# Needle in a Haystack: Finding Supermassive Black Hole-Related Flares in the
Zwicky Transient Facility Public Survey

Yael Dgany The School of Physics and Astronomy, Tel Aviv University, Tel Aviv
69978, Israel  Iair Arcavi The School of Physics and Astronomy, Tel Aviv
University, Tel Aviv 69978, Israel  CIFAR Azrieli Global Scholars program,
CIFAR, Toronto, Canada  Lydia Makrygianni The School of Physics and Astronomy,
Tel Aviv University, Tel Aviv 69978, Israel  Craig Pellegrino Las Cumbres
Observatory, 6740 Cortona Drive, Suite 102, Goleta, CA 93117-5575, USA
Department of Physics, University of California, Santa Barbara, CA 93106-9530,
USA  D. Andrew Howell Las Cumbres Observatory, 6740 Cortona Drive, Suite 102,
Goleta, CA 93117-5575, USA  Department of Physics, University of California,
Santa Barbara, CA 93106-9530, USA Iair Arcavi<EMAIL_ADDRESS>
###### Abstract

Transient accretion events onto supermassive black holes (SMBHs), such as
tidal disruption events (TDEs), Bowen Fluorescence Flares (BFFs), and active
galactic nuclei (AGNs), which are accompanied by sudden increases of activity,
offer a new window onto the SMBH population, accretion physics, and stellar
dynamics in galaxy centers. However, such transients are rare and finding them
in wide-field transient surveys is challenging. Here we present the results of
a systematic real-time search for SMBH-related transients in Zwicky Transient
Facility (ZTF) public alerts, using various search queries. We examined 345
rising events coincident with a galaxy nucleus, with no history of previous
activity, of which 223 were spectroscopically classified. Of those, five
(2.2%) were TDEs, one (0.5%) was a BFF, and two (0.9%) were AGN flares.
Limiting the search to blue events, the fraction of TDEs nearly doubles to
4.1%, and no TDEs are missed. Limiting the search further to candidate post-
starburst galaxies increases the relative number of TDEs to 16.7%, but the
absolute numbers in such a search are small. The main contamination source is
supernovae (95.1% of classified events), of which the majority (82.2% of
supernovae) are of Type Ia. In a comparison set of 39 events with limited
photometric history, the AGN contamination increases to $\sim$30%. Host galaxy
offset is not a significant discriminant of TDEs in current ZTF data, but
might be useful in higher-resolution data. Our results can be used to quantify
the efficiency of various SMBH-related transient search strategies in optical
surveys such as ZTF and the Legacy Survey of Space and Time.

Supernovae (1668), Tidal disruption (1696), Supermassive black holes (1663),
Active galactic nuclei (16), Astronomical methods (1043)

##  1 Introduction

Wide-field optical surveys have recently found new types of transients
occurring exclusively in galaxy centers. These transients are thought to be
associated with enhanced accretion events onto supermassive black holes
(SMBHs). As such, they have the potential to reveal the presence and
properties of otherwise inactive SMBHs, as well as constrain physics of
accretion and related radiative processes. Notable examples of such transients
are optical-ultraviolet tidal disruption events (TDEs) and Bowen Fluorescence
Flares (BFFs). Both types of events are characterized by a sudden increase of
flux by several orders of magnitude and are thus much more dramatic than the
few tens of percent level variability seen in most active galactic nuclei
(AGNs), which host SMBHs with steadily accreting material.

A TDE is the result of the disruption of a star by an SMBH (Hills, 1975). In
such an event, half of the stellar material is expected to accrete onto the
SMBH (Rees, 1988). For disruptions occurring outside the event horizon
(expected for solar-type stars disrupted by SMBHs of masses $\lesssim
10^{8}\,\textrm{M}_{\odot}$, for example), the accretion event will be
accompanied by an observable flare. Several such flares have been detected in
X-rays, as expected from directly observed accretion emission (see Saxton et
al. 2020 for a recent review). However, somewhat surprisingly, a class of
optical-ultraviolet TDEs has also been discovered (Gezari et al., 2012; Arcavi
et al., 2014). These events are mostly blue, with blackbody temperatures of a
few $10^{4}$ K lasting for several months to years, and show broad emission
lines of H and/or He in their spectra. The emission mechanism leading to these
observed properties is a topic of active debate (see van Velzen et al. 2020
and Gezari 2021 for recent reviews).

In addition to their emission mechanism puzzle, optical-ultraviolet TDEs show
a peculiar and strong host galaxy preference for post-starburst galaxies
(Arcavi et al., 2014; French et al., 2016, 2020ab). This preference is not yet
fully understood, but might be related to the spatial distribution and
dynamics of various stellar populations in the centers of such galaxies
(French et al., 2020). Studying optical-ultraviolet TDEs can thus also help
shed light on the stellar dynamics in galaxy nuclei, which are responsible for
driving up TDE rates in post-starburst environments (e.g. Madigan et al.,
2018).

Like TDEs, BFFs are also blue and show H and He lines in their spectra,
leading Tadhunter et al. (2017) to classify the first such observed event as a
TDE. However, a second (Gromadzki et al., 2019) and then third (Trakhtenbrot
et al., 2019) event showed that their typical spectral line widths are much
narrower than those of TDEs, and that their light curves decline much more
slowly than those of TDEs. This led Trakhtenbrot et al. (2019) to classify
BFFs as a separate observational class. There are now also hints that BFFs
occur in previously existing AGN (Makrygianni et al., 2023), meaning that they
could be the result of accretion instabilities in an AGN disk, or of a TDE
occurring in an AGN and interacting with its existing accretion disk (e.g.
Chan et al., 2019).

BFFs are named as such because they exhibit certain emission lines (such as He
II 4686Å and N III 4640Å, among others) that are associated with the Bowen
fluorescence mechanism (Bowen, 1928). In this mechanism, extreme-ultraviolet
and X-ray photons excite certain He II transitions, which in turn launch a
cascade of transitions observed in the optical and ultraviolet regimes. This
process requires the presence of extreme-ultraviolet photons hitting high-
density and high-optical-depth material, and is was indeed predicted decades
ago to occur in AGNs (Netzer et al., 1985). Since the identification of this
mechanism in BFFs, it has also been suggested to occur in some TDEs
(Blagorodnova et al., 2019; Leloudas et al., 2019), hinting at a possible
connection between the conditions of matter and radiation in these two types
of events related to SMBH accretion.

It is clear that studying more TDEs and BFFs is necessary in order to better
constrain their nature, emission mechanisms, and the physics they can teach us
in relation to SMBHs and their associated accretion processes. However, these
events are intrinsically rare. The exact TDE rate remains uncertain, but is
likely to be in the range of $10^{-5}$–$10^{-4}$ events per galaxy per year
(e.g. Wang & Merritt, 2004; Stone & Metzger, 2016). The BFF rate is not yet
estimated at all, but observationally, they are less common than TDEs (this
could be due in large part to selection effects, as discussed below). In
addition to their intrinsic rarity, finding TDEs and BFFs is also
observationally challenging. As events that occur in galaxy centers, their
detection is contaminated by image-subtraction artifacts, “regular” AGN
activity, unresolved non-SMBH-related transients, and even variable stars that
cannot be easily distinguished from distant or compact galaxies. For these
reasons, only a few dozen TDEs and a few BFFs have been identified so far.

Attempts have been made to devise selection criteria to weed out such
transients from the large alert streams produced by wide-field transient
surveys. Such criteria typically include selecting candidates by the
significance of the flare (since TDEs and BFFs are luminous), color (since
TDEs and BFFs are blue), and host properties (since TDE hosts are mostly
quiescent).

Hung et al. (2018) searched a set of 493 nuclear transients (0$\farcs$8 from
their host galaxy center) from the intermediate Palomar Transient Factory, for
events with $g-R<0$ mag residing in galaxies with $u-g>1$ mag and $g-r>0.5$
mag. These cuts reduced the set of candidates to just 26, of which two are
TDEs. Still, the contamination fraction is large. A substantial amount of
telescope time is required to vet 13 candidates (through spectroscopy or
ultraviolet colors) for each bona fide TDE.

One way to further reduce the amount of transient contamination in TDE
searches is to focus the search on galaxies most similar to the post-starburst
hosts that TDEs seem to prefer. French & Zabludoff (2018) used galaxies from
the Sloan Digital Sky Survey (SDSS; York et al., 2000) Data Release 12 main
galaxy survey (Strauss et al., 2002; Alam et al., 2015), with similar spectral
properties as those of actual TDE hosts, to train a machine-learning algorithm
to identify such galaxies from photometry alone. They then used this algorithm
to identify several tens of thousands of TDE host galaxy candidates in
archival survey data. Arcavi et al. (2022c) found indeed that using the French
& Zabludoff (2018) catalog of galaxies reduces contamination by roughly a
factor of 3-50 (depending on the subset of galaxies used from the catalog)
compared to filtering just on quiescent galaxies, and that the only
contaminant transients in such galaxies are Type Ia supernovae (SNe). That
study, however, was based on archival data alone.

Here we perform a systematic real-time search for TDEs, BFFs, and other
possible SMBH flares in the Zwicky Transient Facility (ZTF; Bellm, 2014;
Graham et al., 2019) alert stream, as parsed by the Lasair broker (Smith et
al., 2019). We use various search criteria that rely on candidate brightness
and color and their host galaxy properties, and compare their effectiveness in
selecting TDEs and BFFs against actual spectroscopic classifications obtained
by us and by the rest of the community. We focus on rising events (i.e. events
discovered before their peak), selected using visual inspection, for two main
reasons. First, such events are more scientifically valuable, as they include
the peak time and brightness, as well as the early pre-peak emission, both of
which contain important information for constraining models of SNe and TDEs.
In addition, rising events present a way of decreasing the number of events to
a more manageable subsample for spectroscopic classification, while avoiding
biasing the sample toward a particular class (the selection of rising events
is done before their classifications are known).111One exception is that we
would be less likely to catch rapidly rising events during their rise compared
to slower-rising events, but the typical ZTF public survey cadence is
$\lesssim 3$ days, which should be enough to catch most rapidly evolving
transients (e.g. Ho et al., 2021).

Our goal is to quantify the contamination fraction for the various search
criteria and to check whether any of them miss TDEs and BFFs. Here, we do not
constrain the intrinsic rates of SNe, TDEs, or BFFs in nature, but rather the
observed fractions of events, to help guide searches in ongoing and future
transient surveys and to help prioritize limited spectroscopic classification
resources. We detail our search criteria in Section 2, present and analyze our
results in Section 3, and discuss them and conclude in Section 4.

##  2 Methods

We searched the ZTF real-time alert stream for transients in galaxy centers
every day between 2020 November 3 and 2022 March 6 (UT dates), with the
exception of a $\sim$2-month break due to a ZTF technical outage between 2021
December 5 and 2022 February 17. In total, our search includes alerts from 414
days. We used the custom query builder on version 1.0 of the Lasair
broker222https://lasair.lsst.ac.uk to filter the alerts. Lasair uses a
contextual classifier called
Sherlock333https://lasair.readthedocs.io/en/develop/core_functions/sherlock.html.
Sherlock is a boosted decision tree algorithm that provides an initial
classification of every nonmoving object by performing a spatial crossmatch
against data from historical and ongoing astronomical surveys, including
catalogs of nearby galaxies, variable stars, and AGNs (see Section 4.2 of
Smith et al. 2020 for more details).

Our queries, which are based on the TDE queries by M. Nicholl on version 1.0
of Lasair444https://lasair.roe.ac.uk/filters/94/ and
https://lasair.roe.ac.uk/filters/95/, filter ZTF alerts according to the
following criteria (for each, we state the corresponding Lasair query
condition):

  1. 1.

The candidate is within a certain threshold distance of the nearest Sherlock
catalog source. For 80% of our sample, we choose a threshold of
0$\farcs$5555This value was chosen given that the ZTF pixel scale of 1″ per
pixel results in a typical centroiding accuracy of $\lesssim$0$\farcs$3, which
we increase to 0$\farcs$5 to be inclusive. For the rest, we increased the
separation threshold to 1″ to check if this has a strong effect on the
results:
sherlock_classifications.separationArcsec < 0.5
or
sherlock_classifications.separationArcsec < 1
We found that the value of the threshold has no significant effect on the
results (see Appendix B), and therefore analyzed the joint sample of both
separation thresholds together to increase our sample size. This condition,
regardless of the separation threshold, filters out “hostless” events, i.e.
those with no host in the Sherlock catalog.

  2. 2.

The nearest catalog source is likely a galaxy rather than a star666sgscore1 is
based on a random forest classifier trained and implemented in the ZTF alerts
by Tachibana & Miller (2018). An sgscore value closer to 0 means that the
nearest source in the Panoramic Survey Telescope and Rapid Response System
(Pan-STARRS; Kaiser et al., 2010) first survey (PS1; Chambers et al., 2016)
catalog is more likely a galaxy, while a value closer to 1 means it is more
likely a star.:
objects.sgscore1 < 0.5

  3. 3.

The Sherlock classification of the candidate is either “SN” (Supernova) or
“NT” (Nuclear Transient):777The other Sherlock classifications, which we
exclude from our search, are: “VS” (Variable Star), “CV” (Cataclysmic
Variable), “BS” (Bright Star), “AGN”, “Orphan” (if the transient fails to
match against any cataloged source), and “Unclear”.
sherlock_classifications.classification in (‘SN’,‘NT’)

  4. 4.

The candidate does not have detections more than 100 days ago (indicating that
it might be a variable, rather than a transient source, though some past
detections could be artifacts):888A ZTF alert is reported to the brokers with
a 30 day history, which may contain prediscovery detections. Lasair marks the
time of the first detection in this 30 day history as jdmin.
objects.jdmin > JDNOW()-100

  5. 5.

The candidate does not have a ZTF17 or ZTF19 name, meaning that it was not
created by ZTF in 2017 or 2019 (this is another way of filtering out variable
sources):999Sporadic false detections in galaxy centers may occur, sometimes
years before a real event occurs at the same position, and based on false
detections that are later filtered out by the brokers. In such a case, the
real event would have an old name from when the false detection occurred years
before. Removing such events might thus undesirably filter out interesting
candidates. In order to avoid losing many candidates, but still not being
inundated with variable sources, we decided to allow events with ZTF18 names
(several bad subtractions in 2018 caused false events then; E. Bellm, private
communication), while removing those with ZTF17 and ZTF19 names.
objects.objectId NOT LIKE ‘ZTF17%’ AND objects.objectId NOT LIKE ‘ZTF19%’

  6. 6.

The candidate is not a previously classified SN:
crossmatch_tns.tns_prefix != ‘SN’

  7. 7.

The candidate has $<$3 of its detections deemed unreliable (i.e. which are not
marked as good quality and/or the candidate is dimmer than the
reference):101010ncand is the total number of detections from ZTF, which can
be either positive or negative subtraction residuals (i.e. a brightening or
fading with respect to the reference image). ncandgp counts only ‘good and
positive’ detections, i.e. a positive flux with respect to the reference and
having a ZTF machine-learning real-bogus score $>$0.75. This criterion
requires that most detections are good and positive, but allows for one or two
light-curve points with poor real-bogus scores if, for example, the transient
was detected when it was very young and the subtraction residuals at the
earliest epochs have a low real-bogus score due to a relatively low signal-to-
noise ratio.
objects.ncand - objects.ncandgp < 3

  8. 8.

At least one of those detections was no more than 14 days ago (in order to
avoid old objects that might already be fading):
objects.ncandgp_14 > 1

  9. 9.

The candidate is more than 10∘ away from the Galactic plane (in order to
filter stellar flares or variability):
objects.glatmean > 10 OR objects.glatmean < \- 10

Conditions 4 and 5 could introduce a bias against finding BFFs (which might be
associated with preexisting AGNs; Makrygianni et al., 2023) and TDEs occurring
in AGNs. However, these conditions are necessary in order to remove “normal”
AGN activity, which can otherwise be a major contaminant (see below).

In addition to conditions 1–9, we create two variations of the query, each
with a different magnitude limit:

  1. 10a.

The latest $g$\- or $r$-band magnitude of the candidate is brighter than 19:
objects.rmag < 19 OR objects.gmag < 19

  2. 10b.

The latest $g$\- or $r$-band magnitude of the candidate is brighter than 19.5:
objects.rmag < 19.5 OR objects.gmag < 19.5

The motivation for these variations is due to several spectrographs on
dynamically scheduled telescopes (which are ideal for the rapid classification
of transients) - such as the Floyds spectrographs (Brown et al., 2013) on the
Las Cumbres Observatory Faulkes Telescopes North (FTN) and South (FTS) and the
SPectrograph for the Rapid Acquisition of Transients (SPRAT; Piascik et al.,
2014) on the Liverpool Telescope - being on 2 m class telescopes, with a
typical limiting magnitude of 19. Similarly, the advanced extended Public
European Southern Observatory (ESO) Spectroscopic Survey for Transient Objects
(ePESSTO+, a continuation of PESSTO; Smartt et al., 2013), responsible for a
large number of transient classifications, uses the ESO Faint Object
Spectrograph and Camera v2 (EFOSC2; Buzzoni et al., 1984) on the 3.6 m New
Technology Telescope (NTT) to reach a magnitude of 19.5.

For each of these variations, we create three subvariations: one without any
additional conditions, one with an additional condition on the color of the
event:

  1. 11.

The candidate has a $g-r$ magnitude difference $<$0.05 (in order to select
only blue events, since TDEs are observed to maintain roughly constant blue
colors for months; see e.g. van Velzen et al. 2020):111111The $g-r$ color is
evaluated on the most recent night with positive detections (relative to the
reference) in both bands. We choose a threshold of 0.05, to be slightly more
conservative than the threshold of 0 suggested by Hung et al. (2018). We do
not take into account magnitude errors here.
objects.g_minus_r < 0.05

and, finally, one subvariation that searches for candidates coincident with
galaxies from the French & Zabludoff (2018) catalog of likely TDE hosts
(hereafter referred to as post-starburst, or PS, galaxies; Arcavi et al.,
2014; French et al., 2016), as implemented in the “E+A Galaxies”
watchlist121212https://lasair.roe.ac.uk/watchlist/321/ on Lasair.

In total we have six queries, which we hereby number as follows:

  1. I.

Conditions 1–9, with a limiting magnitude $<$19 (Condition 10a), blue
(Condition 11), and in a PS galaxy.

  2. II.

Conditions 1–9, with a limiting magnitude $<$19 (Condition 10a) and blue
(Condition 11).

  3. III.

Conditions 1–9, with a limiting magnitude $<$19 (Condition 10a).

  4. IV.

Conditions 1–9, with a limiting magnitude $<$19.5 (Condition 10b), blue
(Condition 11), and in a PS galaxy.

  5. V.

Conditions 1–9, with a limiting magnitude $<$19.5 (Condition 10b) and blue
(Condition 11).

  6. VI.

Conditions 1–9, with a limiting magnitude $<$19.5 (Condition 10b).

Obviously, these are not independent, with some queries being subsets of
others, and all being subsets of Query VI. These queries produced roughly 30
new candidates per day in total, which we inspected manually. Only those
showing a coherently rising light curve were marked as candidates of interest.
Candidates not obviously rising at discovery were monitored for an extra epoch
of ZTF photometry and checked again. Candidates for which it was still not
clear whether they were rising or not were monitored for another week. This
step removed events that had a flat, varying, or incoherent light curve, which
could be due to “normal” AGN variability, the stellar variability of Galactic
objects, or artifacts of the ZTF image-subtraction pipeline. In addition, this
removed true transients that were already after their peak luminosity and that
are not part of our sample as defined here. Of all our filtering steps, this
is the most subjective, as it requires visual inspection, rather than some
strict criterion for what constitutes a “coherently rising” light curve.
However, by checking each candidate during multiple epochs, we aim to make
this step as inclusive as possible. In addition, since this step is performed
before the classification of the candidate is known, it should not bias the
search against a particular type of transient (except extremely rapidly rising
events, with rise times $\lesssim$3 days).

After these cuts, we were left with a total of 345 candidates of interest
(from our entire 414 day search, i.e. $\sim$0.83 candidates of interest per
day, on average), which we attempted to classify spectroscopically within a
few days of discovery.

Version 3.0 of Lasair (also known as “Iris”) was released in 2021 March. To
improve performance, not all of the information that was available in version
1.0 (such as the full detection histories of all candidate events) was carried
over to version 3.0. To check for any differences in query results, we add
four more queries that we ran on Lasair 3.0 (Iris) between 2022 April 6 and
2022 August 2 (for a total of 118 days):

  1. VII.

Conditions 1–9, with a limiting magnitude $<$19 (Condition 10a).

  2. VIII.

Conditions 1–9, with a limiting magnitude $<$19.5 (Condition 10b).

  3. IX.

Conditions 1–9, with a limiting magnitude $<$19 (Condition 10a) and in a PS
galaxy.

  4. X.

Conditions 1–9, with a limiting magnitude $<$19.5 (Condition 10b) and in a PS
galaxy.

Color information was not available as a query parameter in Iris, therefore
here we cannot filter by condition 11 here. We perform the same manual cuts as
above and are left with 39 events, which is an average of 0.33 candidates per
day. Version 4.0 of Lasair was released in 2022 May, but we do not test it
here.

We obtain a total of 345 candidates of interest from Lasair 1.0 (310 of which
from using a separation threshold of 0$\farcs$5 in Criterion 1, with the rest
from using a separation threshold of 1″) and 39 candidates of interest from
Lasair 3.0 (all of which from using a separation threshold of 1″).

For all those brighter than 19th magnitude, we requested spectra through the
Las Cumbres Observatory Floyds spectrographs mounted on the 2 m FTN and FTS
telescopes at Haleakala (United States) and Siding Spring (Australia)
observatories, respectively. Weather, technical issues, and oversubscription
of the telescopes mean that not all the requested spectra were obtained or
that some were obtained first by the community and reported to the Transient
Name Server (TNS)131313http://www.wis-tns.org. We were able to obtain spectra
of 83 candidates of interest, taken through a 2″ slit placed on the candidate
along the parallactic angle (Filippenko, 1982). One-dimensional spectra were
extracted and the flux and wavelength were calibrated using the
floyds_pipeline141414https://github.com/LCOGT/floyds_pipeline (Valenti et al.,
2013). Fainter targets, accessible from La Silla Observatory, were sent for
consideration to the ePESSTO+ collaboration, for classification with the NTT.

All of our classification spectra, as well as those obtained by the ePESSTO+
collaboration, were publicly reported to the TNS. Many of our candidates of
interest were classified by other members of the community and also reported
to the TNS. In total, 246 of our 384 candidates of interest (64.1%) were
classified on the TNS. We take these classifications as reported to the TNS
and analyze their distribution in the next section.

Figure 1: Redshift distribution (shown in a stacked histogram) of the 241
classified candidates of interest with redshifts on the TNS. The number of
events in each class is denoted in parentheses in the legend.

##  3 Results and Analysis

Table 1: Numbers and fractions of the classes of candidates of interest from the different queries. Query | Total | Not | SN | AGN | TDE | Other | Galaxy | BFF | Varstar
---|---|---|---|---|---|---|---|---|---
| Transients | Classified |  |  |  |  |  |  |
Lasair 1.0
I: $<$19 Mag, Blue, and in PS | 6 | 1 | 4 | 0 | 1 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 16.67% | 66.67% | 0 | 16.67% | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 80.00% | 0 | 20.00% | 0 | 0 | 0 | 0
II: $<$19 Mag and Blue | 116 | 19 | 91 | 0 | 5 | 0 | 1 | 0 | 0
Percentage of All Transients |  | 16.38% | 78.45% | 0 | 4.31% | 0 | 0.86% | 0 | 0
Percentage of Classified Transients |  |  | 93.81% | 0 | 5.15% | 0 | 1.03% | 0 | 0
III: $<$19 Mag | 213 | 41 | 163 | 2 | 5 | 1 | 1 | 0 | 0
Percentage of All Transients |  | 19.25% | 76.53% | 0.94% | 2.35% | 0.47% | 0.47% | 0 | 0
Percentage of Classified Transients |  |  | 94.77% | 1.16% | 2.91% | 0.58% | 0.58% | 0 | 0
IV: $<$19.5 Mag, Blue, and in PS | 9 | 3 | 5 | 0 | 1 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 33.33% | 55.56% | 0 | 11.11% | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 83.33% | 0 | 16.67% | 0 | 0 | 0 | 0
V: $<$19.5 Mag and Blue | 193 | 71 | 116 | 0 | 5 | 0 | 1 | 0 | 0
Percentage of All Transients |  | 36.79% | 60.10% | 0 | 2.59% | 0 | 0.52% | 0 | 0
Percentage of Classified Transients |  |  | 95.08% | 0 | 4.10% | 0 | 0.82% | 0 | 0
VI: $<$19.5 Mag | 345 | 121 | 213 | 2 | 5 | 1 | 1 | 1 | 1
Percentage of All Transients |  | 35.07% | 61.74% | 0.58% | 1.45% | 0.29% | 0.29% | 0.29% | 0.29%
Percentage of Classified Transients |  |  | 95.09% | 0.89% | 2.23% | 0.45% | 0.45% | 0.45% | 0.45%
Lasair 3.0 (Iris)
VII: $<$19 Mag in Iris | 33 | 13 | 14 | 6 | 0 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 39.39% | 42.42% | 18.18% | 0 | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 70.00% | 30.00% | 0 | 0 | 0 | 0 | 0
VIII: $<$19.5 Mag in Iris | 39 | 17 | 16 | 6 | 0 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 43.59% | 41.03% | 15.38% | 0 | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 72.73% | 27.27% | 0 | 0 | 0 | 0 | 0
IX: $<$19 Mag, in Iris, and in PS | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 0 | 0 | 100.00% | 0 | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 0 | 100.00% | 0 | 0 | 0 | 0 | 0
X: $<$19.5 Mag, in Iris, and in PS | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 50.00% | 0 | 50.00% | 0 | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 0 | 100.00% | 0 | 0 | 0 | 0 | 0

The full list of our candidates of interest can be found in Table 8 in
Appendix C. The redshift distribution of all classified transients with a
determined redshift on the TNS (244 events) is presented in Figure 1.151515One
classified transient, AT 2022amc, has no redshift determination, since its
spectrum consists of a blue continuum with no clearly identifiable lines While
our queries can in principle find TDEs out to a redshift of $z\sim$0.16 (using
our magnitude limit of 19.5 and a TDE typical peak absolute magnitude of -20;
van Velzen et al., 2020), the median redshift of our classified candidates of
interest is $z=0.069$, and all but one of the TDEs are at redshifts $z<0.04$
(the most distant TDE, AT 2022csn at a redshift of $z=0.148$, is also more
luminous than typical TDEs; Y. Dgany et al. in preparation). The reason that
most classified events are much closer than our redshift limit is likely
because nearby events are typically prioritized for spectroscopic
classification over more distant events. At the median redshift, our angular
cut of 0$\farcs$5 from the galaxy nucleus corresponds to a physical cut of
$\sim$0.66 kpc (assuming the cosmology of Hinshaw et al., 2013).

Figure 2: Numbers and fractions of the classes of candidates of interest from
the different Lasair 1.0 queries, as detailed in the text and in Table 1. The
numbers in parentheses next to each subplot title denote the total number of
events in that query. Figure 3: The same as Figure 2 but for classified events
only.

The distribution of the classifications of our candidates of interest, per
query, are listed in Table 1 and presented in Figures 2 and 3. In the interest
of simplicity, we consolidate the various SN classifications into one
category, which we name “SN”. These include SNe of undetermined type, SNe I of
undetermined subtype, SNe Ia and their various subtypes, as well as SNe Ib,
Ic, Ic-BL, II, IIn, IIb, and superluminous SNe (SLSNe) of Types I and II. A
breakdown of the number of events per SN type is available in Table 3 in
Appendix A.

For all of our events of interest in Lasair 1.0 (Query VI), we find that the
vast majority (95.09%) of classified events are SNe, 2.23% (five events) are
TDEs, 0.45% are BFFs, and 0.89% are flaring AGN.161616Here, the term “flaring
AGN” refers to events with coherent brightening episodes much stronger than
any typical variability seen in their historical light curves. Specifically,
the flares seen here rose by 0.25 magnitudes on average in one week, which is
much higher than normal AGN variability (e.g. MacLeod et al., 2012; Caplar et
al., 2017) The remaining 1.34% consist of one variable star, one event
classified as “Galaxy” (which means that it was either an artifact or it faded
before the spectrum was obtained), and one classified as “Other”. The “Other”
event is AT 2022amc, which displays a featureless blue continuum. This could
have been a young core-collapse SN or some other hot flare, including an SMBH-
related one, such as a TDE or BFF. Unfortunately, no follow-up spectra were
posted to TNS or, to our knowledge, published elsewhere, so its nature remains
undetermined.

The five TDEs are AT 2020vwl (ZTF20achpcvt, also named ATLAS20bdgk and
Gaia20etp; Hammerstein et al., 2021), AT 2021ehb (ZTF21aanxhjv, also named
ATLAS21jdy; Gezari et al., 2021; Yao, 2021; Alexander et al., 2021; Yao,
2022), AT 2022bdw (ZTF22aaahtqz, also named ATLAS22dth, Gaia22baj, and
PS22avi; Arcavi et al., 2022b, a), AT 2022csn (ZTF22aabimec, also named
ATLAS22ggz, Gaia22ayp and PS22bju; Arcavi & Pellegrino, 2022), and AT 2022dbl
(ZTF18aabdajx, also named ASASSN-22ci; Arcavi et al., 2022d; Sfaradi et al.,
2022). AT 2020vwl was classified as a “TDE H+He” (van Velzen et al., 2020) by
the ZTF group, with a spectrum obtained from the Spectral Energy Distribution
Machine (SEDM; Blagorodnova et al., 2018) on the Palomar 60 inch telescope
(Cenko et al., 2006). Despite the low spectral resolution, broad He II and
H$\alpha$ can be clearly identified, on top of a blue continuum, making the
TDE classification secure. AT 2021ehb was also classified by the ZTF team
using an SEDM spectrum, but that spectrum is not publicly available on the
TNS. Follow-up spectra that are available on the TNS do not show clear TDE
signatures, but X-ray detections (Yao et al., 2021) make the TDE
classification likely. AT 2022bdw, AT 2022csn, and AT 2022dbl were classified
by the effort presented here, using the Las Cumbres Observatory Floyds
spectrographs, based on broad H and He II spectral features on top of a blue
continuum. AT 2022csn was initially classified by the ePESSTO+ collaboration
as a Type I SLSN (Srivastav et al., 2022, 2022ab) but later reclassified by us
as a TDE after the emergence of TDE spectral features. AT 2022dbl is also
listed on the TNS as AT 2018mac, due to a sporadic detection in 2018 at a
similar position, likely resulting from an image-subtraction artifact. This is
the only TDE out of the five found in a PS host from the French & Zabludoff
(2018) catalog. We conclude that all five TDE classifications are secure, with
the possible exception of AT 2021ehb, since its classification spectrum is not
available on the TNS.

The BFF is AT 2021seu (ZTF21abjciua, also named ATLAS21bbfi; Arcavi et al.,
2021), also classified by this effort using Floyds (Arcavi, 2021). The
classification is based on a possible N III / He II emission complex on top of
a blue continuum, not seen in an archival SDSS spectrum at that position
(Arcavi et al., 2021), and resembling the spectra of BFFs in Trakhtenbrot et
al. (2019).

All five TDEs pass the “blue criterion” (Criterion 11) and were found by Query
V, but the number of SNe passing this criterion is much smaller, nearly
doubling the percentage of TDEs among classified blue transients. Limiting the
search to candidates that are both blue and brighter than magnitude 19 at
discovery (Query II) keeps all TDEs and further increases their percentage to
5.15%. Looking at events in PS galaxies (Query IV),171717Here, we study all
events that were both in a PS host and blue. There were two more transients in
PS hosts that were not blue: ZTF20acselme (a Type Ia SN) and ZTF22aabsemf (an
unclassified event). only one of the five TDEs remains, but it is one of six
(16.67%) classified transients there. This is consistent (to 1.2$\sigma$) with
the finding of Arcavi et al. (2022c) that $10.0\%\pm 5.5\%$ of classified
transients in PS galaxies should be TDEs.

For all of our events of interest in Lasair 3.0 (Query VIII), SNe are still
the majority of classified events (72.73%), with the rest all flaring AGNs.
The fraction of flaring AGNs in Lasair 3.0 is thus 30 times larger than in
Lasair 1.0. Some of this difference is likely explained by the fact that, at
least initially, Lasair 3.0 did not provide the full multiyear light-curve
history of each candidate. This precluded filtering most AGNs by their
historical activity.

In order to quantify the significance of the difference in fractions between
queries, we calculate their confidence bounds using the Clopper-Pearson method
(Clopper & Pearson, 1934). Gehrels (1986) discusses how this method, which
uses binomial statistics to estimate lower and upper confidence bounds for
ratios, is especially useful for ratios of different event types, when the
numbers of observed events are small. The $1\sigma$ confidence bounds
calculated with this method (and used hereafter) are shown in Table 2.

The fraction of TDEs in our global Lasair 1.0 query (Query VI) is $2.23\%\pm
0.98\%$ and that of BFFs is $0.45\%\pm 0.44$. Requiring candidates be blue
(Query V), increases the TDE fraction by a factor of $1.84\pm 1.11$. Adding
the requirement for a PS host (Query IV) increases the TDE fraction by a
factor of $7.47\pm 7.53$ compared to the global query (Query VI). Without the
full light-curve history of Lasair 3.0, the fraction of AGNs there increases
by a factor of $30.55\pm 23.86$ in Query VIII compared to Query VI.

Table 2: Fractions of classified candidates of interest from the different queries with $1\sigma$ Clopper-Pearson confidence bounds. Query | SN | AGN | TDE | Other | Galaxy | BFF | Varstar
---|---|---|---|---|---|---|---
I | $80.00\%\pm 17.79\%$ | $0$ | $20.00\%\pm 17.79\%$ | $0$ | $0$ | $0$ | $0$
II | $93.81\%\pm 2.43\%$ | $0$ | $5.15\%\pm 2.23\%$ | $0$ | $1.03\%\pm 1.02\%$ | $0$ | $0$
III | $94.77\%\pm 1.69\%$ | $1.16\%\pm 0.81\%$ | $2.91\%\pm 1.27\%$ | $0.58\%\pm 0.58\%$ | $0.58\%\pm 0.58\%$ | $0$ | $0$
IV | $83.33\%\pm 15.13\%$ | $0$ | $16.67\%\pm 15.13\%$ | $0$ | $0$ | $0$ | $0$
V | $95.08\%\pm 1.95\%$ | $0$ | $4.10\%\pm 1.78\%$ | $0$ | $0.82\%\pm 0.81\%$ | $0$ | $0$
VI | $95.09\%\pm 1.44\%$ | $0.89\%\pm 0.63\%$ | $2.23\%\pm 0.98\%$ | $0.45\%\pm 0.44\%$ | $0.45\%\pm 0.44\%$ | $0.45\%\pm 0.44\%$ | $0.45\%\pm 0.44\%$
VII | $70.00\%\pm 10.19\%$ | $30.00\%\pm 10.19\%$ | $0$ | $0$ | $0$ | $0$ | $0$
VIII | $72.73\%\pm 9.44\%$ | $27.27\%\pm 9.44\%$ | $0$ | $0$ | $0$ | $0$ | $0$
IX | $0$ | $100.00\%\pm 0.00\%$ | $0$ | $0$ | $0$ | $0$ | $0$
X | $0$ | $100.00\%\pm 0.00\%$ | $0$ | $0$ | $0$ | $0$ | $0$

We wish to check whether the offset of a source from its host galaxy center
can be used as a way to better select for TDEs and BFFs. To do that, we
retrieved the distnr parameter of each detection of each SN, TDE, and the BFF
in our sample using the Automatic Learning for the Rapid Classification of
Events (ALeRCE) broker181818https://alerce.science/ (Förster et al., 2021).
The distnr parameter is provided with each detection of a source in the ZTF
alert packets191919https://zwickytransientfacility.github.io/ztf-avro-
alert/schema.html. It denotes the distance of that detection to the nearest
source in the reference-image PSF catalog (within $30\arcsec$), in units of
pixels (which are equal to units of $\arcsec$, since the ZTF pixel scale is 1
pixel per $\arcsec$). We plot the distribution of these values in Figure 4.
TDEs show a slightly lower average distnr value than SNe, but the difference
is much smaller than the spread of values of each type of event and hence not
significant. Our one BFF actually shows a larger average distnr value than the
SNe. However, this is based on detections of a single event, and could be
driven by the centroid measurement of its particular host galaxy in ZTF. We
conclude that there is no significant difference in the distnr values between
SNe, TDEs, and BFFs in ZTF, and therefore that this parameter is not a good
discriminant.

Figure 4: Distribution of the distnr parameter, which quantifies the offset of
a source from its host galaxy center (shown in a stacked histogram) for all
detections of SNe, TDEs, and the BFF. The vertical lines at the top denote the
average value for each class. The number of total detections per class of
events is shown in parentheses in the legend. While TDEs show a lower average
offset compared to SNe, the difference is not large enough to be used a
discriminant.

There are 24 additional events that are classified as TDEs on the TNS, from
the time period of our search, which do not appear here. Of those, 16 have
robust TDE classifications (i.e. at least one public spectrum showing clear
broad He II and/or H$\alpha$ emission and blue colors). The rest have either
no public spectrum available, very noisy spectra, or show no clear spectral
features. Of the 16 robust TDEs, most (11) have not been identified here
because they were deemed not to have a coherent rising light curve at
discovery. Three events were missed due to a bug in the queries (which was
later fixed), and two did not pass Criterion 7, regarding the number of
unreliable detections (one required increasing the number of unreliable
detections from $<$3 to $<$5, and one required it be increased to $<$21).
However, relaxing Criterion 7 would have likely also led to an increase in
contaminant candidates.

##  4 Discussion and Conclusions

Our results quantify the “needle in a haystack” problem of finding TDEs and
BFFs in wide-field transient surveys. We find that the photometric history of
candidates is crucial for removing most AGN contamination. Even so, roughly
one in 35–45 events is a TDE, and one in 170–220 is a BFF. This sets a
significant challenge for identifying these events in current transient
surveys, and for identifying even a small subset of the thousands of TDEs
expected to be discovered by the Legacy Survey of Space and Time (LSST; Ivezić
et al., 2019) every year (Bricman & Gomboc, 2020).

The fraction of TDEs increases by almost a factor of 2 to roughly one in 20–25
events when selecting only blue ($g-r<0.05$) transients. This cut does not
remove any TDEs. The fraction further increases by another factor of $\sim$3
to roughly one in 5–6 events when selecting probable TDE host galaxies.
However, such galaxies are rare, making the total number of TDEs discoverable
in this way small. Given the huge increase in expected TDE discovery
fractions, though, it would be beneficial to update the French & Zabludoff
(2018) galaxy catalog and to expand its coverage using new spectroscopic and
photometric surveys such as SDSS-V (Kollmeier et al., 2017).

An additional $\sim$50% of TDEs are found when relaxing Criterion 7 to allow
events with a smaller number of reliable detections, and roughly three times
as many TDEs are found when relaxing the condition that the event be rising in
brightness at discovery. However, the number of contaminants that relaxing
such criteria adds is significant. For example, changing the threshold of
Criterion 7 from $<$3 to $<$5 (which would have added one TDE to the sample)
increased the number of daily candidates by a factor of 2–3, and changing it
to $<$21 (which would have added a second TDE to the sample) increased the
number of daily candidates by an order of magnitude.

We find no significant difference between the offsets of TDEs vs. nuclear SNe
from their host galaxy centers. Therefore, this parameter cannot be used as a
discriminant for selecting more likely TDEs, at least not in ZTF, as
quantified by the distnr parameter. LSST, with its higher spatial resolution,
might be able to make host nucleus offsets a more viable distinguishing
parameter.

An additional possible discriminant for selecting TDEs, but which was not
tested here, is their ultraviolet to optical colors (e.g. Hung et al., 2018).
Obtaining ultraviolet photometry rapidly and for many targets is currently
possible almost exclusively with the the Neil Gehrels Swift Observatory
(Gehrels et al., 2004) Ultraviolet/Optical Telescope (Roming et al., 2005).
Indeed, Hung et al. (2018) have shown that selecting transients by a
combination of their ultraviolet to optical colors using Swift and the optical
colors of their host galaxies increased the fraction of TDEs to 1 in 4.5.
However, Swift is limited in the number of transients it can vet. The upcoming
Ultraviolet Transient Astronomy Satellite (ULTRASAT; Sagiv et al., 2014), with
its wide-field ultraviolet imager, is expected to obtain ultraviolet
photometry for thousands of TDEs. This will be an excellent way to
discriminate TDEs from other transients without the need for substantial
classification resources.

Another approach is to train machine-learning algorithms to classify
transients from photometry alone. This has been done for distinguishing
between some SN types (e.g. Richards et al., 2012; Möller et al., 2016;
Charnock & Moss, 2017; Boone, 2019; Ishida et al., 2019; Villar et al., 2019;
Gomez et al., 2020; Hosseinzadeh et al., 2020; Villar et al., 2020). Until
recently, the number of observed TDEs has been too small to be used for
classification training, leading Muthukrishna et al. (2019) to train their
algorithm on simulated TDE light curves. Today, with TDE light curves
available for dozens of events, it might be possible to effectively train a
machine-learning algorithm to distinguish TDE light curves from those of other
SNe. How effectively this can be done, and at what phase of the light curve a
robust classification can be obtained (if at all), remains to be tested.

Of course, any such filtering based on the photometric properties of the
transient or the characteristics of its host galaxy can also bias population
studies of TDEs and BFFs. A specific population that almost all current
searches (including ours) are biased against is that of slowly evolving
transients with years-long evolution. Such events are already suppressed by
the alert mechanisms of most transient surveys, even before reaching the
brokers. ZTF alerts, for example, are generated by comparing a new image to a
reference image taken up to a few months to a few years earlier. Therefore,
events that rise on a time scale of several years will not be much brighter in
the new image compared to the reference, and thus an alert might never be
issued. Since the set of images used as references is updated from time to
time, such transients could remain hidden during the lifetimes of the surveys.
Indeed, when Lawrence et al. (2016) compared images from PS1 to images
obtained a decade earlier by SDSS, they found a population of slowly rising
nuclear transients. This population is not seen in current transient surveys,
which are optimized to find transients that change on shorter time scales.

It will thus continue to be challenging to find TDEs and BFFs in optical
transient surveys in an unbiased way, even for events evolving on time scales
of days to months. One way forward is to use some combination of well-defined
photometric and host galaxy filters, such as those used here. However, making
searches as complete as possible will still require ample spectroscopic
resources for vetting large numbers of SMBH-related transient candidates.

We thank B. Trakhtenbrot for providing some of the spectroscopy time on the
Las Cumbres network used to classify candidates identified here, and A.
Lawrence, K. Smith, R. Williams, and D. Young for assistance with using Lasair
and for helpful comments. We also thank M. Nicholl for implementing many of
the queries used here on Lasair, as well as the French & Zabludoff (2018)
galaxy catalog as a watchlist there, and A. Riba for developing a Target and
Observation Manager used to manually inspect candidates and schedule followup
observations. We are grateful to B. Zackay for helpful comments regarding host
offsets.

Y.D., I.A., and L.M. acknowledge support from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation program
(grant agreement number 852097). I.A. is a CIFAR Azrieli Global Scholar in the
Gravity and the Extreme Universe Program and acknowledges support from that
program, from the Israel Science Foundation (grant No. 2752/19), from the
United States – Israel Binational Science Foundation (BSF; grant No. 2018166),
and from the Israeli Council for Higher Education Alon Fellowship. This work
makes use of observations with the Las Cumbres Observatory global telescope
network. The Las Cumbres Observatory group is supported by NSF grants
AST-1911151 and AST-1911225 and BSF grant 2018166.

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##  Appendix A Fractions of SN Subtypes

We list the division of SN types found by each query in Table 3. The majority
of contaminants across all queries are Type Ia SNe, with the next most likely
contaminant being Type II SNe (including their subvariants IIb and IIn). The
ZTF Bright Transient Survey (BTS; Perley et al., 2020) aims to classify all
transients brighter than certain magnitude cuts (similar to our Query III),
but, unlike this work, does not focus on galaxy nuclei. Their Type Ia SN
fraction is lower than ours ($\sim$73% vs. $\sim$82% here) and their Type II
SN fraction is higher ($\sim$20% vs. $\sim$10%
here)202020https://sites.astro.caltech.edu/ztf/bts/bts.php. Since Type Ia and
Type II SNe have similar radial distributions in their host galaxies (e.g.
Prieto et al., 2008), this difference might be a selection effect whereby Type
Ia SNe are preferentially detected in this work compared to the BTS, since
they are more luminous than Type II events and stand out more clearly in
bright galaxy nuclei. As found previously by Arcavi et al. (2022c), Type Ia
SNe are the only contaminant of TDEs and BFFs in PS galaxies (unless AGNs
cannot be fully filtered out, as in the case of the Lasair 3.0 queries).

Table 3: Internal division of SN types from the different queries. Query | Total | SN Ia | SN Ib | SN Ic | SN Ic-BL | SN I | SN II | SN IIb | SN IIn | SN | SLSN
---|---|---|---|---|---|---|---|---|---|---|---
| SNe |  |  |  |  |  |  |  |  |  |
I | 4 | 4 (100.0%) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
II | 91 | 76 (83.5%) | 1 (1.1%) | 0 | 2 (2.2%) | 0 | 10 (11.0%) | 0 | 2 (2.2%) | 0 | 0
III | 163 | 134 (82.2%) | 2 (1.2%) | 2 (1.2%) | 4 (2.5%) | 0 | 17 (10.4%) | 0 | 3 (1.8%) | 1 (0.6%) | 0
IV | 5 | 5 (100.0%) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
V | 116 | 97 (83.6%) | 1 (0.9%) | 0 | 2 (1.7%) | 0 | 13 (11.2%) | 0 | 2 (1.7%) | 0 | 1 (0.9%)
VI | 213 | 175 (82.2%) | 2 (0.9%) | 3 (1.4%) | 4 (1.9%) | 1 (0.5%) | 21 (9.9%) | 1 (0.5%) | 3 (1.4%) | 1 (0.5%) | 2 (0.9%)
VII | 14 | 8 (57.1%) | 0 | 0 | 0 | 0 | 4 (28.6%) | 0 | 2 (14.3%) | 0 | 0
VIII | 16 | 10 (62.5%) | 0 | 0 | 0 | 0 | 4 (25.0%) | 0 | 2 (12.5%) | 0 | 0
IX | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
X | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0

##  Appendix B Results per Separation Threshold

As mentioned in Section 2, we split the Lasair 1.0 sample into two subsamples,
each with a different separation threshold for Condition 1 (0$\farcs$5 and
1″). The fractions (Table 1) of the split per separation threshold are
presented in Table 4 (for the 0$\farcs$5 threshold) and Table 5 (for the 1″
threshold).

Table 4: The same as Table 1 but only for events in Lasair 1.0 selected with a separation (Condition 1) threshold of 0$\farcs$5. Query | Total | Not | SN | AGN | TDE | Other | Galaxy | BFF | Varstar
---|---|---|---|---|---|---|---|---|---
| Transients | Classified |  |  |  |  |  |  |
Lasair 1.0
I: $<$19 Mag, Blue, and in PS | 4 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 25.00% | 75.00% | 0 | 0 | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 100.00% | 0 | 0 | 0 | 0 | 0 | 0
II: $<$19 Mag and Blue | 96 | 17 | 76 | 0 | 2 | 0 | 1 | 0 | 0
Percentage of All Transients |  | 17.71% | 79.17% | 0 | 2.08% | 0 | 1.04% | 0 | 0
Percentage of Classified Transients |  |  | 96.20% | 0 | 2.53% | 0 | 1.27% | 0 | 0
III: $<$19 Mag | 186 | 38 | 143 | 1 | 2 | 1 | 1 | 0 | 0
Percentage of All Transients |  | 20.43% | 76.88% | 0.54% | 1.08% | 0.54% | 0.54% | 0 | 0
Percentage of Classified Transients |  |  | 96.62% | 0.68% | 1.35% | 0.68% | 0.68% | 0 | 0
IV: $<$19.5 Mag, Blue, and in PS | 7 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 42.86% | 57.14% | 0 | 0 | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 100.00% | 0 | 0 | 0 | 0 | 0 | 0
V: $<$19.5 Mag and Blue | 169 | 67 | 99 | 0 | 2 | 0 | 1 | 0 | 0
Percentage of All Transients |  | 39.64% | 58.58% | 0 | 1.18% | 0 | 0.59% | 0 | 0
Percentage of Classified Transients |  |  | 97.06% | 0 | 1.96% | 0 | 0.98% | 0 | 0
VI: $<$19.5 Mag | 310 | 113 | 191 | 1 | 2 | 1 | 1 | 1 | 0
Percentage of All Transients |  | 36.45% | 61.61% | 0.32% | 0.65% | 0.32% | 0.32% | 0.32% | 0
Percentage of Classified Transients |  |  | 96.95% | 0.51% | 1.02% | 0.51% | 0.51% | 0.51% | 0
Table 5: The same as Table 1 but only for events in Lasair 1.0 selected with a separation (Condition 1) threshold of 1″. Query | Total | Not | SN | AGN | TDE | Other | Galaxy | BFF | Varstar
---|---|---|---|---|---|---|---|---|---
| Transients | Classified |  |  |  |  |  |  |
Lasair 1.0
I: $<$19 Mag, Blue, and in PS | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 0 | 50.00% | 0 | 50.00% | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 50.00% | 0 | 50.00% | 0 | 0 | 0 | 0
II: $<$19 Mag and Blue | 20 | 2 | 15 | 0 | 3 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 10.00% | 75.00% | 0 | 15.00% | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 83.33% | 0 | 16.67% | 0 | 0 | 0 | 0
III: $<$19 Mag | 27 | 3 | 20 | 1 | 3 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 11.11% | 74.07% | 3.70% | 11.11% | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 83.33% | 4.17% | 12.50% | 0 | 0 | 0 | 0
IV: $<$19.5 Mag, Blue, and in PS | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 0 | 50.00% | 0 | 50.00% | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 50.00% | 0 | 50.00% | 0 | 0 | 0 | 0
V: $<$19.5 Mag and Blue | 24 | 4 | 17 | 0 | 3 | 0 | 0 | 0 | 0
Percentage of All Transients |  | 16.67% | 70.83% | 0 | 12.50% | 0 | 0 | 0 | 0
Percentage of Classified Transients |  |  | 85.00% | 0 | 15.00% | 0 | 0 | 0 | 0
VI: $<$19.5 Mag | 35 | 8 | 22 | 1 | 3 | 0 | 0 | 0 | 1
Percentage of All Transients |  | 22.86% | 62.86% | 2.86% | 8.57% | 0 | 0 | 0 | 2.86%
Percentage of Classified Transients |  |  | 81.48% | 3.70% | 11.11% | 0 | 0 | 0 | 3.70%

In order to assess if there is a statistically significant difference between
the fractions resulting from the different thresholds, we calculate the
1$\sigma$ Clopper-Pearson confidence bounds per each separation threshold
subsample in Table 6 (for the 0$\farcs$5 threshold) and Table 7 (for the 1″
threshold).

Table 6: The same as Table 2 but only for events in Lasair 1.0 selected with a separation (Condition 1) threshold of 0$\farcs$5. Query | SN | AGN | TDE | Other | Galaxy | BFF | Varstar
---|---|---|---|---|---|---|---
I | $100.00\%\pm 0.00\%$ | $0$ | $0$ | $0$ | $0$ | $0$ | $0$
II | $96.20\%\pm 2.14\%$ | $0$ | $2.53\%\pm 1.76\%$ | $0$ | $1.27\%\pm 1.25\%$ | $0$ | $0$
III | $96.62\%\pm 1.48\%$ | $0.68\%\pm 0.67\%$ | $1.35\%\pm 0.94\%$ | $0.68\%\pm 0.67\%$ | $0.68\%\pm 0.67\%$ | $0$ | $0$
IV | $100.00\%\pm 0.00\%$ | $0$ | $0$ | $0$ | $0$ | $0$ | $0$
V | $97.06\%\pm 1.66\%$ | $0$ | $1.96\%\pm 1.37\%$ | $0$ | $0.98\%\pm 0.97\%$ | $0$ | $0$
VI | $96.95\%\pm 1.22\%$ | $0.51\%\pm 0.50\%$ | $1.02\%\pm 0.71\%$ | $0.51\%\pm 0.50\%$ | $0.51\%\pm 0.50\%$ | $0.51\%\pm 0.50\%$ | $0$
Table 7: The same as Table 2 but only for events in Lasair 1.0 selected with a separation (Condition 1) threshold of 1″. Query | SN | AGN | TDE | Other | Galaxy | BFF | Varstar
---|---|---|---|---|---|---|---
I | $50.00\%\pm 35.16\%$ | $0$ | $50.00\%\pm 35.16\%$ | $0$ | $0$ | $0$ | $0$
II | $83.33\%\pm 8.74\%$ | $0$ | $16.67\%\pm 8.74\%$ | $0$ | $0$ | $0$ | $0$
III | $83.33\%\pm 7.57\%$ | $4.17\%\pm 4.06\%$ | $12.50\%\pm 6.71\%$ | $0$ | $0$ | $0$ | $0$
IV | $50.00\%\pm 35.16\%$ | $0$ | $50.00\%\pm 35.16\%$ | $0$ | $0$ | $0$ | $0$
V | $85.00\%\pm 7.94\%$ | $0$ | $15.00\%\pm 7.94\%$ | $0$ | $0$ | $0$ | $0$
VI | $81.48\%\pm 7.43\%$ | $3.70\%\pm 3.61\%$ | $11.11\%\pm 6.01\%$ | $0$ | $0$ | $0$ | $3.70\%\pm 3.61\%$

There are no statistically significant differences between the results of the
two separation thresholds. It does appear that there is a much higher fraction
of TDEs in the subsample of the 1″ threshold (of order 10%) compared to the
0$\farcs$5 threshold (of order 1%). However, this is not statistically
significant and is a result of small number statistics. To demonstrate this,
we plot all of the separationArcsec values of all the detections of our five
TDEs in Figure 5.

Figure 5: Distribution of the separationArcsec parameter from Criterion 1
(shown in a stacked histogram) for all detections of our five TDEs. There is
no significant difference in the separations between the TDEs in the
0$\farcs$5 threshold subsample (AT 2020vwl and AT 2021ehb) and the TDEs in the
1″ threshold subsample (AT 2022bdw, AT 2022csn, and AT 2022dbl), meaning that
the change of separation threshold does not have a strong effect on the number
of TDEs discovered.

Most detections for the two TDEs in the 0$\farcs$5 threshold subsample and the
three TDEs in the 1″ threshold subsample are below 0$\farcs$5 separation. This
means that the increase of the threshold to 1″ is not responsible for the
larger fraction of TDEs in that subsample, but rather it is a small number
statistics fluctuation.

##  Appendix C List of Events

We list in Table 8 the full set of events considered transients of interest in
this work, their publicly available classification and redshift, and the query
number(s) in which they were found.

Table 8: Candidates of interest.

ZTF Name | TNS Name | Other Names | R.A. | Decl. | Query | Classification | Redshift | Note | Reference(s)
---|---|---|---|---|---|---|---|---|---
|  |  | (deg) | (deg) |  |  |  |  |
ZTF18aabdajx |  AT 2022dbl (also AT 2018mac) |  ASASSN-22ci | 185.1878 | 49.55128 | I  | TDE | 0.0284 |  | Arcavi et al. (2022d); Sfaradi et al. (2022) |  |
ZTF18aacnlxz |  SN 2020aavr |  | 134.95468 | 38.10909 | III  | SN II | 0.072475 |  | Burke et al. (2020b) |  |
ZTF18aadsuxd |  AT 2020yui |  ATLAS20bfcp, PS20kyb, ZTF18aaefvaq | 129.53396 | 31.66792 | III  |  |  |  |  |  |
ZTF18aagtwyh |  SN 2021oud |  Gaia21cyq, ZTF18aahbypm | 189.92466 | 16.53793 | II  | SN Ia | 0.066041 |  | SNIascore (2021) |  |
ZTF18aahfbqp |  SN 2020acua |  | 156.67845 | 21.7239 | III  | SN Ia | 0.041362 |  | Hiramatsu et al. (2020) |  |
ZTF18aahkoqq |  SN 2021slw |  ATLAS21bbji, Gaia21dnq, ZTF21abnabtv | 203.17888 | 32.4431 | III  | SN Ia | 0.034457 |  | Hiramatsu et al. (2021ad); Chu et al. (2021a) |  |
ZTF18aahvpcy |  SN 2022eaz |  ATLAS22ina, Gaia22bhf | 208.40005 | 37.73573 | II  | SN Ia | 0.0607 |  | Gonzalez et al. (2022) |  |
ZTF18aaisqmw |  SN 2020acqt |  | 176.35752 | 55.52702 | III  | SN Ia | 0.0527 |  | Burke et al. (2020g) |  |
ZTF18aaiwewk |  SN 2022cox |  ATLAS22gie, PS22biu | 196.64354 | 54.9627 | V  | SN Ia | 0.087 |  | Pellegrino et al. (2022) |  |
ZTF18aaizgoq |  SN 2021gad |  ATLAS21ipu | 231.51665 | 36.32309 | III  | SN Ia | 0.07 |  | Tucker (2021) |  |
ZTF18aajhkjz |  AT 2020acub |  ATLAS20bkaw, PS20nox, ZTF20acxtayx | 149.88365 | 15.84735 | V  |  |  |  |  |  |
ZTF18aajhkjz |  AT 2021mrp |  | 166.45372 | 15.12587 | V  |  |  |  |  |  |
ZTF18aakeanj |  AT 2021dc |  Gaia21ald, ZTF21aaaollj | 203.76788 | 53.87351 | II  |  |  | aaNot clear if it is indeed rising at discovery. |  |  |
ZTF18aamtiwb |  SN 2022dcf |  ATLAS22gxr, PS22cdu | 185.06648 | 56.62635 | II  | SN Ia-91T-like | 0.066 |  | Pellegrino et al. (2022ab) |  |
ZTF18aansqom |  SN 2021hbg |  | 251.0451 | 39.78875 | III  | SN Ia | 0.03 |  | Angus (2021) |  |
ZTF18aarefcm |  AT 2022drv |  ATLAS22iga | 204.58714 | 40.15554 | II  |  |  |  |  |  |
ZTF18aasypqx |  SN 2022cvt |  ATLAS22gxa | 214.68999 | 22.36559 | II  | SN Ia-91T-like | 0.073 |  | SNIascore (2022); Smith et al. (2022, 2022ab) |  |
ZTF18aauvmnq |  SN 2021tty |  ATLAS21bden | 207.19808 | 26.55212 | II  | SN Ia | 0.0621 |  | Gonzalez et al. (2021); Chu et al. (2021b) |  |
ZTF18aaviokz |  AT 2021duz |  | 207.86824 | 40.44794 | IV  |  |  |  |  |  |
ZTF18aaviokz |  AT 2021duz |  | 207.86828 | 40.44793 | I  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF18abfdxbt |  SN 2022dme |  Gaia22bak | 255.92866 | 78.65643 | II  | SN Ia | 0.048914 |  | Burke et al. (2022a) |  |
ZTF18abmakin |  |  | 298.62199 | 55.58944 | II  |  |  |  |  |  |
ZTF18abrfccm |  SN 2020yxo |  ATLAS20beyo | 26.86302 | 0.96765 | III  | SN Ia | 0.1 |  | Gromadzki et al. (2020); Callis et al. (2020) |  |
ZTF18abrzevn |  AT 2019byp |  | 269.35527 | 26.84119 | VII  | AGN | 0.045399 |  | Newsome et al. (2022) |  |
ZTF18abwitkf |  SN 2021xy |  ATLAS21bsl, Gaia21amh | 15.70534 | -21.83647 | III  | SN Ia | 0.061 |  | Smith et al. (2021, 2021ab) |  |
ZTF18abwnmnc |  AT 2020aczj |  ZTF20acxuyqs | 330.52799 | -5.66265 | VII  |  |  |  |  |  |
ZTF18abxusti |  AT 2021gvi |  | 119.03849 | 65.87328 | III  |  |  |  |  |  |
ZTF18acfhmhp |  AT 2020aaxb |  | 13.13143 | 46.33825 | III  |  |  |  |  |  |
ZTF18acjxyak |  SN 2021efe |  | 281.14242 | 33.72181 | III  | SN Ia | 0.0585 |  | Perley (2021) |  |
ZTF18acsremz |  SN 2021hmc |  | 148.62747 | 53.16909 | I  | SN Ia | 0.025136 |  | Balcon (2021); Dahiwale & Fremling (2021a) |  |
ZTF18actytgu |  SN 2021lyz |  ATLAS21barz | 11.03286 | 30.351 | II  | SN Ia | 0.019 |  | Tinyanont et al. (2021) |  |
ZTF18acwyvak |  AT 2021jmb |  | 174.04786 | 74.77397 | VII  | AGN | 0.055785 |  | Newsome et al. (2022) |  |
ZTF20aanlygx |  AT 2020abea |  | 56.01208 | -18.62466 | III  | AGN | 0.063 |  | 2023TNSCR.853....1A |  |
ZTF20aanzldk |  AT 2022cwl |  ATLAS22gfj, PS22bps | 139.37062 | -2.08112 | III  | AGN | 0.053 |  | Arcavi et al. (2022e) |  |
ZTF20aaoluli |  AT 2021aqe |  | 14.09962 | 13.90157 | VI  |  |  |  |  |  |
ZTF20aaprrgg |  SN 2021aayn |  PS21oli | 62.01957 | -6.30023 | IV  | SN Ia | 0.087 |  | Carini et al. (2021a, b) |  |
ZTF20aaqhlvq |  AT 2022jiw |  ATLAS22nmb, Gaia22dmk | 196.76539 | -4.30944 | VII  |  |  |  |  |  |
ZTF20aaqhlvq |  AT 2022jiw |  ATLAS22nmb, Gaia22dmk | 196.7654 | -4.30943 | VIII  |  |  |  |  |  |
ZTF20aartqux |  AT 2021hed |  ATLAS21jyx | 208.60376 | 23.67644 | II  |  |  |  |  |  |
ZTF20aarvpbq |  SN 2021gen |  ATLAS21ikj | 222.98006 | 10.12371 | III  | SN Ia | 0.080611 |  | Dahiwale & Fremling (2021b) |  |
ZTF20aasivtu |  AT 2020fhp |  Gaia20dxw | 276.63611 | 21.15216 | VII  | AGN | 0.223 |  | Newsome et al. (2022ab) |  |
ZTF20aavfogu |  AT 2020hoj |  | 173.45788 | -20.81444 | VI  |  |  | aaNot clear if it is indeed rising at discovery. |  |  |
ZTF20abajndx |  AT 2020acvj |  | 187.25093 | -15.50851 | II  |  |  | aaNot clear if it is indeed rising at discovery. |  |  |
ZTF20abbhlct |  AT 2020mma |  | 188.91823 | -21.04658 | VII  | AGN | 0.061759 |  | Newsome et al. (2022) |  |
ZTF20abeyroj |  AT 2022ebh |  Gaia22dfz | 198.5069 | -3.47522 | VII  |  |  |  |  |  |
ZTF20abmtbpa |  AT 2021adlq |  ATLAS21blqh | 31.75077 | 6.96475 | VI  |  |  |  |  |  |
ZTF20abpbmxi |  AT 2020qnk |  | 297.46295 | -18.15618 | II  |  |  | bbLower than the expected signal-to-noise ratio in the spectrum attempt. |  |  |
ZTF20absulsv |  AT 2021adhi |  ATLAS21blnl, PS21mqk | 358.86024 | -4.2969 | V  |  |  |  |  |  |
ZTF20abtcijp |  SN 2021iat |  ATLAS21kmm | 258.99041 | 66.6909 | II  | SN Ia | 0.07 |  | Dahiwale & Fremling (2021c) |  |
ZTF20abynbhh |  AT 2021hsv |  ATLAS21kna | 112.67908 | 41.46758 | III  |  |  |  |  |  |
ZTF20achpcvt |  AT 2020vwl |  ATLAS20bdgk, Gaia20etp | 232.65758 | 26.98244 | II  | TDE | 0.035 |  | Hammerstein et al. (2021) |  |
ZTF20ackzfvn |  SN 2021adcw |  ATLAS21blvp, PS21ojv | 31.137 | -21.11931 | VII  | SN II | 0.017 |  | Ragosta et al. (2021ab, 2021) |  |
ZTF20acnwgtl |  SN 2020yhh |  | 329.42018 | -5.67019 | III  | SN Ia | 0.14 |  | Gromadzki et al. (2020ac, 2020ab) |  |
ZTF20acobevy |  SN 2020yub |  ATLAS20bezd | 0.56296 | -23.49506 | III  | SN Ia | 0.08 |  | Burke et al. (2020a) |  |
ZTF20acoxmsr |  SN 2020yza |  ATLAS20bfpa, PS20mwz | 160.39647 | -3.44147 | III  | SN Ia | 0.071 |  | Burke et al. (2020c) |  |
ZTF20acpwjus |  SN 2020zjv |  ATLAS20bfsh, PS20nfq | 141.63857 | -1.65029 | VI  | SN Ia-91T-like | 0.075 |  | Ihanec et al. (2020ab); Gromadzki et al. (2020ad) |  |
ZTF20acpylns |  SN 2020zlc |  | 145.34409 | 15.9613 | III  | SN Ia | 0.076388 |  | Burke et al. (2020d) |  |
ZTF20acqjmzk |  SN 2020zpj |  | 340.91099 | -14.06795 | VI  | SN Ia | 0.1 |  | Gromadzki et al. (2020ae); Ihanec et al. (2020) |  |
ZTF20acqzpta |  SN 2020aaez |  ATLAS20bgmh | 24.47855 | 30.10691 | III  | SN Ia | 0.07135 |  | Hiramatsu et al. (2020ab); Dahiwale & Fremling (2021d) |  |
ZTF20acrheie |  SN 2020aarw |  | 81.31309 | -8.62605 | III  | SN Ia | 0.049431 |  | Hiramatsu et al. (2020ab) |  |
ZTF20acsdykp |  SN 2020aaxd |  Gaia20fox, PS20lul | 124.86338 | 2.10144 | III  | SN Ia | 0.1 |  | Hiramatsu et al. (2020ab) |  |
ZTF20acselme |  SN 2021huw |  | 134.13621 | 39.14126 | III  | SN Ia | 0.042 |  | Leadbeater (2021) |  |
ZTF20actdeqw |  SN 2020abfd |  ATLAS20bgfy, Gaia20fqk | 26.94368 | -21.76087 | III  | SN Ia | 0.045 |  | Burke et al. (2020e) |  |
ZTF20actlvpy |  SN 2020aavu |  ATLAS20bgwg, PS20lyk | 328.80104 | -11.32326 | III  | SN Ia | 0.11 |  | Burke et al. (2020b) |  |
ZTF20actrovz |  SN 2020aazy |  ATLAS20bgju, PS20nkl | 357.11884 | -5.39563 | III  | SN Ia | 0.09 |  | Burke et al. (2020b) |  |
ZTF20actvgsy |  SN 2020aazk |  ATLAS20bhjy, PS20mhg | 139.69959 | 30.35468 | III  | SN Ia | 0.10196 |  | Burke et al. (2020f) |  |
ZTF20actvlbq |  SN 2020abaf |  ATLAS20bgnd, PS20mnx | 152.56933 | 7.95562 | III  | SN Ia | 0.083038 |  | Hiramatsu et al. (2020ac) |  |
ZTF20acuaqlf |  SN 2020abbo |  Gaia20fsb | 357.77519 | 6.94249 | III  | SN II | 0.017 |  | Burke et al. (2020b); Dahiwale & Fremling (2020a) |  |
ZTF20acujaft |  SN 2020abgn |  ATLAS20bgmz, Gaia20fsf, PS20mwx | 32.4228 | -6.73706 | III  | SN Ia | 0.08 |  | Hinkle (2020); Dahiwale & Fremling (2021e) |  |
ZTF20aculruc |  SN 2020abij |  ATLAS20bgvv | 358.55995 | -4.80556 | III  | SN Ia | 0.077 |  | Dahiwale & Fremling (2020b) |  |
ZTF20acumcrz |  SN 2020abln |  | 22.91202 | 18.24501 | VI  | SN Ia | 0.054 |  | Anderson et al. (2020); Pessi et al. (2020ae) |  |
ZTF20acuoclk |  SN 2020abqd |  ATLAS20bgyf, PS20mnz | 124.06977 | 37.40383 | III  | SN Ia | 0.082 |  | Hiramatsu et al. (2020ad) |  |
ZTF20acveadu |  SN 2020absk |  ATLAS18bcmv, PS20mbv | 136.00384 | -0.08888 | III  | SN II | 0.01889 |  | Perley (2020) |  |
ZTF20acvebcu |  SN 2020abqx |  ATLAS20bgye, PS20mkm | 178.10281 | 67.5477 | III  | SN Ib | 0.063 |  | Burke et al. (2021a) |  |
ZTF20acvexen |  AT 2020abrr |  ATLAS20bjsp, PS20nwf | 195.44765 | 32.60167 | V  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF20acvjagm |  SN 2020abtf |  ATLAS20bgve, PS21akm | 119.89367 | 15.29998 | VI  | SN II | 0.014 |  | Anderson et al. (2020); Pessi et al. (2020ae) |  |
ZTF20acvkqxy |  SN 2020abve |  ATLAS20bjhg | 142.22477 | 40.88916 | III  | SN Ia | 0.063327 |  | Dahiwale & Fremling (2020c) |  |
ZTF20acwnrty |  SN 2020acef |  ATLAS20bhob, PS20mhi | 146.09988 | 22.97916 | VI  | SN Ia-91T-like | 0.065 |  | Pessi et al. (2020ac, 2020) |  |
ZTF20acwofhd |  SN 2020acfp |  ATLAS20bjsc | 171.89415 | 47.37943 | III  | SN Ic | 0.032816 |  | Dahiwale & Fremling (2021f, g) |  |
ZTF20acwpaov |  SN 2020acfq |  ATLAS20bjnr | 132.39613 | 9.58738 | III  | SN Ia | 0.05913 |  | Dahiwale & Fremling (2020d) |  |
ZTF20acwpixx |  SN 2020aceu |  ATLAS20bjqf, PS20nuq | 178.07599 | 7.89155 | II  | SN Ia | 0.077451 |  | Dahiwale & Fremling (2021h) |  |
ZTF20acwqmul |  AT 2020acbn |  ATLAS20bhzn, ZTF18aatfpbe | 190.1667 | 13.81572 | III  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF20acxdawc |  SN 2020acmf |  ATLAS20bigv | 40.86169 | 16.66293 | VI  | SN Ia | 0.025 |  | Pessi et al. (2020ab, 2020ad) |  |
ZTF20acxycfh |  SN 2020acwp |  | 147.03499 | 6.81964 | III  | SN Ia | 0.078525 |  | Burke et al. (2020g) |  |
ZTF20acynlkj |  SN 2020aden |  ATLAS20bkdg | 17.00425 | -15.40552 | III  | SN Ia | 0.055008 |  | Hiramatsu et al. (2020ae) |  |
ZTF20acywefl |  SN 2020adka |  ATLAS21asd | 118.36714 | 28.26255 | III  | SN Ia | 0.058596 |  | Dahiwale & Fremling (2021i); Burke et al. (2021b) |  |
ZTF20acyxomd |  AT 2020aeuf |  ATLAS20bkau | 150.36432 | 19.46194 | VI  |  |  |  |  |  |
ZTF20adaftef |  SN 2020adra |  ATLAS21atx | 151.3117 | 21.91095 | II  | SN Ia | 0.084025 |  | Dahiwale & Fremling (2021j) |  |
ZTF21aaaaddl |  SN 2021D |  ATLAS21asf | 358.64203 | 15.74521 | VI  | SN Ia | 0.07459 |  | Dahiwale & Fremling (2021k) |  |
ZTF21aaaosmn |  SN 2021ct |  ATLAS21axd | 221.76091 | 56.31489 | III  | SN Ia | 0.0375 |  | Dahiwale & Fremling (2021k); Pellegrino et al. (2021) |  |
ZTF21aaaovuq |  AT 2021bc |  ATLAS21czg | 233.88764 | 18.14889 | II  |  |  |  |  |  |
ZTF21aaapesv |  AT 2021gk |  | 223.30836 | 28.85542 | III  |  |  |  |  |  |
ZTF21aaapfal |  AT 2021da |  ATLAS21crw | 250.53913 | 35.60025 | II  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aaarmti |  SN 2021ek |  ATLAS21ajr, PS21fo | 50.95792 | -10.0448 | V  | SLSN-I | 0.193 |  | Srivastav et al. (2021ab); Gillanders et al. (2021) |  |
ZTF21aaaytny |  SN 2021mc |  ATLAS21cvk | 240.43947 | 18.01928 | II  | SN Ia | 0.0459 |  | Pellegrino et al. (2021ab) |  |
ZTF21aacigst |  AT 2021sq |  ATLAS21bsd | 149.86789 | 28.37349 | V  |  |  | aaNot clear if it is indeed rising at discovery. |  |  |
ZTF21aacnjlf |  AT 2021acw |  ATLAS21bnv | 25.84651 | 2.22959 | V  |  |  |  |  |  |
ZTF21aacudxe |  SN 2021vt |  ATLAS21chd, PS21bys | 201.78258 | 11.35239 | V  | SN Ia | 0.087 |  | Muñoz et al. (2021ac, 2021ab) |  |
ZTF21aadahyi |  SN 2021akb |  ATLAS21cac | 170.90465 | -5.53246 | VI  | SN Ia | 0.097 |  | Delgado et al. (2021ab, 2021) |  |
ZTF21aadatfg |  SN 2021xv |  ATLAS21cmb | 241.88673 | 36.77951 | II  | SN Ic-BL | 0.05 |  | Schulze & Sollerman (2021, 2021ab) |  |
ZTF21aadkgpm |  SN 2021zu |  ATLAS21cgz, Gaia21ahe, PS21aqu | 168.74875 | 27.45333 | II  | SN Ia | 0.06 |  | Dahiwale & Fremling (2021f); Delgado et al. (2021ab, 2021) |  |
ZTF21aadrmok |  AT 2021acb |  | 261.05295 | 46.82644 | II  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aadrmsv |  AT 2021amq |  ATLAS21czo | 242.97524 | 21.0087 | V  |  |  |  |  |  |
ZTF21aadrtcs |  SN 2021abt |  ATLAS21cae | 199.82393 | -7.2492 | II  | SN Ia | 0.0014285 |  | Gonzalez & Dgany (2021) |  |
ZTF21aadrtqz |  SN 2021abn |  ATLAS21bmj | 208.7036 | -13.60863 | II  | SN Ia | 0.058 |  | Hiramatsu et al. (2021) |  |
ZTF21aaekkbv |  SN 2021ait |  ATLAS21crv, Gaia21anh | 264.64777 | 42.21554 | II  | SN Ia | 0.07 |  | Srivastav et al. (2021) |  |
ZTF21aaekmoy |  SN 2021ais |  ATLAS21cry | 223.53354 | 2.97951 | II  | SN Ia | 0.080003 |  | Hiramatsu et al. (2021ab) |  |
ZTF21aaevrjl |  SN 2021arg |  ATLAS21dkx, PS21aia | 67.82827 | -10.39637 | V  | SN II | 0.031 |  | González et al. (2021); Gonzalez et al. (2021ah, 2021ai); González et al. (2021ab) |  |
ZTF21aafdvxz |  SN 2021bkb |  ATLAS21dhf, PS21ewl | 236.87341 | 10.93365 | III  | SN II | 0.05 |  | Dahiwale & Fremling (2021l) |  |
ZTF21aafkktu |  SN 2021avg |  ATLAS21cux, Gaia21aku, PS21bkp | 174.99587 | 14.52796 | II  | SN II | 0.031 |  | Schulze & Sollerman (2021, 2021ab) |  |
ZTF21aaflcbk |  SN 2021arr |  ATLAS21dab, Gaia21aml, PS21bzh | 189.12726 | 19.82585 | VI  | SN Ia | 0.07 |  | Dahiwale & Fremling (2021l) |  |
ZTF21aaglrzc |  SN 2021bmc |  ATLAS21djm | 127.24882 | 12.41938 | II  | SN II | 0.05 |  | Magee et al. (2021); Terwel et al. (2021ab); Dahiwale & Fremling (2021m) |  |
ZTF21aagnbsu |  AT 2021cld |  ATLAS21efr | 124.66522 | 6.12749 | V  |  |  |  |  |  |
ZTF21aagrzqz |  AT 2021chg (also AT 2013kz) |  ATLAS21ehz | 144.49052 | 48.3884 | II  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aagsbot |  AT 2021bvu |  ATLAS21emi | 158.1223 | 16.16226 | V  |  |  |  |  |  |
ZTF21aagshha |  AT 2021byg |  ATLAS21emb | 171.75836 | 16.73231 | III  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aagskhr |  AT 2021bpz |  ATLAS21end | 191.98612 | 29.72008 | V  |  |  | aaNot clear if it is indeed rising at discovery. |  |  |
ZTF21aagtcgb |  SN 2021bmb |  ATLAS21djl, PS21zq | 224.32537 | 43.45972 | II  | SN Ia | 0.0375 |  | Dahiwale & Fremling (2021l) |  |
ZTF21aagtexi |  SN 2021bqo |  ATLAS21eie | 188.74309 | 45.44921 | II  | SN Ia | 0.08 |  | Dahiwale & Fremling (2021n) |  |
ZTF21aagtquu |  AT 2021brg |  ATLAS21eem | 244.44327 | 65.12367 | III  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aagxmcs |  SN 2021cca |  ATLAS21efx, PS21zy | 154.0805 | -1.78745 | V  | SN Ia | 0.085 |  | Dimitriadis & Foley (2021) |  |
ZTF21aagzswk |  |  | 26.91632 | -1.97598 | VI  |  |  |  |  |  |
ZTF21aagzwod |  |  | 19.41428 | -2.94091 | III  |  |  |  |  |  |
ZTF21aahfizx |  AT 2021bwg |  ATLAS21egb | 225.31005 | 13.0221 | V  |  |  |  |  |  |
ZTF21aahfjbs |  SN 2021cky |  ATLAS21eev | 229.39754 | 18.11804 | V  | SN Ia-pec | 0.073 |  | Prentice et al. (2021, 2021ab) |  |
ZTF21aahfjlo |  SN 2021clz |  ATLAS21flj, Gaia21bfk, PS21axb | 214.64922 | 52.1011 | II  | SN Ia | 0.039 |  | Dahiwale & Fremling (2021e) |  |
ZTF21aahhmev |  AT 2021bzb |  ATLAS21els | 76.697 | 3.75387 | V  |  |  |  |  |  |
ZTF21aahnkut |  SN 2021cez |  ATLAS21epx | 197.81538 | -16.54579 | II  | SN Ia | 0.07 |  | Dahiwale & Fremling (2021o) |  |
ZTF21aahnqnn |  AT 2021dng |  ATLAS21gvh | 167.32333 | -5.75498 | V  |  |  |  |  |  |
ZTF21aahpoxv |  AT 2021csz |  ATLAS21gau, PS21cqr | 174.10466 | 4.8955 | V  |  |  |  |  |  |
ZTF21aahpxww |  AT 2021cff |  ATLAS21fwh | 158.95735 | 10.91143 | VI  |  |  |  |  |  |
ZTF21aahzspd |  SN 2021cjx |  ATLAS21fkc | 148.00477 | 21.17413 | V  | SN Ia | 0.11 |  | Dahiwale & Fremling (2021o) |  |
ZTF21aaiucta |  AT 2021cfu |  ATLAS21fza, PS21cnz | 254.84455 | -1.20244 | VI  |  |  |  |  |  |
ZTF21aaiucta |  AT 2021cfu |  ATLAS21fza, PS21cnz | 254.84454 | -1.20245 | III  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aajgdeu |  SN 2021cjd |  ATLAS21ekw | 193.27745 | 36.81955 | III  | SN IIP | 0.027929 |  | Dahiwale & Fremling (2021p) |  |
ZTF21aajgdpo |  SN 2021cjy |  ATLAS21hdg, PS21aei | 194.82941 | 38.88605 | II  | SN II | 0.036088001 |  | Dahiwale & Fremling (2021q) |  |
ZTF21aakbgpf |  SN 2021crv |  ATLAS21gdz, PS21bhh | 122.63426 | -8.12558 | II  | SN Ia | 0.05 |  | Dahiwale & Fremling (2021r) |  |
ZTF21aakfqwq |  AT 2021crk |  ATLAS21hdc, PS21cpw | 176.27889 | 18.54037 | V  |  |  |  |  |  |
ZTF21aalgboj |  AT 2021dfo |  ATLAS21hcp | 135.79646 | 51.78792 | II  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aalgqsv |  SN 2021dic |  PS21bxl | 154.08238 | 0.65215 | II  | SN Ia | 0.079358 |  | Dahiwale & Fremling (2021s) |  |
ZTF21aalhgqi |  SN 2021dib |  ATLAS21gtb, PS21aie | 173.7361 | 9.03865 | VI  | SN I | 0.086 |  | Moran et al. (2021ab, 2021) |  |
ZTF21aalnxkl |  SN 2021djz |  PS21bze | 202.98122 | -2.45151 | V  | SN Ia | 0.087 |  | Harvey et al. (2021, 2021ab) |  |
ZTF21aalxxzn |  AT 2021fxu |  Gaia22dgm | 206.82834 | 2.18269 | VII  | AGN | 0.1097 |  | Newsome et al. (2022ab) |  |
ZTF21aamjzki |  AT 2020hoj |  ZTF20aavfogu | 173.45805 | -20.81437 | III  |  |  |  |  |  |
ZTF21aamkxbl |  SN 2021dwo |  ATLAS21gwk | 216.18103 | -1.57031 | II  | SN Ia | 0.057153 |  | Burke et al. (2021c) |  |
ZTF21aamucom |  SN 2021dsb |  ATLAS21hgi, Gaia21byu, PS21bxv | 158.82831 | -13.00542 | II  | SN Ia-91T-like | 0.025 |  | Dahiwale & Fremling (2021s) |  |
ZTF21aamxduf |  SN 2021eij |  ATLAS21hjg | 217.38808 | 10.94154 | VI  | SN Ia | 0.1 |  | Moran et al. (2021ab, 2021) |  |
ZTF21aanswls |  SN 2021ell |  ATLAS19bftq | 298.58614 | 1.58498 | III  | SN Ia | 0.025 |  | Hiramatsu et al. (2021ac) |  |
ZTF21aanuyro |  SN 2021eno |  ATLAS21huk, Gaia21blr, PS21ctb | 224.82649 | 59.90032 | II  | SN Ia | 0.07 |  | Dahiwale & Fremling (2021t) |  |
ZTF21aanvtng |  AT 2021ehx |  | 167.89582 | 5.58533 | V  |  |  |  |  |  |
ZTF21aanxhjv |  AT 2021ehb |  ATLAS21jdy | 46.94925 | 40.3113 | II  | TDE | 0.018 |  | Gezari et al. (2021); Yao (2021); Yao et al. (2021); Alexander et al. (2021); Yao (2022) |  |
ZTF21aaocgci |  AT 2021eox |  ATLAS21hnu | 128.46705 | -11.86269 | II  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aaocibg |  SN 2021epo |  ATLAS21hpb, Gaia21biq, PS21cgw | 131.54246 | 56.12765 | II  | SN Ia | 0.071 |  | Dahiwale & Fremling (2021u) |  |
ZTF21aaoexjt |  SN 2021ezs |  ATLAS21hsb | 208.39581 | 27.08093 | II  | SN Ia | 0.11 |  | Dahiwale & Fremling (2021b) |  |
ZTF21aaomiwf |  |  | 194.63988 | 83.11293 | III  |  |  |  |  |  |
ZTF21aaootdm |  AT 2021gny (also AT 2015dv) |  ATLAS21ith, PS21cgk | 211.95257 | 7.1261 | V  |  |  |  |  |  |
ZTF21aaopupn |  AT 2021fap |  ATLAS21hon | 217.35744 | 9.06336 | V  |  |  |  |  |  |
ZTF21aaoqayg |  AT 2021eyw |  ATLAS21iuk | 252.98655 | 28.92162 | III  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aaoqcnh |  SN 2021feu |  ATLAS21ixy | 272.47882 | 11.30154 | III  | SN Ia | 0.04 |  | Kankare et al. (2021, 2021ab) |  |
ZTF21aaovdlh |  SN 2021fck |  ATLAS21huq, PS21bxe | 130.34449 | 2.88978 | II  | SN Ia | 0.056 |  | Dahiwale & Fremling (2021u) |  |
ZTF21aapfmut |  AT 2021fyr |  ATLAS21ikk, PS21fzt | 154.93818 | 32.24469 | III  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aaphjwb |  AT 2021ghw |  ATLAS21kdb | 177.84396 | 24.65899 | V  |  |  |  |  |  |
ZTF21aaphzor |  AT 2021hcd |  | 154.5871 | 5.92311 | VI  |  |  |  |  |  |
ZTF21aapjmgf |  SN 2021fzp |  ATLAS21iif, Gaia21bkh, PS21clp | 220.98371 | 16.41324 | II  | SN II | 0.054 |  | Williams et al. (2021, 2021ab); Dahiwale & Fremling (2021p) |  |
ZTF21aapjmpq |  SN 2021fzq |  ATLAS21imm, PS21cgr | 225.28036 | 12.31399 | VI  | SN Ia-91T-like | 0.13 |  | Pessi et al. (2021ae, 2021ac) |  |
ZTF21aapjmpq |  SN 2021fzq |  ATLAS21imm, PS21cgr | 225.28036 | 12.31399 | VI  | SN Ia-91T-like | 0.13 |  | Pessi et al. (2021ae, 2021ac) |  |
ZTF21aapjpee |  AT 2021hdo |  ATLAS21jdj | 202.63239 | -6.3692 | V  |  |  |  |  |  |
ZTF21aapkuur |  SN 2021gba |  ATLAS21iuc, PS21bxj | 219.26622 | -8.07644 | V  | SN Ia | 0.1 |  | Dennefeld et al. (2021, 2021ab) |  |
ZTF21aappehx |  SN 2021gwu |  | 139.10441 | -17.35608 | II  | SN Ia | 0.054514 |  | Hiramatsu et al. (2021ac) |  |
ZTF21aaprfkc |  SN 2021gwp |  ATLAS21jcn, PS21ego | 199.01655 | -15.50397 | III  | SN Ia | 0.056 |  | Dahiwale & Fremling (2021v) |  |
ZTF21aaptmrn |  SN 2021gdm |  ATLAS21itu | 237.38765 | 4.67097 | VI  | SN Ia | 0.112 |  | Pessi et al. (2021af, 2021ab) |  |
ZTF21aapuwry |  AT 2021gdw |  PS21clx | 249.19601 | -16.22766 | VI  |  |  |  |  |  |
ZTF21aapvxnf |  SN 2021gyw |  ATLAS21kpq | 281.88222 | 64.46884 | II  | SN Ia-91T-like | 0.085 |  | Dahiwale & Fremling (2021w) |  |
ZTF21aapxogj |  SN 2021ghj |  PS21cfn | 125.95185 | 38.38982 | II  | SN II | 0.060214 |  | Dahiwale & Fremling (2021x) |  |
ZTF21aaqgrrf |  SN 2021gqm |  ATLAS21jlu, PS21eeh | 153.76115 | 3.79654 | V  | SN Ia | 0.082 |  | Mitra et al. (2021); Pessi et al. (2021ad) |  |
ZTF21aaqhqke |  SN 2021gpw |  ATLAS21iwv, Gaia21czj | 200.47858 | 16.74437 | II  | SN IIn | 0.075 |  | Perley (2021ab) |  |
ZTF21aaqjuyt |  |  | 165.70851 | 23.85029 | VI  |  |  |  |  |  |
ZTF21aaqmoof |  SN 2021gvv |  ATLAS21jlj, PS21fbg | 256.14998 | 14.08037 | V  | SN II | 0.038 |  | Pessi et al. (2021, 2021ag) |  |
ZTF21aaqpykm |  SN 2021hay |  ATLAS21jli, PS21gdb | 229.5394 | 15.17631 | V  | SN Ia | 0.06 |  | Pellegrino et al. (2021ag) |  |
ZTF21aaqwbgq |  SN 2021hjb |  ATLAS21jgx | 133.93957 | 16.92466 | III  | SN Ia | 0.06651 |  | Dahiwale & Fremling (2021a) |  |
ZTF21aarigsr |  SN 2021iaf |  ATLAS21kat, Gaia21chu | 227.84385 | 26.79227 | II  | SN Ia | 0.059 |  | SNIascore (2021ab) |  |
ZTF21aarmuxl |  AT 2021hpp |  ATLAS21kne, PS21cus | 141.24411 | 42.80907 | III  |  |  | ccFaded before the spectrum was attempted (could have been rapidly evolving or an artifact). |  |  |
ZTF21aarohyu |  SN 2021hzo |  ATLAS21khv | 180.16705 | 51.84093 | II  | SN Ia | 0.063 |  | Dahiwale & Fremling (2021y) |  |
ZTF21aarrkwn |  AT 2021igm |  ATLAS21kdi, PS21fxj | 226.85561 | 18.31667 | V  |  |  |  |  |  |
ZTF21aarteuc |  AT 2021hzc |  ATLAS21kxo, PS21dfc | 151.98128 | -8.28587 | V  |  |  |  |  |  |
ZTF21aarycyl |  SN 2021hvu |  ATLAS21jpb, Gaia21bxb, PS21fia | 153.41 | -24.52076 | II  | SN Ia-91T-like | 0.104 |  | Hiramatsu et al. (2021ae) |  |
ZTF21aasgcve |  SN 2021idn |  ATLAS21kye | 129.16748 | 1.16559 | V  | SN Ia-91T-like | 0.09 |  | Dahiwale & Fremling (2021z) |  |
ZTF21aasgrpw |  SN 2021idh |  ATLAS21laz, PS21dhx | 155.82763 | -0.42939 | VI  | SN Ia | 0.095 |  | Dimitriadis & Angus (2021) |  |
ZTF21aaskuth |  AT 2021icw |  ATLAS21lge, PS21fxn | 262.75059 | 7.96276 | VI  |  |  |  |  |  |
ZTF21aassamj |  SN 2021ify |  ATLAS21lot, PS21fig | 261.72373 | 13.17885 | II  | SN Ia | 0.056 |  | Pessi et al. (2021); Dahiwale & Fremling (2021v); Pessi et al. (2021ag) |  |
ZTF21aaswvyc |  SN 2021ijy |  ATLAS21lyk | 258.4873 | 39.0988 | III  | SN Ia | 0.07 |  | Team (2021) |  |
ZTF21aatdzmt |  SN 2021imq |  ATLAS21lqa, Gaia21cfl, PS21fih | 227.36531 | 36.81745 | II  | SN Ia | 0.076 |  | SNIascore (2021ac) |  |
ZTF21aatlbsi |  SN 2021ipy |  ATLAS21ncf | 292.34717 | 74.07815 | II  | SN Ia | 0.07 |  | Dahiwale & Fremling (2021v) |  |
ZTF21aatyxox |  AT 2021jtw |  ATLAS21mtq, PS21fwq | 206.9273 | -26.37541 | V  |  |  |  |  |  |
ZTF21aauufbz |  |  | 160.2291 | 24.74699 | IV  |  |  | aaNot clear if it is indeed rising at discovery. |  |  |
ZTF21aauufgo |  AT 2021jjp |  ATLAS21mwb, PS21eik | 211.12748 | -24.86573 | V  |  |  |  |  |  |
ZTF21aauurqh |  |  | 204.13567 | -5.05061 | VI  |  |  |  |  |  |
ZTF21aavpgvx |  AT 2021jui |  ATLAS21mwc, PS21fnl | 186.71367 | -17.77826 | V  |  |  |  |  |  |
ZTF21aavrywc |  AT 2021jwa |  ATLAS21njq | 236.35534 | 8.20475 | V  |  |  |  |  |  |
ZTF21aawaguv |  AT 2021kae |  | 181.57238 | 5.81541 | V  |  |  |  |  |  |
ZTF21aawckpe |  |  | 198.85353 | 1.12187 | III  |  |  |  |  |  |
ZTF21aawtazf |  SN 2021kkz |  ATLAS21nnp, PS21gar | 248.98476 | 18.08285 | III  | SN Ia | 0.061948 |  | Dahiwale & Fremling (2021aa); Jaeger (2021) |  |
ZTF21aaxjiii |  SN 2021klq |  ATLAS21nqa | 351.50055 | 46.65778 | III  | SN Ia | 0.04 |  | Burke et al. (2021f) |  |
ZTF21aaxkckg |  SN 2021kmv |  ATLAS21nme, PS21elb | 152.84195 | 41.78835 | II  | SN Ia | 0.063 |  | SNIascore (2021ad) |  |
ZTF21aaxstsv |  SN 2021ktw |  ATLAS21nqy | 215.87831 | 8.34491 | II  | SN Ia | 0.09 |  | SNIascore (2021ae) |  |
ZTF21aaxtpty |  AT 2021kqp |  ATLAS21oaf, PS21doo | 223.79475 | -6.98593 | V  |  |  |  |  |  |
ZTF21aaxxihx |  SN 2021ktv |  ATLAS21nqw | 165.76618 | 8.86105 | III  | SN Ic-BL | 0.06 |  | Tucker (2021ab) |  |
ZTF21aaxxjdr |  AT 2021ktu |  ATLAS21nqv | 169.0091 | 21.29421 | II  |  |  |  |  |  |
ZTF21aaxxjen |  SN 2021kun |  ATLAS21ofj | 166.41407 | 19.46031 | I  | SN Ia | 0.097 |  | Gonzalez et al. (2021ac) |  |
ZTF21aaxyecd |  SN 2021kui |  ATLAS21ohm | 194.25355 | 40.43447 | II  | SN Ia | 0.061249 |  | SNIascore (2021af) |  |
ZTF21aaydomp |  AT 2021ldb |  ATLAS21oan | 190.5094 | -14.12457 | VI  |  |  |  |  |  |
ZTF21aaydtqk |  AT 2021kxh |  ATLAS21owr | 178.90335 | 5.54756 | V  |  |  |  |  |  |
ZTF21aayoiis |  AT 2021mpc |  ATLAS21pes, PS21gct | 179.09923 | -17.53295 | V  |  |  |  |  |  |
ZTF21aayqrgx |  SN 2021lax |  Gaia21cof | 212.33849 | 72.11727 | II  | SN Ib | 0.033 |  |  |  |
ZTF21aazlxiw |  SN 2021lta |  ATLAS21oez, PS21dqe | 246.50245 | 28.42744 | II  | SN Ia | 0.1 |  | SNIascore (2021ag) |  |
ZTF21aazmjaf |  AT 2021lkq |  ATLAS21ovp | 316.71025 | 10.73573 | V  |  |  |  |  |  |
ZTF21aazpyza |  AT 2021lob |  | 222.37709 | -27.24104 | V  |  |  |  |  |  |
ZTF21aazpzqo |  AT 2021loa |  | 214.60334 | -27.65508 | III  |  |  |  |  |  |
ZTF21aazqtor |  SN 2021lny |  ATLAS21oyt | 205.26646 | -25.60261 | II  | SN Ia | 0.0954 |  | Hiramatsu et al. (2021af) |  |
ZTF21aazqynq |  AT 2021lsw |  ATLAS21osi | 186.93889 | 26.77057 | V  |  |  |  |  |  |
ZTF21aazybsh |  |  | 16.08828 | 55.74215 | III  |  |  |  |  |  |
ZTF21abaagct |  AT 2021lrw |  ATLAS21orf, PS21gdn | 305.75781 | -6.41894 | V  |  |  |  |  |  |
ZTF21abalkop |  AT 2021mho |  ATLAS21rcr | 183.01062 | 19.04756 | VI  |  |  |  |  |  |
ZTF21abamnyi |  AT 2021mck |  ATLAS21rch | 220.04695 | 66.13794 | V  |  |  |  |  |  |
ZTF21abawvgt |  AT 2021mbe |  ATLAS21pjw | 326.67805 | 8.54946 | VI  |  |  |  |  |  |
ZTF21abbliav |  SN 2021mqv |  ATLAS21peg, Gaia21cte | 322.88652 | 11.14894 | VI  | SN Ia | 0.049 |  | SNIascore (2021ah) |  |
ZTF21abboaaz |  AT 2021mkx |  ATLAS21qbe | 179.52419 | -27.75361 | III  |  |  |  |  |  |
ZTF21abbuxzr |  AT 2021mmq |  ATLAS21pad | 167.3917 | -1.5678 | III  | Varstar |  |  | Hiramatsu et al. (2021ag) |  |
ZTF21abbxdcm |  SN 2021msu |  ATLAS21pis | 223.63703 | 9.22811 | II  | SN Ia-91T-like | 0.05 |  | Nicholl et al. (2021, 2021ab) |  |
ZTF21abbxlbu |  AT 2021mqq |  ATLAS21pju | 315.86938 | 4.94065 | II  |  |  |  |  |  |
ZTF21abbytmm |  SN 2021msv |  ATLAS21piv | 195.09912 | -11.9655 | II  | SN Ia | 0.054 |  | SNIascore (2021ai) |  |
ZTF21abbzjeq |  SN 2021mwb |  ATLAS21pjy, PS21dxq | 242.23797 | 35.42108 | II  | SN Ia | 0.043 |  |  |  |
ZTF21abcfgwv |  AT 2021mxu |  ATLAS21rfm | 171.34341 | 24.90104 | V  |  |  |  |  |  |
ZTF21abcixor |  SN 2021nip |  ATLAS21prb, PS21ety | 244.21006 | 12.64293 | II  | SN II | 0.033 |  | Dahiwale & Fremling (2021ab); Perez-Fournon et al. (2021) |  |
ZTF21abcmepi |  SN 2021nlj |  ATLAS21pvm, PS21hrs | 187.86256 | 4.22648 | II  | SN Ia | 0.089 |  | SNIascore (2021aj) |  |
ZTF21abcrhpk |  SN 2021nqo |  ATLAS21pvj, PS21gzv | 322.36694 | 4.8875 | II  | SN Ia | 0.0744 |  | Terwel (2021); SNIascore (2021ak) |  |
ZTF21abdbodg |  SN 2021ohp |  Gaia21czd | 206.99498 | -13.78255 | III  | SN Ia | 0.064 |  | SNIascore (2021) |  |
ZTF21abdmevk |  SN 2021ont |  ATLAS21qky, PS21ivp | 246.67702 | 39.14524 | II  | SN II | 0.02833 |  | Makrygianni et al. (2021, 2021ab); Bruch et al. (2021); Chu et al. (2021c) |  |
ZTF21abfoyac |  SN 2021pni |  Gaia21ebi | 268.07435 | 8.23976 | III  | SN II | 0.033 |  | Terwel (2021ab); Chu et al. (2021c) |  |
ZTF21abfxibf |  SN 2021qtn |  | 355.38227 | 14.12417 | II  | SN Ia | 0.063044 |  | Gonzalez et al. (2021ad) |  |
ZTF21abgxbvi |  AT 2021quh |  | 250.76254 | -26.69082 | III  |  |  |  |  |  |
ZTF21abhhbmd |  SN 2021qnv |  ATLAS21wwc, Gaia21dox | 338.16656 | 28.20678 | III  | SN Ia | 0.042 |  | Hiramatsu et al. (2021ah); Chu et al. (2021c) |  |
ZTF21abhshmt |  SN 2021qvg |  ATLAS21ztt, Gaia21dpf | 200.08813 | 48.72436 | II  | SN Ia-91T-like | 0.055 |  | Hiramatsu et al. (2021ah) |  |
ZTF21abhuoyz |  SN 2021qyh |  ATLAS21baec, PS21hwf | 215.89982 | 14.11976 | II  | SN Ia | 0.0875 |  | Hiramatsu et al. (2021ai) |  |
ZTF21abhymom |  SN 2021qyf |  ATLAS21zvh | 205.34227 | 1.98462 | III  | SN Ia | 0.08 |  | Newsome et al. (2021) |  |
ZTF21abhzboh |  SN 2021qyc |  ATLAS21zwq | 237.23744 | 6.84263 | II  | SN Ia | 0.051097 |  | Hiramatsu et al. (2021ai) |  |
ZTF21abidtrd |  SN 2021rax |  ATLAS21baeg | 283.24974 | 28.95966 | III  | SN Ia | 0.048 |  | Hung (2021) |  |
ZTF21abifutc |  SN 2021rdq |  ATLAS21baen | 190.19495 | 42.30288 | II  | SN Ia | 0.072 |  | SNIascore (2021al) |  |
ZTF21abjbjba |  SN 2021sbv |  ATLAS21bbfc | 196.47762 | 2.14131 | II  | SN Ia | 0.069122 |  | Gonzalez et al. (2021) |  |
ZTF21abjciua |  AT 2021seu |  ATLAS21bbfi | 215.39427 | 37.90969 | II  | BFF | 0.06 |  | Arcavi et al. (2021); Arcavi (2021) |  |