019). GRB100316D is a more energetic GRB, but
its emission shows a soft spectral peak, similar to other X-ray flashers
(Bufano et al., 2012). While none of the other objects in our sample have any
coincident $\gamma$-ray emission, some have X-ray and radio detections and
upper limits. SN 2018gep, the FBOT/SN Ic-BL, has both X-ray and radio
detections that are consistent with the host galaxy emission (Ho et al.,
2019). On the other hand, SN 2020bvc has mildly-relativistic X-ray and radio
emission characteristic of LLGRBs (Ho et al., 2020a). Corsi et al. (2022) also
obtained radio and X-ray follow-up for several ZTF SNe, a subset of which are
part of the sample discussed in this work. In Table 1 we display radio
observations with the Very Large Array (VLA) and X-ray observations with the
Swift X-ray Telescope (XRT) for those SNe; the remainder of SNe which lack
radio/X-ray coverage have dashes in those respective columns. Only two of the
objects in the sample presented here (SN 2020tkx and SN 2021ywf) have a
detected point-source-like radio counterpart, but their low velocities suggest
that they are not the same as GRB-associated SNe (Corsi et al., 2022).

The fact that none of these SNe are linked to standard, classical long GRBs
prevents us from exploring the proposed theoretical connection between the GRB
energetics and _r_ -process production. If the GRB jet energy, which scales
with the mass accreted by the disk, correlates with the amount of _r_ -process
mass produced in the disk winds, then collapsars with no GRBs may not be able
to produce detectable _r_ -process signatures. Siegel et al. (2019) find that
for black hole accretion rates between 0.003$-$1.0 M⊙ s-1 needed to power
relativistic outflows, the disk winds are neutron-rich and can synthesize
heavy and light _r_ -process elements. The association of a GRB with a SN Ic-
BL could point towards a central engine that harbors high enough accretion
rates to potentially generate _r_ -process elements.

It is yet unclear to what extent the populations of SNe Ic-BL and long GRBs
overlap. Furthermore, LLGRBs, short-duration GRBs with collapsar progenitors
(Ahumada et al., 2021) and long-duration GRBs from compact binaries
(Rastinejad et al., 2022) present evidence towards a broad diversity in
collapsar central engines, ranging from mildly relativistic to ultra-
relativistic explosion energies. One possibility is that a subset of SNe Ic-BL
could correspond to failed GRBs with low black hole accretion rates. Multi-
wavelength observations of SN 2006aj suggest that another subset may be
associated with a progenitor whose jet runs into a cocoon of extended stellar
material (Nakar, 2015), even when an LLGRB is not detected, as in the case of
SN 2017iuk (Izzo et al., 2019). Yet another subset could be off-axis GRBs.

This diversity of collapsar central engines and jet properties could lend
itself naturally to a scenario where some collapsars are capable of producing
_r_ -process elements while others are not. However, given that only
$\sim$half of the SNe in our sample have X-ray or radio observations, a more
systematic NIR follow-up campaign with SNe Ic-BL associated with classical
long GRBs, LLGRBs, X-ray/radio counterparts, and lacking any multi-wavelength
counterparts is needed to investigate whether only those SNe that produce
relativistic emission are able to create conditions conducive to _r_ -process
nucleosynthesis.

Another possibility we acknowledge is that collapsars could be a very low-
yield source of _r_ -process nucleosynthesis. The expected yields from the
Siegel et al. 2019 and Barnes & Metzger 2022 models (0.01-0.1M⊙) are mainly
set by the joint constraints from the literature on _r_ -process
nucleosynthesis sites (see for e.g. Hotokezaka et al., 2018). However, the
discovery of minuscule amounts of Sr and Ba in an extremely metal-poor star
(Casey & Schlaufman, 2017) motivates the need for core-collapse supernovae
with an extremely low yield of _r_ -process material whose nucleosynthesis is
consistent with the Solar _r_ -process abundance pattern. Due to the
limitations of these models and the dataset presented here, our study only
searches for enrichment levels of $M_{r\mathrm{p}}$$\gtrsim 0.01\rm
M_{\odot}$. Detailed analysis of the nebular-phase spectra of SNe Ic-BL would
likely be required to probe such low levels of enhancement robustly.

Despite the fact that we find no evidence for _r_ -process enrichment in the
SNe Ic-BL in our sample, we must also acknowledge a number of caveats to this
work.

First, we note that the _r_ -process enriched and _r_ -process-free models
make different predictions about the relationship between nickel mass and SN
luminosity. While the inferred central values from the GPR inference of both
$\beta_{\rm ej}$ and $M_{\rm ej}$ based on the _r_ -process grid are generally
within the 1$\sigma$ errorbar of our explosion property estimates, the nickel
mass inferred shows a larger deviation from the Arnett value. Arnett-like
models are constructed such that the radioactive energy-generation rate
crosses the bolometric light curve precisely at peak luminosity. The _r_
-process enriched models, in which energy diffuses through a series of
concentric shells, do not reproduce this behavior; they generally have $L_{\rm
bol}(\rm tpk)<Q_{\rm dot}(\rm tpk)$. As a result, the amounts of nickel
inferred by each model for a given luminosity are inconsistent.

Given the differences between the _r_ -process enriched and _r_ -process-free
models we use, a more robust approach would be to conduct an apples-to-apples
comparison between _r_ -process-free and _r_ -process enriched models from the
same underlying grid. Initially, we performed fitting to both the _r_
-process-free and _r_ -process enriched models from (Barnes & Metzger, 2022),
but found that the colors of the _r_ -process-free models were consistently
much redder than the observed colors of our objects at all epochs. To
construct the _r_ -process-free SED, Barnes & Metzger (2022) uses the light
curve of SN 2007gr as it has well-sampled $B$\- to $K$-band photometry up to
late-times, but the detection of the CO molecule in its nebular phase NIR
spectra may affect the $K-$band flux of the object (Hunter et al., 2009). A
closer look at the optical-NIR colors for a systematic sample of stripped-
envelope and Type II SNe may reveal whether the late-time SED of SN 2007gr is
truly representative of standard _r_ -process-free SNe Ic.

Furthermore, our understanding of the emission of _r_ -process ejecta in the
nebular phase is quite limited. The radiation from _r_ -process-enriched
ejecta layers has a strong impact on the predictions of late-time photometry
for the _r_ -process grid. Barnes & Metzger (2022) adopts a temperature of
2500 K for the _r_ -process SED because a black-body at this temperature
reproduces the optical and NIR photometric colors of the nebular-phase
kilonova model spectrum of Hotokezaka et al. (2021). However, kilonova
nebular-phase modeling is still a topic of active investigation. Future
studies of kilonova nebulae, both observational and theoretical, may refine
our understanding of nebular emission from pure _r_ -process outflows.
Furthermore, differences between kilonovae and _r_ -process-enriched SN (e.g.,
in their densities or their compositions) may mean that nebular-phase emission
from the former is not a perfect predictor of nebular-phase emission from the
latter.

Finally, we acknowledge the limitations of the dataset we present here for
testing whether collapsars synthesize _r_ -process elements. Due to the nature
of our classical observing runs with WIRC, our NIR light curves are very
sparse, and in some cases our upper limits are too shallow to be constraining.
In contrast, future wide field of view NIR facilities (i.e. WINTER, DREAMS,
PRIME) will enable systematic follow-up of nearby SNe Ic-BL discovered by
contemporaneous wide-field optical surveys (i.e. ZTF, Pan-STARRS, ATLAS, Vera
Rubin Observatory, etc) as well as counterparts to nearby long GRBs to late-
times. The James Webb Space Telescope will grant the unique ability to probe
the mid-infrared wavelengths and acquire IR spectroscopy to search for further
signatures of _r_ -process production. Higher cadence NIR photometry and
nebular spectroscopy to search for the _r_ -process signatures from collapsars
would substantiate the results of this paper as well as determine whether the
presence of a relativistic jet in the explosion is required for heavy element
production. The authors plan to investigate the relative contribution of
collapsars, neutron star mergers, and neutron star–black hole mergers towards
the _r_ -process abundance in the Universe in a future work. The next
generation of optical and IR telescopes will open new windows to discoveries
providing valuable insights into the open questions about _r_ -process
nucleosynthesis from collapsars.

SN | $M_{\rm ej}$ | $E_{\rm K}$ | $M_{\rm Ni}$ | E(B-V)host |  $T_{\rm eff}$ [phase] | Ref.
---|---|---|---|---|---|---
| (M⊙) | (foe) | (M⊙) |  | (K [day]) |
1998bw | 10 | 50 | 0.4 | 0.06* | 5919 [24.5] | Clocchiatti et al. (2011); Nakamura et al. (2001)
2002ap | 2.5-5 | 4-10 | 0.07 (0.02) | 0.09 | 5126 [30.5] | Mazzali et al. (2002)
2007ce | 2.90 (0.63) | 1.85 (0.89) | 0.48 (0.01) | 0.00 | 6310 [18.5] |  Modjaz et al. (2008)**
2007I | 6.87 (0.80) | 7.63 (1.99) | 0.10 (0.00) | 0.34 | 4064 [33.5] |  Modjaz et al. (2008)**
2009bb | 3.4 (0.4) | 6.2 (0.8) | 0.20 (0.02) | 0.540 | 3584 [22.6] | Taddia et al. (2018)
2010bh | 2.21 (0.10) | 11.34 (0.52) | 0.21 (0.03) | 0.30 | 6102 [23.5] | Olivares E. et al. (2012)
2016coi | 4-7 | 7-8 | 0.15 | 0.00 | 4727 [32.1] | Terreran et al. (2019)
Table 3: Explosion properties and inferred _r_ -process ejecta masses and
mixing fractions of low-redshift SNe with contemporaneous optical and NIR
imaging from our literature search. Where available, we quote the 1$\sigma$
uncertainties on the parameters in brackets. For SN 1998bw and SN 2002ap, we
quote the ranges of explosion parameters corresponding to the best-fitting
light curve models. *Clocchiatti et al. (2011) already corrected for host
extinction; we use the assumed host extinction to correct only the NIR
photometry. **For SN 2007I and SN 2007ce, as explosion properties were not
estimated in the literature, we conduct light curve analysis to derive the
best fit properties as described in Sec. 7.

Table 4: Optical properties of the BL-Ic SNe in our sample.

SN | $t_{\rm peak}$ | $M_{\rm peak,r}$ | $t_{\rm expl}$ | $M_{\rm Ni}$ | $\tau_{m}$ | $M_{\rm ej}$ | $E_{\rm kin}$ | $v_{\rm ph}$ |  $T_{\rm eff}$ [phase]
---|---|---|---|---|---|---|---|---|---
| (MJD) | (mag) | (day) | (M⊙) | (day) | (M⊙) | ($10^{51}$erg) | (c) | (K [day])
2018jaw | 58455.70 | -18.63 (0.08) | -18.74 ${}_{-0.66}^{+0.66}$ | 0.33 ${}_{-0.02}^{+0.02}$ | 13.63 ${}_{-1.38}^{+1.10}$ |  $>$ 1.41 (0.33) |  $>$ 0.40 (0.16) | 0.022 (0.004) | –
2018kva | 58487.05 | -18.70 (0.02) | -15.81 ${}_{-0.60}^{+0.49}$ | 0.29 ${}_{-0.01}^{+0.01}$ | 12.13 ${}_{-0.87}^{+1.06}$ | 2.51 (0.39) | 3.53 (0.76) | 0.051 (0.004) | 5431 [47.2]
2019gwc | 58650.58 | -18.48 (0.01) | -12.78 ${}_{-0.46}^{+0.46}$ | 0.22 ${}_{-0.01}^{+0.01}$ | 6.96 ${}_{-0.15}^{+0.12}$ |  $>$ 0.60 (0.05) |  $>$ 0.44 (0.08) | 0.037 (0.003) | 5953 [33.6]
2019hsx | 58647.07 | -17.08 (0.02) | -15.63 ${}_{-0.53}^{+0.38}$ | 0.07 ${}_{-0.01}^{+0.01}$ | 12.12 ${}_{-1.26}^{+1.10}$ | 1.64 (0.43) | 0.99 (0.50) | 0.033 (0.007) | 11002 [36.1]
2019moc | 58715.76 | -19.16 (0.03) | -20.02 ${}_{-3.23}^{+0.27}$ | 0.52 ${}_{-0.02}^{+0.01}$ | 10.60 ${}_{-0.30}^{+0.37}$ | 2.09 (0.50) | 3.48 (1.85) | 0.056 (0.013) | 8537 [63.0]
2019qfi | 58753.56 | -18.01 (0.02) | -15.09 ${}_{-1.40}^{+1.40}$ | 0.13 ${}_{-0.01}^{+0.01}$ | 10.58 ${}_{-1.71}^{+1.44}$ |  $>$ 1.22 (0.33) |  $>$ 0.70 (0.24) | 0.032 (0.004) | 5698 [25.0]
2019xcc | 58844.59 | -16.58 (0.06) | -10.62 ${}_{-2.51}^{+2.51}$ | 0.04 ${}_{-0.01}^{+0.01}$ | 5.04 ${}_{-0.95}^{+1.36}$ | 0.68 (0.30) | 2.40 (1.14) | 0.081 (0.007) | –
2020dgd | 58914.05 | -17.74 (0.02) | -18.03 ${}_{-2.50}^{+2.50}$ | 0.13 ${}_{-0.03}^{+0.03}$ | 13.68 ${}_{-2.70}^{+3.78}$ | 2.81 (1.50) | 3.07 (2.42) | 0.045 (0.013) | –
2020lao | 59003.92 | -18.66 (0.02) | -10.60 ${}_{-0.99}^{+0.99}$ | 0.23 ${}_{-0.01}^{+0.01}$ | 7.71 ${}_{-0.21}^{+0.22}$ | 1.22 (0.16) | 2.48 (0.71) | 0.048 (0.005) | –
2020rph | 59092.34 | -17.48 (0.02) | -19.88 ${}_{-0.02}^{+0.02}$ | 0.07 ${}_{-0.01}^{+0.01}$ | 17.23 ${}_{-0.89}^{+1.19}$ | 3.83 (1.59) | 3.08 (2.81) | 0.039 (0.016) | 5857 [30.5]
2020tkx | 59116.50 | -18.49 (0.05) | -12.77 ${}_{-4.54}^{+4.54}$ | 0.22 ${}_{-0.01}^{+0.01}$ | 10.95 ${}_{-0.77}^{+0.67}$ |  $>$ 1.75 (0.24) |  $>$ 1.82 (0.35) | 0.044 (0.003) | 7116 [32.8]
2021bmf | 59265.12 | -20.60 (0.04) | -23.76 ${}_{-5.52}^{+5.68}$ | 0.98 ${}_{-0.17}^{+0.16}$ | 18.08 ${}_{-7.94}^{+6.64}$ | 8.05 (5.37) | 23.63 (16.14) | 0.073 (0.005) | 15618 [41.4]
2021too | 59434.09 | -19.66 (0.02) | -23.23 ${}_{-0.41}^{+0.41}$ | 0.92 ${}_{-0.03}^{+0.03}$ | 17.67 ${}_{-0.66}^{+0.65}$ | 5.06 (0.78) | 6.42 (2.09) | 0.048 (0.007) | 5363 [23.5]
2021xv | 59235.56 | -18.92 (0.07) | -12.79 ${}_{-0.33}^{+0.24}$ | 0.30 ${}_{-0.02}^{+0.01}$ | 7.72 ${}_{-0.49}^{+0.66}$ | 0.89 (0.15) | 0.96 (0.23) | 0.045 (0.004) | 5969 [13.5]
2021ywf | 59478.64 | -17.10 (0.05) | -10.67 ${}_{-0.49}^{+0.49}$ | 0.06 ${}_{-0.01}^{+0.01}$ | 8.87 ${}_{-0.81}^{+0.81}$ | 1.06 (0.19) | 0.92 (0.26) | 0.040 (0.004) | 5238 [19.5]

Note. — Table containing the explosion properties of all of the ZTF-discovered
SNe in our sample that have not yet been published. Our methods for deriving
the quantities given above are described in Sec. 7. The velocities shown here
are measured at various different phases, so do not represent the photospheric
velocity of the supernova at peak. We report effective temperatures from
blackbody fits around $\sim$30 days post-peak for each of the objects that has
one or more NIR detections.

The authors express gratitude towards Ehud Nakar, Daniel Siegel, Leo Singer,
Navin Sridhar, and Peter Tsun Ho Pang for helpful discussions in preparing
this work. Based on observations obtained with the Samuel Oschin Telescope
48-inch and the 60-inch Telescope at the Palomar Observatory as part of the
Zwicky Transient Facility project. ZTF is supported by the National Science
Foundation under Grants No. AST-1440341 and AST-2034437 and a collaboration
including current partners Caltech, IPAC, the Weizmann Institute of Science,
the Oskar Klein Center at Stockholm University, the University of Maryland,
Deutsches Elektronen-Synchrotron and Humboldt University, the TANGO Consortium
of Taiwan, the University of Wisconsin at Milwaukee, Trinity College Dublin,
Lawrence Livermore National Laboratories, IN2P3, University of Warwick, Ruhr
University Bochum, Northwestern University and former partners the University
of Washington, Los Alamos National Laboratories, and Lawrence Berkeley
National Laboratories. Operations are conducted by COO, IPAC, and UW. SED
Machine is based upon work supported by the National Science Foundation under
Grant No. 1106171. Based on observations made with the Nordic Optical
Telescope, owned in collaboration by the University of Turku and Aarhus
University, and operated jointly by Aarhus University, the University of Turku
and the University of Oslo, representing Denmark, Finland and Norway, the
University of Iceland and Stockholm University at the Observatorio del Roque
de los Muchachos, La Palma, Spain, of the Instituto de Astrofisica de
Canarias. S. Anand acknowledges support from the National Science Foundation
GROWTH PIRE grant No. 1545949. J. Barnes is supported by the Gordon and Betty
Moore Foundation through Grant GBMF5076. M. W. Coughlin acknowledges support
from the National Science Foundation with grant numbers PHY-2010970 and
OAC-2117997. S. Schulze acknowledges support from the G.R.E.A.T research
environment, funded by Vetenskapsrådet, the Swedish Research Council, project
number 2016-06012. L.T. acknowledges support from MIUR (PRIN 2017 grant
20179ZF5KS)

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