likely anomalies (blue highlights) did _not_ have transient spectra. Of those
that did, 6/20 (30%) were flagged by LAISS at least a week prior to the
classification spectrum.

##  5 Approximate Nearest Neighbor Similarity Search with ANNOY

In this current age of large time-domain surveys like ZTF (Bellm et al., 2019)
and the imminent LSST (Ivezić et al., 2019), the bottleneck of object
discovery lies in the ability of automated algorithms to quickly isolate and
prioritize detections of interest amid the flood of alert events. ZTF
generates approximately 70 GB/night (Patterson et al., 2019) and discovers of
order $\sim$10000 SNe/year545454For this estimate, we use the Search function
from www.wis-tns.org/, and search for all transients reported to TNS from ZTF
first light on 2017 November 1 to 2023 November 1 with ZTF listed as the
discovery source, and find the per year average., and LSST is estimated to
produce an alerts volume of 10x that at approximately 782 GB/night, observing
an estimated of order $\sim$1 million
SNe/year555555https://dmtn-102.lsst.io/DMTN-102.pdf.

With high present-day discovery rates and climbing, we cannot rely on
computationally expensive operations for SNe discovery and characterization.
In this section, we argue that a low-latency, sublinear time-complexity
approximate nearest neighbors search can be an effective tool to search for
similar SNe for applications of SN discovery, reclassfication, and more.

Previous studies have shown that data mining strategies involving a nearest
neighbors search for retroactive SN discovery can be successful (Aleo et al.,
2022). However, Aleo et al. (2022) used a brute-force kD-tree approach to
search for neighbors in feature space, which at worst case scales linearly
with the number of data points in runtime ($\mathcal{O}(n)$). This approach
would be impractical for the real-time data streams of ZTF and LSST. We use
their approach as inspiration, and instead opt for an approximate similarity
search, which at worst case scales in logarithmic time with respect to the
number of data points, $\mathcal{O}(log(n))$.

For our ANN algorithm, we use the open-source package
ANNOY565656https://github.com/spotify/annoy (Bernhardsson, 2018), the method
developed by Spotify for song recommendations. Essentially, ANNOY allows for
an efficient ANN search in high-dimensional spaces, such as the large light
curve and host galaxy feature dataset we use in this work575757Note that in
this work we do not use data imputation. Although in principle ANNOY can run
on data sets with imputed entries, it may introduce bias and impact the
nearest neighbor search accuracy, particularly if many values are imputed..
This process involves dividing the high-dimensional space into smaller
subspaces using random hyperplanes. These subspaces are organized into a
binary tree structure, where each node represents a subspace and each leaf
node corresponds to a specific point in the original high-dimensional space.
During a search operation, the algorithm begins at the root of the tree and
recursively traverses the tree, selecting the branch that is closest to the
query point. At each leaf node, the distance between the query point and the
represented point is recorded. Simultaneously, a list of the $k$ closest
points encountered thus far is maintained. To optimize the search process,
recorded distances are utilized to prune branches unlikely to contain points
closer than the current $k$th closest point. This iterative search is repeated
for each query point. The ANNOY library is designed to ensure efficient memory
usage and high performance, enabling indexing of millions of high-dimensional
vectors.

In the LAISS pipeline (Figure 1), we build an ANNOY index of our databank—the
reference dataset to which new, incoming, or manually chosen objects will be
compared. However, we do so after scaling our databank with the
sklearn.preprocessing.StandardScaler()585858https://scikit-
learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
module and applying Principal Component Analysis (PCA, Jolliffe 2002) to
reduce the dimensionality of our data from 120 dimensions to 60 dimensions,
retaining $\sim$98% of the variance. This will partially speed up the ANN
search without important information loss; moreover, having less than 100
dimensions is recommended by the code authors for the best
performance595959Though, in principle it can perform “surprisingly well” up to
1,000 dimensions; see https://github.com/spotify/annoy#summary-of-features.

LAISS can run the ANN search to calculate any $k$ neighbors (default is $k=8$)
for any ZTF object for which its light curve and host galaxy association and
features can be extracted, regardless if it exists in the databank or not; all
that is needed is a ZTF Object ID of the the user’s choosing. For example,
when supplied a ZTF Object ID, LAISS queries ANTARES for the object of
interest’s ZTF photometry and manually extracts the light curve features in
the same manner as the lc_feature_extractor _Filter_ , followed by running
GHOST (Gagliano et al., 2021) to retrieve the matched host galaxy and its
features. The object will undergo the same scaling and PCA transformation as
the data set bank, and then ANNOY will find the ANNs in the 60 dimensional PC-
space. As the ANNs are found, LAISS queries the ANTARES broker to retrieve the
up-to-date ZTF photometry, IAU name, spectroscopic classification (if exists),
and spectroscopic redshift (if exists) from TNS for each ANN. Plotting
functions are then run to overlay the reference transient and its $k$ ANNs’
light curves and host galaxy thumbnails. Currently, this ANN similarity search
feature is only available in the Python module on
Github606060https://github.com/patrickaleo/LAISS-local. Efforts to create the
same functionality within an ANTARES _Filter_ is ongoing. This will remove the
additional outside API requests for extracting light curve and host galaxy
features and up-to-date TNS information.

Within our databank of 5472 transients, the default $k=8$ ANN search alone
takes anywhere from 1 to 350 ms on a 2 GHz Quad-Core Intel Core i5. This
results in $\leq$5 minutes of total processing time to find $k=8$ ANNs for all
$\sim$1000 SN/night estimated with LSST. If an object needs to have its light
curve and host features extracted from scratch, the entire process takes about
1 minute per transient (thus, $\sim$16 hours of total processing time for all
$\sim$1000 SN/night), where the overwhelming majority time bottleneck stems
from the GHOST host association—specifically time spend on catalog queries
(e.g., PS1); such catalogs could be downloaded to disk to dramatically speed
up associations. Utilizing faster host association methods such as DELIGHT
($\sim$60 ms/transient, Förster et al. 2022) and modifying GHOST to use
internal disk-downloaded catalogs is a subject of future work.

###  5.1 ANN Similarity Search Results on Light Curves and Host Galaxies

The ability to quickly find analogs to an object of interest is crucial to
many useful applications, including but not limited to:

  * •

Predicting Behavior: Based on the known properties and evolution of analogs,
one can predict the potential behavior of newly observed transients across
their time evolution. Nominal events will likely evolve as expected in common
galactic environments, and deviations from this behavior (or in the case where
analogs are anomalous) could indicate rare or misunderstood phenomena;

  * •

Calibrating Models: By comparing observations of known analogs with
theoretical predictions, experts can improve the accuracy of models describing
the underlying physical processes of transient events, such as those used for
simulations (e.g., models developed for SNANA (Kessler et al., 2009) and their
use in PLAsTiCC Hložek et al. (2020)).

  * •

Studying Rare Events: By associating a similarity score, experts can measure
how similar or dissimilar an event is from the rest of the existing dataset.
Objects strongly dissimilar to its nearest neighbors can indicate truely
unusual phenomena, or out-of-distribution (OOD) behavior for the particular
reference dataset.

  * •

Probing Host Galaxy Properties: Analogous transients in different host
galaxies allow experts to investigate the influence of host galaxy properties
on the characteristics of the transient events. This information is valuable
for understanding the connections between stellar populations, galactic
environments, and transient phenomena.

  * •

Improving Classification: As more analogs are discovered and characterized,
classification systems can be refined, leading to more accurate and automated
identification of new transients.

  * •

Reclassification: A given transient of a known type whose analogs are
classified differently can direct the expert’s attention into reinspecting
their spectra and classifications, possibly updating their classification. We
show this to be true in Section 5.2 and Table 7, where we update
classifications for 17 unique SNe from our databank, prompted by an ANN
search.

  * •

Optimizing Observational Strategies: Observers can prioritize targets based on
the likelihood of finding analogs or because they are analogs to a known rare
or scientifically useful event, helping to maximize the efficiency of
observing campaigns and the utilization of telescope time. This can be used in
tandem with active learning strategies like RESSPECT (Kennamer et al., 2020).

  * •

Retroactive Discovery: Analogs of known transients can also be used as a data
mining method to find similar but previously undiscovered (i.e., not publicly
reported) transients in large datasets, successfully demonstrated by Aleo et
al. (2022). In this work, we showcase 84 transients discovered from the ZTF
Alert Stream in 2018-2021 using this method, as shown in Table F and Section
5.4. Similarly, analogs can be used to suggest likely candidates of a certain
class that were originally reported but have no follow-up spectra for
classification, and thus represent possible missed opportunities (Section
5.3).

To our knowledge there is no popular and lightweight tool for finding
transient analogs like SNe. To demonstrate such a utility, we present an ANN
similarity search of the most commonly observed SNe (a Type Ia) in Figure 9.
As a reminder, we only input the 60-dimensional PC feature-space (from the
120-dimensional light curve and associated host galaxy feature-space). We _do
not_ input the photometric observations themselves, nor the redshift, SN type,
galaxy type (spiral, elliptical), or thumbnail image (though this is a topic
to be explored in future work). For visual aid, in the left panel we overlay
the object of interest (ZTF21aaublej/SN 2021ixf) and its $k=8$ closest ANNs,
shifting the light curves such that the peak brightness in the ZTF-$r$ band
and ZTF-$g$ band is at 0 d since peak (both passbands fit independently). In
the right panel, we show the thumbnail of the host galaxy of our object of
interest in the top left, and the 8 ANNs continuing from left to right, top to
bottom. This similarity search in our database was completed in 3 ms.

Figure 9:  Stacked light curve (left panel) and grid of host galaxy thumbnails
(right panel) of Type Ia SN 2021ixf (bolded) and its 8 approximate nearest
neighbors within 120 d feature-space (faded). The similarity search in our
database was completed in only 3 ms. Note that we do not include SN type of
redshift information in the similarity search. Thus, the similar light curve
evolution, SN types, redshift values, and host galaxy environments are as a
result of the close proximity of SN 2021ixf and its neighbors in
120-dimensional feature-space.

We find that the reference SN 2021ixf, in part because it is a well-sampled,
full-phase, common SN Ia at a nearby redshift, is indeed highly similar in
light curve evolution and host galaxy environment as its 8 ANNs. All light
curves increase in brightness by $\approx$3 mag in 16 d to 20 d to peak from
their first observation, and have a prominent secondary ZTF-$r$ band bump at
around 30 d post-peak, lasting about 100 d in total. Our reference object of
class SN Ia-normal at $z=0.041$ is matched to 6 SN Ia-normal and 2 SN
Ia-91T-like spanning $z\in[0.021,0.05]$, with a mean redshift of
$\bar{z}=0.039$. The host galaxies are all nearby, face-on spiral galaxies
except for one which is edge-on (SN 2021mid/ZTF21abbkefe). Another also
exhibits evidence of a violent merging history (SN 2021vtq/ZTF21absvlrr).

###  5.2 Reclassification of SNe

Here, we briefly test the ansatz that similar transient types broadly have
similar light curve evolutions and host galaxy environments, investigating
whether or not it proves fruitful to look for nearest neighbors of rare
reference objects. Although there is no guarantee that nearest neighbors of an
object are of the same type, it is a higher likelihood than random sampling.
In this search, we find that in doing so we additionally recover transients of
rare types that were initially misclassified.

Figure 10:  Same as Figure 9, but for Type Ia-91bg-like SN
2021jvp/ZTF21aawihwx. In this case, ANN=5 (SN 2021abpz/ZTF21achjwus) and ANN=7
(SN 2020yje/ZTF20acnznol) are in fact SN Ia-91bg-like SNe previously
missclassified as SN Ia-normal after re-examination of their TNS
classification spectra, prompted by the results of this similarity search (see
Table 7). We bold these re-classified events (dark green and dark red for
ZTF-$g$ and ZTF-$r$, respectively) for visual purposes. The similarity search
in our database was completed in only $\sim$300 ms.

In Figure 10, we show the Type Ia-91bg-like SN 2021jvp/ZTF21aawihwx and its 8
ANNs, achieved in $\sim$300 ms. Initially, if we look at the original TNS
classification labels only, we find 4 SN Ia, 1 SN II, and 3 with no
classification spectrum (for which we make no definitive determination).
Prompted by the nearest neighbor results, we manually re-inspect the
classification spectra from TNS for these 5 objects using the Supernova
Identification package (SNID, Blondin & Tonry 2007). We use the 5.0 version of
SNID with additional template sets from the Berkeley Supernova Ia Program
(BSNIP, Silverman et al. 2012), Modjaz et al. (2014); Liu & Modjaz (2014); Liu
et al. (2016); Modjaz et al. (2016); Gutiérrez et al. (2017); Williamson et
al. (2019), totalling 6145 spectra from 811 templates. We use the forcez
argument for any object that has a known host-$z$.

Table 7: Reclassification of SNe Updated classification of 17 unique SNe driven by ANN=8 nearest neighbor matches to all SNe classes using SNID ZTF ID | IAU Name | TNS Class. | New Class. | Reference SNe | ANN | Remarks
---|---|---|---|---|---|---
Reference Class: SN IIn (59 objects)
ZTF21abcjpnm | 2021njo | SN II | SN IIn | 2020abku | 1 | Second peak likely CSM interaction.
ZTF21aaizyqc | 2021ckb | SN II | SLSN (He-rich) | 2021hur | 2 | Match to PTF10hgi (Quimby et al., 2010).
Reference Class: SN Ia-91T-like (37 objects)
ZTF21aagoliy | 2021cjc | SN Ia | SN Ia-91T-like | 2020zjv | 1 | Match to SN 1997br, SN 1991T.
ZTF21abjtqyq | 2021sis | SN Ia | SN Ia-91T-like | 2020acef | 1 | Match to SN 1999aa.
ZTF21abothvq | 2021uib | SN Ia | SN Ia-91T-like | ” | 6 | Matches to SN 2001V, SN 1991T.
ZTF21abicgai | 2021sju | SN Ia | SN Ia-91T-like | 2020adis | 3 | Match to 1997br.
ZTF21abcxner | 2021nxh | SN Ia | SN Ia-91T-like | 2021qvg | 8 | Lack of Si. Match to SN 1991T.
Reference Class: SN Ib (25 objects)
ZTF21aaqwfqe | 2021hen | SN I | SN Ib | 2021gno | 2 | Match to iPTF13bvn.
ZTF21aabyifm | 2021qv | SN Ib/c | SN Ib | ” | 5 | Matches to iPTF13bvn, SN 2009iz.
Reference Class: SN IIb (21 objects)
ZTF21abnvlnj | 2021tyf | SN II | SN IIb | 2021M | 8 | LC has shock-cooling peak.
Reference Class: SN Ic-BL (14 objects)
ZTF21aacufip | 2021vz | SN Ic | SN Ic-BL | 2021too | 4 | Match to SN 2007ce. Bad quality spectrum.
Reference Class: SN Ia-pec (10 objects)
ZTF20ackkejs | 2020xyd | SN Ia | SN Ia-91T-like | 2021cky | 8 | Lack of Si and bright ($M_{abs}=-19.65$).
ZTF21abothvq | 2021uib | SN Ia | SN Ia-91T-like | 2021ebb | 1 | Matches to SN 2001V, SN 1991T.
ZTF21abiawpf | 2021rce | SN Ia | SN Ia-91T-like | ” | 5 | Matches to SN 2006cz, SN 2007S.
Reference Class: SN Ia-91bg-like (6 objects)
ZTF20acnznol | 2020yje | SN Ia | SN Ia-91bg-like | 2021jvp | 7 | Matches to SN 2007ba, SN 2000cn.
” | ” | SN Ia | SN Ia-91bg-like | 2021wzb | 6 | ”
ZTF21achjwus | 2021abpz | SN Ia | SN Ia-91bg-like | 2021jvp | 5 | Matches to SN 1986G, SN 1999bh.
” | ” | SN Ia | SN Ia-91bg-like | 2021wzb | 8 | ”
ZTF21acfigoo | 2021aazj | SN Ia | SN Ia-91bg-like | 2021fnr | 2 | Matches to SN 1986G, SN 2007ax.
ZTF21abmwgow | 2021ttg | SN Ia | SN Ia-91bg-like | 2021wzb | 2 | Matches to SN 2008R, SN 2007ap.
NOTE: The light pink highlight designates transients that appear at least
twice in the Table. NOTE: The double quotation ” marks a repeated entry from
the above row.

We find SN 2021abpz (ANN=5) and SN 2020yje (ANN=7) are better explained by the
SN Ia-91bg-like classification—the same as reference SN 2021jvp. In SN
2021abpz, there is a visible titanium trough at 4200Å (Hachinger et al., 2009;
Heringer et al., 2017) and strong SNID matches to subluminous / Type Ia-91bg-
like SN 1986G, SN 1999bh a few days after peak, in phase with when the
spectrum was taken. SN 2021abpz has a peak absolute magnitude of $M\sim-18.5$
mag, consistent with subluminous Ia. Similarly, SN 2020yje has strong matches
to Type Ia-91bg-like SN 2007ba and SN 2000cn a few days before peak, with a
peak absolute magnitude of $M\sim-17.6$ mag, also consistent with subluminous
Ia. Moreover, both SN 2021abpz and SN 2020yje are in elliptical hosts and
offset from the center—common for Type Ia-91bg-like events (Barkhudaryan et
al., 2019). Their light curves are red and fast declining. We additionally
find both objects are among the first 8 ANNs of another SN Ia-91bg-like
object, SN 2021wzb.

We repeat this procedure and manually inspect the TNS classification spectra
of all classified transients within the first 8 ANNs for the spectroscopic
objects in our databank. Any updated classifications for classes in which at
least one discovery was made is shown in Table 7. In the majority of cases,
the original classification labels are correct and we do not reclassify. Of
our entire spectroscopic sample of 1620 objects, we find 17 unique objects
that are better explained by a different classification label, or about 1%.
Although this percentage is small, of the 17 objects we reclassify, 8 (47%)
were reclassified from a non-anomalous class (which in this work are SN Ia, SN
Ia-91T-like, SN II, and SN IIP) to an anomalous class. This brings the total
anomalous spectroscopic sample from 228 objects to 236, an increase of 3.5%.
Because of the intrinsic rarity of anomalous objects, a 3.5% increase in our
overall spectroscopically anomalous sample from a directed manual re-
evaluation of their spectra is a marked difference.

In our original spectroscopic sample of 1620 objects, only 6 were originally
of this SN Ia-91bg-like classification. After searching this same data set for
the 8 ANNs of the 6 known SN Ia-91bg-like objects, we find an additional 4 SN
Ia-91bg-like objects originally misclassified as SN Ia-normal, an increase of
67%. This nearly doubles our total to 10 SN Ia-91bg-like objects.

###  5.3 Missed Opportunity SNe - Previously Reported to TNS

We can only reclassify SNe that have follow-up spectra. However, in principle
we can repeat the same ANN=8 search of anomalous transients to look for
potential anomalous candidates that were reported to TNS whilst active but
have no classification spectrum. We call these “missed opportunity”
transients. Although these events have faded, we identify unclassified ZTF
transients with a high likelihood of being anomalous. While the entire search
is ongoing and its entirety is beyond the scope of this work, we do identify a
few candidates for which there is a strong possibility of being a TDE, using
the FLEET-TDE classifier from Gomez et al. (2023d).

Figure 11:  Same as Figures 9, 10 but for TDE 2021gje/ZTF21aapvvtb. In this
case, ANN=2 (AT 2021ovg/ZTF21aazenvp) was previously reported to TNS but never
previously identified as a TDE candidate until this work (FLEET=85% TDE), and
ANN=6 (AT 2021ahwl/ZTF21aasdcgt) is a missed TDE candidate (FLEET=70% TDE; now
reported to TNS from this work). We bold these new TDE candidates (dark green
and dark red for ZTF-$g$ and ZTF-$r$, respectively) for visual purposes. The
similarity search in our database was completed in only $\sim$230 ms.

For a directed search, we examine the first 8 ANNs of each of the 20
spectroscopic TDEs from our databank to look for missed opportunity TDEs that
have yet to be reported in the literature. For instance, we use as reference
2021gje, a TDE at $z=0.358$. We find the 8 ANNs in $\sim$230 ms, as shown in
Figure 11. Of those, 2 are AGNs which have survived all our selection cuts
(ANN=3, ZTF21aafktkn; ANN=8, ZTF18abmnoau), 3 are likely SN Ia (AT
2021cmq616161This object likely has a host misassociation, but is correct in
the currently running GHOST version., AT 2021Z, AT 2021ugf), 1 is an SLSN
candidate626262AT 2021hzr has $P(\text{SLSN-II})=68$% according to FLEET,
though, FLEET is not optimized for SLSN-II classifications. (AT 2021hzr), and
2 are likely TDEs or AGN flares with no previous reporting as such in the
literature (AT 2021ovg, AT 2021ahwl). Moreover, AT 2021ahwl was never reported
to TNS until this work (and thus was not considered in Gomez et al. 2023d).

AT 2021ovg has a $P(\text{TDE})=85\%$ from FLEET from the late-time
classifier, but there is no post-peak photometry, and thus the usage of the
late-time classifier is not fully appropriate here. From the FLEET-TDE
classifier for pre-peak, this object is assigned a $P(\text{AGN})=60\%$ and
$P(\text{TDE})=32\%$. It is nuclear, blue, and there are no previous signs of
variability according to ZTF DR data. The ALeRCE light curve classifier
(Sánchez-Sáez et al., 2021) assigns the highest probability as a SLSN at 34%
(there is no TDE option), with the second highest probability being an AGN
(16%). At an estimated redshift of $z=0.241$ from SDSS (Csabai et al., 2003),
we estimate an absolute magnitude of at least $M_{abs}\approx-20.5$ mag, as
there is no visible peak. AT 2021ovg was not reported in Gomez et al. (2023d).

AT 2021ahwl is a moderately strong missed TDE candidate at
$P(\text{TDE})=70\%$ from FLEET. It is nuclear, blue, visible for $\sim$60 d,
and there are no previous signs of strong variability according to ZTF DR
data. At an estimated redshift of $z=0.364$ from SDSS (Csabai et al., 2003),
we estimate a peak absolute magnitude of $M_{abs}\approx-21.8$ mag. Using the
estimated redshift of $z=0.372$ from PS1-STRM (Beck et al., 2021), we estimate
a peak absolute magnitude of $M_{abs}\approx-21.9$ mag. Both redshift
estimates, if correct, are consistent with the reference $z$ at 0.358.

We repeat a likewise examination for the 20 spectroscopic TDEs from our
databank, and find one strong TDE candidate (AT 2021agpi with
$P(\text{TDE})=95\%$ from the FLEET late-time classifier), and three
questionable ones (AT 2020hip, AT 2020yaf, AT 2021stx) that could be TDE or
possibly AGN/ AGN flares, or enhanced accretion-driven AGN flares
(Trakhtenbrot et al., 2019). Spectroscopic observations are warranted for
confirmation, though this opportunity has passed.

Similarly, because FLEET is also optimized for SLSN-I classification and
discovery, we perform another directed search and examine the first 8 ANNs of
each of the 11 spectroscopic SLSN-I and 14 spectroscopic SLSN-II from our
databank to look for missed opportunity SLSNe-I that have yet to be reported
in the literature. We identify one moderately strong SLSN-I candidate (AT
2021fao with $P(\text{SLSN-I})=64\%$ from the FLEET late-time classifier) and
one borderline candidate (AT 2021lnu with $P(\text{SLSN-I})=49\%$ from the
FLEET late-time classifier636363Although not formally discussed in the
literature, this object does have an Astronote identifying it as a ZTF SLSN
candidate (Perley et al., 2021).).

###  5.4 Missed Opportunity SNe - Previously Unreported to TNS

Beyond identifying possible rare transients or anomaly candidates that were
reported but have no spectroscopic follow-up, we can go the next step further
and search for ANNs of known transients to find previously undiscovered or
unreported transients (i.e., not reported to public catalogs like TNS). The
idea here is that if a known transient has a specific feature distribution,
(approximate) nearest neighbors with similar distributions are likely to also
be transients, and perhaps a fraction are not reported. An illustration of
this directed search is found in Figure 12, whose search was completed in
$\sim$140 ms.

Figure 12:  Same as Figures 9, 10, 11, but for Type Ia SN
2020whs/ZTF20aciuftf. In this case, ANN=1 (AT 2018mfm/ZTF18abnzney), ANN=3 (AT
2021ahvn/ZTF21abcluco), and ANN=5 (AT 2021ahvo/ZTF17aadqidc) were missed SNe
candidates (now reported to TNS from this work). We bold these SN discoveries
(dark green and dark red for ZTF-$g$ and ZTF-$r$, respectively) for visual
purposes. The similarity search in our database was completed in only
$\sim$140 ms.

The reference used is Type Ia SN 2020whs/ ZTF20aciuftf at $z=0.13$. From this
ANN=8 search, 5 were already reported to TNS, and thus we manually
investigated the remaining 3 with no submission. Upon expert analysis, we find
that all 3 are missed SNe candidates: ANN=1 (AT 2018mfm/ ZTF18abnzney), ANN=3
(AT 2021ahvn/ ZTF21abcluco), and ANN=5 (AT 2021ahvo/ ZTF17aadqidc).

The 1st ANN, AT 2018mfm/ ZTF18abnzney is approximately 1$\farcs$38 from its
host (WISEA J000044.86+152956.7). It peaks in ZTF-$g$ at an apparent magnitude
of 19.8 mag with a rise of 11 d, compared to the 19.6 mag peak apparent
magnitude of reference SN 2020whs with an 8 d rise. At the time of the
transient, AT 2018mfm has a peak brightness about 1 mag brighter than the
baseline from ZTF DR5. Because of the short timescale and evolution, this is
likely an SN Ia.

The 3rd ANN, AT 2021ahvn/ ZTF21abcluco, is remarkably similar in evolution to
reference SN 2020whs. Both have a first epoch in ZTF-$g$ at an apparent
magnitude of 20.2 mag, rise to peak in 8 d to a peak apparent magnitude in
ZTF-$g$ of 19.6 mag, and fade in 12-13 d. This candidate has an estimated
photo-$z$ from from PS1-STRM (Beck et al., 2021) of $z=0.168$, similar to the
spectroscopic redshift of reference SN 2020whs ($z=0.13$). This candidate, at
approximately 0$\farcs$61 from the center of host WISEA J115609.31+210137.8,
has an estimated peak absolute magnitude consistent with that of an SN Ia.

The 5th ANN, AT 2021ahvo/ ZTF17aadqidc, is at the center (0$\farcs$17) of a
galaxy (WISEA J100313.80+283944.8) with a spectroscopic redshift of 0.087,
placing the peak $M_{abs}\sim-18.6$ mag, well within the expected $M_{abs}$
range of an SN I. Moreover, this object has 7 ZTF-$r$ and 5 ZTF-$g$ alert
stream observations and an apparent peak magnitude of $m\sim 19.4$ mag, which
is about one magnitude brighter than the limiting magnitude of ZTF, making
this a viable candidate for SN detection algorithms. A possible reason for why
this was initially missed is due to its nuclear location, though this
candidate’s photometry is $\sim 0.3$ mag above the baseline of from the ZTF DR
and is red $g-r\approx 0.4$ mag (disfavoring an AGN flare). The ALeRCE light
curve classifier Sánchez-Sáez et al. (2021) predicts this candidate as an SN
Ib/c.

We likewise perform a directed search by calculating the first 8 ANNs of all
spectroscopic transients in our databank, and investigate any non-reported
ANNs for transient activity. In summary, we find that a fraction were never
reported in public catalogs, totaling 84 transient candidate discoveries (not
including likely AGN), all of which can be found in Table F.

Finally, to be complete in recovering missed SNe from our databank, we perform
an exhaustive search, visually inspecting each unique object not previously
reported to TNS and not previously discovered through our ANN search. That
search yielded 241 SNe candidates, as shown in Table F.

In total, we report the discovery of 325 transients, all observed between
2018-2021 and absent from public catalogs ($\sim$1% of all ZTF AT reports to
TNS through 2021).

To better understand how our retroactive candidate SNe discoveries are
distributed as a function of time, we plot in Figure 13 the unweighted
percentage (top panel) and weighted (bottom panel) percentage relative to the
fraction present in our original databank (blue) identified by our ANN=8
search method (gold) and exhaustive method (green). The majority (134 out of
325, or 41%) of our candidate discoveries are from 2018 (with contributions of
35 out of 84 (42%) from our ANN=8 search method and 99 out of 241 (41%) from
our exhaustive search method). Contrary to our expectation, this is nearly
equal to that of 2021, where we identify 131 out of 325 (also 40%) total
missed candidates. However, our databank contains an uneven distribution of
reported candidates per year. Grouping the IAU names, approximately 0% are
from 2017, 11% are from 2018, 4% are from 2019, 20% are from 2020, 62% are
from 2021, and 3% are from 2022. Thus, when we account for this unequal
weighting, our result of 40% of our identified missed candidates from 2021
becomes unsurprising given that 62% of our databank is from 2021; it follows
that there would be relatively more missed transients (numbers wise) for that
year.

Figure 13:  The unweighted percentage (top panel) and weighted (bottom panel)
percentage relative to the fraction present in our original databank (blue) of
the retroactive SNe candidate discoveries recovered by our ANN=8 search method
(gold) and exhaustive method (green). From both methods combined, we recover
nearly equal numbers from 2018 and 2021 (134 out of 325 (41%) and 131 out of
325 (40%), respectively). However, relative to the yearly fractions that
comprise our databank, we find that the fraction of our total 2018 discoveries
relative to all discoveries from this work (41%) is nearly 3 times that of the
fraction relative to that which comprise our databank (11%), whereas our total
2021 discoveries (40%) make up nearly two-thirds relative to our databank
(62%). Overall, the weighted fraction of discoveries is greatest in 2018, the
first full year of the ZTF survey, and decreasing every year through 2022,
implying an improvement in detection and reporting methods via increasing
numbers of active broker teams in the ZTF Alert Stream.

Note the weighted fraction of objects (bottom panel) missed in 2018 and 2019
is greater than that relative to 2020 and 2021. In fact, the weighted fraction
is greatest in 2018, the first full year of the ZTF survey, and decreasing
every year through 2022, implying an improvement in detection and reporting
methods via increasing numbers of active broker teams in the ZTF Alert
Stream.646464Only three objects in our databank have a ZTF ObjectID from 2017,
so although it is included for completeness, it effectively bears no impact on
this analysis. For instance, the ALeRCE broker did not start real–time ML
classification of the ZTF alert stream until early 2019 (Förster et al.,
2021), and the ANTARES broker did not start reporting to TNS until 2021. Fink
did not begin processing the ZTF public live-alert stream until November 2019
(Möller et al., 2021). Between additional teams reporting to TNS and the
addition of object detection algorithms characterizing the ZTF alert stream in
subsequent years (e.g., Muthukrishna et al., 2019; Andreoni et al., 2021; Duev
& van der Walt, 2021; van Roestel et al., 2021; Coughlin et al., 2021; Förster
et al., 2021; Sánchez-Sáez et al., 2021; Carrasco-Davis et al., 2021; Leoni et
al., 2022; Aleo et al., 2022; Reyes-Jainaga et al., 2023), it is sensible that
we discover progressively fewer SNe candidates relative to those present in
our databank. We encourage more retroactive study of the ZTF survey,
particularly in 2018 and 2019 as these data are likely rife with undiscovered
SNe and interesting candidates.

##  6 Discussion

###  6.1 Light curve extrapolation

Astronomical transients are the observational counterpart of terminal events,
and a study of their photometric evolution can offer insights to underlying
astrophysical properties. This can be performed on an individual basis (e.g.,
Gagliano et al. 2021), or across a population (e.g., Nyholm et al. 2020). Due
to cadence, weather, and other factors intrinsic to astronomical observations,
our photometry is irregularly sampled, noisy, and at times has large gaps
spanning several epochs. Thus, in order to obtain homogeneous data suited for
feature extraction and training machine and deep learning models requires fast
but accurate approximation. For such reasons, there are popular parametric
light curve models in the literature (Bazin et al., 2009; Villar et al., 2019;
Russeil et al., 2023) and non-parameteric models such as Gaussian process (GP,
Boone, 2019; Demianenko et al., 2022) both of which predict the temporal flux
evolution.

Note that although we make no such rigorous attempt at a formal light curve
fit model, we can approximate a possible light curve evolution through
considering the additional photometry and host galaxy environment from analogs
to “reconstruct” a light curve. To our knowledge, this idea has not been
demonstrated in the literature for light curve fits, but similar ideas of
applying “twin” supernovae in regards to spectra for SN Ia standardization
(Fakhouri et al., 2015; Boone et al., 2021) have shown promise.

Although we cannot recover photometry that was missed, we can place a soft
constraint on the possible evolution by stacking light curves of ANNs, which
broadly will be of a similar SN class, redshift, and host galaxy environment
to the reference. Despite poor sampling of the light curve, or poor sampling
at a critical phase, it is reasonable to investigate ANNs of these objects to
gain insight of possible light curve evolutionary paths and the associated
variance. We show an example of reference SN Ia 2022cox/ZTF18aaiwewk in Figure
14, which is poorly sampled post peak.

Figure 14:  Same as Figures 9, 10, 11, 12, but for Type Ia SN
2022cox/ZTF18aaiwewk, overplotted with the best-fit SALT3 model (Kenworthy et
al., 2021). In this case, even though SN 2022cox is poorly sampled after peak
with a 30 d gap, we can see fuller light curve evolution and accompanying host
galaxies of analogs through the similarity search, providing useful insights
as to the possible classification and redshift range as well as the possible
(unobserved) light curve evolution of our reference. The SALT3 fit provides
additional context, and we observe that analog light curve epochs are
positioned on or near this curve. The similarity search in our database was
completed in only $\sim$2 ms.

Despite having a large 30 d gap immediately after peak, we know from spectra
SN 2022cox is an SN Ia at $z$=0.087. Four of its ANN matches have
spectroscopic classifications; three SN Ia with redshift ranging $z$=0.07 to
$z$=0.08, and one poor SN II match at $z$=0.023. The photometric evolution of
the other 4 ANNs appear to also be of an SN Ia nature. In total, only one
appears to occur in an early-type galaxy. Stacking the light curves, the
possible evolution post-peak (where the original gap is) is now well-sampled.
In fact, if we overplot the best-fit SALT3 model (Kenworthy et al., 2021), we
find that the ZTF-$r$ fit is well-traced by the majority of the analogs’
$r$-band photometry post-peak as well as the late-time photometry of our
reference. Moreover, despite there being no ZTF-$g$ photometry post-peak, the
SALT3 fit is, too, well-traced by the analogs’ $g$-band photometry. This
method may provide viable supporting evidence for light curve constraints (or
priors), particularly in cases where the SALT3 fit is poorly constrained.

The attraction of this approach is that this provides critical potential
photometry where there is little to none, which contrasts nicely with a
popular light curve approximator: GP (Boone, 2019; Demianenko et al., 2022;
Gagliano et al., 2023). GP regression (Rasmussen & Williams, 2005), while
producing zero error at the learning points, incurs a large error in large
gaps between subsequent epochs. A rigorous analytic comparison between the
error of GP-fit light curves to those fit with analogs’ photometry is left for
future work.

Because our approach relies on the quality of the ANN search and consistency
in light curve profiles, it is necessary for there to be sufficient numbers of
the SN type at varying redshifts and host galaxies to have requisite matches
for tracing possible light curve evolutions. Thus, for this work, it is
unlikely this method will be effective for any SNe beyond normal SN Ia.

###  6.2 Common failure modes of our anomaly detection model

Despite the success of our anomaly detection model and our hyperparameter
tuning to create a high fidelity sample, there are still cases where our model
tags a non-anomaly as anomalous. Visual inspection of such objects from Tables
3, 4, 5, 6, including their light curve evolution and host galaxy association
and environment, reveals some common patterns. The majority of misclassified
anomalies tend to:

  1. 1.

have an incorrect host association (SN 2021aff, SN 2021uzt, SN 2021ojn, AT
2022ywi, SN 2023dgp, SN 2023eqx, AT 2023fli, AT 2023sws). This often occurs
where the likely true host is faint and small (correlated with high-$z$).
Thus, normal light curve behavior in one host galaxy environment could be
exceedingly peculiar if wrongly associated to another vastly different host
galaxy. However, we note that there are many instances where a spectroscopic
or behavioral anomaly is correctly tagged anomalous despite a misassociation
(SN 2018lnb, AT 2021dpa, AT 2023atr, SN 2021axu, SN 2023khp, SN 2023omf, AT
2023otw), potentially indicating that the object’s light curve behavior is
peculiar enough alone to warrant being flagged anomalous.

  2. 2.

be very blue (SALT3 $c\leq-0.2$) SN Ia or SN Ia candidates (AT 2020iga, AT
2020itp, AT 2020wwt, AT 2023tdy, AT 2023tim, AT 2023tjc, AT 2023tnr, AT
2023sws). This is likely a byproduct of feature_linear_fit_slope_magn_g) being
our model’s most discriminating feature for anomaly detection, where strong
blue color is correlated with TDEs and some pre-peak SNe IIn light curves.
Thus, it is unsurprising that SNe Ia significantly bluer than the SALT3 model
are flagged anomalous;

  3. 3.

be luminous (transient peak $M_{abs}\leq-19.8$, using spectroscopic or
photometric redshift estimates) Ia or Ia candidates whose host galaxy is faint
(apparent brightness) and/or small (apparent size) and/or blue (likely spiral,
star-forming) according to the DESI Legacy Survey Imaging Surveys (AT 2020hjv,
SN 2020qkx, SN 2020qql, AT 2020sgy, SN 2020wfg)656565Note that SN Ia-CSM
2020kre is an example of successfully tagged event that falls in this
category.;

  4. 4.

have no rise information (SN 2023cyx, SN 2023dgp, AT 2023fbj, AT 2023huz) or
have no observations until the light curve is well in decline (SN 2020tnq, SN
2021vwx, SN 2023mcs). The latter possibly masquerades as a transient that is
far fainter than is typical or is at a lower $z$ than the host galaxy would
imply, and would cause light curve features that are out-of-distribution (OOD)
with respect to similar spectroscopic members due to the features’ dependence
on quantifying early (and intermediate) phases of light curve behavior. In
other words, two of the exact same SN Ia, where one is observed post peak and
one that is observed for the entirety of the evolution will have some
differing light curve features despite being the same event;

  5. 5.

be SN II with constant color evolution, thus have light curves that appear
more SN Ib/c-like (SN 2019wmr, SN 2020acun). Both examples were in YSE DR1 and
misclassified by both ParSNIP and SuperRAENN as SN Ib/c, potentially
indicating these events are in fact of the SNe IIb subtypes (the most common
such case, as cited in Aleo et al. 2023b);

  6. 6.

have a significant (>10 d) gap in one passband during a period of intense
color evolution of the light curve (SN 2023bfv, AT 2023glx). Because ZTF-$g$
and ZTF-$r$ features are calculated independently, the passbands then no
longer represent a similar phase in the evolution, but offset phases (where
one lags behind), leading to behavior that is flagged as anomalous;

  7. 7.

or have some combination of the above.

###  6.3 Host-galaxy SED Modeling with PROSPECTOR

As an additional analysis, we fit a spectral energy density model using
stellar population synthesis to matched host galaxies for each real-time
anomalous transient candidate (i.e., tagged by LAISS_RFC_AD_filter Filter) to
understand the connection between transients and their host galaxy
environments. We perform Bayesian statistical inference to report the star
formation rate (SFR [$M_{\odot}$/Gyr]), the stellar mass of the galaxy
($\log(M)$), the specific star formation rate ($\log(\text{spSFR})$), the age
of the stellar population (Age [Gyr]), and the V-band optical depth ($A_{V}$).

Our goal is to begin an investigation of the locations of tagged anomalies in
host galaxy parameter feature-space (e.g., log(SFR) vs. $\log(M)$), relative
to larger derived systematic samples across many normal and anomalous SNe
classes from Schulze et al. (2021) and Sharma et al. (2023). We note that the
sample from Schulze et al. (2021) contains the entirety of core-collapse SNe
from 2009 and 2017 by the Palomar Transient Factory — 888 SNe of 12 distinct
classes out to $z\leavevmode\nobreak\ \approx\leavevmode\nobreak\ 1$ —
including host galaxy photometric properties spanning the far-ultraviolet
(FUV) to the mid-infrared (MIR) with modeled host-galaxy spectral energy
distributions (SEDs) to derive physical properties. Meanwhile, the sample from
Sharma et al. (2023) added 12 BTS SNe Ia-CSM hosts in comparison to BTS SNe Ia
collected by Irani et al. (2022), with all such works overplotting hosts into
SFR vs. stellar mass feature-space. By comparing the locations of derived SFR
and stellar mass for our tagged anomalies to larger systematic samples, we can
incrementally add to the literature of derived host galaxy parameters for rare
SNe, as well as investigate the nature of LAISS’ tagged spectroscopically
“Normal” SNe (e.g., SN Ia, SN II)—do they reside in sparse regions of host
galaxy parameter space?

We analyze the set of matched-host galaxies from Table 4 using
PROSPECTOR666666https://github.com/bd-j/prospector, a framework integrating
Bayesian statistical inference through nested
(DYNESTY676767https://github.com/joshspeagle/dynesty) or distributed
(EMCEE686868https://github.com/dfm/emcee) samplers with stellar population
synthesis models (FSPS696969https://github.com/cconroy20/fsps) (Foreman-Mackey
et al., 2013; Leja et al., 2017a; Johnson et al., 2021). We begin with the
previously matched PanSTARRS host galaxies of anomaly candidate transients
calculated via GHOST (Gagliano et al., 2021) within the LAISS_RFC_AD_filter
Filter. Using the matched host astrometry, we re-query
MAST707070https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html for
PanSTARRS photometry (Chambers et al., 2016), choosing the Kron magnitude Kron
(1980) from the Forced Mean table in each band. The Kron magnitude utilizes an
adaptive size aperture, the size of which we also query. Using the Kron
aperture in the PanSTARRS $r$ band, we query for the nearest fixed-size
aperture and uncertainties in GALEX (Gezari et al., 2013), 2MASS (Skrutskie et
al., 2006), AllWISE (Jarrett et al., 2011), and UKIDSS (Lawrence et al., 2007)
catalogs. Because the matched host galaxies via GHOST are inherently from the
PanSTARRS catalog, we are guaranteed to have PanSTARRS photometry.

To properly model the SED to extract physical and phenomenological parameters
of the host galaxies, we require a vast range of host galaxy photometry from
the FUV to the MIR. Thus, we only include tagged anomalies that have at least
one observation in GALEX (filters FUV, NUV spanning 1542–2274Å), PanSTARRS
(filters $g$, $r$, $i$, $z$, $y$ spanning 4776–9603Å), either 2MASS (filters
$J$, $H$, $K_{s}$ spanning 12350–21590Å) or UKIDSS (filters $Z$, $Y$, $J$,
$H$, $K$ spanning 8360-23800Å), and AllWISE (filters $W1$, $W2$, $W3$, $W4$
spanning 33526–285500Å). Moreover, we require a known redshift from
spectroscopy, and the transient class (which is not an AGN) from at least one
classification spectrum. This results in 5 matched hosts from the original
sample of 45 tagged anomalies.

Our fitting routine follows that given in Leja et al. (2017b); we first make a
fit of our SPS model parameters using the Levenberg–Marquardt algorithm (Moré,
1978) to initialize the MCMC chains to a sensible parameterization. We then
use the Dynesty sampler to perform Bayesian parameter estimation. Additional
details on the SED fitting methods can be found in Appendix D.

Our reported galaxy properties are calculated from the parameter samples
following Section 4.1 of Nugent et al. (2020) and we report the parameter
estimates in Table 8. It is important to note that while PROSPECTOR samples
over the total solar masses formed, we estimate and report the stellar mass of
the galaxy using the approximation given in Leja et al. (2013).

Of the five remaining objects after cuts, we only consider two bonafide
anomalies, both of which are _spectroscopic_ anomalies. However, we will find
that all five objects lie in sparse regions of SFR vs. stellar mass feature-
space compared to Schulze et al. (2021) and Sharma et al. (2023), and could
indicate that LAISS’ tagged spectroscopically “Normal” SNe have uncommon host
galaxy parameters for their respective SNe classes despite small number
statistics.

ZTF23aajkisd (SN IIn) at a $\log(M)$ of $10.118^{+0.1125}_{-0.110}$
$M_{\odot}$ and $\log(\text{SFR})$ of $-1.469^{+0.167}_{-0.181}$ $M_{\odot}$
yr-1, resulting in a $\log(\text{spSFR})$ of $-11.594^{+0.204}_{-0.209}$ yr-1,
lies beyond the outer 90% contour of host galaxy populations in the mass–SFR
plane as determined by a kernel density estimate of 111 SN IIn in the Schulze
et al. (2021) sample (see their Figure 10). This indicates that SN 2023iex has
an uncommonly low SFR for its relatively larger galaxy mass, resulting in a
lower spSFR. However, in the mass-redshift plane (see their Figure 9), the
host of SN 2023iex resides in the inner $\sim$20% contour, indicating a common
mass for its redshift (0.029).

ZTF23aatdcey (SN IIn) at a $\log(M)$ of $10.128^{+0.100}_{-0.096}$ $M_{\odot}$
and $\log(\text{SFR})$ of $-1.591^{+0.198}_{-0.238}$ $M_{\odot}$ yr-1,
resulting in a $\log(\text{spSFR})$ of $-11.719^{+0.241}_{-0.272}$ yr-1,
follows the same pattern as SN 2023iex (ZTF23aajkisd). The host of SN 2023nof
(ZTF23aatdcey) also lies beyond the outer 90% contour of host galaxy
populations in the mass–SFR plane from the Schulze et al. (2021) sample. Its
host galaxy also resides in a sparsely populated, low region of spSFR, where
the SFR is low considering the relatively larger mass of the galaxy. In the
mass-redshift plane, it resides in the inner $\sim$20% contour, indicating a
common mass for its redshift (0.069).

ZTF23aberpzw (SN II), at a $\log(M)$ of $10.204^{+0.071}_{-0.083}$ $M_{\odot}$
and $\log(\text{SFR})$ of $-1.949^{+0.175}_{-1.87}$ $M_{\odot}$ yr-1,
resulting in a $\log(\text{spSFR})$ of $-12.154^{+0.232}_{-0.216}$ yr-1, lies
beyond the outer 90% contour of host galaxy populations in the mass–SFR plane
as determined by a kernel density estimate of 498 SN II in the Schulze et al.
(2021) sample (see their Figure 10). Moreover, it lies in what Schulze et al.
(2021) found to be in the lower extreme of SFR—up to $\lesssim$2% of regular
CC SNe (SN Ibc, SN IIb, SN II, and SN IIn) whose hosts exhibit SFR $\lesssim$
0.01 $M_{\odot}$ yr-1 (a $\log(\text{SFR})$ of $-2$ $M_{\odot}$ yr-1) and
spSFR between $10^{-14}$ yr-1 and $10^{-11}$ yr-1, often with sizeable
uncertainties. Thus, we consider this object to be a member of the lower
extreme SFR hosts. In the mass-redshift plane the host of SN 2023swf resides
in the inner $\sim$10% contour, indicating an extremely common mass at its
redshift (0.024). Despite the light curve and spectra indicating a normal SN
II, there is some evidence this object may be anomalous when considering the
extremely low spSFR, and thus may be considered a _contextual_ anomaly.

Overall, all core-collapse SNe (ZTF23aajkisd, ZTF23aatdcey, ZTF23aberpzw)
reside in a sparsely populated region of uncommonly low spSFR, where their
galaxies have low SFR for their relatively larger size (all above the median
and mode $\log(M)$ values as shown in Schulze et al. (2021), their Table 5).

ZTF23aapgswu (SN Ia) at a $\log(M)$ of $8.367^{+0.077}_{-0.095}$ $M_{\odot}$,
is a peculiar case. Its host is a relatively compact, strongly blue and likely
high star-forming galaxy. One such consequence is a heavily negatively skewed
SFR distribution, with one peak and long tail, resulting in a (more negative)
median value of $\log(\text{SFR})$ of $-4.389^{+0.570}_{-59.850}$ $M_{\odot}$
yr-1 and a $\log(\text{spSFR})$ of $-12.751^{+0.600}_{-59.850}$ yr-1. However,
because of the heavy negative skew that biases the median, we also note a
marginally more realistic (but still unphysical) mode value of
$\log(\text{SFR})$ of $-3.998$ $M_{\odot}$ yr-1 and a $\log(\text{spSFR})$ of
$-12.424$ yr-1. Such an unphysically low SFR value can likely be traced to our
choice of SFH function: it has been suggested that parameterized SFH are not
flexible enough to deal with the actual complexities inherent in galaxy
systems (Leja et al., 2019a). As a further point of caution, no SN Ia from
Irani et al. (2022) of BTS SNe Ia have such a low $\log(\text{SFR})$, and thus
we make no strong claims as to the validity of the fit. More investigation of
the host galaxy and derived host parameters of SN 2023mcs is encouraged,
especially when considering it is host to a SN Ia.

ZTF23abayyjm (SN Ia) at a $\log(M)$ of $10.815^{+0.110}_{-0.044}$ $M_{\odot}$
(the largest in our sample of five objects) and $\log(\text{SFR})$ of
$0.147^{+0.263}_{-0.842}$ $M_{\odot}$ yr-1, resulting in a
$\log(\text{spSFR})$ of $-10.705^{+0.234}_{-0.761}$ yr-1, is the only event
that resides in a host galaxy with above Milky Way metallicity ([Z/H] = 0.0;
see Choi et al. (2016)). When compared to BTS SNe Ia from Irani et al. (2022)
and Sharma et al. (2023), it is at the upper regime of massive, highly star-
forming and high metallicity host galaxies beyond the red and blue contours
corresponding to Galaxy Zoo ellipticals and spirals (see Figure 14 of Irani et
al. (2022)). However, the spSFR is average after normalizing SFR by the very
large galaxy mass, and is among the highest mass hosts compared to the Irani
et al. (2022) and Sharma et al. (2023) samples.

The goal of this exercise was to lay the groundwork of building a downstream
_Filter_ to fit SPS host galaxy models. In this pursuit, we have discerned
valuable insights. Moreover, we recognize the need for additional technologies
and software infrastructure that needs to be developed before this process can
be scaled to _Rubin_ -era data streams.

First, it was critical to use matched-aperture size photometry to get sensible
results. Even so, we chose to only use pre-calculated magnitudes from reported
fixed-size apertures, when in principle the apertures should be matched
exactly across each band. Additional work would be necessary to create a
robust pipeline that works on galaxies at various distances. Our fits could
likely be improved by utilizing non-parameteric SFH functions. Additionally,
MCMC sampling is computationally expensive. Each object’s sampling takes
approximately 2 hr on a shared compute node equipped with an AMD EPYC 7502
CPU. To rectify this, other works have suggested speeding up SPS models with
neural network emulators (Kwon et al., 2023), which are reported to achieve a
100x speed-up; or with approximate posterior distributions of galaxy
parameters efficiently via Simulation-Based inference (SBI; Cranmer et al.,
2020) and amortized Neural Posterior Estimation (NPE; Papamakarios & Murray,
2016) techniques, which can achieve an amortized calculation of galaxy
parameter posteriors in less than 1s (see Hahn & Melchior, 2022; Khullar et
al., 2022; Wang et al., 2023, and references therein); or with a physics-
informed variational autoencoder (Gagliano & Villar, 2023). These efforts are
promising and ongoing, but beyond the scope of this work.

Table 8: Host Galaxy Parameter Estimation with Prospector. We fit a spectral model using photometry from matched host galaxies to each transient in Table 4 that pass strict selection cuts. We report the transient redshift, the log star formation rate ($\log(\text{SFR})$ [$M_{\odot}$]), the total stellar mass of the galaxy ($\log(M)$ [$M_{\odot}$ yr-1]), the log specific star formation rate ($\log(\text{sSFR})$ [yr-1]), the age of the stellar population (Age [Gyr]) and the V-band optical depth ($A_{V}$). We also report the value of $\chi^{2}$/n.o.f. (number of filters). For all distributions, we report the median values with upper and lower bounds equivalent to the $84^{\text{th}}$ and $16^{\text{th}}$ quantiles, respectively, unless otherwise noted (light green highlighter; see below). ZTF ID | Spec. Class | Redshift | $\log(M)$ | Age [Gyr] | $A_{V}$ | $\log(\text{SFR})$ | $\log(\text{spSFR})$ | $\chi^{2}/n.o.f$ |
---|---|---|---|---|---|---|---|---|---
ZTF23aatdcey | SN IIn | $0.069$ | $10.128^{+0.100}_{-0.096}$ | $4.406^{+1.952}_{-1.331}$ | $1.636^{+0.267}_{-0.278}$ | $-1.591^{+0.198}_{-0.238}$ | $-11.719^{+0.241}_{-0.272}$ | 39.451/14 |
ZTF23aajkisd | SN IIn | $0.029$ | $10.118^{+0.125}_{-0.110}$ | $3.120^{+1.959}_{-1.031}$ | $2.097^{+0.191}_{-0.184}$ | $-1.469^{+0.167}_{-0.181}$ | $-11.594^{+0.204}_{-0.209}$ | 23.446/14 |
ZTF23aapgswu | SN Ia | $0.030$ | $8.367^{+0.077}_{-0.095}$ | $10.401^{+2.368}_{-2.913}$ | $0.117^{+0.189}_{-0.087}$ | $-4.389^{+0.570}_{-59.729}$ | $-12.751^{+0.600}_{-59.850}$ | 28.920/10 |
ZTF23aberpzw | SN II | $0.024$ | $10.204^{+0.071}_{-0.083}$ | $9.418^{+2.611}_{-2.387}$ | $1.221^{+0.194}_{-0.188}$ | $-1.949^{+0.175}_{-0.187}$ | $-12.154^{+0.232}_{-0.216}$ | 19.036/14 |
ZTF23abayyjm | SN Ia | $0.140$ | $10.815^{+0.110}_{-0.044}$ | $1.471^{+1.006}_{-0.461}$ | $3.275^{+0.184}_{-0.170}$ | $0.147^{+0.263}_{-0.824}$ | $-10.705^{+0.234}_{-0.761}$ | 28.451/11 |
The light green highlighter indicates distributions that have unreliable median values (displayed as default in the table) due to their heavy negative skew. For these, the mode is better representative. For ZTF23aapgswu, the mode of $\log(\text{SFR})$ is $-3.998$ $M_{\odot}$ yr-1 and the mode of $\log(\text{spSFR})$ is $-12.424$ yr-1. |

##  7 Conclusion

In this work, we present LAISS (Lightcurve Anomaly Identification and
Similarity Search), a pipeline for real-time anomaly detection and approximate
similarity searches of astronomical transients within large volumetric data
streams. We debut our anomaly detection model as a _Filter_ on the ANTARES
broker to process the nightly ZTF Alert Stream. Our model is based on a RFC
architecture using extracted light curve and contextual host galaxy features
without the need for redshift information, designed to classify several types
of transients as anomalies (“Anomaly”) with high purity:

  1. 1.

Spectroscopic anomalies (i.e., designate all SNe other than normal Type Ia,
Type Ia-91T-like, and normal Type II, IIP as anomalous);

  2. 2.

Contextual anomalies (e.g., a SN in an atypical galactic environment);

  3. 3.

Behavioral anomalies (e.g., a SN re-brightening due to CSM-interaction).

We run our anomaly detection model on the active ZTF alert stream for real-
time discovery, as well as legacy subsets of the ZTF alert stream and the
Young Supernova Experiment Data Release 1 for retroactive discovery. Moreover,
we construct a low-latency approximate similarity search model within our
derived light curve and host galaxy feature-space to find transient analogs of
similar light curve evolution and host galaxy environments.

Our conclusions and key takeaways for the LAISS anomaly detection and
approximate similarity search pipeline are as follows:

  * •

The most important _light curve features_ for distinguishing anomalies from
other transients according to our RFC model using both impurity and
permutation importances are the light curve slope in a least squares fit of
the linear stochastic model with Gaussian noise described by observation
errors {$\delta_{i}$} (feature_linear_fit_slope_magn_g) and its error in
$r$-band
(feature_linear_fit_slope_sigma_magn_r), and the unbiased Anderson–Darling
normality test statistic for $g$-band flux
(feature_anderson_darling_normal_flux_g).

  * •

The most important _host galaxy features_ for distinguishing anomalies from
other transients according to our RFC model using both impurity and
permutation importances are radial offset and color-derived features, namely
the aperture magnitude $i-z$ color difference (i-z) and the normalized host
separation dist/DLR. Notably, the high importance of these contextual host
galaxy features are in agreement with that of FLEET (Gomez et al., 2020,
2023a, 2023d) and GHOST (Gagliano et al., 2021), despite the nature of their
different anomaly detection and SNe classification tasks.

  * •

Using the spectroscopic label alone from TNS (before vetting) as indicator of
an anomalous transient, we can recover anomalies with a purity of $\approx$50%
using the full light curve classifier for events with
$P(anom)\leavevmode\nobreak\ \textgreater\leavevmode\nobreak\ 0.5$. If we
assume anomalies (defined in this work as everything not Type Ia-normal,
91T-like, II-normal, and IIP) comprise about 10% of the SNe population in a
magnitude-limited survey like ZTF, this is a factor of $\sim$5 improvement
compared to random selection. The corresponding completeness for this
threshold is 29%. However, if we additionally consider _updated spectroscopic
classifications_ , _contextual anomalies_ , and _behavioral anomalies_ (such
as SNe in an atypical environment or peculiar light curve or spectral
behavior) as a result of expert vetting, we can recover anomalies with an
_observed purity_ of $\approx$89% using the full light curve classifier for
events with $P(anom)\leavevmode\nobreak\ \textgreater\leavevmode\nobreak\ 0.5$
at a completeness threshold of 37%. This is a factor of $\sim$8 improvement
compared to random selection.

  * •

When using any SNe as input into our approximate similarity search, we can
retrieve 8 ANNs in our 5472 object, 60-dimensional principal component-space
in $\mathcal{O}(100)$ ms. If needed, we can re-extract light curve features in
in $\sim$1 s and host galaxy association and features in $\sim$1 min. We show
that an ANN solution to finding SNe analogs with pre-computed features can
scale to Rubin data streams.

  * •

We demonstrate an approximate similarity search for finding SNe analogs is
useful in many applications, such as but not limited to: finding similar SNe
evolution and host galaxy environment, finding missed SNe in legacy datasets,
providing possible light curve evolution of poorly sampled SNe, and prompting
some reclassification of SNe (often to a rarer subclass of the SNe type).

  * •

From our approximate similarity search, we report 17 unique reclassified SNe
and 84 previously undiscovered SNe candidates found by an ANN=8 search of our
databank spectroscopic sample. From a final exhaustive search of our databank,
we report the remaining 241 SNe candidates to TNS. In total, we report 325
discoveries, all from 2018-2021 and absent from public catalogs ($\sim$1% of
all ZTF AT reports to TNS through 2021717171https://tinyurl.com/ZTF-AT-TNS-
reports-thru2021).

  * •

Despite small number statistics, there is some evidence to suggest that
objects tagged anomalous by LAISS have host galaxy properties such as mass,
star formation rate, and specific star formation rate, that reside in sparse
regions of latent space of equivalent SN type when compared to larger samples
in the literature.

###  7.1 List of Anomalies

We report the following new _spectroscopic_ and _behavioral_ anomalies, either
flagged in real-time or retrospectively by our anomaly detection model. They
are confirmed via follow-up observations or from a retroactive
reclassification of existing spectra prompted by approximate similarity
search. Additionally, we report likely anomalous transient candidates based on
their light curve evolution, host galaxy environment, and results from
photometric classifiers such as FLEET (Gomez et al., 2020). We newly identify
a myriad of peculiar and rare transients, including:

  * •

SLSN (11 total; 2 spectroscopic; 9 photometric): We identify 11 new candidate
members: 2 spectroscopic (ZTF21aaizyqc / SN 2021ckb, ZTF22abfdzrv / SN
2022vmg); 9 photometric (ZTF20acbiwfi / AT 2020afew, ZTF20aczhbki / AT
2020afex, ZTF21aajrgkw / AT 2021dpa, ZTF21aaualdi / AT 2021ahwa, ZTF21abasbpe
/ AT 2021ahwh, ZTF20acyroio / AT 2021ahwp, ZTF23aahjdxa / AT 2023gpp,
ZTF23aaqqeek / AT 2023mne, ZTF23aawblmi / AT 2023otw).

  * •

TDE (10 total; 1 spectroscopic, 9 photometric): We identify 9 new candidate
members: 1 spectroscopic (ZTF23aamsetv / SN 2023kvy); 8 photometric
(ZTF18abtjrbt / AT 2018mfz, ZTF18acvwkvc / AT 2018mkd, PS19gzf / AT 2019vuz,
ZTF20acpgnmw / AT 2020afev, ZTF20acpzjsk / AT 2020affx, ZTF21aazenvp / AT
2021ovg, ZTF21aasdcgt / AT 2021ahwl, ZTF22absuavp / AT 2022zyh, ZTF22abzajwl /
AT 2023adr).

  * •

Long-rising ($\textgreater$ 40 d) SN II (6 total; 2 spectroscopic, 4
photometric): We identify 6 new candidate members: 2 spectroscopic
(ZTF20abqlmwn/ SN 2020rmk, ZTF21aaqyifh/ SN 2021hqe); 4 photometric
(ZTF18abiitmq/ AT 2018mhh, ZTF18abkmfaj/ AT 2018mhj, ZTF21abasjcd/ AT 2021rmq,
ZTF23aajestr / AT 2023inr).

  * •

SN Ic-BL (1 total; 1 spectroscopic, 0 photometric): We identify 1 new
candidate member; 1 spectroscopic (ZTF21aacufip / SN 2021vz); 0 photometric.

  * •

SN Ib (2 total; 2 spectroscopic, 0 photometric): We identify 2 new candidate
members: 2 spectroscopic (ZTF21aaqwfqe / SN 2021hen, ZTF21aabyifm / SN
2021qv); 0 photometric.

  * •

SN Ib/c (6 total; 0 spectroscopic, 6 photometric): We identify 6 new candidate
members: 0 spectroscopic; 6 photometric (ZTF18abwhsnx / AT 2018mgd,
ZTF18aajgowk / AT 2018mgw, ZTF20acuyxki / AT 2020afes, ZTF21aaiapis / AT
2021ahwg, ZTF23aafgmaz / AT 2023frg, ZTF23abedgfr / AT 2023syt).

  * •

SN IIb (4 total; 3 spectroscopic, 1 photometric): We identify 4 new candidate
members: 3 spectroscopic (ZTF21aajvukh/ SN 2021cpi, ZTF21abnvlnj / SN 2021tyf,
ZTF23aarktow/ SN 2023myo); 1 photometric (ZTF18acvgmpx / AT 2018mkc).

  * •

SN IIn (19 total; 4 spectroscopic, 15 photometric): We identify 19 new
candidate members: 4 spectroscopic (ZTF21abcjpnm / SN 2021njo, ZTF23aatdcey /
SN 2023nof, ZTF23aatcsou / SN 2023nwe, ZTF23aavtugd / SN 2023omf); 15
photometric (ZTF18ablqjws / AT 2018mga, PS20czv / AT 2020jvi, PS20mgo / AT
2020acyu, ZTF20aclghmy / AT 2020affa, ZTF20acbptqx / 2020affg, ZTF20acngnvf /
AT 2020affn, ZTF20acrssoi / AT 2020affz, ZTF20actkqax / AT 2020afgb,
ZTF20acxyrkk / AT 2020afge, ZTF21abiggqx / AT 2021rjf, ZTF22aatwxrl / AT
2022oym, ZTF23aaahnss / AT 2023atr, ZTF23aaveoxd / AT 2023ofr, ZTF23aaufkak /
AT 2023nwk, ZTF23aaewyhm / AT 2023gzn).

  * •

SN Ia-CSM (3 total; 2 spectroscopic, 1 photometric): We identify 3 new
candidate members: 2 spectroscopic (ZTF20acpbboa / SN 2020ywo, ZTF23aatabje /
SN 2023ocx); 1 photometric (ZTF23aaqbyzr / SN 2023mic).

  * •

SN Ia-91bg-like (10 total; 7 spectroscopic, 3 photometric): We identify 10 new
candidate members: 7 spectroscopic (ZTF20acnznol / SN 2020yje, ZTF21abmwgow /
SN 2021ttg, ZTF21acfigoo / SN 2021aazj, ZTF21achjwus / SN 2021abpz,
ZTF21acjgafq / SN 2021ackd, ZTF21acmnpqa / SN 2021adpx, ZTF23abhafym / SN
2023tsw); 3 photometric (ZTF19aavoqbe / AT 2019aatq, ZTF21aaiahsu / AT
2021ahxg, ZTF23aaflptz / AT 2023gbk).

  * •

Unknown blue transients (2 total; 2 photometric): We identify 2 blue
transients of unknown type: 0 spectroscopic; 2 photometric (ZTF21aabyoxk / AT
2021ahwu, ZTF21aawzmne / AT 2021ahyj).

Lastly, we aggregate the _contextual_ anomalies:

  * •

SN candidates in rare ring host galaxy (2): ZTF18acvgmpx / AT 2018mkc,
ZTF21aakjxhg / AT 2021ahxn.

  * •

SN Ia in flocculent spiral/merger (1): ZTF20aazpwen / SN 2020kbl.

  * •

SN II or SN II candidate in possible elliptical host galaxy (3): ZTF19abljudj
/ AT 2019aats, ZTF20aawlmfu / AT 2020kmj, ZTF23abcinum / SN 2023sds.

Facilities: ZTF, ADS, TNS, NED (https://doi.org/10.26132/ned1), ATel, ANTARES,
ALeRCE

Software: ANNOY (Bernhardsson, 2018), antares-client (NOIRLab CSDC Team,
2023), Astropy (Astropy Collaboration et al., 2013; Price-Whelan et al.,
2018), FLEET (Gomez et al., 2020), GHOST (Gagliano et al., 2021), Matplotlib
(Hunter, 2007), numpy (Walt et al., 2011), Pandas (pandas development team,
2020), Scikit-Learn (Pedregosa et al., 2011), YSE-PZ (Coulter et al., 2022,
2023)

##  8 Acknowledgments

We thank Anya Nugent, Nell Byler, Maggie Verrico, Nicholas Earl, Yuxin Dong,
Charlie Conroy, and Ben Johnson for helpful correspondence regarding host-
galaxy SED Modeling.

Author contributions are listed below.
P. D. Aleo as project lead and YSE collaboration meeting co-lead contributed
to ideation, development, and deployment of LAISS; statistical and data
analysis; analysis of classified objects; lead writing and lead editing;
figures.
A. Engel contributed to writing text, making figures, and performing analysis
using Prospector.
G. Narayan as YSE Executive Committee member contributed to oversight;
editing; helpful discussions.
C. R. Angus contributed to observing and reducing NOT data used for
classification; analysis of classified objects; and sniffing and/or sorting
and/or flagging transients.
K. Malanchev contributed to ANTARES operations (software); _Filter_
management; draft review.

K. Auchettl contributed to WiFeS observations to confirm candidates; to draft
review.
V. F. Baldassare as YSE Executive Committee member contributed to helpful
discussions.
A. Berres contributed to the creation of the Slack-bot.
T. J. L. de Boer contributed to PS1 operations.
B. M. Boyd contributed as a YSE junior review panelist.
K. C. Chambers contributed to PS1 operations.
K. W. Davis contributed to observing and reducing Lick-3m data used for
classification.
N. Esquivel contributed to ANTARES operations (software); ANTARES operations
(hardware).
D. Farias contributed as a YSE junior review panelist.
R. J. Foley as YSE Executive Committee member contributed to helpful
discussions, managed the YSE survey, and was PI of spectroscopic observing
programs.
A. T. Gagliano contributed to draft review; helpful discussions and to
sniffing and/or sorting and/or flagging transients.
C. Gall contributed to sniffing and/or sorting and/or flagging transients. H.
Gao contributed to PS1 operations.
S. Gomez contributed to observing and reducing SOAR data used for
classification in co-ordination with the FLEET program; to helpful
discussions.
M. Grayling contributed as lead YSE junior review panelist.
C.-C. Lin contributed to PS1 operations.
E. A. Magnier contributed to PS1 operations.
K. S. Mandel as YSE Executive Committee contact contributed to oversight and
helpful discussions.
T. Matheson contributed to ANTARES operations (software); discussions; draft
review.
S. I. Raimundo contributed to sniffing and/or sorting and/or flagging
transients.
V. G. Shah contributed as a YSE junior review panelist.
M. D. Soraisam contributed to ANTARES operations (software); discussions;
draft review.
K. M. de Soto contributed as a YSE junior review panelist.
S. Vicencio contributed to ANTARES operations (software); _Filter_ management.
V. A. Villar as YSE Executive Committee member contributed to helpful
discussions.

P.D.A. and G.N.’s contributions to this work were directly supported by NSF
AST–2206195, and we gratefully acknowledge this funding. P.D.A. has also been
supported by the Illinois Survey Science Graduate Fellowship from the Center
for AstroPhysical Surveys (CAPS)727272https://caps.ncsa.illinois.edu/ at the
National Center for Supercomputing Applications (NCSA). A.W.E. was partially
supported by the Open Call Initiative, under the Laboratory Directed Research
and Development (LDRD) Program at Pacific Northwest National Laboratory
(PNNL). PNNL is a multi-program national laboratory operated for the U.S.
Department of Energy (DOE) by Battelle Memorial Institute under Contract No.
DE-AC05-76RLO 1830. G.N. is also supported by NSF CAREER grant AST–2239364,
funded in-part by a grant by Charles Simonyi, and NSF OAC–2311355, DOE support
through the Department of Physics at the University of Illinois, Urbana-
Champaign (# 13771275), and support from the HST Guest Observer Program
through HST-GO-16764 and HST-GO-17128 (PI: R. Foley). V.F.B.’s contributions
to this work were supported by NSF AST-2206165. B.M.B. is supported by the
Cambridge Centre for Doctoral Training in Data-Intensive Science funded by the
UK Science and Technology Facilities Council (STFC). The UCSC team is
supported in part by NASA grant NNG17PX03C, NSF grants AST–1815935 and
AST–2307710, the Gordon & Betty Moore Foundation, the Heising-Simons
Foundation, and by a fellowship from the David and Lucile Packard Foundation
to R.J.F. A.T.G. is supported by the National Science Foundation under
Cooperative Agreement PHY-2019786 (The NSF AI Institute for Artificial
Intelligence and Fundamental Interactions, http://iaifi.org/). C.G. is
supported by a VILLUM FONDEN Young Investigator Grant (project number 25501).
M.G. and K.S.M. are supported by the European Union’s Horizon 2020 research
and innovation programme under ERC Grant Agreement No. 101002652 and Marie
Skłodowska-Curie Grant Agreement No. 873089. K.M.dS. acknowledges support by
the NSF through grant AST–2108676. K.dS. thanks the LSST-DA Data Science
Fellowship Program, which is funded by LSST-DA, the Brinson Foundation, and
the Moore Foundation; her participation in the program has benefited this
work. V.S. acknowledges the support of the LSST Corporation’s 2021 Enabling
Science award for undergraduates. V.A.V. acknowledges support by the NSF
through grant AST–2108676.

The Young Supernova Experiment (YSE) and its research infrastructure is
supported by the European Research Council under the European Union’s Horizon
2020 research and innovation programme (ERC Grant Agreement 101002652, PI K.
Mandel), the Heising-Simons Foundation (2018-0913, PI R. Foley; 2018-0911, PI
R. Margutti), NASA (NNG17PX03C, PI R. Foley), NSF (AST–1720756, AST–1815935,
AST–2307710, PI R. Foley; AST-1909796, AST-1944985, PI R. Margutti), the David
& Lucille Packard Foundation (PI R. Foley), VILLUM FONDEN (project 16599, PI
J. Hjorth), and the Center for AstroPhysical Surveys (CAPS) at the National
Center for Supercomputing Applications (NCSA) and the University of Illinois
Urbana-Champaign.

The ANTARES project has been supported by the National Science Foundation
through a cooperative agreement with the Association of Universities for
Research in Astronomy (AURA) for the operation of NOIRLab, through an NSF
INSPIRE grant to the University of Arizona (CISE AST-1344024, PI: R.
Snodgrass), and through a grant from the Heising-Simons Foundation.

YSE-PZ was developed by the UC Santa Cruz Transients Team with support from
The UCSC team is supported in part by NASA grants NNG17PX03C, 80NSSC18K0303,
80NSSC19K0113, 80NSSC19K1386, 80NSSC20K0953, 80NSSC21K2076, 80NSSC22K1513,
80NSSC22K1518, and 80NSSC23K0301; NSF grants AST–1720756, AST–1815935, and
AST–1911206; grants associated with Hubble Space Telescope programs DD–14925,
DD–15600, GO–15876, GO–16238, SNAP–16239, GO–16690, SNAP–16691, and GO–17128;
the Gordon & Betty Moore Foundation; the Heising-Simons Foundation;
fellowships from the Alfred P. Sloan Foundation and the David and Lucile
Packard Foundation to R.J.F.; Gordon and Betty Moore Foundation postdoctoral
fellowships and a NASA Einstein fellowship, as administered through the NASA
Hubble Fellowship program and grant HST-HF2-51462.001, to D.O.J.; and a
National Science Foundation Graduate Research Fellowship, administered through
grant No. DGE-1339067, to D.A.C.

Pan-STARRS is a project of the Institute for Astronomy of the University of
Hawaii, and is supported by the NASA SSO Near Earth Observation Program under
grants 80NSSC18K0971, NNX14AM74G, NNX12AR65G, NNX13AQ47G, NNX08AR22G,
80NSSC21K1572 and by the State of Hawaii. The Pan-STARRS1 Surveys (PS1) and
the PS1 public science archive have been made possible through contributions
by the Institute for Astronomy, the University of Hawaii, the Pan-STARRS
Project Office, the Max-Planck Society and its participating institutes, the
Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute
for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham
University, the University of Edinburgh, the Queen’s University Belfast, the
Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory
Global Telescope Network Incorporated, the National Central University of
Taiwan, STScI, NASA under grant NNX08AR22G issued through the Planetary
Science Division of the NASA Science Mission Directorate, NSF grant
AST-1238877, the University of Maryland, Eotvos Lorand University (ELTE), the
Los Alamos National Laboratory, and the Gordon and Betty Moore Foundation.

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

Parts of this research were supported by the Australian Research Council
Discovery Early Career Researcher Award (DECRA) through project number
DE230101069.

A major upgrade of the Kast spectrograph on the Shane 3 m telescope at Lick
Observatory was made possible through generous gifts from the Heising-Simons
Foundation as well as William and Marina Kast. Research at Lick Observatory is
partially supported by a generous gift from Google.

This research has made use of the VizieR catalogue access tool, CDS,
Strasbourg, France (DOI : 10.26093/cds/vizier). The original description of
the VizieR service was published in 2000, A&AS 143, 23

## References

  * Aleo et al. (2023a) Aleo, P., Auchettl, K., & Ransome, C. 2023a, Transient Name Server Classification Report, 2023-2506, 1
  * Aleo et al. (2022) Aleo, P. D., Malanchev, K. L., Pruzhinskaya, M. V., et al. 2022, New A, 96, 101846, doi: 10.1016/j.newast.2022.101846
  * Aleo et al. (2023b) Aleo, P. D., Malanchev, K., Sharief, S., et al. 2023b, ApJS, 266, 9, doi: 10.3847/1538-4365/acbfba
  * Alves et al. (2022) Alves, C. S., Peiris, H. V., Lochner, M., et al. 2022, ApJS, 258, 23, doi: 10.3847/1538-4365/ac3479
  * Andreoni et al. (2021) Andreoni, I., Coughlin, M. W., Kool, E. C., et al. 2021, ApJ, 918, 63, doi: 10.3847/1538-4357/ac0bc7
  * Angus (2023) Angus, C. 2023, Transient Name Server Classification Report, 2023-2466, 1
  * Angus & Aleo (2023) Angus, C., & Aleo, P. 2023, Transient Name Server Classification Report, 2023-2467, 1
  * Astropy Collaboration et al. (2013) Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068
  * Ayala et al. (2023) Ayala, B., Ramirez, M., Dastidar, R., et al. 2023, Transient Name Server Classification Report, 2023-2482, 1
  * Babu & Mahabal (2016) Babu, G. J., & Mahabal, A. 2016, International Statistical Review / Revue Internationale de Statistique, 84, 506.  http://www.jstor.org/stable/44162510
  * Baldeschi et al. (2020) Baldeschi, A., Miller, A., Stroh, M., Margutti, R., & Coppejans, D. L. 2020, ApJ, 902, 60, doi: 10.3847/1538-4357/abb1c0
  * Barkhudaryan et al. (2019) Barkhudaryan, L. V., Hakobyan, A. A., Karapetyan, A. G., et al. 2019, MNRAS, 490, 718, doi: 10.1093/mnras/stz2585
  * Barris et al. (2004) Barris, B. J., Tonry, J. L., Blondin, S., et al. 2004, ApJ, 602, 571, doi: 10.1086/381122
  * Bazin et al. (2009) Bazin, G., Palanque-Delabrouille, N., Rich, J., et al. 2009, A&A, 499, 653, doi: 10.1051/0004-6361/200911847
  * Beck et al. (2021) Beck, R., Szapudi, I., Flewelling, H., et al. 2021, MNRAS, 500, 1633, doi: 10.1093/mnras/staa2587
  * Bell et al. (2004) Bell, E. F., Wolf, C., Meisenheimer, K., et al. 2004, ApJ, 608, 752, doi: 10.1086/420778
  * Bellm et al. (2019) Bellm, E. C., Kulkarni, S. R., Graham, M. J., et al. 2019, PASP, 131, 018002, doi: 10.1088/1538-3873/aaecbe
  * Bernhardsson (2018) Bernhardsson, E. 2018, Annoy: Approximate Nearest Neighbors in C++/Python.  https://pypi.org/project/annoy/
  * Blondin & Tonry (2007) Blondin, S., & Tonry, J. L. 2007, ApJ, 666, 1024, doi: 10.1086/520494
  * Böhm et al. (2023) Böhm, V., Kim, A. G., & Juneau, S. 2023, MNRAS, 526, 3072, doi: 10.1093/mnras/stad2773
  * Boone (2019) Boone, K. 2019, AJ, 158, 257, doi: 10.3847/1538-3881/ab5182
  * Boone (2021) —. 2021, AJ, 162, 275, doi: 10.3847/1538-3881/ac2a2d
  * Boone et al. (2021) Boone, K., Aldering, G., Antilogus, P., et al. 2021, ApJ, 912, 71, doi: 10.3847/1538-4357/abec3b
  * Breiman (2001) Breiman, L. 2001, Machine Learning, 45, 5, doi: 10.1023/A:1010933404324
  * Brout & Scolnic (2021) Brout, D., & Scolnic, D. 2021, ApJ, 909, 26, doi: 10.3847/1538-4357/abd69b
  * Burhanudin & Maund (2022) Burhanudin, U. F., & Maund, J. R. 2022, arXiv e-prints, arXiv:2208.01328.  https://arxiv.org/abs/2208.01328
  * Byler et al. (2017) Byler, N., Dalcanton, J. J., Conroy, C., & Johnson, B. D. 2017, ApJ, 840, 44, doi: 10.3847/1538-4357/aa6c66
  * Cardelli et al. (1989) Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245, doi: 10.1086/167900
  * Carrasco-Davis et al. (2021) Carrasco-Davis, R., Reyes, E., Valenzuela, C., et al. 2021, AJ, 162, 231, doi: 10.3847/1538-3881/ac0ef1
  * Chabrier (2003) Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392
  * Chambers et al. (2016) Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv e-prints, arXiv:1612.05560.  https://arxiv.org/abs/1612.05560
  * Chambers et al. (2023) Chambers, K. C., Boer, T. D., Bulger, J., et al. 2023, Transient Name Server Discovery Report, 2023-1750, 1
  * Charnock & Moss (2017) Charnock, T., & Moss, A. 2017, ApJ, 837, L28, doi: 10.3847/2041-8213/aa603d
  * Chawla et al. (2002) Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. 2002, Journal of Artificial Intelligence Research, 16, 321, doi: 10.1613/jair.953
  * Chiang (2023) Chiang, Y.-K. 2023, The Astrophysical Journal, 958, 118, doi: 10.3847/1538-4357/acf4a1
  * Choi et al. (2016) Choi, J., Dotter, A., Conroy, C., et al. 2016, The Astrophysical Journal, 823, 102, doi: 10.3847/0004-637X/823/2/102
  * Chu et al. (2021) Chu, M., Dahiwale, A., & Fremling, C. 2021, Transient Name Server Classification Report, 2021-3328, 1
  * Conroy & Gunn (2010) Conroy, C., & Gunn, J. E. 2010, The Astrophysical Journal, 712, 833, doi: 10.1088/0004-637X/712/2/833
  * Conroy et al. (2009) Conroy, C., Gunn, J. E., & White, M. 2009, The Astrophysical Journal, 699, 486, doi: 10.1088/0004-637X/699/1/486
  * Coughlin et al. (2021) Coughlin, M. W., Burdge, K., Duev, D. A., et al. 2021, MNRAS, 505, 2954, doi: 10.1093/mnras/stab1502
  * Coulter et al. (2022) Coulter, D. A., Jones, D. O., McGill, P., et al. 2022, YSE-PZ: An Open-source Target and Observation Management System, v0.3.0, Zenodo, doi: 10.5281/zenodo.7278430
  * Coulter et al. (2023) Coulter, D. A., Jones, D. O., McGill, P., et al. 2023, PASP, 135, 064501, doi: 10.1088/1538-3873/acd662
  * Cranmer et al. (2020) Cranmer, K., Brehmer, J., & Louppe, G. 2020, Proceedings of the National Academy of Science, 117, 30055, doi: 10.1073/pnas.1912789117
  * Csabai et al. (2003) Csabai, I., Budavári, T., Connolly, A. J., et al. 2003, AJ, 125, 580, doi: 10.1086/345883
  * Cui et al. (2023) Cui, K., Armstrong, D. J., & Feng, F. 2023, Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework.  https://arxiv.org/abs/2311.08080
  * Dálya et al. (2018) Dálya, G., Galgóczi, G., Dobos, L., et al. 2018, MNRAS, 479, 2374, doi: 10.1093/mnras/sty1703
  * Davis et al. (2023) Davis, K., Foley, R., Izzo, L., et al. 2023, Transient Name Server Classification Report, 2023-2235, 1
  * Davis et al. (2022) Davis, K. W., Taggart, K., Tinyanont, S., et al. 2022, arXiv e-prints, arXiv:2211.05134.  https://arxiv.org/abs/2211.05134
  * Demianenko et al. (2022) Demianenko, M., Malanchev, K., Samorodova, E., et al. 2022, Toward an understanding of the properties of neural network approaches for supernovae light curve approximation, arXiv, doi: 10.48550/ARXIV.2209.07542
  * D’Isanto et al. (2016) D’Isanto, A., Cavuoti, S., Brescia, M., et al. 2016, MNRAS, 457, 3119, doi: 10.1093/mnras/stw157
  * Donoso-Oliva et al. (2023) Donoso-Oliva, C., Becker, I., Protopapas, P., et al. 2023, A&A, 670, A54, doi: 10.1051/0004-6361/202243928
  * Dotter (2016) Dotter, A. 2016, The Astrophysical Journal Supplement Series, 222, 8, doi: 10.3847/0067-0049/222/1/8
  * Dressler et al. (1987) Dressler, A., Lynden-Bell, D., Burstein, D., et al. 1987, ApJ, 313, 42, doi: 10.1086/164947
  * Duev & van der Walt (2021) Duev, D. A., & van der Walt, S. J. 2021, arXiv e-prints, arXiv:2111.12142, doi: 10.48550/arXiv.2111.12142
  * Etsebeth et al. (2023) Etsebeth, V., Lochner, M., Walmsley, M., & Grespan, M. 2023, arXiv e-prints, arXiv:2309.08660, doi: 10.48550/arXiv.2309.08660
  * Fakhouri et al. (2015) Fakhouri, H. K., Boone, K., Aldering, G., et al. 2015, ApJ, 815, 58, doi: 10.1088/0004-637X/815/1/58
  * Foley & Mandel (2013) Foley, R. J., & Mandel, K. 2013, ApJ, 778, 167, doi: 10.1088/0004-637X/778/2/167
  * Foley et al. (2003) Foley, R. J., Papenkova, M. S., Swift, B. J., et al. 2003, PASP, 115, 1220, doi: 10.1086/378242
  * Foreman-Mackey et al. (2013) Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067
  * Förster et al. (2021) Förster, F., Cabrera-Vives, G., Castillo-Navarrete, E., et al. 2021, AJ, 161, 242, doi: 10.3847/1538-3881/abe9bc
  * Förster et al. (2022) Förster, F., Muñoz Arancibia, A. M., Reyes-Jainaga, I., et al. 2022, AJ, 164, 195, doi: 10.3847/1538-3881/ac912a
  * Fossey et al. (2014) Fossey, S. J., Cooke, B., Pollack, G., Wilde, M., & Wright, T. 2014, Central Bureau Electronic Telegrams, 3792, 1
  * Frederick et al. (2021) Frederick, S., Gezari, S., Graham, M. J., et al. 2021, ApJ, 920, 56, doi: 10.3847/1538-4357/ac110f
  * Fremling (2023a) Fremling, C. 2023a, Transient Name Server Discovery Report, 2023-1858, 1
  * Fremling (2023b) —. 2023b, Transient Name Server Discovery Report, 2023-1800, 1
  * Fremling (2023c) —. 2023c, Transient Name Server Discovery Report, 2023-1885, 1
  * Fremling (2023d) —. 2023d, Transient Name Server Discovery Report, 2023-2306, 1
  * Fremling et al. (2020) Fremling, C., Miller, A. A., Sharma, Y., et al. 2020, ApJ, 895, 32, doi: 10.3847/1538-4357/ab8943
  * Gagliano et al. (2023) Gagliano, A., Contardo, G., Foreman-Mackey, D., Malz, A., & Aleo, P. 2023, in American Astronomical Society Meeting Abstracts, Vol. 55, American Astronomical Society Meeting Abstracts, 103.02
  * Gagliano et al. (2021) Gagliano, A., Narayan, G., Engel, A., Carrasco Kind, M., & LSST Dark Energy Science Collaboration. 2021, ApJ, 908, 170, doi: 10.3847/1538-4357/abd02b
  * Gagliano & Villar (2023) Gagliano, A., & Villar, V. A. 2023, arXiv e-prints, arXiv:2312.16687, doi: 10.48550/arXiv.2312.16687
  * Gagliano et al. (2022) Gagliano, A., Izzo, L., Kilpatrick, C. D., et al. 2022, ApJ, 924, 55, doi: 10.3847/1538-4357/ac35ec
  * Galama et al. (1998) Galama, T. J., Vreeswijk, P. M., van Paradijs, J., et al. 1998, Nature, 395, 670, doi: 10.1038/27150
  * Gallazzi et al. (2005) Gallazzi, A., Charlot, S., Brinchmann, J., White, S. D. M., & Tremonti, C. A. 2005, MNRAS, 362, 41, doi: 10.1111/j.1365-2966.2005.09321.x
  * Gezari et al. (2013) Gezari, S., Martin, D. C., Forster, K., et al. 2013, ApJ, 766, 60, doi: 10.1088/0004-637X/766/1/60
  * Giles & Walkowicz (2019) Giles, D., & Walkowicz, L. 2019, MNRAS, 484, 834, doi: 10.1093/mnras/sty3461
  * Giles & Walkowicz (2020) Giles, D. K., & Walkowicz, L. 2020, MNRAS, 499, 524, doi: 10.1093/mnras/staa2736
  * Gomez et al. (2023a) Gomez, S., Berger, E., Blanchard, P. K., et al. 2023a, ApJ, 949, 114, doi: 10.3847/1538-4357/acc536
  * Gomez et al. (2020) —. 2020, ApJ, 904, 74, doi: 10.3847/1538-4357/abbf49
  * Gomez et al. (2023b) Gomez, S., Hiramatsu, D., Berger, E., Blanchard, P., & Gezari, S. 2023b, Transient Name Server Classification Report, 2023-2347, 1
  * Gomez et al. (2023c) Gomez, S., Hiramatsu, D., Kumar, H., Berger, E., & Blanchard, P. 2023c, Transient Name Server Classification Report, 2023-2877, 1
  * Gomez et al. (2023d) Gomez, S., Villar, V. A., Berger, E., et al. 2023d, ApJ, 949, 113, doi: 10.3847/1538-4357/acc535
  * Graur et al. (2017) Graur, O., Bianco, F. B., Modjaz, M., et al. 2017, ApJ, 837, 121, doi: 10.3847/1538-4357/aa5eb7
  * Grayling et al. (2024) Grayling, M., Thorp, S., Mandel, K. S., et al. 2024, arXiv e-prints, arXiv:2401.08755, doi: 10.48550/arXiv.2401.08755
  * Gupta et al. (2024) Gupta, R., Muthukrishna, D., & Lochner, M. 2024, arXiv e-prints, arXiv:2403.14742, doi: 10.48550/arXiv.2403.14742
  * Gutiérrez et al. (2017) Gutiérrez, C. P., Anderson, J. P., Hamuy, M., et al. 2017, ApJ, 850, 89, doi: 10.3847/1538-4357/aa8f52
  * Hachinger et al. (2009) Hachinger, S., Mazzali, P. A., Taubenberger, S., Pakmor, R., & Hillebrandt, W. 2009, MNRAS, 399, 1238, doi: 10.1111/j.1365-2966.2009.15403.x
  * Hahn & Melchior (2022) Hahn, C., & Melchior, P. 2022, ApJ, 938, 11, doi: 10.3847/1538-4357/ac7b84
  * Hambleton et al. (2022) Hambleton, K. M., Bianco, F. B., Street, R., et al. 2022, Rubin Observatory LSST Transients and Variable Stars Roadmap.  https://arxiv.org/abs/2208.04499
  * Hansson et al. (2012) Hansson, K. S. A., Lisker, T., & Grebel, E. K. 2012, MNRAS, 427, 2376, doi: 10.1111/j.1365-2966.2012.21659.x
  * Henne et al. (2017) Henne, V., Pruzhinskaya, M. V., Rosnet, P., et al. 2017, New A, 51, 43, doi: 10.1016/j.newast.2016.08.009
  * Heringer et al. (2017) Heringer, E., van Kerkwijk, M. H., Sim, S. A., & Kerzendorf, W. E. 2017, ApJ, 846, 15, doi: 10.3847/1538-4357/aa8309
  * Hložek et al. (2020) Hložek, R., Ponder, K. A., Malz, A. I., et al. 2020, arXiv e-prints, arXiv:2012.12392.  https://arxiv.org/abs/2012.12392
  * Horne (1986) Horne, K. 1986, PASP, 98, 609, doi: 10.1086/131801
  * Howell et al. (2005) Howell, D. A., Sullivan, M., Perrett, K., et al. 2005, ApJ, 634, 1190, doi: 10.1086/497119
  * Hunter (2007) Hunter, J. D. 2007, Computing in science & engineering, 9, 90
  * Irani et al. (2022) Irani, I., Prentice, S. J., Schulze, S., et al. 2022, ApJ, 927, 10, doi: 10.3847/1538-4357/ac4709
  * Ishida et al. (2021) Ishida, E. E. O., Kornilov, M. V., Malanchev, K. L., et al. 2021, A&A, 650, A195, doi: 10.1051/0004-6361/202037709
  * Itagaki (2023) Itagaki, K. 2023, Transient Name Server Discovery Report, 2023-1158, 1
  * Ivezić et al. (2019) Ivezić, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c
  * Jacobson-Galán et al. (2022a) Jacobson-Galán, W., Venkatraman, P., Margutti, R., et al. 2022a, arXiv e-prints, arXiv:2203.03785.  https://arxiv.org/abs/2203.03785
  * Jacobson-Galán et al. (2022b) Jacobson-Galán, W. V., Dessart, L., Jones, D. O., et al. 2022b, ApJ, 924, 15, doi: 10.3847/1538-4357/ac3f3a
  * Jacobson-Galán et al. (2024) Jacobson-Galán, W. V., Dessart, L., Davis, K. W., et al. 2024, arXiv e-prints, arXiv:2403.02382, doi: 10.48550/arXiv.2403.02382
  * Jarrett et al. (2011) Jarrett, T. H., Cohen, M., Masci, F., et al. 2011, ApJ, 735, 112, doi: 10.1088/0004-637X/735/2/112
  * Johansson et al. (2013) Johansson, J., Thomas, D., Pforr, J., et al. 2013, MNRAS, 435, 1680, doi: 10.1093/mnras/stt1408
  * Johnson et al. (2021) Johnson, B. D., Leja, J., Conroy, C., & Speagle, J. S. 2021, ApJS, 254, 22, doi: 10.3847/1538-4365/abef67
  * Jolliffe (2002) Jolliffe, I. 2002, Principal component analysis (New York: Springer Verlag)
  * Jones et al. (2021) Jones, D. O., Foley, R. J., Narayan, G., et al. 2021, ApJ, 908, 143, doi: 10.3847/1538-4357/abd7f5
  * Kelly et al. (2010) Kelly, P. L., Hicken, M., Burke, D. L., Mandel, K. S., & Kirshner, R. P. 2010, ApJ, 715, 743, doi: 10.1088/0004-637X/715/2/743
  * Kelsey et al. (2023) Kelsey, L., Sullivan, M., Wiseman, P., et al. 2023, MNRAS, 519, 3046, doi: 10.1093/mnras/stac3711
  * Kennamer et al. (2020) Kennamer, N., Ishida, E. E. O., Gonzalez-Gaitan, S., et al. 2020, arXiv e-prints, arXiv:2010.05941, doi: 10.48550/arXiv.2010.05941
  * Kenworthy et al. (2021) Kenworthy, W. D., Jones, D. O., Dai, M., et al. 2021, ApJ, 923, 265, doi: 10.3847/1538-4357/ac30d8
  * Kessler et al. (2009) Kessler, R., Bernstein, J. P., Cinabro, D., et al. 2009, PASP, 121, 1028, doi: 10.1086/605984
  * Khullar et al. (2022) Khullar, G., Nord, B., Ćiprijanović, A., Poh, J., & Xu, F. 2022, Machine Learning: Science and Technology, 3, 04LT04, doi: 10.1088/2632-2153/ac98f4
  * Kim et al. (2014) Kim, D.-W., Protopapas, P., Bailer-Jones, C. A. L., et al. 2014, A&A, 566, A43, doi: 10.1051/0004-6361/201323252
  * Kron (1980) Kron, R. G. 1980, ApJS, 43, 305, doi: 10.1086/190669
  * Kuncarayakti et al. (2023) Kuncarayakti, H., Sollerman, J., Izzo, L., et al. 2023, A&A, 678, A209, doi: 10.1051/0004-6361/202346526
  * Kunkel et al. (1987) Kunkel, W., Madore, B., Shelton, I., et al. 1987, IAU Circ., 4316, 1
  * Kwon et al. (2023) Kwon, K. J., Hahn, C., & Alsing, J. 2023, The Astrophysical Journal Supplement Series, 265, 23, doi: 10.3847/1538-4365/acba14
  * Lanusse et al. (2023) Lanusse, F., Parker, L., Golkar, S., et al. 2023, arXiv e-prints, arXiv:2310.03024, doi: 10.48550/arXiv.2310.03024
  * Lavrukhina & Malanchev (2023) Lavrukhina, A., & Malanchev, K. 2023, Performant feature extraction for photometric time series.  https://arxiv.org/abs/2302.10837
  * Lawrence et al. (2007) Lawrence, A., Warren, S. J., Almaini, O., et al. 2007, MNRAS, 379, 1599, doi: 10.1111/j.1365-2966.2007.12040.x
  * Leja et al. (2019a) Leja, J., Carnall, A. C., Johnson, B. D., Conroy, C., & Speagle, J. S. 2019a, The Astrophysical Journal, 876, 3, doi: 10.3847/1538-4357/ab133c
  * Leja et al. (2017a) Leja, J., Johnson, B. D., Conroy, C., van Dokkum, P. G., & Byler, N. 2017a, The Astrophysical Journal, 837, 170, doi: 10.3847/1538-4357/aa5ffe
  * Leja et al. (2017b) —. 2017b, The Astrophysical Journal, 837, 170, doi: 10.3847/1538-4357/aa5ffe
  * Leja et al. (2013) Leja, J., van Dokkum, P. G., Momcheva, I., et al. 2013, The Astrophysical Journal Letters, 778, L24, doi: 10.1088/2041-8205/778/2/L24
  * Leja et al. (2019b) Leja, J., Johnson, B. D., Conroy, C., et al. 2019b, The Astrophysical Journal, 877, 140, doi: 10.3847/1538-4357/ab1d5a
  * Leoni et al. (2022) Leoni, M., Ishida, E. E. O., Peloton, J., & Möller, A. 2022, A&A, 663, A13, doi: 10.1051/0004-6361/202142715
  * Li et al. (2011) Li, W., Leaman, J., Chornock, R., et al. 2011, MNRAS, 412, 1441, doi: 10.1111/j.1365-2966.2011.18160.x
  * Li et al. (2022) Li, X., Ragosta, F., Clarkson, W. I., & Bianco, F. B. 2022, ApJS, 258, 2, doi: 10.3847/1538-4365/ac3bca
  * Liang et al. (2023) Liang, Y., Melchior, P., Lu, S., Goulding, A., & Ward, C. 2023, AJ, 166, 75, doi: 10.3847/1538-3881/ace100
  * Lidman et al. (2023) Lidman, C., Martin, B., Brout, D., et al. 2023, Transient Name Server Classification Report, 2023-1786, 1
  * Liu et al. (2012) Liu, F. T., Ting, K. M., & Zhou, Z.-H. 2012, ACM Trans. Knowl. Discov. Data, 6, doi: 10.1145/2133360.2133363
  * Liu & Modjaz (2014) Liu, Y., & Modjaz, M. 2014, SuperNova IDentification spectral templates of 70 stripped-envelope core-collapse supernovae, arXiv, doi: 10.48550/ARXIV.1405.1437
  * Liu et al. (2016) Liu, Y.-Q., Modjaz, M., Bianco, F. B., & Graur, O. 2016, ApJ, 827, 90, doi: 10.3847/0004-637X/827/2/90
  * Lochner & Bassett (2021) Lochner, M., & Bassett, B. A. 2021, Astronomy and Computing, 36, 100481, doi: 10.1016/j.ascom.2021.100481
  * Lochner et al. (2016) Lochner, M., McEwen, J. D., Peiris, H. V., Lahav, O., & Winter, M. K. 2016, ApJS, 225, 31, doi: 10.3847/0067-0049/225/2/31
  * Lokken et al. (2023) Lokken, M., Gagliano, A., Narayan, G., et al. 2023, MNRAS, 520, 2887, doi: 10.1093/mnras/stad302
  * Lyman et al. (2018) Lyman, J. D., Taddia, F., Stritzinger, M. D., et al. 2018, MNRAS, 473, 1359, doi: 10.1093/mnras/stx2414
  * Magnier et al. (2016) Magnier, E. A., Sweeney, W. E., Chambers, K. C., et al. 2016, arXiv: Instrumentation and Methods for Astrophysics
  * Mahabal et al. (2017) Mahabal, A., Sheth, K., Gieseke, F., et al. 2017, arXiv e-prints, arXiv:1709.06257, doi: 10.48550/arXiv.1709.06257
  * Malanchev (2021) Malanchev, K. 2021, light-curve: Light curve analysis toolbox, Astrophysics Source Code Library, record ascl:2107.001.  http://ascl.net/2107.001
  * Malanchev et al. (2021) Malanchev, K. L., Pruzhinskaya, M. V., Korolev, V. S., et al. 2021, MNRAS, 502, 5147, doi: 10.1093/mnras/stab316
  * Margutti et al. (2019) Margutti, R., Metzger, B. D., Chornock, R., et al. 2019, ApJ, 872, 18, doi: 10.3847/1538-4357/aafa01
  * Martínez-Galarza et al. (2021) Martínez-Galarza, J. R., Bianco, F. B., Crake, D., et al. 2021, MNRAS, 508, 5734, doi: 10.1093/mnras/stab2588
  * Matheson et al. (2021) Matheson, T., Stubens, C., Wolf, N., et al. 2021, AJ, 161, 107, doi: 10.3847/1538-3881/abd703
  * McInnes et al. (2018) McInnes, L., Healy, J., Saul, N., & Großberger, L. 2018, Journal of Open Source Software, 3, 861, doi: 10.21105/joss.00861
  * McIntosh et al. (2014) McIntosh, M., Bieryla, A., Newton, E. R., et al. 2014, in American Astronomical Society Meeting Abstracts, Vol. 224, American Astronomical Society Meeting Abstracts #224, 121.17
  * Modjaz et al. (2016) Modjaz, M., Liu, Y. Q., Bianco, F. B., & Graur, O. 2016, ApJ, 832, 108, doi: 10.3847/0004-637X/832/2/108
  * Modjaz et al. (2014) Modjaz, M., Blondin, S., Kirshner, R. P., et al. 2014, AJ, 147, 99, doi: 10.1088/0004-6256/147/5/99
  * Möller & de Boissière (2020) Möller, A., & de Boissière, T. 2020, MNRAS, 491, 4277, doi: 10.1093/mnras/stz3312
  * Möller et al. (2021) Möller, A., Peloton, J., Ishida, E. E. O., et al. 2021, MNRAS, 501, 3272, doi: 10.1093/mnras/staa3602
  * Moré (1978) Moré, J. J. 1978, in Lecture Notes in Mathematics, Berlin Springer Verlag, Vol. 630, 105–116, doi: 10.1007/BFb0067700
  * Moreno-Cartagena et al. (2023) Moreno-Cartagena, D., Cabrera-Vives, G., Protopapas, P., et al. 2023, arXiv e-prints, arXiv:2308.06404, doi: 10.48550/arXiv.2308.06404
  * Munoz-Arancibia et al. (2023a) Munoz-Arancibia, A., Hernandez-Garcia, L., Bauer, F. E., et al. 2023a, Transient Name Server AstroNote, 138, 1
  * Munoz-Arancibia et al. (2023b) Munoz-Arancibia, A., Pignata, G., Bauer, F. E., et al. 2023b, Transient Name Server Discovery Report, 2023-2237, 1
  * Munoz-Arancibia et al. (2023c) Munoz-Arancibia, A., Pignata, G., Forster, F., et al. 2023c, Transient Name Server Discovery Report, 2023-1714, 1
  * Munoz-Arancibia et al. (2023d) Munoz-Arancibia, A., Pignata, G., Bauer, F. E., et al. 2023d, Transient Name Server Discovery Report, 2023-2237, 1
  * Muthukrishna et al. (2022) Muthukrishna, D., Mandel, K. S., Lochner, M., Webb, S., & Narayan, G. 2022, MNRAS, 517, 393, doi: 10.1093/mnras/stac2582
  * Muthukrishna et al. (2019) Muthukrishna, D., Narayan, G., Mandel, K. S., Biswas, R., & Hložek, R. 2019, PASP, 131, 118002, doi: 10.1088/1538-3873/ab1609
  * Nair & Abraham (2010) Nair, P. B., & Abraham, R. G. 2010, ApJS, 186, 427, doi: 10.1088/0067-0049/186/2/427
  * Narayan & ELAsTiCC Team (2023) Narayan, G., & ELAsTiCC Team. 2023, in American Astronomical Society Meeting Abstracts, Vol. 55, American Astronomical Society Meeting Abstracts, 117.01
  * NOIRLab CSDC Team (2023) NOIRLab CSDC Team. 2023, ANTARES Client, https://gitlab.com/nsf-noirlab/csdc/antares/client/
  * Nordin et al. (2019) Nordin, J., Brinnel, V., van Santen, J., et al. 2019, A&A, 631, A147, doi: 10.1051/0004-6361/201935634
  * Nugent et al. (2023) Nugent, A. E., Polin, A. E., & Nugent, P. E. 2023, arXiv e-prints, arXiv:2304.10601, doi: 10.48550/arXiv.2304.10601
  * Nugent et al. (2020) Nugent, A. E., Fong, W., Dong, Y., et al. 2020, The Astrophysical Journal, 904, 52, doi: 10.3847/1538-4357/abc24a
  * Nun et al. (2014) Nun, I., Pichara, K., Protopapas, P., & Kim, D.-W. 2014, ApJ, 793, 23, doi: 10.1088/0004-637X/793/1/23
  * Nun et al. (2016) Nun, I., Protopapas, P., Sim, B., & Chen, W. 2016, AJ, 152, 71, doi: 10.3847/0004-6256/152/3/71
  * Nyholm et al. (2020) Nyholm, A., Sollerman, J., Tartaglia, L., et al. 2020, A&A, 637, A73, doi: 10.1051/0004-6361/201936097
  * pandas development team (2020) pandas development team, T. 2020, pandas-dev/pandas: Pandas, latest, Zenodo, doi: 10.5281/zenodo.3509134
  * Papamakarios & Murray (2016) Papamakarios, G., & Murray, I. 2016, arXiv e-prints, arXiv:1605.06376, doi: 10.48550/arXiv.1605.06376
  * Pasquet et al. (2019) Pasquet, J., Pasquet, J., Chaumont, M., & Fouchez, D. 2019, A&A, 627, A21, doi: 10.1051/0004-6361/201834473
  * Patterson et al. (2019) Patterson, M. T., Bellm, E. C., Rusholme, B., et al. 2019, PASP, 131, 018001, doi: 10.1088/1538-3873/aae904
  * Pedregosa et al. (2011) Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of Machine Learning Research, 12, 2825
  * Perez-Carrasco et al. (2023) Perez-Carrasco, M., Cabrera-Vives, G., Hernandez-García, L., et al. 2023, AJ, 166, 151, doi: 10.3847/1538-3881/ace0c1
  * Perley et al. (2021) Perley, D. A., Yan, L., Lunnan, R., et al. 2021, Transient Name Server AstroNote, 198, 1
  * Perley et al. (2022) Perley, D. A., Sollerman, J., Schulze, S., et al. 2022, ApJ, 927, 180, doi: 10.3847/1538-4357/ac478e
  * Perlmutter et al. (1997) Perlmutter, S., Gabi, S., Goldhaber, G., et al. 1997, ApJ, 483, 565, doi: 10.1086/304265
  * Pierel et al. (2018) Pierel, J. D. R., Rodney, S., Avelino, A., et al. 2018, PASP, 130, 114504, doi: 10.1088/1538-3873/aadb7a
  * Pierel et al. (2023) Pierel, J. D. R., Arendse, N., Ertl, S., et al. 2023, ApJ, 948, 115, doi: 10.3847/1538-4357/acc7a6
  * Pimentel et al. (2023) Pimentel, Ó., Estévez, P. A., & Förster, F. 2023, AJ, 165, 18, doi: 10.3847/1538-3881/ac9ab4
  * Popovic et al. (2021) Popovic, B., Brout, D., Kessler, R., Scolnic, D., & Lu, L. 2021, ApJ, 913, 49, doi: 10.3847/1538-4357/abf14f
  * Price-Whelan et al. (2018) Price-Whelan, A. M., Sipőcz, B. M., Günther, H. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f
  * Pruzhinskaya et al. (2019) Pruzhinskaya, M. V., Malanchev, K. L., Kornilov, M. V., et al. 2019, MNRAS, 489, 3591, doi: 10.1093/mnras/stz2362
  * Qin et al. (2022) Qin, Y.-J., Zabludoff, A., Kisley, M., et al. 2022, ApJS, 259, 13, doi: 10.3847/1538-4365/ac2fa1
  * Qu et al. (2021) Qu, H., Sako, M., Möller, A., & Doux, C. 2021, AJ, 162, 67, doi: 10.3847/1538-3881/ac0824
  * Quimby et al. (2010) Quimby, R. M., Kulkarni, S., Ofek, E., et al. 2010, The Astronomer’s Telegram, 2740, 1
  * Rasmussen & Williams (2005) Rasmussen, C. E., & Williams, C. K. I. 2005, Gaussian Processes for Machine Learning (The MIT Press), doi: 10.7551/mitpress/3206.001.0001
  * Rebbapragada et al. (2009) Rebbapragada, U., Protopapas, P., Brodley, C. E., & Alcock, C. 2009, in Astronomical Society of the Pacific Conference Series, Vol. 411, Astronomical Data Analysis Software and Systems XVIII, ed. D. A. Bohlender, D. Durand, & P. Dowler, 264
  * Reyes & Estévez (2020) Reyes, E., & Estévez, P. A. 2020, arXiv e-prints, arXiv:2005.07779, doi: 10.48550/arXiv.2005.07779
  * Reyes-Jainaga et al. (2023) Reyes-Jainaga, I., Förster, F., Muñoz Arancibia, A. M., et al. 2023, ApJ, 952, L43, doi: 10.3847/2041-8213/ace77e
  * Richardson et al. (2014) Richardson, D., Jenkins, Robert L., I., Wright, J., & Maddox, L. 2014, AJ, 147, 118, doi: 10.1088/0004-6256/147/5/118
  * Russeil et al. (2023) Russeil, E., et al. 2023.  https://arxiv.org/abs/2310.02916
  * Sánchez et al. (2017) Sánchez, P., Lira, P., Cartier, R., et al. 2017, ApJ, 849, 110, doi: 10.3847/1538-4357/aa9188
  * Sánchez-Sáez et al. (2021) Sánchez-Sáez, P., Reyes, I., Valenzuela, C., et al. 2021, AJ, 161, 141, doi: 10.3847/1538-3881/abd5c1
  * Schmidt et al. (1998) Schmidt, B. P., Suntzeff, N. B., Phillips, M. M., et al. 1998, ApJ, 507, 46, doi: 10.1086/306308
  * Schulze et al. (2021) Schulze, S., Yaron, O., Sollerman, J., et al. 2021, The Astrophysical Journal Supplement Series, 255, 29, doi: 10.3847/1538-4365/abff5e
  * Sharma et al. (2023) Sharma, Y., Sollerman, J., Fremling, C., et al. 2023, ApJ, 948, 52, doi: 10.3847/1538-4357/acbc16
  * Shi & Horvath (2006) Shi, T., & Horvath, S. 2006, Journal of Computational and Graphical Statistics, 15, 118, doi: 10.1198/106186006X94072
  * Siebert et al. (2020) Siebert, M. R., Dimitriadis, G., Polin, A., & Foley, R. J. 2020, ApJ, 900, L27, doi: 10.3847/2041-8213/abae6e
  * Silverman et al. (2012) Silverman, J. M., Foley, R. J., Filippenko, A. V., et al. 2012, MNRAS, 425, 1789, doi: 10.1111/j.1365-2966.2012.21270.x
  * Sit et al. (2022) Sit, T., Kasliwal, M., Tzanidakis, A., De, K., & ZTF Collaboration. 2022, in American Astronomical Society Meeting Abstracts, Vol. 54, American Astronomical Society Meeting Abstracts, 131.03
  * Skrutskie et al. (2006) Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163, doi: 10.1086/498708
  * Solarz et al. (2017) Solarz, A., Bilicki, M., Gromadzki, M., et al. 2017, A&A, 606, A39, doi: 10.1051/0004-6361/201730968
  * Soraisam et al. (2022) Soraisam, M., Matheson, T., Lee, C.-H., et al. 2022, ApJ, 926, L11, doi: 10.3847/2041-8213/ac4e99
  * Soraisam et al. (2020) Soraisam, M. D., Saha, A., Matheson, T., et al. 2020, ApJ, 892, 112, doi: 10.3847/1538-4357/ab7b61
  * Stein et al. (2021) Stein, G., Harrington, P., Blaum, J., Medan, T., & Lukic, Z. 2021, arXiv e-prints, arXiv:2110.13151, doi: 10.48550/arXiv.2110.13151
  * Stern et al. (2012) Stern, D., Assef, R. J., Benford, D. J., et al. 2012, ApJ, 753, 30, doi: 10.1088/0004-637X/753/1/30
  * Stetson (1996) Stetson, P. B. 1996, PASP, 108, 851, doi: 10.1086/133808
  * Storey-Fisher et al. (2021) Storey-Fisher, K., Huertas-Company, M., Ramachandra, N., et al. 2021, MNRAS, 508, 2946, doi: 10.1093/mnras/stab2589
  * Strateva et al. (2001) Strateva, I., Ivezić, Ž., Knapp, G. R., et al. 2001, AJ, 122, 1861, doi: 10.1086/323301
  * Sullivan et al. (2010) Sullivan, M., Conley, A., Howell, D. A., et al. 2010, MNRAS, 406, 782, doi: 10.1111/j.1365-2966.2010.16731.x
  * Tachibana & Miller (2018) Tachibana, Y., & Miller, A. A. 2018, PASP, 130, 128001, doi: 10.1088/1538-3873/aae3d9
  * Tonry et al. (2023) Tonry, J., Denneau, L., Weiland, H., et al. 2023, Transient Name Server Discovery Report, 2023-1398, 1
  * Tonry et al. (2012) Tonry, J. L., Stubbs, C. W., Lykke, K. R., et al. 2012, ApJ, 750, 99, doi: 10.1088/0004-637X/750/2/99
  * Trakhtenbrot et al. (2019) Trakhtenbrot, B., Arcavi, I., Ricci, C., et al. 2019, Nature Astronomy, 3, 242, doi: 10.1038/s41550-018-0661-3
  * Tucker (2021) Tucker, M. A. 2021, Transient Name Server Classification Report, 2021-2720, 1
  * van der Maaten & Hinton (2008) van der Maaten, L., & Hinton, G. 2008, Journal of Machine Learning Research, 9, 2579.  http://jmlr.org/papers/v9/vandermaaten08a.html
  * van Roestel et al. (2021) van Roestel, J., Duev, D. A., Mahabal, A. A., et al. 2021, AJ, 161, 267, doi: 10.3847/1538-3881/abe853
  * van Velzen et al. (2019) van Velzen, S., Gezari, S., Cenko, S. B., et al. 2019, ApJ, 872, 198, doi: 10.3847/1538-4357/aafe0c
  * Villar et al. (2021) Villar, V. A., Cranmer, M., Berger, E., et al. 2021, The Astrophysical Journal Supplement Series, 255, 24, doi: 10.3847/1538-4365/ac0893
  * Villar et al. (2019) Villar, V. A., Berger, E., Miller, G., et al. 2019, ApJ, 884, 83, doi: 10.3847/1538-4357/ab418c
  * Villar et al. (2020) Villar, V. A., Hosseinzadeh, G., Berger, E., et al. 2020, ApJ, 905, 94, doi: 10.3847/1538-4357/abc6fd
  * Vincenzi et al. (2019) Vincenzi, M., Sullivan, M., Firth, R. E., et al. 2019, MNRAS, 489, 5802, doi: 10.1093/mnras/stz2448
  * Walt et al. (2011) Walt, S. v. d., Colbert, S. C., & Varoquaux, G. 2011, Computing in Science & Engineering, 13, 22
  * Wang et al. (2023) Wang, B., Leja, J., Villar, V. A., & Speagle, J. S. 2023, ApJ, 952, L10, doi: 10.3847/2041-8213/ace361
  * Webb et al. (2020) Webb, S., Lochner, M., Muthukrishna, D., et al. 2020, MNRAS, 498, 3077, doi: 10.1093/mnras/staa2395
  * Williamson et al. (2019) Williamson, M., Modjaz, M., & Bianco, F. B. 2019, ApJ, 880, L22, doi: 10.3847/2041-8213/ab2edb
  * Wright et al. (2010) Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868, doi: 10.1088/0004-6256/140/6/1868
  * Yao (2023) Yao, Y. 2023, Transient Name Server Classification Report, 2023-2004, 1

##  Appendix A Light Curve Features

Our light curve features are extracted with the lc_feature_extractor filter in
ANTARES using the light-curve package. We extract the same features for $r$
and $g$ band, comprising 62 total features (31 $r$ band, 31 $g$ band). A brief
description of each feature is as follows:

  * •

feature_amplitude_magn: Half amplitude of magnitude (Malanchev et al., 2021).

  * •

feature_anderson_darling_normal_magn: Unbiased Anderson–Darling normality test
statistic for magnitude (Malanchev et al., 2021).

  * •

feature_beyond_1_std_magn: Fraction of observations beyond $n=1\sigma_{m}$
from the mean magnitude $\langle m\rangle$ (D’Isanto et al., 2016).

  * •

feature_beyond_2_std_magn: Fraction of observations beyond $n=2\sigma_{m}$
from the mean magnitude $\langle m\rangle$ (D’Isanto et al., 2016).

  * •

feature_cusum_magn: A range of cumulative sums dependent on the number of
observations, mean magnitude, and magnitude standard deviation (Kim et al.,
2014).

  * •

feature_inter_percentile_range_2_magn: Inter-percentile range for $p=0.02$,
where $p$ is the pth quantile of the magnitude distribution (Malanchev et al.,
2021).

  * •

feature_inter_percentile_range_10_magn: Inter-percentile range for $p=0.10$,
where $p$ is the pth quantile of the magnitude distribution. A special case of
the interpercentile range known as the interdecile range (Malanchev et al.,
2021).

  * •

feature_inter_percentile_range_25_magn: Inter-percentile range for $p=0.25$,
where $p$ is the pth quantile of the magnitude distribution. A special case of
the interpercetile range known as the interquartile range (Malanchev et al.,
2021).

  * •

feature_kurtosis_magn: Excess kurtosis of magnitude (Malanchev et al., 2021).

  * •

feature_linear_fit_slope_magn: The slope of the light curve in the least
squares fit of the linear stochastic model with Gaussian noise described by
observation errors {$\delta_{i}$} (Malanchev et al., 2021).

  * •

feature_linear_fit_slope_sigma_magn: The error of the slope of the light curve
in the least squares fit of the linear stochastic model with Gaussian noise
described by observation errors {$\delta_{i}$} (Malanchev et al., 2021).

  * •

feature_magnitude_percentage_ratio_40_5_magn: The magnitude 40 to 5 ratio,
written in terms of the magnitude distribution quantile function $Q$.
(D’Isanto et al., 2016).

  * •

feature_magnitude_percentage_ratio_20_5_magn: The magnitude 20 to 5 ratio,
written in terms of the magnitude distribution quantile function $Q$.
(D’Isanto et al., 2016).

  * •

feature_mean_magn: The non-weighted mean magnitude.

  * •

feature_median_absolute_deviation_magn: The median of the absolute value of
the difference between magnitude and its median (D’Isanto et al., 2016).

  * •

feature_percent_amplitude_magn: The maximum deviation of magnitude from its
median (D’Isanto et al., 2016).

  * •

feature_median_buffer_range_percentage_10_magn: The fraction of observations
inside Median($m$) $\pm 10\times$ (max($m$)-min($m$))/2 interval (D’Isanto et
al., 2016).

  * •

feature_median_buffer_range_percentage_20_magn: The fraction of observations
inside Median($m$) $\pm 20\times$ (max($m$)-min($m$))/2 interval (D’Isanto et
al., 2016).

  * •

feature_percent_difference_magnitude_percentile_5_magn: Ratio of $p$=5th
inter-percentile range to the median (Malanchev et al., 2021).

  * •

feature_percent_difference_magnitude_percentile_10_magn: Ratio of $p$=10th
inter-percentile range to the median (Malanchev et al., 2021).

  * •

feature_skew_magn: Skewness of magnitude, $G_{1}$ (Malanchev et al., 2021).

  * •

feature_standard_deviation_magn: Standard deviation of magnitude, $\sigma_{m}$
(Malanchev et al., 2021).

  * •

feature_stetson_k_magn: Stetson $K$ coefficient described light curve shape of
magnitude (Stetson, 1996).

  * •

feature_weighted_mean_magn: Weighted mean magnitude (Malanchev et al., 2021).

  * •

feature_anderson_darling_normal_flux: Unbiased Anderson–Darling normality test
statistic for flux (Malanchev et al., 2021).

  * •

feature_cusum_flux: A range of cumulative sums dependent on the number of
observations, mean flux, and flux standard deviation (Kim et al., 2014).

  * •

feature_excess_variance_flux: Measure of the flux variability amplitude
(Sánchez et al., 2017).

  * •

feature_kurtosis_flux: Excess kurtosis of flux (Malanchev et al., 2021).

  * •

feature_mean_variance_flux: Standard deviation of flux to mean flux ratio
(Malanchev et al., 2021).

  * •

feature_skew_flux: Skewness of flux (Malanchev et al., 2021).

  * •

feature_stetson_k_flux: Stetson $K$ coefficient described light curve shape of
flux (Stetson, 1996).

The full documentation, including equations, can be found here:
https://docs.rs/light-curve-
feature/0.2.2/light_curve_feature/features/index.html.

##  Appendix B Host Galaxy Features

Our host galaxy features and a brief description are as follows737373Those
from PS1 can be found here:
https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables:

  * •

gmomentXX: Second moment $M_{xx}$ for $g$ filter stack detection.

  * •

gmomentXY: Second moment $M_{xy}$ for $g$ filter stack detection.

  * •

gmomentYY: Second moment $M_{yy}$ for $g$ filter stack detection.

  * •

gmomentR1: First radial moment for $g$ filter stack detection.

  * •

gmomentRH: Half radial moment ($r^{0.5}$ weighting) for $g$ filter stack
detection.

  * •

gPSFFlux: PSF flux from $g$ filter stack detection.

  * •

gApFlux: Aperture flux from $g$ filter stack detection.

  * •

gKronFlux: Kron (1980) flux from $g$ filter stack detection.

  * •

gKronRad: Kron (1980) radius from $g$ filter stack detection.

  * •

gExtNSigma: An extendedness measure for the $g$ filter stack detection based
on the deviation between PSF and Kron (1980) magnitudes, normalized by the PSF
magnitude uncertainty.

  * •

rmomentXX: Second moment $M_{xx}$ for $r$ filter stack detection.

  * •

rmomentXY: Second moment $M_{xy}$ for $r$ filter stack detection.

  * •

rmomentYY: Second moment $M_{yy}$ for $r$ filter stack detection.

  * •

rmomentR1: First radial moment for $r$ filter stack detection.

  * •

rmomentRH: Half radial moment ($r^{0.5}$ weighting) for $r$ filter stack
detection.

  * •

rPSFFlux: PSF flux from $r$ filter stack detection.

  * •

rApFlux: Aperture flux from $r$ filter stack detection.

  * •

rKronFlux: Kron (1980) flux from $r$ filter stack detection.

  * •

rKronRad: Kron (1980) radius from $r$ filter stack detection.

  * •

rExtNSigma: An extendedness measure for the $r$ filter stack detection based
on the deviation between PSF and Kron (1980) magnitudes, normalized by the PSF
magnitude uncertainty.

  * •

imomentXX: Second moment $M_{xx}$ for $i$ filter stack detection.

  * •

imomentXY: Second moment $M_{xy}$ for $i$ filter stack detection.

  * •

imomentYY: Second moment $M_{yy}$ for $i$ filter stack detection.

  * •

imomentR1: First radial moment for $i$ filter stack detection.

  * •

imomentRH: Half radial moment ($r^{0.5}$ weighting) for $i$ filter stack
detection.

  * •

iPSFFlux: PSF flux from $i$ filter stack detection.

  * •

iApFlux: Aperture flux from $i$ filter stack detection.

  * •

iKronFlux: Kron (1980) flux from $i$ filter stack detection.

  * •

iKronRad: Kron (1980) radius from $i$ filter stack detection.

  * •

iExtNSigma: An extendedness measure for the $i$ filter stack detection based
on the deviation between PSF and Kron (1980) magnitudes, normalized by the PSF
magnitude uncertainty.

  * •

zmomentXX: Second moment $M_{xx}$ for $z$ filter stack detection.

  * •

zmomentXY: Second moment $M_{xy}$ for $z$ filter stack detection.

  * •

zmomentYY: Second moment $M_{yy}$ for $z$ filter stack detection.

  * •

zmomentR1: First radial moment for $z$ filter stack detection.

  * •

zmomentRH: Half radial moment ($r^{0.5}$ weighting) for $z$ filter stack
detection.

  * •

zPSFFlux: PSF flux from $z$ filter stack detection.

  * •

zApFlux: Aperture flux from $z$ filter stack detection.

  * •

zKronFlux: Kron (1980) flux from $z$ filter stack detection.

  * •

zKronRad: Kron (1980) radius from $z$ filter stack detection.

  * •

zExtNSigma: An extendedness measure for the $z$ filter stack detection based
on the deviation between PSF and Kron (1980) magnitudes, normalized by the PSF
magnitude uncertainty.

  * •

ymomentXX: Second moment $M_{xx}$ for $y$ filter stack detection.

  * •

ymomentXY: Second moment $M_{xy}$ for $y$ filter stack detection.

  * •

ymomentYY: Second moment $M_{yy}$ for $y$ filter stack detection.

  * •

ymomentR1: First radial moment for $y$ filter stack detection.

  * •

ymomentRH: Half radial moment ($r^{0.5}$ weighting) for $y$ filter stack
detection.

  * •

yPSFFlux: PSF flux from $y$ filter stack detection.

  * •

yApFlux: Aperture flux from $y$ filter stack detection.

  * •

yKronFlux: Kron (1980) flux from $y$ filter stack detection.

  * •

yKronRad: Kron (1980) radius from $y$ filter stack detection.

  * •

yExtNSigma: An extendedness measure for the $y$ filter stack detection based
on the deviation between PSF and Kron (1980) magnitudes, normalized by the PSF
magnitude uncertainty.

  * •

i-z: Aperture magnitude for the $i$ filter stack detection minus aperture
magnitude for the $z$ filter stack detection. (iApMag - zApMag).

  * •

gApMag_gKronMag: Aperture magnitude minus the Kron (1980) magnitude for the
$g$ filter stack detection.

  * •

rApMag_rKronMag: Aperture magnitude minus the Kron (1980) magnitude for the
$r$ filter stack detection.

  * •

iApMag_iKronMag: Aperture magnitude minus the Kron (1980) magnitude for the
$i$ filter stack detection.

  * •

zApMag_zKronMag: Aperture magnitude minus the Kron (1980) magnitude for the
$z$ filter stack detection.

  * •

yApMag_yKronMag: Aperture magnitude minus the Kron (1980) magnitude for the
$y$ filter stack detection.

  * •

4DCD: A 4-dimensional color distance in $g-r$, $r-i$, $i-z$, and $z-y$ from
the PS1 stellar locus, the path traced by stars in color-color space (Tonry et
al., 2012).

  * •

dist/DLR: Transient-host separation (arcsec) normalized by the directional
light radius.

##  Appendix C Feature Correlations

With a large 120 dimensional feature-space, we are likely to have dependent or
correlated features. In this subsection, we argue that this is an intentional
choice that proffers quantitative benefits rather than suffers from poor
design.

We present a Spearman rank-correlation matrix of our training set in Figure
C.1. Note that we choose Spearman’s rank correlation in favor of Pearson
correlation because the former can quantifiably describe linear and non-linear
relationships (those which can be described with a monotonic function),
whereas the latter describes only linear relationships.

For light curve features, we use the same set of 31 for both ZTF-$r$, ZTF-$g$
bands. Broadly, the features that measure amplitude or amplitude-adjacent
properties are correlated across intra-passband and inter-passband. However,
those that are correlated intra-passband are measuring different degrees of
amplitude or amplitude variation during a supernova’s evolution (e.g., those
measuring the 2nd, 10th, and 25th quantile of the magnitude distribution).
Meanwhile, those that are inter-passband correlated provide utility because of
the color information they capture. Moreover, inter-passband correlation
vectors are less correlated than their intra-passband counterparts.

For host galaxy features, it is not only known that some features are highly
correlated with each other; in fact, it has been leveraged in its use in
empirical relationships such as the Fundamental Plane for ellipticals
(Dressler et al., 1987), the color-magnitude relation (Bell et al., 2004), and
more recently in cosmological analyses with SNe Ia in regard to host-galaxy
stellar mass (Popovic et al., 2021; Kelsey et al., 2023). See, e.g., Sullivan
et al. (2010); Kelly et al. (2010); Brout & Scolnic (2021); Grayling et al.
(2024) for additional discussion regarding the relationship between SN and
galaxy properties.

We find that with the exception of the second moment {g,r,i,z,y}momentXY, all
of our intra-passband host features are positively correlated, but to varying
degrees. However, the same features inter-passband tend to exhibit strong
correlations (e.g., {g,r,i,z,y}momentR1). Easier to see is the appearance of
blocks within the full correlation matrix, which are the strong positive
correlations between brightness and radial moments of host galaxies in each
band. These findings are largely consistent with those found in Gagliano et
al. (2021) (see further details in their Section 4).

From a broader viewpoint, the correlation between light curve and host galaxy
features is poor at best, with the largest positive Spearman correlation of
$\rho\sim+0.30$ existing between the slope of the light curve in the least
squares fit of the linear stochastic model feature_linear_fit_slope_magn_g,r
and nearly all host galaxy features (with the exception of the second moment
{g,r,i,z,y}momentXY, aperture magnitude $i-z$ color difference i-z, and
normalized directional light radius dist/DLR). The most negative Spearman
correlation of $\rho\sim-0.30$ relates the majority of host galaxy features to
the non-weighted mean magnitude feature_mean_magn_{g,r} and the weighted mean
magnitude feature_weighted_mean_magn_{g,r}. Perhaps the most interesting
feature is the normalized directional light radius dist/DLR, which is not
correlated with any other feature except for itself, and is the most
discriminating host galaxy feature used in our RFC model (see Figure 6).

Typically, the large number of features used in this work could fall prey to
the _curse of dimensionality_ and would prohibit a brute-force
($\mathcal{O}(n)$ time) search across SNe. We circumvent this by 1) using a
RFC for our AD model, which inherently performs feature selection when
building each decision tree in the forest (where a random subset of
max_features features at each split is used747474This process effectively
reduces the dimensionality because not all features are used for each tree,
helping to avoid overfitting, and is robust to more irrelevant features.), and
2) using dimensionality reduction via PCA for our ANN similarity search with
ANNOY (which itself is natively $\mathcal{O}(log(n))$ time) to efficiently
search the entire feature space.

Figure C.1:  The Spearman rank correlation matrix for the LAISS databank of
extracted ZTF-$g$, ZTF-$r$ light curve and PS1-$grizy$ host galaxy features
(see Appendix B), with red corresponding to positively correlated features and
blue corresponding to negatively correlated features. Broadly, the light curve
features that measure amplitude or amplitude-adjacent properties are
correlated, more strongly intra-passband versus inter-passband. There exists
strong positive correlations between brightness and radial moments of host
galaxies in each band, forming a repeating block structure. From a high-level,
the correlation between light curve features to host galaxy features is poor.
Overall, these features are chosen to maximize color and capture related but
different information of the supernova light curve and host galaxy, and to
reduce the impact of dependent or irrelevant features.

##  Appendix D Methods for PROSPECTOR Host-galaxy SED Modeling

We correct photometry by subtracting an extinction correction using the
corrected SFD map (Chiang, 2023), provided through the
DUSTMAP757575https://github.com/gregreen/dustmaps package. To make these
corrections we assume an average $R_{v}$ = 3.1. We set a lower bound on the
photometric error in any filter as 15% to allow for systematic error— which we
note is a larger bound than used in Leja et al. (2019b). We felt it was
necessary to increase the lower bound of uncertainty given our inexact matched
aperture sizes.

The final vector of photometric observations and uncertainties are used to fit
our stellar population synthesis model. Prospector calls the PYTHON-
FSPS767676https://github.com/dfm/python-fsps library to perform the fits
(Conroy et al., 2009; Conroy & Gunn, 2010). The library uses the MIST
isochrones to build the models (Choi et al., 2016; Dotter, 2016). We use a
Chabrier initial mass function (Chabrier, 2003) with a delayed-tau stellar
formation history ($t$ $\times$ $\exp{-t/\tau}$). We set the redshift to the
value of the known transient’s redshift. We model dust attenuation using the
Milky Way Dust extinction law (Cardelli et al., 1989), and additionally
attenuate young stars with an extra static dust contribution of 0.5 (for
additional details see Conroy et al. 2009). We model the contribution from
Nebular Emission but fix the gas phase metallicity and the gas ionization
parameters (Byler et al., 2017). We use the Gallazzi et al. (2005) mass-
metallicity relationship as a prior on the stellar metallicity, following its
use in Nugent et al. (2023). The final model is a function of 5 free
parameters. Our priors are described in Table D.1.

Table D.1: PROSPECTOR Parameter Priors. We describe our model, initial parameter estimates, and priors below. We broadly follow the suggestions of model complexity following the procedure of Nugent et al. (2023). Below, $\mathcal{U}(a,b)$ is the Top Hat distribution on the interval $(a,b)$. $\delta$ means that the parameter is always fixed. Parameter | Prior distribution | Notes
---|---|---
mass | $\log{\mathcal{U}(1e6,1e13)}$ |  PROSPECTOR samples masses formed; we report stellar masses
logzsol | $\mathcal{U}(-2,0.4)$ | Limited by availability of MIST Isochrones777777The MIST models developed for ancient, metal-poor populations from -4.0$\leavevmode\nobreak\ \leq$[Z/H]$\textless$ -2.0 from Choi et al. (2016) are not included in FSPS. (Choi et al., 2016)
dust2 | $\mathcal{U}(0.0,4.0)$ | —
tage | $\mathcal{U}(0.0,13.8)$ | Age of stellar population
tau | $\log{\mathcal{U}(0.01,10)}$ | —
IMF | $\delta(1)$ | Chabrier IMF
Dust Type | $\delta(1)$ | Milky Way Extinction Law
SFH | $\delta(4)$ | Delayed-Tau

##  Appendix E Spectra

Here we describe our observations and data reduction for the 9 spectra whose
classifications are used in Table 4.

TDE 2023kvy was discovered on 2023 June 16 23.59 by Tonry et al. (2023) and
classified as a TDE on 2023 September 9.97 (Gomez et al., 2023b) as part of
the FLEET program (Gomez et al., 2020) from a SOAR / Goodman spectrum acquired
on 2023 August 27. LAISS correctly tagged TDE 2023kvy as an anomaly on 2023
August 26. This spectrum was acquired in coordination with FLEET, whose
program identified this object independently.

SN 2023sds was discovered on 2023 September 11.38 by Munoz-Arancibia et al.
(2023b) and classified by us as an SN II on 2024 March 24.10 from a Lick-3m /
KAST spectrum acquired by us on 2023 October 22.19. LAISS correctly tagged SN
2023sds as an anomaly on 2023 September 24.

SN 2023nof was discovered on 2023 July 23.34 by Munoz-Arancibia et al. (2023c)
and originally classified as an SN II on 2023 July 27.34 by Lidman et al.
(2023) from an ANU-2.3m / WiFeS spectrum acquired on 2023 July 26. Later, we
reclassified as an SN IIn on 2023 October 06.77 from an ANU-2.3m / WiFeS
spectrum acquired by us on 2023 September 29 (Aleo et al., 2023a). LAISS
correctly tagged SN 2023nof as an anomaly on 2023 September 24.

SN 2023nwe was discovered on 2023 July 24.43 by Chambers et al. (2023) and
classified by us as an SN IIn on 2023 September 11.69 (Davis et al., 2023)
from a Lick-3m / KAST spectrum acquired by us on 2023 September 06.30. LAISS
correctly tagged SN 2023nwe as an anomaly on 2023 August 28.

SN 2023omf was discovered on 2023 July 31.35 by Fremling (2023a) and
classified by us as an SN IIn on 2024 March 24.15 from a Lick-3m / KAST
spectrum acquired by us on 2023 October 22.11. LAISS correctly tagged SN
2023nwe as an anomaly on 2023 October 10.

SN 2023ocx was discovered on 2023 July 20.22 by Fremling (2023b) and
classified as an SN Ia-CSM by us on 2023 October 02.52 (Angus & Aleo, 2023)
from a NOT / ALFOSC spectrum acquired by us on 2023 September 19. LAISS
correctly tagged SN 2023ocx as an anomaly on 2023 August 28.

SN 2023otw was discovered on 2023 July 29.46 by Fremling (2023c) and
classified as an SN II on 2023 November 07.56 (Gomez et al., 2023c) as part of
the FLEET program (Gomez et al., 2020) from a SOAR / Goodman spectrum acquired
on 2023 October 11. This spectrum was acquired in coordination with FLEET,
whose program identified this object independently. LAISS incorrectly tagged
SN 2023otw as an anomaly on 2023 September 20.

SN 2023swf was discovered on 2023 September 16.25 by Fremling (2023d) and
classified as an SN II on 2023 October 04.72 (Ayala et al., 2023) from a ESO-
NTT / EFOSC2-NTT (ePESSTO+) spectrum acquired on 2023 October 04.21. We
retained the original SN II classification from a ANU-2.3m / WiFeS spectrum
acquired by us on 2023 October 11. LAISS incorrectly tagged SN 2023swf as an
anomaly on 2023 October 09.

SN 2023sed was discovered on 2023 September 08.35 by Munoz-Arancibia et al.
(2023d) and classified by us as an SN Ia on 2023 October 02.55 (Angus, 2023)
from a NOT / ALFOSC spectrum acquired by us on 2023 September 28. LAISS
incorrectly tagged SN 2023sed as an anomaly on 2023 September 24.

To reduce the Kast data, we used the UCSC Spectral
Pipeline787878https://github.com/msiebert1/UCSC_spectral_pipeline (Siebert et