# Linking Extragalactic Transients and their Host Galaxy Properties:
Transient Sample, Multi-Wavelength Host Identification, and Database
Construction

Yu-Jing Qin Department of Astronomy and Steward Observatory, University of
Arizona, 933 N Cherry Ave, Tucson, AZ 85721  Ann Zabludoff  Department of
Astronomy and Steward Observatory, University of Arizona, 933 N Cherry Ave,
Tucson, AZ 85721  Marina Kisley  Department of Computer Science, University of
Arizona, 1040 4th St, Tucson, AZ 85721  Yuantian Liu  Department of Physics,
University of Arizona, 1118 E. 4th Street, PO Box 210081, Tucson, AZ 85721
Iair Arcavi The School of Physics and Astronomy, Tel Aviv University, Tel Aviv
69978, Israel  CIFAR Azrieli Global Scholars program, CIFAR, Toronto, Canada
Kobus Barnard Department of Computer Science, University of Arizona, 1040 4th
St, Tucson, AZ 85721  Peter Behroozi Department of Astronomy, University of
Arizona, 933 N Cherry Ave, Tucson, AZ 85721  K. Decker French Observatories of
the Carnegie Institute for Science, 813 Santa Barbara St. Pasadena, CA 91001
Hubble Fellow  Curtis McCully  Las Cumbres Observatory  Nirav Merchant  Data
Science Institute, University of Arizona, BSRL Building (#242), 1230 N. Cherry
Ave, Tucson, AZ 85721

###### Abstract

Understanding the preferences of transient types for host galaxies with
certain characteristics is key to studies of transient physics and galaxy
evolution, as well as to transient identification and classification in the
LSST era. Here we describe a value-added database of extragalactic
transients—supernovae, tidal disruption events, gamma-ray bursts, and other
rare events—and their host galaxy properties. Based on reported coordinates,
redshifts, and host galaxies (if known) of events, we cross-identify their
host galaxies or most likely host candidates in various value-added or survey
catalogs, and compile the existing photometric, spectroscopic, and derived
physical properties of host galaxies in these catalogs. This new database
covers photometric measurements from the far-ultraviolet to mid-infrared.
Spectroscopic measurements and derived physical properties are also available
for a smaller subset of hosts. For our 36333 unique events, we have cross-
identified 13753 host galaxies using host names, plus 4480 using host
coordinates. Besides those with known hosts, there are 18100 transients with
newly identified host candidates. This large database will allow explorations
of the connections of transients to their hosts, including a path toward
transient alert filtering and probabilistic classification based on host
properties.

astronomical databases: catalogs – supernovae: general – galaxies: general

††journal: ApJS

##  1 Introduction

Extragalactic transients cover a wide range of energetic, fast, and usually
cataclysmic events observed across the electromagnetic spectrum, as well as
the recently revealed gravitational wave and neutrino windows. These events
include, but are not limited to, supernovae (SNe), gamma-ray bursts (GRBs),
tidal distribution events (TDEs), fast radio bursts (FRBs), and gravitational
wave events (GWs). With better observational facilities and computational
technologies, our understanding of these transient phenomena has advanced
considerably in the new century. However, their connection to the evolution
history and observational properties of their host galaxies remains to be
further explored.

Transients are stellar or supermassive black hole phenomena in nature. They
occur on much smaller spatial scales than the typical size of galaxies. They
are also ephemeral compared to timescales of galaxy evolution. Notwithstanding
the dramatic contrast in spatial and temporal scales, there are examples of
well-established transient-host connections:

  1. 1.

Core-collapse supernovae (CC SNe) show stronger preference for late type,
star-forming galaxies than thermonuclear Type-Ia supernovae (SNe Ia) (e.g.,
van den Bergh, 1959; Tammann, 1978; Oemler & Tinsley, 1979; Sullivan et al.,
2006; Li et al., 2011; Foley & Mandel, 2013; Graur et al., 2017);

  2. 2.

Compared to “normal” SNe Ia, SNe Ia-91bg tend to occur in early-type hosts,
while SNe Ia-91T and Ia-02cx favor late-types (Taubenberger, 2017, and
references therein);

  3. 3.

The hosts of short-duration gamma-ray bursts (SGRBs) are brighter, larger in
size, more metal-rich, and, on average, more quiescent than typical hosts of
long-duration gamma-ray bursts (LGRBs) (Berger, 2009; Fong et al., 2010; Lyman
et al., 2017).

  4. 4.

The rate of TDEs is dramatically boosted in post-starburst galaxies (French et
al., 2016), although the underlying driving mechanism is subject to debate
(Graur et al., 2018; Stone et al., 2018);

  5. 5.

The recently discovered electromagnetic counterpart of gravitational wave
event GW170817 (Abbott et al., 2017), also detected as a SGRB, lies in an
early-type galaxy with shell-like merger features;

  6. 6.

The known hosts of FRBs show rather diverse properties, with a possible excess
of galaxies in the “green valley,” or galaxies with harder ionizing radiation
than provided by star formation alone (e.g., Heintz et al., 2020).

This synopsis is certainly not exhaustive but nevertheless demonstrates that
some transients prefer certain host environments. There may be still other
connections that, while subtle, could be discovered with a large, well-curated
database gleaned from archived surveys.

To a certain extent, such transient-host connections can be interpreted within
our existing picture of transient physics and galaxy evolution. From a
statistical and empirical perspective, the event rates of optical supernovae
and their counterparts at other wavelengths depend on the recent star
formation history and the delay time distribution of supernova explosion since
star formation; theoretically, the preferred channel and mechanism of the
explosion could also depend on metallicity and dynamical environment, which
directly shape the evolutionary path of progenitors and indirectly affect
stellar binarity and initial mass function. For TDEs, the event rates not only
are related to the integrated stellar populations of the host but also
regulated by the concentration of star formation, availability of gas near the
galactic nucleus, and/or the disturbed central stellar and gas dynamics in the
host. Due to the uncertainties in our theoretical models, it is unclear to
what extent the rates of transients depend on the global properties of their
hosts. Potential biases in transient surveys further complicate the
interpretation of the observed transient-host connections. Nevertheless, the
study of transient-host connections points to a better-refined view of
transient phenomena and galaxy evolution, including the physics of massive
stars, interacting binaries, and compact objects and the role of supermassive
black holes and active nuclei in galaxy evolution.

Transient-host connections are also of practical interest. Modern transient
surveys, particularly the Legacy Survey of Space and Time (LSST) at Vera C.
Rubin Observatory (Ivezić et al., 2019), are expected to deliver nearly 10
million transient alerts every night in real-time. For extragalactic events,
over a year of operation, LSST is estimated to detect about 0.2 million SNe
Ia, a smaller but comparable sample of CC SNe, and orders of magnitude fewer
other uncommon transients inundated in the torrent of alerts111LSST Science
Book v2, Chapter 8 & Chapter 11.. Selecting a subset of potentially
interesting events would be essential for any study that requires follow-up
observations using other facilities. Often this must be done quickly in the
early phase of transient evolution, when evanescent phenomena may give us
valuable insights into their physical models. Host properties thus open up a
new avenue towards automatized alert filtering and classification, even “pre-
classification,” i.e., assigning event rates or probabilities to galaxies in
the field based on their properties, before any alerts arrive. Indeed, the
viability and reliability of host-based transient classification have been
clearly demonstrated by the early conceptual work of Foley & Mandel (2013) and
recent attempts of Gagliano et al. (2020). Even photometric data alone may
effectively distinguish potential hosts of certain rare transients (e.g.,
French & Zabludoff, 2018). Meanwhile, new-generation transient brokers for
Zwicky Transient Facility (ZTF; Bellm et al., 2019) and the future LSST, such
as ANTARES (Narayan et al., 2018), ALeRCE (Förster et al., 2020), Lasair
(Smith et al., 2019), MARS222mars.lco.global and Fink (Möller et al., 2020),
are all capable of cross-matching transient alerts with external source
catalogs, providing the necessary infrastructure for host-based event
filtering and classification. Given this potential to provide a baseline
estimate of transient probabilities (i.e., a categorical prior function of
different types) or rates independent of any transient characteristics, the
power of host properties must be fully utilized now for imminent transient
surveys.

Exploring the connections of extragalactic transient and their host properties
entails a complete census of known transient hosts, including their properties
measured by various sky surveys across the electromagnetic spectrum. Existing
samples and catalogs of transient host galaxies, such as the time-controlled
sample of Lick Observatory Supernova Search (LOSS; Leaman et al., 2011; Li et
al., 2011), also the host samples of SDSS-II Supernova Survey (Sako et al.,
2018) and DES Supernova Programme (Wiseman et al., 2020), usually focus on a
dedicated transient survey or a specific class of transients. The recently
released GHOST database (Gagliano et al., 2020), which has a considerably
larger transient sample, is mostly limited to the photometric properties of
supernova hosts obtained by the Pan-STARRS survey. There is no single catalog
to date of consistently measured host properties, across a wide wavelength
range, for a large and up-to-date sample of transient events. Reasons include:

  1. 1.

Host galaxies are not always reported by transient surveys or summarized in
catalogs or platforms. Consequently, a considerable fraction of transients in
the literature, even with accurate sky coordinates, redshifts, and
classifications, have no easily accessible host data of any kind.

  2. 2.

Beyond that, new transients are continuously being discovered at ever-
improving efficiencies, posing an immediate challenge to human-based host
identifying and reporting. Automated host identifying algorithms, using either
catalogs or images, are favored in this situation. However, only a few such
algorithms have been developed up to date. (e.g., Sullivan et al., 2006; Gupta
et al., 2016; Sako et al., 2018; Gagliano et al., 2020).

  3. 3.

Even for transients with identifiable hosts, obtaining their properties
involves substantial efforts. Some surveys conduct dedicated observations of
hosts. Accessing archival data of public surveys, a more viable approach for
many other cases, also requires host matching, quality control, and data
compilation.

Constructing a transient host database with better coverage of previous
transient events and available host properties remains a crucial step for the
in-depth exploration and analysis of transient-host connections. Here we
present such a new transient-host database. We find counterparts of host
galaxies in external catalogs using the reported host name designations or
coordinates, as presented in the data sources. For events without reported
host galaxies, we identify their best host candidates within nearby
extragalactic objects, cross-matched in various external catalogs. Our goal
here is to locate the correct counterparts of transient host galaxies in
various catalogs, which entails cross-matching sources in multiple catalogs,
and, when necessary, ranking potential host galaxies to identify the most
likely candidates. Therefore, we refer to the process as the cross-
identification of transient host galaxies. We then collate properties of our
identified host galaxies and host candidates, including photometry across UV
to IR wavelengths, morphology, spectral lines and indices, and derived
physical parameters such as stellar mass, metallicity, and star formation rate
provided in those catalogs.

This first paper describes our database of transient host galaxies and the
workflow to construct it. In Section 2, we review our data sources for
transient events (published catalogs and online services) and the procedure to
create a sample of unique transient events. In Section 3, we describe our
method to search host properties in external catalogs and collect their
information. We also discuss our strategy to identify new host galaxies for
those without hosts reported in our data sources. We then describe the
detailed object selection criteria and data coverage in each external catalog
in Section 4 and present the basic statistics of our transient-host pairs in
Section 5. Finally, we discuss our software implementation and data format in
Section 6 and summarize in Section 7.

##  2 The transient sample

This section describes our selected upstream data sources for transient events
and the procedure to assemble a transient sample. We also present the basic
statistics in these upstream data sources and the compiled transient sample as
of June 1, 2021, i.e., the cut-off date of transient events for the initial
release of our database presented in this paper.

Table 1: Upstream Data Sources for Transients

Source | Number | Type
---|---|---
| Total | Used |
The Open Supernova Catalog | 82864 | 34547 | SN
French et al. (2020) Review | 43 | 43 | TDE
The Open TDE Catalog | 97 | 96 | TDE
Swift GRB Catalog | 1651 | 1493 | GRB
Fermi/LAT GRB Catalog | 145 | 78 | GRB
INTEGRAL/IBAS GRB Catalog | 140 | 140 | GRB
The Fast Radio Burst Catalogue | 118 | 13 | FRB
LIGO Detections | 12 | 1 | GW
The Open Kilonova Catalog | 6 | 6 | KN
GRB Host Studies (GHostS)aaAncillary data source for GRB localization, $T_{90}$, and redshifts. | 235 | – | GRB
Jochen Greiner’s GRB listaaAncillary data source for GRB localization, $T_{90}$, and redshifts. | 2226 | – | GRB
Gamma-Ray Burst Online IndexaaAncillary data source for GRB localization, $T_{90}$, and redshifts. | 2082 | – | GRB
GRBweb DatabaseaaAncillary data source for GRB localization, $T_{90}$, and redshifts. | 7559 | – | GRB
FRB Host Galaxy DatabasebbAncillary data source for FRB host association. | 19 | – | FRB
Taubenberger (2017) ReviewccAncillary data source for SNe Ia classification. | 60 | – | SN

Note. — Number counts include all records in the original data source (Total)
and the number of records that contributed any data in our sample (Used).
Besides French et al. (2020), other reference sources are actively updated.
Numbers are as of June 1, 2021.

###  2.1 Upstream data sources for transients

Existing transient events are detected by various surveys or missions,
reported at multiple circulars or platforms, and cataloged by several sites or
numerous individual publications. There is no single data source that covers
all previously known transient events. Therefore, we choose a few
representative data sources with relatively complete records for each major
transient type, organized in a machine-readable format for easier access.
Below we discuss our transient data sources for each major type.

####  2.1.1 Supernovae

Our supernova records are imported from the Open Supernova Catalog (OSC;
Guillochon et al., 2017)333sne.space, github.com/astrocatalogs/supernovae. The
OSC compiles supernovae in numerous published catalogs, individual works, and
online services, notably the Transient Name Server444www.wis-tns.org, Padova-
Asiago Supernova Catalog (Barbon et al., 1999)555graspa.oapd.inaf.it/cgi-
bin/sncat.php, the Latest Supernova
website666www.rochesterastronomy.org/supernova.html (Gal-Yam et al., 2013),
the Weizmann Interactive Supernova data REPository
(WISeREP)777wiserep.weizmann.ac.il/, and the CfA Supernova Data
Archive888www.cfa.harvard.edu/supernova/SNarchive.html, etc. Apart from the
basic information (or “metadata”) of supernovae and supernova candidates, the
catalog also compiles their light curves, spectra, X-ray, and radio
measurements. For every single event, all related data are self-contained in a
single file, where the historical versions of each file are traceable via
their git-based version control solution.

Although great efforts are made in Guillochon et al. (2017) to collect and
homogenize supernova data in various sources, for our transient sample, there
are a few issues to be resolved when importing their supernova records. First,
the supernova basic data (or “metadata”) in OSC – such as classification, sky
coordinates, and redshifts – trace back to multiple automatically-updated or
manually-maintained reference sources. As a result, slightly different,
inconsistent, or even erroneous values are often provided for the same data
field (or column), requiring further selection and validation. Secondly, not
all the records in OSC are directly detected and reliably classified
supernovae. There are a large fraction of transient candidates in OSC without
classification. There are also supernova remnants and historical supernovae
(i.e., discovered before photometric techniques became available) in the Milky
Way or nearby galaxies. Long-duration GRBs in the CGRO/BATSE Catalog and
grbcatalog.com, even without associated supernovae detected, are also included
for the association of LGRBs with some core-collapse supernovae. Thirdly, the
type labels (including major types and detailed subtypes) in OSC can sometimes
be ambiguous, incomplete, and even inconsistent. Reliable classification
requires high-quality light curves and spectra covering critical phases of
evolution, which are not always available. The criteria and technique for
transient classification could also differ among survey programs or research
groups. Even the classification scheme is evolving as our knowledge of
supernova physics advances. As a result, events are not classified
consistently among different sources.

For our work, we only select a subset of records in OSC, with further data
curation for clarity and consistency. Records with classification, redshift or
host information of any kind (names, or coordinates) are selected. Supernova
candidates and unclassified events are omitted unless their redshifts or hosts
are also reported. Those temporarily ignored candidates and unclassified
events will be added back to our database during future updates once they are
confirmed or classified in OSC. We also skip ancient supernovae and supernova
remnants, even if their types are indirectly inferred in the literature.
Furthermore, we only select GRBs in OSC with associated supernovae detected
(i.e., classified as supernovae at the same time). Similarly, we omit records
labeled as TDEs at this stage. TDEs and GRBs are added via other data sources
(discussed later).

The snapshot of OSC we use here has 82864 records, where 26868 records are
classified (i.e., with type labels assigned), and 21592 records have host
galaxy information, either name designations or coordinates. As new records
and data are added into OSC by programs and users regularly, we set up an
automatic script to pull the latest version of OSC hosted at github.com and
update our transient records accordingly. Our database does not include
detailed supplementary data of each event in OSC, including transient light
curves, spectra, X-ray, or radio fluxes.

####  2.1.2 Tidal disruption events

Currently, there are only a few dozen tidal disruption events reported in the
literature. For the initial release of our database, we use the summarized
list in the review of French et al. (2020) as our primary data source.
Additionally, we use the Open TDE Catalog999tde.space, a close sibling of the
Open Supernova Catalog, as our secondary data source101010For future updates
of the database, we may switch our primary data source to the Transient Name
Server.. Like OSC, the Open TDE Catalog is designed as a complete sample of
known TDEs, including likely ones, in the literature or transient circulars,
where basic data are compiled from various reference sources. This catalog
also includes unconfirmed or possible events where alternative explanations or
classifications may exist, which requires manual inspection and selection.

To incorporate these records into the database, we combined events in both
data sources into a single, unified list of TDEs. We manually match the events
in these two data sources by name designations (including variants) to ensure
that the combined list is free of duplicates. To verify our name-based
matching and to further identify unique events in each data source, we also
spatially cross-match events using an angular distance threshold of 5”.
Finally, we use the metadata (name, coordinate, redshift, and host galaxy) in
French et al. (2020) whenever available, but also keeping other names and
aliases in the Open TDE catalog.

We also unified the type labels in our combined list of TDEs. The type labels
within the Open TDE Catalog are still to be standardized. Some events are
classified by their inferred progenitors or physical scenarios (e.g.,
“MS+SMBH”), while others are only labeled as “TDE” or “TDE?”. We first assign
“TDE” labels to all the events in the combined list. For events in the list of
French et al. (2020), we further assign labels to indicate their detected
wavelengths, like “UVOptTDE” for events detected in UV or optical bands and
“XrayTDE” for X-ray detected events. Possible and likely X-ray detected events
in French et al. (2020) are labeled as “PossibleXrayTDE” and “LikelyXrayTDE,”
based on the samples of Auchettl et al. (2017). There are other proposed
classification schemes in the literature (e.g., van Velzen et al., 2020),
which could be included in the future release of our database.

There are 43 TDEs in the list of French et al. (2020) and 97 events in the
Open TDE catalog (as of June 1, 2021). Excluding 41 common ones, the combined
list has 98 unique events.

####  2.1.3 Gamma-ray bursts

We use the following catalogs of individual GRB missions for their relatively
better localization accuracy than other catalogs.

  * -

Swift Catalog111111swift.gsfc.nasa.gov/archive/grb_table/fullview/ (Lien et
al., 2016). This is a catalog of events detected by the Burst Alert Telescope
(BAT) on Swift. BAT localizes GRBs to arcminute-level accuracy. Some events
also have afterglow detection by the X-ray Telescope (XRT) and UV/Optical
Telescope (UVOT) on board, which further improves the positioning accuracy to
arcsecond-level.

  * -

Fermi/LAT
Catalog121212fermi.gsfc.nasa.gov/ssc/observations/types/grbs/lat_grbs/table.php
(Ackermann et al., 2013). This catalog includes events detected by the Large
Area Telescope (LAT) on Fermi, with arcminute-level positioning accuracy. This
does not include events only detected by the Gamma-ray Burst Monitor (GBM),
which has a larger error circle (degree-level in the best case).

  * -

INTEGRAL/IBIS Catalog (Vianello et al., 2009; Bošnjak et al., 2014) and later
events localized with the INTEGRAL Burst Alert System131313ibas.iasf-
milano.inaf.it (Mereghetti et al., 2003). These events are detected by the
Imager on-Board the INTEGRAL Satellite (IBIS), with a typical positional error
of about $2^{\prime}$. For high signal-to-noise events, the accuracy may reach
sub-arcminute level.

The catalogs listed above certainly do not cover all well-localized GRBs.
Other missions, such as HETE-2, BeppoSAX, and AGILE, also report events that
are well-localized via afterglows or by associated supernovae, usually with
other facilities. We do not use their original catalogs due to the lack of
precise coordinates there.

We also accessed several ancillary data sources for GRBs, including the
catalog of the GRB Host Studies (GHostS) Project141414www.grbhosts.org
(Savaglio, 2006; Savaglio et al., 2006), the GRB list maintained by Jochen
Greiner151515https://www.mpe.mpg.de/~jcg/grbgen.html, the Gamma-Ray Burst
Online Index (GRBOX)161616https://sites.astro.caltech.edu/grbox/grbox.php, and
the GRBweb catalog171717https://user-web.icecube.wisc.edu/~grbweb_public/.
These ancillary data sources summarize GRB properties from multiple missions
and follow-up programs, including data reported to GCN
circulars181818gcn.gsfc.nasa.gov/gcn3_archive.html that are not machine-
readable at the moment. These ancillary data sources provide us 1) potentially
better coordinates than reported in the three mission catalogs above; 2) more
well-localized GRBs that are not reported in those mission catalogs; 3) most
importantly, GRB redshifts measured from afterglows, host galaxies, or
associated supernovae.

We use the latest online version of these mission catalogs and ancillary data
sources to ensure that our GRB records are up-to-date. We combine records from
these reference sources into a single list, during which we cross-match GRBs
by their standardized names to eliminate duplicates. For each event, we choose
the best sky coordinate (i.e., with the smallest 90% error circle) within
these reference sources. Redshift and $T_{90}$ (i.e., the time when $90\%$
photons arrive), when available, are usually consistent across these reference
sources. If not, we take the median value of all reported measurements.

Events in our list are classified as GRB by default. According to their
$T_{90}$ values, we further classify them into long-duration GRBs (LGRBs;
$T_{90}\geq 2\,\text{s}$) and short-duration GRBs (SGRBs;
$T_{90}<2\,\text{s}$). Events without $T_{90}$ in any of our data sources are
only classified as GRB. The traditional long-short dichotomy might be further
refined using the spectral hardness (e.g., Zhang et al., 2012), which remains
a part of our future work.

It should be emphasized that GRBs are, on average, detected at higher
redshifts than other transients. The drastically decreased completeness of
survey catalogs, combined with the large error in coordinates, makes the host
hard or even impossible to be identified in some cases. Therefore, we use a
rather conservative sample selection and catalog accessing strategy to reduce
the fraction of misidentified hosts. We only include GRBs with a $90\%$ error
radius under $5^{\prime\prime}$ in our sample. Meanwhile, the default search
radius for GRBs without redshifts is reduced to minimize the confusion due to
other nearby galaxies (Section 3). Even with more accurate coordinates and
smaller search radii, the chance remains that some GRB hosts are undetected or
incorrectly identified. Checking quality control metrics (Section 3.5) is
always encouraged when using their compiled host properties.

Besides nine GRBs with associated optical supernovae in OSC, we have 7939
unique GRBs from our selected data sources, where 1621 of them have a $90\%$
error radius better than $5^{\prime\prime}$ and are thus included in the
transient sample.

####  2.1.4 Other rare transients

Our database also includes rare transients such as fast radio bursts (FRBs)
and gravitational wave events (GWs). These rare events are generally poorly
localized, so we only include 14 FRBs and one GW event with host galaxies
reported in the literature out of over 100 FRBs and nine GWs. These events
include GW170817 (Abbott et al., 2017), FRB121102 (Chatterjee et al., 2017),
FRB180916 (Marcote et al., 2020), FRB180924 (Bannister et al., 2019),
FRB181112 (Prochaska et al., 2019), FRB190102 (Macquart et al., 2020),
FRB190523 (Ravi et al., 2019), FRB190608 (Marcote et al., 2020), and FRB190614
(Law et al., 2020). 191919Heintz et al. (2020) and Bhandari et al. (2020) have
recently reported the host galaxies of FRB190611, FRB190711, FRB190714,
FRB191001, FRB200434, which will be included in the next release of the
database. 202020FRB150418 (Keane et al., 2016) is not included as its
“afterglow,” with which the host was located, is likely a coincident variable
source (Williams & Berger, 2016). Also, the case of FRB171020 (Mahony et al.,
2018) is not included due to its indefinitive host association. Their metadata
are referred from the Fast Radio Burst Catalogue (FRBCAT; Petroff et al.,
2016) and the list of LIGO detections212121www.ligo.org/detections.php. We
also refer to the FRB Host database222222frbhosts.org (Heintz et al., 2020)
for host association of known FRBs. For the rest of the events, we do not
attempt to identify their host candidates.

Besides the one associated with GW170817, there are several more kilonova or
“macronova” events (including candidates) reported in the literature. We label
the event associated with GW170817 as “Kilonova.” Events listed in the Open
Kilonova Catalog232323kilonova.space (another sibling of OSC), mostly short
GRBs, are labeled as kilonova candidates (“PossibleKilonova”). These kilonova
candidates include GRB050709 (Jin et al., 2016), GRB060614 (Yang et al.,
2015), GRB080503 (Perley et al., 2009), GRB130603B (Fong et al., 2014) and
GRB150101B (Troja et al., 2018).

####  2.1.5 Remarks on data source selection

There are a few more actively maintained catalogs and online services for
extragalactic transients, including the Transient Name Server242424wis-
tns.weizmann.ac.il, which cross-identifies transient events; the Astronomer’s
Telegram (ATel)252525www.astronomerstelegram.org where initial discovery,
confirmation, and classification of transients are posted; and the GCN
circular262626gcn.gsfc.nasa.gov where high energy transients are reported and
archived. Many sky surveys or space missions also produce their own catalogs
of detected events and candidates. There are also catalogs that are no longer
being actively updated (e.g., the supernova catalog of Lennarz et al. 2012).

We use the data sources above as they are relatively complete (including
events in multiple missions, surveys, and catalogs), and data are summarized
and tabulated in a machine-readable form. The basic statistics of our data
sources are summarized in Table 1. We may include other transient data sources
for transients with records in an easily accessible format in future releases.

Table 2: Type Hierarchy and Number Counts of Transients

Type | Total | Host
---|---|---
0.5em0.8em0.2em0.6pt1.6pt  .1 Full Sample. .2 Thermonuclear SNe (Ia). .3 Ia (unspecified). .3 Ia-91bg. .3 Ia-91T. .3 Ia-02cx. .3 Ia-CSM. .3 Ia-09dc. .3 Ia-02es. .3 Ia-99aa. .3 Ia-00cx. .3 Ca-rich. .2 Core-collapse SNe (CC). .3 Stripped-envelope SNe (SE). .4 Type Ib-like (Ib). .5 Ib (unspecified). .5 Ibn. .5 IIb. .4 Type Ic (Ic). .5 Ic (unspecified). .5 Ic-BL. .4 Ib/c. .3 H-rich Core-collapse SNe (II). .4 II (unspecified). .4 II P. .4 II L. .4 IIn. .3 Superluminous SNe (SLSN). .4 SLSN (unspecified). .4 SLSN-R. .4 SLSN-I. .4 SLSN-II. .2 Tidal Disruption Events (TDE). .3 TDE (unspecified). .3 UVOptTDE. .3 XrayTDE (+ likely events). .2 Gamma-ray Bursts (GRB). .3 GRB (unspecified). .3 SGRB. .3 LGRB. .2 Fast Radio Bursts (FRB). .2 Kilonova (optical only). .2 Gravitational Wave (GW). |  0pt0pt0pt0pt0pt  .1 36333 . .1 15435 . .1 14534 . .1 135 . .1 398 . .1 57 . .1 25 . .1 8 . .1 6 . .1 6 . .1 4 . .1 21 . .1 8437 . .1 1619 . .1 757 . .1 363 . .1 51 . .1 323 . .1 710 . .1 672 . .1 137 . .1 315 . .1 6685 . .1 5023 . .1 918 . .1 55 . .1 681 . .1 230 . .1 13 . .1 6 . .1 154 . .1 61 . .1 99 . .1 56 . .1 22 . .1 21 . .1 1621 . .1 20 . .1 115 . .1 1486 . .1 14 . .1 1 . .1 1 .  |  0pt0pt0pt0pt0pt  .1 19268 . .1 8093 . .1 7558 . .1 91 . .1 177 . .1 38 . .1 9 . .1 8 . .1 5 . .1 3 . .1 3 . .1 15 . .1 5629 . .1 1123 . .1 546 . .1 270 . .1 27 . .1 235 . .1 481 . .1 452 . .1 73 . .1 250 . .1 4605 . .1 3514 . .1 688 . .1 34 . .1 373 . .1 29 . .1 1 . .1 2 . .1 20 . .1 8 . .1 93 . .1 54 . .1 19 . .1 20 . .1 22 . .1 0 . .1 1 . .1 21 . .1 14 . .1 1 . .1 1 .

Note. — Total indicates the number of events with a certain type label,
including events classified as its sub-types. Host indicates the number events
with known host galaxies (names or coordinates). Rows with “unspecified” in
Type exclude events with sub-type labels under the same parent type. Transient
types here are not mutually exclusive. Some types may have common physical
origin, and the same event may receive multiple types.

Figure 1:  The number of transients under each type label, illustrated with
stacked bars to show the relative sample sizes. Here, labels with ellipses (…)
only count events with any subtype classification under this parent type,
while labels with asterisks (*) count events without any further refined
subtype label in the hierarchy (i.e., “unspecified” as defined in Table 2).
Events with multiple incompatible labels (i.e., labels that are not subtypes
of each other) are accounted for multiple times. Type Ia supernova is the most
representative type in our sample, followed by core-collapse supernovae (Type
Ib, Ic, II). Gamma-ray bursts dominate the remaining $7\%$ of classified
events. Relatively rare events like superluminous supernovae (SLSNe) and tidal
disruption events (TDEs) only account for about $1\%$ of all records.

###  2.2 Construction of transient sample

Having transient records selected from those upstream data sources, we combine
these records into a single, unified transient sample. We only keep their
metadata for each event, including sky coordinates, redshift, type labels,
host name, and/or coordinate (if available). We take the following approaches
to ensure that events in the combined transient sample are unique,
consistently classified, and having their best-available metadata compiled.

####  2.2.1 Ensuring the uniqueness of events

We first make our transient sample unique to each event. A transient can be
independently detected, with unique identifiers assigned by different surveys,
missions, or research groups, in their own naming conventions. When events are
reported to circulars like TNS and ATel, there could be another assigned
designation (e.g., “AT” prefix + year + alphabetic identifier) for the event.
Finally, when confirmed with follow-up observations, there might be an
“official” name assigned using the naming convention of the community. These
aliases and designations should point to the same event in our transient
sample. Meanwhile, future events could be detected by multiple messengers, and
they should point to the same entry in the database, provided that clear
associations are established. This is already partly done within OSC but not
across all our upstream data sources.

To make the table unique to each event, besides the preferred or most commonly
used designations, we also keep a list of aliases for each event. When a new
event was reported at our transient data sources, we first search the alias
lists of existing events to check if this is already assigned to a record. We
create a new record for this event only when all its names are new in our
transient sample. When maintaining alias lists, we also considered variants of
transient designations in the literature. For example, starting on Jan 1,
2010, the first GRB detected on a day is always labeled as “A,” even there is
no other GRB on that day (Barthelmy et al., 2009). However, such a change of
naming convention is not strictly obeyed in the literature. There are also
minor variations like with or without whitespaces or dash (e.g., ASASSN vs.
ASAS-SN). We created possible variants of event names, even though they do not
strictly follow the naming conventions of certain transient communities or
survey programs. This effectively prevents duplicates when the database is
updated in the future.

Finally, we create unique identifiers for each transient event in our database
(“_id”), which are used consistently everywhere for host cross-matching and
host candidate identification.

####  2.2.2 Finding the best-available metadata

When assembling the transient sample using several different data sources, we
may also have multiple coordinates or redshifts reported for the same event
besides multiple type labels. Finding the best available sky coordinates and
redshifts would thus be necessary for our later host cross-matching or
identification. We only use the “most referred” coordinates for supernovae,
i.e., the sky coordinates that are most widely used in various data sources.
We do this by pairing RA/Dec by their data sources and counting the number of
primary references linked to each coordinate pair reported in OSC. When
available, we always prefer the spectroscopic redshift of the host galaxy over
the spectroscopic redshift of the supernovae, as we are assembling a catalog
of transient host galaxies. Photometric redshifts of any kind have the lowest
priority. For redshift values without types indicated, we assume that they are
the spectroscopic redshifts of the transients. Finally, when more than one
redshift of the same priority level is reported, we take the median value.

Most GRBs have no associated supernovae detected, except for a handful of
cases. We always use the coordinates with the smallest $90\%$ error circle for
these events. We also include positional accuracy ($90\%$ error radius in
arcseconds) for GRBs. Many GRBs have X-ray or UV/optical afterglows detected,
which provides more accurate localization (arcsecond-level) than coarse
coordinates determined with wide-angle, coded mask detectors (usually
arcminute level in the best cases). When available, we use the most accurate
position determined using X-ray or optical afterglows. Finally, when
associated optical supernovae are detected, we prefer supernova coordinates
and redshifts over those reported for GRB for the same reason.

Commonly assumed as nuclear events, the reported transient coordinates of TDEs
are usually their host coordinates in optical wavelengths. Instead, a small
subset of TDEs may only have X-ray determined transient coordinates reported,
with relatively poor localization accuracy. We take the as-reported positions
as their nominal host coordinates when matching objects in other catalogs (see
Section 3). Using the host coordinates of their optical counterparts in our
final compiled summary table is always recommended over their nominal host
coordinates.

Finally, the metadata of other rare events, including FRBs and GWs, are
generally unique and consistent across different reference sources, so we do
not make a further selection.

Figure 2:  (Left) The cumulative distribution of transient redshifts in our
database. The redshift axis is stretched so that events are uniformly
distributed along the axis. Major supernova types, including Type-Ia, Type-II,
and stripped-envelope (Ib, Ic), are highlighted here. Compared to Type-II and
SE SNe, SNe Ia generally have higher redshifts in our sample, likely due to
their higher luminosities. Meanwhile, SE SNe are distributed at even lower
redshifts than Type-II. Other transients, mainly GRBs with redshifts reported,
are distributed at the higher redshift side of our sample. (Right) The
fraction of each major type across the redshift range, with similar, stretched
redshift so that the shaded area scales with number counts. Labels with
ellipses count events of any subtype under this parent type, while labels with
asterisks only include events without any subtype labels. A large fraction of
events at low redshift are core-collapse supernovae, and the relative fraction
of stripped-envelope supernovae (SN Ib and Ic) within CC SNe drops with
increasing the redshift. The contribution of SNe Ia increases with redshift
and peaks near $z\simeq 0.1$, while subtypes of SNe Ia are mostly at the lower
redshifts.

####  2.2.3 Standardization of the classification scheme

To create a master sample of transients, one needs coherent and consistent
classification for events observed in various wavelengths and time scales.
This is a non-trivial task when working with diverse reference sources and
archival records of historical events due to their minor discrepancies of
classification schemes. The existing classification scheme for transients,
particularly for supernovae, is mostly phenomenological. Supernova types are
primarily assigned based on spectral features and sometimes the shape of light
curves. Meanwhile, the grouping of subtypes (taxonomy or “hierarchy” of
supernova types) is usually motivated by physical models. Even most data
sources follow this well-established classification scheme, the detailed type
notations and label systems (i.e., the union of possible types labels) are not
identical, especially when new phenomena are discovered or potentially new
types are identified. From the viewpoint of a retrospective meta-analysis,
there are a few issues to be resolved when unifying transient types in
different reference sources into a single, consistent scheme.

First, the notations (or type labels) for transient classification could vary
in different reference sources, especially for unusual transients that do not
naturally fit the existing classification scheme or newly identified subtypes
that lack a standard notation in the community. When the complexity of
supernova phenomena cannot be described in our existing scheme, hybrid,
intermediate, or transitional types are occasionally used in the literature to
designate these indecisive cases. For example, SNe Ia with circumstellar
interaction have been once classified as “Ia/IIn” or “Ian” by some observers
for their strong narrow Balmer line emission, a definitive signature for Type
IIn supernovae, before they are recognized as a sub-group of SNe Ia (Ia-CSM or
Ia-02ic, Silverman et al., 2013, 2016). Similarly, Type-IIb supernovae, whose
hydrogen lines – for which Type-II is assigned – fades at later stages until
they eventually become SNe Ib-like. Before the popularization of “IIb”
designation, these events occasionally receive “II/Ib” classification for such
transitional behavior. More commonly, several type notations are used
interchangeably to refer to the same newly identified sub-group of events
(e.g., Ia-06gz and Ia-09dc), which should be unified during data compilation.

Second, the label system could be different across reference sources,
requiring a unified classification scheme. Some reference sources may use
complex or fine-grained classification schemes, including more refined
subtypes, while others may only limited to some major types. When combining
type labels in multiple reference sources, as a result, there could be
inconsistencies due to the classification schemes themselves. For example, the
same supernova could be classified as “Ia-91bg” in some reference sources and
as “Ia” in other reference sources with a more limited range of type labels.
As a result, in the combined sample, some “Ia-91bg” events may have coexisting
“Ia” labels assigned, but other “Ia-91bg” events may not, leading to
inconsistent labeling of events. Ideally, we would use the best-refined
subtype label of each event whenever possible so that transients are always
uniquely and unambiguously classified to the finest details. However, in a
combined sample of transients, the same event could have multiple,
incompatible subtype labels, i.e., labels not along the same branch of the
classification tree. This can result from disagreements among reference
sources or independent detection in multiple wavelengths or by multiple
messengers. Therefore, we allow the same transient event to have multiple
labels. Meanwhile, we choose a hierarchical classification scheme so that
variations of classification schemes can naturally fit in.

Our convectional, phenomenologically-based classification scheme for
transients is not strictly connected to the driving physical mechanisms,
including progenitors, exploding mechanisms, and circumstellar environments.
When constructing a hierarchical classification scheme, instead of adhering to
a purely phenomenological scheme, we group transient types based on their
physical origin whenever known. For example, SNe IIb are commonly considered
as close siblings of hydrogen-poor SNe Ib and Ic (or “stripped-envelope
supernovae”), rather than a subtype of Type-II supernova (e.g., Filippenko et
al., 1993). Similarly, Ca-rich events, named after their strong nebular-phase
Calcium lines, have been previously classified as a sub-group of SNe Ib by
their maximum-light spectra. Later studies revealed their thermonuclear origin
– they closely resemble SNe Ia, rather than collapsing massive stars like SNe
Ib (e.g., Perets et al., 2010). These subtypes are preferably grouped by their
physical origin instead of spectral signatures. To attain consistency of
classification when combining labels from reference sources with different
classification schemes, we assign each event its best-refined subtype (there
can be multiple) and all their physical parent labels so that each label in
our transient sample points to a complete subset of events, including its
physical subtypes.

Third, another source of inconsistency is the group of so-called peculiar
events. Supernovae with extreme properties or unusual signatures are labeled
as peculiar events in our conventional classification scheme (e.g., “Pec” in
many catalogs). However, this all-inclusive label is not used consistently in
different reference sources. For example, a few well-defined subtypes of SN
Ia, such as Ia-91T and Ia-91bg, are sometimes (but not always) labeled as “Ia
Pec.” These robustly-defined, relatively uncommon subtypes have characteristic
observational signatures and are potentially related to the diverse channels,
progenitors, or environments of SNe Ia. Many other peculiar supernovae are
just classified by template-matching algorithms, where the “Pec” label is
inherited from the best-matching template, without other detailed and
definitive type labels. Occasionally, individual papers classify events as
peculiar without a new label for their uncommon properties. These unspecified
peculiar supernovae could just be outliers of ordinary events, but they may
also contain new subtypes that are yet to be discovered. Given the context-
specific meaning of “peculiar supernovae,” we use “Pec” as a flag for
relatively uncommon subtypes and individual events with unusual properties
instead of a subtype.

The standardization of the label system and subtype hierarchy must be done on
a case-by-case basis. We discuss our detailed strategy in Appendix B. The
hierarchy of transient type labels is listed in Table 2.

####  2.2.4 Remarks on transient classification

We discussed the standardization of the classification scheme in the previous
subsection. Such a procedure ensures a unified label system and coherent
subtype hierarchy within the database. However, transients in our combined
sample are detected and classified by numerous survey programs and researchers
over decades, using a wide variety of data and techniques. Therefore, the
results may suffer from possible ambiguity or uncertainty, potential
methodological bias, and even human subjectivity.

First, transients are primarily classified by their photometric or
spectroscopic signatures, while the quality, wavelength range, and temporal
coverage of data are sometimes insufficient to precisely and definitively
subtype those events. For example, some supernova subtypes are characterized
by their early-time or nebular phase spectral features, whereas a high-quality
spectrum is not always available when they are far before or after maximum. As
a workaround for time-intensive spectroscopic classification, efforts have
been made to classify transients purely based on light curves of limited
epochs and filters, either with or without redshifts (e.g., Frieman et al.,
2008; Kessler et al., 2010; Lochner et al., 2016). The results of such
classifications bear even larger uncertainties.

Second, the technique and criteria used could also bias the results. Several
active transient follow-up programs rely on template-matching techniques to
classify events, where a spectral library of various transient types and
phases are cross-correlated with the observed spectra to determine the most-
alike types (e.g., Smartt et al., 2015). Such classifications and dating of
transients are implicitly affected by the coverage of types and phases in the
template library and possibly even by the quality and representativeness of
individual spectra. The aforementioned photometric classification is another
example of how technique and criteria may affect the results.

Third, a non-negligible fraction of classifications are visually done by
experienced observers in an empirical and perhaps subjective manner. The
results may therefore vary from one person to another.

Indeed, the ambiguities, biases, and inconsistencies cannot be fully
controlled or corrected without tracing back the original reference sources or
conducting a comprehensive reanalysis of archival data. However, proper
curation of existing classification does, at least, guarantee the consistency
of type labels within our database.

Figure 3:  The distribution of redshift and luminosity distance for each type,
shown as normalized density with respect to distance modulus. Labels with
asterisks are events that are not further classified as any subtype of this
parent type, i.e., “unspecified” events in Table 2. Luminous events, like SNe
Ia and SLSNe, are generally populated at the higher redshift side. Meanwhile,
relative to their parent types, luminous subtypes (e.g., Ia-91T, Ia CSM, Ibn)
are usually at higher redshifts side, while faint subtypes (e.g., Ia-91bg,
Ia-02cx, Ca-rich) are usually at lower redshifts. However, the different
distributions cannot be fully attributed to the classical Malmquist bias. Some
types require detailed observations (e.g., densely sampled light curves,
spectra) to be classified, implicitly biasing them to lower redshifts. Also,
our transients are compiled from various surveys and spontaneous discoveries
that do not share the same detection efficiencies.  Figure 4: Year of
discovery vs. redshift of our transient events, color-coded with the
declination angle. The time axis is stretched so that transients are uniformly
distributed, and the vertical axis is in uniform scale of cosmological
distance modulus. The transient sample is a mixture of shallow, wide-field
surveys and deep, high-redshift surveys with limited sky coverage. There
exists a low-redshift, spatially scattered component peaked at $z\simeq 0.05$,
with another higher redshift, spatially more localized component around
$z\simeq 0.5$. Consequently, the redshift or luminosity distance distribution
of our events is double-peaked, as illustrated in Figure 3.

###  2.3 Transient sample statistics

In this version of our database, the transient sample has 36333 unique events,
including 23703 supernovae, 1621 GRBs, 99 TDEs, 14 FRBs, and 1 GW event. The
remaining ones are not explicitly classified yet. These are mostly candidate
events with either potential host or redshift reported. The numbers above do
not sum up to the total number of classified events in the transient sample,
as some events are detected in multiple wavelengths or messengers and thus are
double-counted. Also, as we have applied a series of selection criteria, the
number counts do not match their corresponding upstream data sources. Number
statistics by type labels are summarized in Table 2 and Figure 1.

More than half of the events in our database are Type Ia supernovae, where
$4\%$ of them are further classified as some subtype of SNe Ia or marked as
peculiar SNe Ia. These $4\%$ SNe Ia include well-defined subtypes like Ia-91T,
Ia-91bg, Ia-02cx, and other less commonly detected subtypes like Ia-09dc
peculiar events without clearly defined subtypes. Core-collapse supernovae
(“CC”), including Ib, Ic, II, and their subtypes, are the second most abundant
major class of events in our sample, accounting for nearly one-third of our
transient events. We further divide CC SNe into hydrogen-poor stripped-
envelope SNe (“SE”, including Ib, Ic, IIb) and hydrogen-rich Type-II SNe. SE
SNe accounts about $20\%$ of CC SNe, which is slightly lower than the common
estimate of volumetric SE-to-CC ratio (e.g., $36.5_{-5.4}^{+5.5}\%$ in Smith
et al. 2011, $30.4_{-4.9}^{+5.0}\%$ in Shivvers et al. 2017) Most CC SNe are
Type-II, but only $24\%$ of Type-II SNe are further classified into subtypes
by their light curve shapes or spectral signatures. The remaining $5\%$ of
events in our transient sample are mostly GRBs, and the majority are LGRBs.
Other rare events, such as TDEs and superluminous supernovae (“SLSN” under CC
SNe), only account for about $1\%$ of all events.

Within the transient sample, 28995 events have redshifts reported in their
upstream data sources. These are spectroscopic redshifts of either transient
or their host galaxies. Very occasionally, host photometric redshifts are
used. The redshift distributions of major transient types are quite distinct
from each other (Figure 2, left). Type Ia supernovae are distributed over the
entire redshift range, with an extended high-redshift tail, likely due to
their higher luminosities. Type-II and stripped-envelope supernovae are
concentrated at lower redshifts, and particularly, SE SNe are at even lower
redshifts than Type-II SNe. Non-SN events, primarily GRBs with redshifts
available in our sample, also extend to very high redshifts compared to other
transients. Regarding the fractions of major transient types (Figure 2,
right), most events at the lower redshift side ($z\lesssim 0.02$) are CC SNe,
and the fraction of SE SNe within CC SNe drops toward higher redshifts. At
medium redshifts ($z\gtrsim 0.02$), SNe Ia quickly dominate the local fraction
of events, within which SNe Ia with detailed subtypes are mostly distributed
at the lower redshift side.

We further inspected the redshift and luminosity distance distributions of
transients across sub-types as defined in Table 2, calculated using WMAP
9-year cosmological parameters (Hinshaw et al., 2013). Luminosity distances
are presented as distance moduli to be more closely related to the
detectability of transients and can better illustrate the biases and
heterogeneity in our database (Figure 3). Notably, SNe Ia in our sample have a
double-peaked distance modulus distribution, where the first peak appears
between $z\sim 0.05$ and $0.1$, with a secondary peak between $z\sim 0.2$ and
$0.5$. Given the large fraction and extended redshift distribution of SNe Ia
in our transient sample, the full-sample redshift distribution (in distance
modulus) is also double-peaked. Regarding subtypes of SNe Ia, we notice that
subluminous Ia-91bg and Ia-02cx are skewed towards lower redshifts than
overluminous Ia-91T and Ia CSM. Ca-rich transients are distributed similarly
to other low-luminosity SNe Ia. Within CC SNe, SE SNe (Ib, IIb, Ic) have very
similar, if not slightly lower, redshifts compared to Type-II SNe, which
dominate the total number of CC SNe. Superluminous supernovae (“SLSN”) are
clearly skewed towards higher redshifts, in which hydrogen-poor SLSNe (SLSN-I)
are distributed further than hydrogen-rich SLSNe (SLSN-II). Meanwhile, Type II
L are at the lower redshift end of Type II, although their peak magnitudes are
not fainter than Type II P, which dominate the number count of photometrically
classified Type II. For other subtypes of CC SNe, the differences are more
nuanced. Type Ibn SNe are at the higher redshift tail of SE SNe, likely due to
their higher peak luminosities. Similarly, relatively brighter SNe Ic BL (SNe
Ic with broad emission lines) are at slightly higher redshifts than normal SNe
Ic. Finally, rare events like TDEs and FRBs are distributed at higher
redshifts than CC SNe, and GRBs have the highest average redshift compared to
other major types.

The double-peaked distance modulus distribution for SNe Ia could be a direct
result of our sample heterogeneity. These transients are discovered by various
surveys with significantly different sky coverage, cadence, and sensitivity.
Consequently, the spatial-temporal coverage of our sample is far more
complicated than a single survey. As illustrated in Figure 4, our events can
be loosely grouped into low-redshift and high-redshift groups. The low-
redshift group is mostly associated with wide-area, shallow surveys, with
steadily improving depth over the years. Meanwhile, the high-redshift group is
primarily contributed by a few deeper surveys that are more localized in their
sky coverage. This is evident in their declination angles on the sky, where
low-redshift events have scattered and well-mixed declination angles, while
high-redshift events only have a few discrete ranges of values (i.e., their
survey fields) that are related to their time of discovery.

Finally, the all-sky map of our transient events clearly shows the footprints
of a few major deep and narrow-field supernova surveys (Figure 5), notably the
SDSS Supernova Survey (Frieman et al., 2008), the Supernova Legacy Survey
(Astier et al., 2006), the ESSENCE Supernova Survey (Miknaitis et al., 2007),
the High-Z Supernova Search (Schmidt et al., 1998), and the Supernova
Cosmology Project. These deep supernova surveys, however, only contribute a
limited fraction of events in our transient sample. Wide-field transient
surveys contribute most records. Labeling transient records by individual
survey programs could be interesting for certain studies, but this cannot be
achieved easily due to the incompleteness of information in our upstream data
sources.

The number statistics, local fraction, and redshift distribution indicate that
our transient sample is highly heterogeneous. Multiple factors could
contribute to such heterogeneity. First, there could be a classical Malmquist
bias, where intrinsically brighter events can be observed at greater
distances. A possible second-order effect is that slowly-evolving events have
a longer time window for detection and classification and are thus easier to
be identified than those that rise and fade quickly. Second, the criteria or
techniques for classification may also introduce some bias, as many subtypes
require high-quality spectra or even spectra at a certain phase of evolution
to identify, which are often unavailable for distant, faint, or rapidly-fading
objects. Third, and perhaps most importantly, our master transient list is a
compilation of various data sources, including spontaneous discoveries and
transient surveys with vastly different detection efficiencies defined by
their survey design (e.g., sky area, field-of-view, single-epoch sensitivity,
and cadence). Therefore, the number count, relative fraction by redshift, and
luminosity distance distribution do not represent their true cosmological
event rates or that of a sensitivity-limited survey.

Figure 5: (Top) Equatorial coordinates of transients in Lambert cylindrical
equal-area projection. Colors indicate their redshifts, and gray points are
events without redshifts. The dashed and dotted lines trace ecliptic and
Galactic planes, respectively. The fields of several deep supernova surveys
are clearly visible, but they only contribute a limited fraction of events.
Large-area surveys discover most events. (Bottom) Similar to the Top panel,
but showing Galactic coordinates instead. The discovery dates of events are
color-coded with uniform number density over the color axis. Gray points
indicate events without discovery dates. The dashed and dotted lines trace
equatorial and ecliptic planes, respectively. The timespans of major deep
supernova surveys, particularly the SDSS-II supernova survey near the
equatorial plane, are clearly visible.

##  3 Host Cross-identification

To compile the properties of host galaxies, one needs to identify their
counterparts in other datasets and then access their observed or derived
properties. The procedure includes three steps: searching for potential
counterparts of host galaxies in other datasets, matching potential
counterparts across datasets to ensure their spatial association and
correspondence relationship, and identifying the true counterparts of host
galaxies.

Through this Section, we use “sources” for records in other datasets,
“objects” for actual galaxies and stars in the sky, “groups” for clumps of
spatially-associated sources in other datasets matched by our algorithm, and
“fields” for patches of sky near transient coordinates containing the host and
other objects.

We aim to cross-match sources in other datasets into groups correctly, then
identify the counterparts of known hosts or rank cross-matched groups to
identify the best host candidates when necessary. Therefore, we refer to the
entire procedure as “cross-identification.” We use “known hosts” for host
galaxies cross-matched using existing host names or coordinates, and “newly-
identified hosts” (or simply “new hosts”) for host galaxies identified by our
ranking algorithm. Many “newly-identified” hosts are probably apparent to
previous authors or observers, and some may have been identified elsewhere.
However, these hosts are not reported to our upstream data sources. Our work
provides independent host cross-identification for these events.

In the following subsections, we describe the detailed procedure of host
cross-identification. The workflow is outlined in Figure 6.

###  3.1 Accessing sources in external catalogs

We search for the counterparts of host galaxies in other datasets, include
value-added catalogs (VACs) and survey catalogs, which we collectively refer
to as “external catalogs.” VACs are online astronomical databases including
NED, SIMBAD, and HyperLEDA. They collect the observed and derived properties
of objects from multiple surveys and in the literature. More importantly, they
are directly accessible using common name designations of astronomical
objects. Survey catalogs are high-level data products of individual
photometric or spectroscopic surveys, either hosted at web services or
provided as static files containing directly measured properties of objects.
Generally, without name-resolving services like
Sesame272727cds.u-strasbg.fr/cgi-bin/Sesame, data of individual objects in
survey catalogs are only accessible using their sky coordinates. External
catalogs we use in this work are summarized in Tables 4 and 5. The number
statistics and source selection criteria of these catalogs are discussed in
Section 4.

When either host name or coordinate is known, finding the counterparts of the
host in external catalogs becomes a trivial problem. Even some survey catalogs
are not directly accessible using host names alone, the corresponding sky
coordinates of these names are still easily obtainable from VACs. However,
host names or coordinates are only known for a fraction of events. About half
of all events, even having redshift or classification, do not have host name
or coordinate reported. Subject to the availability of host information, we
use two different approaches to search for potential counterparts of hosts in
external catalogs. We outline the two approaches here. The details are
discussed in Appendix D.

####  3.1.1 Events with known hosts

Nearly half of transients in our sample have host galaxies reported. Their
host galaxies are usually identified by experienced human observers, whereas
the involvement of automated programs is increasing in recent years (e.g.,
Gupta et al., 2016; Sako et al., 2018; Gagliano et al., 2020). Often host
galaxy names are reported, with or without the corresponding coordinates.
Occasionally only host coordinates are provided. Whenever possible, we use
host names to access their properties in VACs to ensure reliable matching.
Meanwhile, many survey catalogs are only accessible with sky coordinates. We
resolve the existing host names in VACs to find their corresponding host
coordinates. Even some events already have host coordinates reported along
with host names, to avoid possible errors, we would like to update their as-
reported host coordinates with the name-resolved coordinates in VACs.

To ensure that the host names and coordinates we use are reliable, we
conducted a systematic quality check of their original reference sources. Only
host names and coordinates from reliable reference sources are used in this
work. The procedure of quality check and the selection of reference sources
are described in Appendix C. For events with known host names, we first
resolve their names in NED and SIMBAD to find the best-available host
coordinates. The procedure is described in Appendix D.1. Once succeeded, we
use the name-resolved host coordinates to access other catalogs and find host
properties. We use the existing, as-reported host coordinates when no host
name can be resolved, but host coordinates are given to search external
catalogs. When searching external catalogs, we use a fixed radius of 15” for
name-resolved host coordinates and 30” for as-reported coordinates. Such large
search radii minimize the chance of missing the true host due to ambiguous or
inaccurate coordinates, a common situation for irregular, disturbed, or well-
resolved large host galaxies. Even using known host coordinates, the returned
sources do not always correspond to our known host galaxies. Other non-host
objects may also populate the field given our relatively large search radii.
Therefore, we always perform a local cross-matching to ensure that the host
properties we compiled are complete and uncontaminated (Section 3.2). Finally,
we use transient coordinates to access nearby sources for events without any
host coordinate, either name-resolved or as-reported.

####  3.1.2 Events without host information

For events without host galaxies reported, we aim to identify their best host
candidates and then compile their properties in external catalogs. We search
transient coordinates in VACs and survey catalogs with flexible, per-case
search radii to enclose catalog sources that match the true hosts. Transient
coordinates may have significant angular offsets to their hosts. Therefore,
the search radius for each event must be adjusted to optimize the chance to
enclose the true host. We determine the search radius primarily based on
transient redshifts. For events with redshifts reported, we use angular
distance corresponding to a projected distance of $45$ kpc at the reported
redshift to search catalog sources. When there is no redshift reported, we use
a default search radius of $30^{\prime\prime}$. Specifically, for GRBs without
known redshifts, we use 3 times the 90% error radius as the search radius. Due
to this conservative search radius, we may miss their true hosts, but this
also reduces misidentified hosts. We set lower and upper limits of search
radius to $15^{\prime\prime}$ and $2^{\prime}$, but for GRBs, the lower and
upper limits are $5^{\prime\prime}$ and $15^{\prime\prime}$. These catalog
sources are then cross-matched into spatially associated groups (i.e., host
candidates) with compiled properties (Section 3.2).

Figure 6: The general procedure to compile host properties for transient
events. For events with known hosts, we resolve host names in NED and SIMBAD
to obtain the best-available host coordinates and search the coordinates in
external catalogs. The catalog sources that correspond to our known hosts are
selected with a multi-catalog cross-matching process to avoid mismatching. For
events without hosts reported, we search transient coordinates in external
catalogs with larger cone radii, cross-match all nearby catalog sources with a
similar approach, and identify the most likely host candidates within cross-
matched groups using a trained ranking function. The subsections are indicated
on each block.

###  3.2 Cross-matching objects

To compile the properties of host galaxies in external catalogs, we need to
ensure that only robustly detected catalog sources that spatially coincide
with our best-available host coordinates are used. Meanwhile, when identifying
new host galaxies, we also need to select a group of catalog sources in the
field with confirmed spatial association and readily compiled properties,
rather than treating catalog sources as individual objects and choosing the
best host candidates from them. We need to spatially cross-match sources we
obtained in external catalogs and establish their correspondent relationship
for both purposes.

Cross-matching more than 20 catalogs with a wide latitude of angular
resolution, sensitivity, and a mixture of photometric techniques is
challenging. Several factors make the problem complicated. The sensitivities
(or depths) of sky surveys are vastly different, where sources detected in one
survey could be absent in other catalogs. Meanwhile, there could also be
significant field-to-field variations of source densities and survey coverage.
Some sources in certain catalogs could be artifacts or duplicates of existing
sources in the same catalog that should be removed. Finally, even true and
unique sources could be foreground stars rather than distant galaxies that
could host our transients. Therefore, when cross-matching catalog sources, the
criteria should be optimized for every catalog and tuned in each field.
Artifacts and duplicates of existing sources should be excluded to ensure that
cross-matched catalog sources are genuine and unique. Foreground stars should
be flagged whenever possible to facilitate the identification of new host
candidates.

Catalog sources are cross-matched once their coordinates coincide within a
certain threshold. Source coordinates bear random errors depending on the
signal-to-noise ratio of detection and the angular resolution of the survey
(optical and pixel sampling), along with systematic errors inherited from
survey-wide astrometric calibration. For resolved sources such as our host
galaxies, the measured positions also depend on the photometric technique used
and the deblending of detected sources. Irregular, disturbed, or well-resolved
galaxies are particularly affected by this issue. The criterion for spatial
coincidence is therefore essential for any cross-matching algorithm. The
conventional way to cross-match two catalogs relies on a constant angular
distance threshold, which is heuristically tuned to maximize the number of
reliably matched pairs while not causing mismatches. One may also use the
position errors of sources when provided and match sources in a probabilistic
way. Matching multiple catalogs, however, is a substantially more challenging
problem. For example, Pineau et al. (2017) use $\chi^{2}$ test in
combinatorial analysis to cross-match multiple catalogs, while Salvato et al.
(2018) establish the correspondence of sources with Bayesian statistics. These
algorithms either rely on detailed source properties or require extensive
calibration with large samples and are thus unfeasible given our large number
of external catalogs. Due to data availability issues, we attempt not to
elaborate on the detailed source properties (astrometric, geometric or
photometric) when cross-matching. Also, the algorithm must be tuned and
optimized using existing fields only, rather than a significant portion of the
full catalog.

We cross-match these catalogs simultaneously in a single run using connected
components in undirected graphs, where connectivity (i.e., adjacency matrix)
is determined by pairwise angular distances, predefined per-catalog
astrometric tolerances, and per-field matching thresholds. Here, the
astrometric tolerance of each catalog is a predefined global constant
reflecting the average positional error of sources in this catalog, while the
matching threshold in each field represents an “acceptance threshold” of
cross-matching that can be fine-tuned locally to compensate the field-to-field
variation of source density and catalog coverage. The algorithm is a simple
generalization of the two-catalog matching case, which is outlined below and
described as pseudo-code in Appendix E:

  1. -

Selecting catalog sources. We first select true and unique sources in each
catalog in the vicinity of each queried coordinate (or “field”). The detailed
criteria are discussed in Section 4. Real sources with potential quality
issues (e.g., saturated in the image, close to a bright star, or field edge)
are preserved. We leave such quality control of measured properties for those
who use our data.

  2. -

Calculating pairwise distances. We project selected catalog sources in the
field onto a local 2-d Cartesian frame, in which sky coordinates are converted
to directional angular offsets to the queried coordinate. Then we calculate
the pairwise angular distances of catalog sources, excluding pairs within the
same catalog that are always supposed to be disconnected.

  3. -

Finding connected source pairs. The pairwise angular distances are further
normalized with their corresponding pairwise astrometric tolerances. Here the
pairwise astrometric tolerance for any two catalogs is defined as the square
roots of their quadratically summed, per-catalog astrometric tolerances.
Source pairs with normalized angular distances below the per-field matching
threshold are considered as “connected.”

  4. -

Finding groups of connected sources. We finally split sources into
interconnected groups, in which any two sources are either directly connected
or can be indirectly connected via a “chain” of connected sources. Each
interconnected group of catalog sources is considered a cross-matched group.
Isolated sources that are not connected to other sources are also considered
as single-source “cross-matched” groups.

In analogous to a conventional probabilistic two-catalog cross-matching
problem, assuming that per-catalog astrometric tolerances are isotropic
positional errors of sources, the normalized distance is related to the
Mahalanobis distance of centroid position distributions for a source pair. Not
surprisingly, the values of per-catalog astrometric tolerances and per-field
matching thresholds are critical to the accuracy of cross-matching. We choose
per-catalog astrometric tolerances using a global optimization method, and the
per-field matching threshold is tuned with a similar approach, which is
discussed in Appendix F. To further assess the quality of cross-matching, we
introduced several metrics for cross-matched groups, which are discussed in
Appendix G.

Before finding the known host galaxies or best host candidates and compiling
their properties, some issues and complications remain with these cross-
matched groups. First, a substantial fraction of catalog sources is actually
Milky Way stars, rather than distant galaxies that could be transient hosts.
These foreground stars may become interlopers during host identification and
contaminate the compiled host properties if not handled properly. The
situation worsens for transients at lower Galactic latitudes. Therefore,
foreground stars must be properly labeled and excluded when necessary. Another
issue is that some catalogs may contribute more than one source in a single
cross-matched group. Indeed, we do not directly connect sources contributed by
the same catalog, but they can still be “passively” connected via other
sources in the group. This leads to the confusion of multiple catalog sources,
where only one (or rarely, none) of these sources can reasonably represent the
measured properties of the group. When such confusion occurs, we need to
select the right source (or “representative” source) for the properties of the
object measured by this survey. The procedure is described in Appendix H. It
is worth noting that even we distinguish stars from galaxies here, we still
allow possible stellar objects to be identified as host galaxies, given the
inevitable confusion of star/galaxy separation in external catalogs (Section
3.4).

Table 3: Astrometric tolerances for external catalogs

Catalog | Tolerance | Catalog | Tolerance
---|---|---|---
NED | $0.937^{\prime\prime}$ | 2MASS XSC | $0.968^{\prime\prime}$
SIMBAD | $1.230^{\prime\prime}$ | 2MASS PSC | $0.752^{\prime\prime}$
HyperLEDA | $1.227^{\prime\prime}$ | UKIDSS LAS | $0.474^{\prime\prime}$
GALEX MIS | $1.641^{\prime\prime}$ | VHS | $0.904^{\prime\prime}$
GALEX AIS | $1.824^{\prime\prime}$ | AllWISE | $1.211^{\prime\prime}$
SDSS | $0.456^{\prime\prime}$ | unWISE | $1.336^{\prime\prime}$
PS1 | $1.421^{\prime\prime}$ | NSA | $1.433^{\prime\prime}$
DES | $0.227^{\prime\prime}$ | SuperCOSMOS | $0.813^{\prime\prime}$
DESI LS | $1.196^{\prime\prime}$ | Gaia | $0.010^{\prime\prime}$
SkyMapper | $1.077^{\prime\prime}$ | MPA-JHU | $0.010^{\prime\prime}$
VST ATLAS | $0.293^{\prime\prime}$ |  |

Note. — The per-catalog astrometric tolerances are estimated by maximizing the
overall connectivity score, as described in Appendix F. Gaia and MPA-JHU
catalogs have fixed astrometric tolerances of $0.01^{\prime\prime}$. These
catalogs are matched passively with other catalogs.

###  3.3 Finding sources matching known hosts

For events with known hosts, we only need to identify their counterparts in
external catalogs, or more accurately, the cross-matched group near each
transient that contains these catalog sources. We put the name-resolved or as-
provided host coordinates into the field as a virtual source and cross-match
it with other catalog sources using zero per-catalog astrometric tolerance.
The cross-matched group that contains this virtual source is considered as the
counterpart of the known host.

When using name-resolved host coordinates, the coordinate always coincides
with (and thus matches) a NED or SIMBAD source with the same object name. We
then mark the group as a “confirmed-by-name” host. When using as-provided host
coordinates, and the coordinate matches a group that has not been identified
as a star, we consider the group a “confirmed-by-coordinate” host. If any
cross-matched source in the group is believable a star rather than a galaxy,
we only consider the group a primary host candidate instead of a confirmed
host.

Finally, if we cannot identify a “confirmed-by-name” or “confirmed-by-
coordinate” host using host coordinates, or if only transient coordinates are
used for source searching, we proceed to rank cross-matched groups by their
likelihood to be the true host using the procedure described in the following
subsection. This includes the case in which the as-reported host coordinates
coincide with a star or even match nothing in the search radius.

To ensure that these known host coordinates have successfully matched the
indicated galaxies, we perform a comprehensive visual inspection of these
fields (Appendix J.1). Occasionally, if the host coordinate failed to match
the center of the indicated galaxy or the most prominent component of an
irregular host, we manually reassign the host to the correct cross-matched
group. We focus on the results of cross-matching. Generally, we do not judge
the correctness of transient-host association in our upstream data sources.
However, if there is clearly a better choice of host galaxy than the indicated
one, we mark the galaxy as an “alternative host” and flag the case without
reassigning the host. To summarize, in 91 cases, the input host coordinate
missed the indicated galaxy; in 257 cases, we noticed better hosts than the
indicated ones. For various reasons, like image quality issues, we are unable
to inspect 106 cases. The majority of known and cross-matched hosts (17556)
passed the inspection without any issues.

###  3.4 Finding new host candidates

For events without known and confirmed hosts, we identify their best host
candidates among cross-matched groups. Host galaxies reported in previous
surveys are often visually identified by experienced observers in discovery or
archival images. Such a manual or semi-automatic workflow may remain effective
if transients, mainly supernovae, are classified and reported at the current
rate; however, this would become infeasible for future high-efficiency follow-
up programs or large transient samples, as we discussed here. Meanwhile, for
state-of-the-art and future time-domain sky surveys, the growing involvement
of galaxy properties in real-time alert processing and follow-up scheduling
also requires efficient and reliable methods for automated host
identification.

Finding host galaxies is usually straightforward for human observers using
images. However, it may not be an easy problem for automated algorithms when
only cataloged source properties are available. Transients may occur anywhere
within and even far outside the optical radii of their host galaxies.
Therefore, the right host is not always obvious, and confusion of multiple
possible hosts is sometimes inevitable, even for human observers. Only one (or
even none) could be the true host among those cross-matched groups in each
field, assuming that sources are properly matched. We need an effective and
robust method to rank these cross-matched groups by their possibility to be
the host. We formulate the problem as follows: each cross-matched group could
be characterized with a set of numeric or binary-valued parameters. We aim to
construct a continuous scalar function of these parameters, whose value
indicates the possibility that a cross-matched group can be the true host. The
function need not be a probability estimate in nature, as we only use it as a
numeric score to rank groups and identify the best host candidate of each
event. Also, the function should differentiate multiple possible hosts
effectively to avoid confusion, i.e., being reasonably sensitive and
“monotonic.” We noticed that the decision functions inside conventional binary
classifiers might satisfy these requirements if the classifier is trained to
separate cross-matched groups into true hosts and other non-host objects. We,
therefore, train binary classifiers to distinguish known hosts from other
nearby non-host objects and then use the trained decision function to rank
cross-matched groups of events without known hosts.

There are several important aspects of this machine learning-based host
ranking method: the construction of the training dataset, the parameterization
of cross-matched groups, the evaluation of performance, and finally, the
choice of classifiers. Here we outline the overall procedure to construct
ranking functions. Relevant details are discussed in the Appendix I.

The training set lays the groundwork of our ranking functions. Aiming for a
clean training set, we choose known and properly cross-matched hosts that
passed our visual inspection without quality issues or alternative hosts
(Section 3.3, Appendix J). We access external catalogs and cross-match sources
again, supposing that neither host name nor coordinate is available so that
the training set best resembles the actual situation of host ranking. Cross-
matched groups near these transients are labeled as either “true hosts” or
“other objects” based on our existing knowledge about their true hosts. We
include both “true hosts” and “other objects” in the training set, where the
latter enable the ranking function to reject non-host objects near transient
coordinates. The training set, as a result, is much larger than the actual
number of transients used for training.

There is a wide range of options for the input variables of ranking functions,
such as transient-host offsets, results of cross-matching, or detailed source
properties. We call a particular combination of input variables a feature set.
As the starting point, we construct a basic feature set using only those
universally available parameters of each group, regardless of which catalogs
have been cross-matched. We also expand the basic feature set into its
redshift-dependent version using a few transient redshift-relevant parameters.
Some more detailed (but not universally-available) properties in external
catalogs, including optical-infrared magnitude, angular size, photometric
redshift, and other derived parameters, may further improve the performance of
ranking functions. Assuming that some particular wide-field surveys have the
required sensitivity to detect the true hosts, we further expand the basic
feature set with similar source properties measured in these catalogs.
However, these feature sets are only applicable in the coverage of these
surveys, and the choice of host is also limited to groups with required
parameters available.

Since the ranking functions are trained using events with known hosts, we can
evaluate their performance by checking if a ranking function can recover those
hosts. We define the accuracy of a ranking function as the chance that the
group of a known host ranked the highest among all cross-matched groups in its
field. In other words, the accuracy here is the fraction of known hosts that
have been successfully recovered by the ranking function, which differs from
the accuracy commonly used in statistics, machine learning, or other research
disciplines. We choose this particular performance metric because the goal of
training differs from the actual way the trained model was applied. The
classifiers are trained to distinguish individual “true hosts” from “other
objects,” without the contextual information about other groups in the field;
while the trained decision functions are used to rank (instead of to classify)
all cross-matched groups in the same field and identify the best one. A
result-oriented metric may better reflect the expected outcome of the training
process.

The decision functions of trained binary classifiers are the actual ranking
function we use. To choose the best classifier for the purpose, we compare
several conventional algorithms in the training and testing process, including
Logistic Regression, Support Vector Machine (SVM; Cortes & Vapnik, 1995),
Random Forest (RF; Ho, 1995; Breiman, 2001), AdaBoost (Freund & Schapire,
1997), Stochastic Gradient Descent (SGD), and Multilayer Perceptron (MLP). We
test their performance under different input feature sets, including the basic
feature set and its redshift-dependent version, as well as other feature sets
that rely on certain detailed source properties in external catalogs. These
classifiers all reach above 95% accuracy. Logistic Regression classifier, in
particular, achieves $97.3\pm 0.3\%$ and $97.5\pm 0.6\%$ accuracy using the
basic feature set and its redshift-dependent version. It also stably
outperforms other classifiers when using those derived feature sets with
detailed source properties. Therefore, we use Logistic Regression as the
default classifier. When transient redshift is available, we use the redshift-
dependent version of the basic feature set; otherwise, we use the default,
redshift-independent version. When applicable, ranking scores from other
classifier and feature set combinations are also provided, but we do not use
them to rank candidates directly.

As a final note, the highest-ranking group in a field is referred to as the
primary candidate, and other groups are collectively considered secondary
candidates. However, if the primary candidate is already assigned in a field,
other groups are all secondary candidates. We list the primary candidate first
in the dataset, followed by secondary candidates in descending order of their
ranking scores. The structure of the database is described in Section 6.

###  3.5 Quality Control of Newly-identified hosts

We use trained ranking functions to identify the best host candidates for
events without reported hosts. Although performing reasonably well in cross-
validation, the ranking functions have not been tested for performance for
newly identified hosts. Meanwhile, quality control flags and metrics are also
desired for individual events to facilitate the use of the database. We
provide two sets of quality control flags and metrics based on visual
inspection and mock sample tests.

####  3.5.1 Visual Inspection

We perform a comprehensive visual inspection of new hosts identified by us.
The inspection is aimed to 1) evaluate the accuracy of ranking functions on
these new hosts; 2) understand the failure modes of ranking functions; 3)
provide quality flags for the use of their host properties; and 4) make
corrections to misidentified hosts when possible.

We rely on visual inspection because the true hosts remain unknown at this
point. Other transient host catalogs may serve as the “ground truth” here, but
these catalogs may also have misidentified hosts, leading to inaccurate
estimates of accuracy. Indeed, visual inspection is a subjective evaluation of
quality and performance, which depends on the designed workflow, image
quality, and our discernment; while being an independent test, it reveals true
performance and failure modes that are otherwise hard to characterize.

We have previously inspected the cross-matching of known hosts (Section 3.3,
Appendix J.1). For the sample of new hosts identified in this work, we focus
on whether the default ranking functions have identified the most likely host
we noticed in image cutouts. We make corrections when hosts are likely
misidentified. Also, when there are multiple possible hosts, or when we cannot
identify the most likely host in image cutouts, we mark these indecisive cases
with appropriate flags. The procedure and criteria of visual inspection are
discussed in Appendix J.2.

As a summary, without considering cases in which we are unsure about the most
likely hosts, the default ranking functions achieved $97.0\%$ overall
accuracy, comparable to the results from cross-validation. Taking those
indecisive cases into consideration, the accuracy tops at $97.3\%$ under the
optimistic assumption that indecisive cases are always correctly identified;
but this accuracy may also drop to $89.1\%$ in an improbable pessimistic
situation that the ranking function failed in all indecisive cases.

####  3.5.2 Confidence Scores

As a complementary method for quality control, we introduce a numeric metric
for the reliability of new hosts, namely the confidence score. The confidence
score characterizes the degree to which a host candidate stands out among
nearby non-host objects. True hosts are, in fact, outliers among those more
abundant non-host objects in the field. They should have significantly higher
ranking scores than nearby non-host objects, assuming properly trained ranking
functions. If the ranking score of a candidate is comparable to the best
scores that non-host objects can reach by chance, then the candidate is, to a
certain extent, indistinguishable from non-host objects. This makes the
candidate a less reliable one. We use randomized mock transient samples and
metrics for outlier detection to carry out the comparison here.

We feed a randomized mock transient sample into the workflow to construct
baseline distributions, i.e., the distribution for the best-ranking score that
non-host objects can get by chance. With the corresponding cumulative
distributions, we can map the ranking score of a new candidate into a
percentage score, which we refer to as the confidence score. For a well-
behaved ranking function, reliable candidates should have confidence scores
close to $100\%$, because their ranking scores are significantly higher than
non-host field objects; transients with multiple possible hosts may have more
than one group with high confidence scores; finally, if no candidate reached a
high confidence score, then all its candidates are likely indistinguishable
from non-host field objects, and the event itself is likely hostless. Indeed,
the baseline distributions depend on transient redshift and catalog coverage
and should be constructed separately for each ranking function. We discuss the
details of implementation in Appendix K.

Nevertheless, the confidence scores only serve as a reference. They are still
based on the trained ranking functions themselves, which do not directly
indicate the correctness of transient-host association. Misidentified hosts
could have high confidence scores, while genuine hosts may also have low
confidence scores. Cross-checking with visual inspection results, or
performing some basic quality assessment, is encouraged when using confidence
scores.

###  3.6 Accuracy of Ranking Functions

We use trained ranking functions to identify host candidates for events
without reported hosts. The default ranking functions achieve above $97\%$
accuracy from the cross-validation using known hosts, and our visual
inspection reveals a similar empirical accuracy in new hosts identified in
this work. To further understand the behavior of ranking functions and to
characterize the quality of the dataset in detail, here we analyze the
dependence of accuracy on some key transient or host parameters, including
transient redshift, transient type, transient-host angular offset, and host
optical-near infrared (NIR) magnitude.

For the comparison of accuracy across types, we group transients into broad
classes using the hierarchy in Table 2. Non-supernova transients, mainly GRBs,
are grouped into “Other.” Some unclassified transients, which enter our sample
because either redshift or host galaxy is known, are grouped into “Unknown.”
For optical-NIR magnitudes of hosts, we use $r$, $i$, or $z$-band magnitudes
from DESI Legacy Surveys (LS) and PS1. Outside their coverage, SDSS, VST
ATLAS, or SkyMapper, are also used. For continuous variables, we divide events
into bins of equal sample size along the axis of interest to characterize the
dependency of accuracy.

####  3.6.1 Accuracy from Cross-validation

We do not use trained classifiers to rank cross-matched groups when the host
is already known. Instead, we trust the reported host and directly use host
coordinate to match the right group. However, as the training set, we can use
these known hosts to test the accuracy and analyze the behavior of our ranking
functions.

We estimate the accuracy with standard 10-fold cross-validation. We divide the
sample into 10 subsamples, where each subsample contains about the same number
of fields. During each test, we use one subsample for testing and the rest for
training. We repeat the test 10 times and use each subsample once in turn for
testing. The results are averaged over 10 tests to represent the performance.
Besides estimating the overall accuracy, we also derive the dependence of
accuracy on transient redshift, transient type, transient-host angular offset,
and host optical-NIR magnitude by binning the test sample, estimating the per-
bin accuracy, and taking the average of 10 separate tests. For each test, to
estimate the per-bin accuracy to percent level, each bin must contain at least
a few hundred events, which limits the bin number along the axis.

We summarize the dependency of accuracy in the upper sub-panels of Figure 7.
We only show the accuracy curves for the two default ranking functions for
clarity. Accuracy curves using other feature sets are summarized in Appendix
I.3. The change of accuracy over the redshift range is at the level of a few
percent, indicating the stable performance of the ranking functions. The
accuracy is lower than average for low-redshift or high-redshift events. Also,
at larger transient-host offsets or for fainter hosts, the accuracy is lower
than average. The degradation of accuracy for low-redshift events is likely
attributable to larger transient-host angular offsets. Towards higher
redshifts, the properties of true hosts become less distinguishable from those
ubiquitous faint galaxies, possibly leading to higher failure rates. Notably,
even at relatively small transient-host angular offsets, the accuracy is only
at $98\%$. Failure happens here when a more distant object is chosen rather
than the true host in close proximity. We do not see a significant variation
of accuracy across transient types. The accuracy in “SLSN” and “Other” (mainly
TDEs and GRBs) is noisy due to their limited sample size compared to the
number of folds. Using transient redshift-relevant parameters marginally
boosts the overall accuracy, and the improvement is clear at large transient-
host offsets.

For the accuracy of other feature sets (Appendix I.3), we notice that the
inclusion of some source properties can further improve the accuracy above our
basic feature sets. Even only at the percent level, the increase in accuracy
indicates a significant reduction of failure rates. However, this comes with
the cost that some host galaxies may not have the required features and are
thus ignored by these ranking functions.

####  3.6.2 Empirical Accuracy from Visual Inspection

We also estimate the accuracy of new hosts from the results of visual
inspection. Because the inspection relies on our discernment instead of some
“ground truth,” we refer to this as extitempirical accuracy. In the subsample
of events with unambiguous and clearly visible hosts, we use the fraction of
successfully identified cases as the empirical accuracy. There are, however,
many cases in which the most likely host cannot be determined visually,
usually due to image quality issues, confusion of multiple nearby galaxies,
and even the complete absence of any likely hosts. The uncertainty in accuracy
due to these cases should be taken into consideration. Therefore, we further
estimate the optimistic and pessimistic limits of empirical accuracy supposing
that all these indecisive cases are either successfully identified or missed
by the ranking functions (Section 3.5.1).

We focus on the dependence of empirical accuracy on key transient and host
parameters, which is outlined in the lower sub-panels in Figure 7. We find
similar patterns of dependence as we have seen in known hosts. The accuracy
does not change significantly along the axes. We find lower than average
accuracy in low-redshift or high-redshift bins, larger transient-host angular
offsets, and fainter hosts. Considering the loss of accuracy due to indecisive
cases, even in the pessimistic situation, the accuracy of low- to mid-redshift
events remains around $95\%$. However, for high redshift events, the large
fraction of indecisive cases implies a likely lower accuracy than outlined
here – the pessimistic lower limit of the last redshift bin is under $70\%$.
For this reason, “SLSN” and “Other” (mainly GRBs), usually high-redshift
events, have pessimistic lower limits around $80\%$ and $50\%$, respectively.
Using redshift as input may improve the results for high-redshift or fainter
hosts among cases with identifiable hosts, although the improvement may not be
significant considering the broad range of accuracy due to indecisive cases.

Finally, it shall be noted that for indecisive cases, their redshift and type
are intrinsic properties of the events, so that we can quote the optimistic
and pessimistic limits for these two curves. However, transient-host angular
offset and host optical-NIR magnitude depend on the host and are unknown for
indecisive cases. Therefore, we are unable to quote the optimistic and
pessimistic limits for these two panels.

####  3.6.3 Typical Failure Modes

To understand the reason for which the ranking functions miss the true hosts,
we rerun our redshift-independent default ranking function on the entire
training set, i.e., known and properly cross-matched hosts that passed our
visual inspection, and then select cases in which the top-ranking groups are
not the true hosts. On the other hand, from the visual inspection of new
hosts, we also summarize cases where the ranking functions missed the most
likely hosts as we noticed in the images. We analyze the failed cases on
either side and find some common failure modes.

Figure 8 shows examples of failed cases, grouped into four common failure
modes. First, in some fields, the true host could be ambiguous given the
existing input parameters. Some of these cases can be resolved using more
detailed source properties like shape parameters or galaxy redshifts, but some
remain challenging even for human observers. Second, in some cases, the true
host lies beyond the search radius, so another group inside the radius is
mistakenly chosen as the host. These are usually low-redshift events with
large transient-host angular offsets than typical. Since we exclude such cases
for the training set, the examples here are selected from new hosts. Failure
like this can be reduced with larger search radii at the cost of higher
computational and storage overloads. Third, some well-resolved nearby galaxies
are split into multiple sources in certain survey catalogs. These parts or
substructures of galaxies, when cross-matched into groups, become interlopers
that confuse our ranking functions and reduce the accuracy (see also Appendix
G). Fourth, the ranking scores can be undesirably boosted or penalized in
certain situations, leading to the ignorance of a clear and unambiguous nearby
host that other methods may not easily miss. Here the second and third failure
modes are closely related to low-redshift events, which are less of a concern
at higher redshifts. However, the other two failure modes are limits of the
method itself and can only be resolved using better-tailored ranking
functions.

We estimate the accuracy based on the assumption that the true host is always
inside the search radius. In reality, there are occasional cases where the
true host is beyond the radius, as shown in Figure 8. Most of the time, the
true host is just outside the search radius. For the training set, we excluded
262 events due to the absence of the true host in the radius. This represents
a small fraction compared to the resulted training set (17421). For new hosts,
at least 91 events are affected by this issue (Appendix J.2).

###  3.7 Notes on undetected hosts

Our training and testing process implicitly assumes that every transient
should have a host. In reality, it is not uncommon to see transients without
apparent host galaxies in discovery or archival images. We adhere to public,
wide-field surveys to identify host galaxies, while many hosts, especially
those of high-redshift transients (e.g., SLSNe, GRBs), may drop below the
sensitivity limits of these wide-field surveys and are thus undetected.
Intergalactic stars may also contribute to such hostless transients. Their
“host galaxies” are certainly misidentified and should be excluded from any
analysis.

We use visual inspection to flag possible hostless transients (Appendix J.2).
The criteria we apply are rather conservative. We only rely on DESI LS images,
or occasionally, PS1 images without stacking artifacts, to identify them. We
avoid labeling an event as hostless if its vicinity (angular separation
$\lesssim 15^{\prime\prime}$) contains any possible faint galaxies. These
faint and sometimes barely-resolved galaxies are ubiquitous in survey images,
and they are generally distant background galaxies per the photometric
redshift catalog of Zhou et al. (2021). However, we cannot eliminate the
possibility that some of them are actually real host galaxies at large
projected distances compared to their sizes. We check image cutouts of larger
field-of-view for low-redshift cases to exclude possible hosts at extreme
angular distances. Finally, if there is neither object in the image nor
sources in survey catalogs, but the name of a nearby VAC source clearly
indicates the host, we do not label the event as hostless. We assume that
their hosts, if exist, are low-mass galaxies with small sizes. If marginally
detectable, they should be excluded by the criteria above. Our inspection
leaves $408$ likely hostless events at the sensitivity limits of our
background images and external catalogs.

These $408$ possible hostless transients include $176$ GRBs, $158$ SNe Ia,
$30$ SLSNe, $43$ CC SNe that are not SLSNe, and a few more unclassified
events. Since one event can have multiple types, the numbers above do not
match the sample size. More than half of these possible hostless events have
redshifts reported, with a median value of $0.4$. GRBs and SLSNe dominate the
high redshift side of the population, and the low redshift side ($z<0.1$)
consists exclusively of supernovae ($42$ in total). Therefore, both low-
redshift and high-redshift events may contribute to the population of hostless
transients here.

However, the actual number of hostless transients could be underestimated
here. Taking the actually detected GRB hosts in Hjorth et al. (2012) for
instance. At a limiting magnitude of $R\simeq 24$, typical of modern wide-
field sky surveys, only $24\%$ of their hosts are detectable; at a deeper
limiting magnitude of $R\simeq 24.5$, more than half of their hosts remain
undetected. Yet, this does not include the upper limits reported in Hjorth et
al. (2012). Our visually identified sample of hostless transient is certainly
non-exhaustive.

Finally, the confidence score is an alternative, quantitative approach to
identify hostless events. These events do not have hosts; as a result, their
best-ranking candidates are nearby non-host objects, which are expected to
follow the baseline distributions as we discussed in Section 3.5.2. We show
the distribution of confidence scores for possible hostless transients in
Appendix K.

###  3.8 Notes on the workflow

We discussed the cross-identification of host galaxies under two different
circumstances: events with known hosts and events without hosts reported. The
procedure to access external catalogs and cross-match catalog sources are
similar, as outlined in Figure 6. In fact, we combine these two parallel
workflows into a single pipeline. Accessing and compiling properties of known
hosts is just a trivial case of the entire procedure, where the part of
ranking host candidates can be skipped. The entire pipeline is summarized as
pseudo-code in Appendix E.

In Figure 9, we show some typical host galaxies that went through our
pipeline, including both known hosts and new hosts. These hosts are randomly
selected near a few redshift values as examples. Even having more than 20
catalogs with non-negligible catalog-to-catalog offsets of sky coordinates,
our cross-matching algorithm works reasonably well for hosts across the entire
redshift range.

Similar to the transient collection, we store the compiled properties of host
galaxies in a separate data collection, indexed by the unique identifier of
each event. For events with confirmed hosts, either by name or by coordinate,
we store the properties of the cross-matched host and other cross-matched
groups. When there is no confirmed host besides the properties of the primary
candidate, we also store the properties of other cross-matched groups
(“secondary candidates”) in descending order by their ranking score in the
field. Note that when the as-reported host coordinates match a stellar object,
the group is marked as a primary candidate instead of a “confirmed-by-
coordinate” host (Section 3.3). Otherwise, the group with the highest-ranking
score becomes the default primary candidate.

We retrieve and compile host properties separately for each event, and
consequently, there could be duplicates of hosts if several transients have
been detected in the same galaxy. However, this is a relatively uncommon
situation, as only 1886 known hosts in our database have more than one
transient. Therefore, we do not index these cross-matched hosts uniquely to
eliminate duplicates at this moment. Our statistics and analyses are also
based on the idea of transient-host pairs rather than individual hosts.

Figure 7:  The accuracy of host cross-identification with our default ranking
functions, using redshift-dependent variables (red) or not (blue). The four
panels here show the dependence of accuracy on transient redshift, transient-
host angular offset, transient type, and host optical-NIR magnitude. Except
for the panel of transient type, we split the sample into bins of equal number
counts along the axis of interest and plot the per-bin accuracy at the median
value of the bin. We estimate the accuracy in two separate ways. With the
sample of known hosts (i.e., the training set), we calculated the average
accuracy using 10-fold cross-validation, where the standard deviations are
indicated as error bars. For the sample of new hosts, we estimated an
empirical accuracy with a visual inspection, defined as the fraction of
visually identified hosts that are also chosen by our algorithm. Vertical
“error bars” outline the range of accuracy due to the uncertain cases
(Appendix J.2). We plot the distribution of both samples as shaded areas in
each sub-panel. The densities are over the logarithmic axis for redshift and
transient-host angular offset, so the histogram area corresponds to the sample
size. For the dependence on redshift, we also plot the subset of points
without redshift in the gray shaded area of the first panel.

Figure 8:  Examples of incorrectly identified hosts from cross-validation of
known hosts and visual inspection of new hosts. Each panel shows a field
centered at the transient event, with the event name and angular scale of 5”
indicated at the corners. Inside the search radius of each event (dotted
circle), the best host (either previously reported or visually identified) and
algorithm-identified host are highlighted with red and orange crosses, and
other cross-matched sources are marked with white circles. Each row here
represents a common failure mode: 1) inevitable confusion of multiple likely
host candidates in the field, in which the true host could be ambiguous even
for visual identification; 2) the true host is located beyond the search
radius due to an exceptionally large transient-host offset; 3) part of a well-
resolved, nearby large galaxy is identified as the host, instead of its main
component; and 4) another cross-matched object, either nearby or far in the
background, is erroneously identified as the host at higher ranks. Events at
lower redshifts are more susceptible to the second and third cases.

###  3.9 Comparison with similar works

The technique to cross-identify host galaxies and the data products presented
in this work is unique in many aspects. Here we compare our method and dataset
with similar works in the literature and discuss the advantages and drawbacks
of different approaches.

####  3.9.1 The Directional Light Radius Method

The Directional Light Radius (DLR) method (e.g., Sullivan et al., 2006; Gupta
et al., 2016; Sako et al., 2018), which relies on galaxy shapes and sizes to
identify hosts, is probably the most widely used technique in previous works.
The angular distances of potential host galaxies to a transient are normalized
by their elliptical radii in the direction of the transient (i.e., the DLR).
The resulted parameter ($d_{\mathrm{DLR}}$) is a comparison of transient-
galaxy angular distance and galaxy angular size, taking axis ratio and
position angle (inclination or projection effects) into consideration. Nearby
galaxies with smaller $d_{\mathrm{DLR}}$ are more likely to be the host.
282828In this work and in Gupta et al. (2016), $d_{\mathrm{DLR}}$ refers to
the normalized transient-galaxy angular distance by DLR. In Gagliano et al.
(2020), $d_{\mathrm{DLR}}$ refers to the DLR itself, and
$\theta/d_{\mathrm{DLR}}$ is the normalized angular distance (Eq. 9). The DLR
method implicitly assumes that transients in galaxies have a similar radial
extent as the stellar light, which is reasonable for most stellar explosions.
Calculating $d_{\mathrm{DLR}}$ requires no more than the basic shape and size
parameters of galaxies, making the method easy to implement and thus popular
for similar studies.

The method also has some clear drawbacks. First, shape and size parameters,
such as moments of light, are method- and data-dependent. The resulted
$d_{\mathrm{DLR}}$ are thus comparable only within the same survey. Such
parameters are also noisy for low signal-to-noise detections or marginally-
resolved galaxies, leading to significant errors in the estimated
$d_{\mathrm{DLR}}$. Second, even assuming that transients follow stellar light
and galaxy isophotes are elliptical, the dramatic differences in radial light
profiles (or concentration of light) remains a source of bias. For example,
the effective radius ($r_{50}$) is commonly used to calculate
$d_{\text{DLR}}$, but the fraction of enclosed light in units of $r_{50}$
depends on the light profile, even for the simplest models. At four times the
effective radius, the fraction of enclosed stellar light is $99.1\%$ for an
exponential disk, but for de Vaucouleurs’ profile, the fraction is only
$84.7\%$. The same $d_{\text{DLR}}$ value, therefore, corresponds to a
different fraction of enclosed light and hence the probability of being (or
not being) the true host. Third, the comparison of $d_{\mathrm{DLR}}$
implicitly assumes uniform transient rate and detection efficiency across
galaxies, which may become an issue when there are multiple candidates with
comparable $d_{\text{DLR}}$. Finally, for many photometric catalogs, there is
a specific issue with nearby large galaxies. Most photometric pipelines are
not optimized for galaxies of large angular sizes. Their resolved sub-
structures, such as clumps, spiral arms, and even massive star clusters can be
broken into individual sources by profile-fitting or de-blending algorithms.
Such substructures may impede host identifying, where the source with the
smallest $d_{\text{DLR}}$ could be a substructure instead of the galaxy core.

The DLR method is not immediately applicable to our entire transient sample
due to the heterogeneity in transient types, redshift range, and catalog
coverage. For example, shape and size parameters required by the DLR method
are not always provided in external catalogs. Even when available, they are in
diverse representations and are survey-dependent. However, for some specific
catalogs, we implement the method. We test the accuracy when only
$d_{\text{DLR}}$ is used to rank host galaxies. We also include this
$d_{\text{DLR}}$ parameter in some feature sets and analyze the performance.
Only using $d_{\text{DLR}}$ to rank nearby groups, we can recover nearly
$90\%$ of known hosts. The implied $10\%$ misassociation rates is higher than
the estimate of $5\%$ in Gagliano et al. 2020, while close to the mock sample
performance in (Gupta et al., 2016). On the other hand, feature sets including
$d_{\text{DLR}}$ achieve higher accuracy than the default ranking functions.
However, the improved performance may not be fully attributed to this single
parameter.

####  3.9.2 The Gradient Ascent Method

The recent work of Gagliano et al. (2020) represents a more sophisticated
genre of technique, where the gradient of surface brightness extracted from
image cutouts, instead of cataloged source properties, is used to associate
transients with potential hosts. They locate the peak of surface brightness
(i.e., the core of the potential host) in the image cutout and use the
cataloged source at the peak position for host properties. Finding the peak of
light in an image is a non-trivial task in the presence of foreground stars
and resolved galaxy substructures. They remove point sources, smooth out the
fluctuations over the extended light component, and construct the gradient
field for the light. After then, starting from the transient location, the
algorithm ascends to the core of a nearby galaxy following this gradient
field. This method works remarkably well for low-redshift host galaxies with
resolved substructures where the conventional DLR method could easily fail.

However, the technique requires direct analysis of image cutouts, potentially
with higher computational costs. The quality and availability of image data
could also be a constraint of its applicability. Also, the hyper-parameters in
the entire workflow, such as the smoothing scale and the star/galaxy
separation criteria, must be tuned for each survey. Finally, in analogous to
an optimization problem, the chance remains that the current solution (i.e.,
the peak of surface brightness) is only a “local minimum” instead of the
global best solution, or the solution converges in the wrong direction.

####  3.9.3 The Ranking Function Method

In this work, we introduce a machine learning-based host candidate ranking
technique. We characterize potential hosts by various parameters, including
basic parameters that are universally available for every cross-matched group,
and optionally, detailed source properties in external catalogs. These
parameters are then fed into a ranking function, which sorts these potential
hosts by their estimated possibility to be the right host. The ranking
function is trained using known, properly cross-matched, and visually
inspected host galaxies in our dataset, essentially turning the problem into a
regular classification or regression problem.

Using a trained algorithm instead of a designed one based on certain
assumptions is the primary difference of our approach compared to other
methods. Those already identified hosts, including non-host objects in the
same field, are used to train ranking functions and objectively evaluate their
performance. A trained algorithm generally uses fewer assumptions and is thus
less vulnerable to biases inherited from the imposed assumptions. The method
is also extensible and flexible. Any characteristics of potential hosts can be
used as input, and many conventional classification and regression algorithms
can act as the ranking function. Actually, this is a framework or a family of
methods. However, the method relies on a well-constructed and sufficiently
large training set, which may not be available for other similar problems. One
may create a training set using mock transient-host pairs as did in Gupta et
al. (2016), but the assumptions made when generating mock transient-host pairs
could implicitly bias the results. We also assume that the cases to which the
trained ranking function is applied are similar to those in the training
dataset. This is less of a concern since the new hosts have empirical accuracy
comparable to the accuracy from cross-validation, but the transferability of
the trained model should be tested in other similar applications.

We provide a matrix of trained ranking functions, but the default ranking
functions we used deserve further discussion. To ensure the completeness of
input variables, the default ranking functions only take the basic
characteristics of cross-matched groups that are independent of which catalogs
have been matched. Detailed source properties in external catalogs, which
proved to improve the accuracy, are not used. Therefore, the behavior of the
default ranking functions might be partly driven by non-physical factors. We
use a simple Logistic Regression classifier here. Such a simple linear model
may suffer from underfitting, but the model is also insensitive to outliers in
the training set and systematic variations in some variables. In other words,
a simple linear algorithm guarantees the robustness of the trained model, but
it may also lack the capability to differentiate multiple likely candidates.

####  3.9.4 Single-catalog vs. multiple-catalog techniques

We choose hosts from cross-matched groups, where each group may contain
sources from multiple external catalogs. This multi-catalog approach, driven
by our motivation to provide rich host properties across multiple surveys,
clearly differs from previous works. Our method and dataset naturally provide
better coverage of host properties over a wide range of wavelengths, robustly
cross-matched across various catalogs, but the downsides are also clear. To
begin with, cross-matching multiple catalogs, which itself is a challenging
problem for galaxies with a wide range of angular sizes and redshifts, becomes
an extra step. More importantly, due to the sensitivity limits and partial sky
coverage of surveys, cross-identified hosts cannot have complete source
properties across these 21 external catalogs. Consequently, there is a trade-
off between the accuracy and applicability of training functions if one plans
to use detailed source properties as input parameters. Adhering to those
universally available basic features avoids the problem, but the performance
could be driven by non-physical factors. Also, the compiled dataset could be
sparse, especially for high redshift hosts or hosts beyond the coverage of
major sky surveys. On the contrary, single-catalog techniques can fully
utilize the existing source properties when finding host galaxies. Host
properties compiled in this way are also consistently measured and are only
subject to the selection function of one survey. However, the data provided
are always limited without further cross-matching with other catalogs.

####  3.9.5 Comparison with Gagliano et al. (2020) dataset

Finally, besides the different methods to identify host galaxies as we
discussed above, it is also worth comparing the publicly available dataset
presented here with the GHOST database maintained by Gagliano et al. (2020).
First, our work includes more transient types, such as TDEs, GRBs, and other
rare events, with fine-grained taxonomy, while Gagliano et al. (2020) focus
mainly on spectroscopically-confirmed supernovae. Second, our transient sample
includes 18140 events with known and cross-matched hosts and 18100 new hosts.
The larger sample size compared to the GHOST database is mainly due to our
different sample selection criteria and the inclusion of other transient
types. Third, we choose to trust the reported hosts when available and only
use the algorithm when there is no usable host information, while Gagliano et
al. (2020) perform consistent catalog-based and image-based host matching for
all their supernovae. Fourth, the dataset presented here includes cross-
matched properties in 21 catalogs, while Gagliano et al. (2020) provides
source properties mainly in the Pan-STARRS catalog, with limited data in other
catalogs. We also include properties of secondary host candidates. Finally,
besides the static version of the database in various formats, at the moment,
we do not provide visualization and data access tools as Gagliano et al.
(2020) did. This could be a part of our future updates.

Figure 9:  Examples of cross-matching catalog sources using astrometric
tolerances and connectivity of objects. Each panel here shows the color
composite image of a host galaxy on the left side, with transient name,
redshift, and 1” angular scale (red bar) indicated. The cross-matched catalog
sources are plotted with various symbols on the right side, a zoom-in view of
the red circle (radius indicated) centered at the host. To cross-match
objects, the distances of catalog sources are normalized by their pairwise
astrometric tolerances and then compared to their thresholds of connectivity
(Section 3.2). Connected pairs under this criteria are indicated as gray
lines, and groups of inter-connected catalog sources are further labeled as
cross-matched objects. For clarity, we only zoom into the host here, while
other catalog sources in the search radius are also cross-matched into groups
in the same way. These panels are randomly selected near redshifts $0.02$,
$0.05$, $0.1$, $0.2$, $0.5$, and $1$, where the upper six panels are known
hosts, and the lower six panels are new hosts with our algorithm (Section
3.4). The cross-matching algorithm we developed is effective yet flexible for
most hosts, while more complex cases are discussed in Section G.

##  4 Host Data Compilation

The general guideline to choosing external catalogs is that the data should be
available for a large fraction of our hosts, and in combination, these
catalogs should cover the entire UV-optical-IR wavelength range. Following
this principle, we select a wide variety of catalogs (Tables 4, 5). Here, we
discuss the basic characteristics, source selection, and star-galaxy
separation criteria used for our external catalogs. We also briefly summarize
the coverage of host properties in these catalogs. Note that the column names
mentioned are as appeared in our data sources.

###  4.1 GALEX

We choose the All-sky Imaging Survey (AIS GR6/7; Bianchi et al., 2011) and the
Medium-depth Imaging Survey (MIS GR5; Bianchi et al., 2017) catalogs of the
GALEX mission for UV photometry of host galaxies. They provide fixed circular
apertures (8 and 17 pixels) and elliptical Kron magnitudes in FUV and NUV
bands, with related shape parameters for apertures and standard SExtractor
flags in each band. The AIS has point source limiting magnitudes ($5\sigma$,
AB) of $20.8$ (NUV) and $19.9$ (FUV), covering $2.6\times
10^{4}\;\mathrm{deg}^{2}$ ($63\%$ of the sky), while the MIS covers only 1000
$\;\mathrm{deg}^{2}$ but reaches deeper limiting magnitudes of $22.7$ in NUV
and $22.6$ in FUV.

We do not impose source selection criteria for GALEX catalogs. Sources in both
catalogs are always assumed to be valid objects. There are sources that are
affected by dichronic, detector window, or detector edge reflection artifacts.
These sources, although shall be excluded for photometric analysis as
recommended by the references above, usually match true objects in deeper
optical surveys. We, therefore, preserve these sources in our cross-matching
process, and these potentially corrupted photometric measurements can be
excluded later using corresponding artifact bits in Nafl and Fafl columns.
Given the relatively low angular resolution of the GALEX survey, we do not use
their star-galaxy separation parameters as given in the catalogs. Taking both
AIS and MIS into account, 9125 of our known hosts have GALEX magnitudes in at
least one band (usually NUV), while 6791 have magnitudes in both GALEX bands.
Meanwhile, 8161 new hosts have at least one band available, and 5993 have both
bands available.

###  4.2 SDSS photometric and spectroscopic catalogs

We also use the SDSS DR16 source catalog (Ahumada et al., 2020) for optical
photometry of host galaxies. This data release covers $1.4\times
10^{4}\;\mathrm{deg}^{2}$ ($34\%$ the sky) in ugriz bands, reaching point
source sensitivity down to $22.2$ mag in g and r (95% completeness, asinh
magnitude). We accessed the photometric object table at SDSS SkyServer
292929skyserver.sdss.org/dr16/en/home.aspx. The table provides complete PSF,
profile-fitting (de Vaucouleurs, exponential and composite), fixed aperture
and Petrosian photometry of detected objects. We exclude duplicate sources by
selecting primary survey objects (mode=1). Stars are identified using their
pipeline morphological classification (class) when cross-matching objects.
This morphological indicator is based on the comparison of model-fitting
magnitudes with PSF-fitting magnitudes. However, we only perform star-galaxy
separation for sources brighter than $i=20$ (asinh magnitude). Fainter than
this magnitude, the purity of stars begins to
drop303030classic.sdss.org/dr7/products/general/stargalsep.html.

We also search for spectroscopic targets in MPA-JHU catalogs (Kauffmann et
al., 2003; Brinchmann et al., 2004; Tremonti et al., 2004). These catalogs are
based on the spectra of SDSS DR8 (Aihara et al., 2011), which provide spectral
properties (spectral indices, line equivalent widths, and fluxes) and derived
physical parameters (star formation rates, stellar masses and gas-phase
metallicities) based on SDSS spectroscopy. We select objects that are
classified as galaxies (SPECTROTYPE=‘GALAXY’) with reliable measurements
(RELIABLE=1). Spectra are also deduplicated using the list provided by the
authors.

Within our transient-host pairs, 12018 known hosts and 10998 new hosts have