# Searching for Supernovae in HETDEX Data Release 3

József Vinkó Department of Astronomy, University of Texas at Austin, 2515
Speedway, Stop C1400 Austin, Texas 78712-1205, USA  Konkoly Observatory, CSFK,
Konkoly-Thege M. út 15-17, Budapest, 1121, Hungary  ELTE Eötvös Loránd
University, Institute of Physics, Pázmány Péter sétány 1/A, Budapest, 1117
Hungary  Institute of Physics, University of Szeged, Dóm tér 9, Szeged, 6720,
Hungary  Benjamin P. Thomas Department of Astronomy, University of Texas at
Austin, 2515 Speedway, Stop C1400 Austin, Texas 78712-1205, USA  J. Craig
Wheeler Department of Astronomy, University of Texas at Austin, 2515 Speedway,
Stop C1400 Austin, Texas 78712-1205, USA  Anna Y. Q. Ho Department of
Astronomy, Cornell University, Ithaca, NY 14853, USA  Erin Mentuch Cooper
Department of Astronomy, University of Texas at Austin, 2515 Speedway, Stop
C1400 Austin, Texas 78712-1205, USA  Karl Gebhardt Department of Astronomy,
University of Texas at Austin, 2515 Speedway, Stop C1400 Austin, Texas
78712-1205, USA  Robin Ciardullo Department of Astronomy & Astrophysics, The
Pennsylvania State University, University Park, PA 16802, USA  Institute for
Gravitation and the Cosmos, The Pennsylvania State University, University
Park, PA 16802, USA  Daniel J. Farrow University Observatory, Fakultät für
Physik, Ludwig-Maximilians University Munich, Scheinerstrasse 1, 81679 Munich,
Germany  Max-Planck Institut für extraterrestrische Physik, Giessenbachstrasse
1, 85748 Garching, Germany  Gary J. Hill McDonald Observatory, The University
of Texas at Austin, 2515 Speedway, Stop 1402, Austin, TX 78712, USA
Department of Astronomy, University of Texas at Austin, 2515 Speedway, Stop
C1400 Austin, Texas 78712-1205, USA  Zoltán Jäger  Baja Observatory,
University of Szeged, Szegedi út POB 766, Baja, 7900, Hungary  ELTE Eötvös
Loránd University, Gothard Astrophysical Observatory, Szent Imre h. u. 112,
Szombathely, 9700, Hungary  MTA-ELTE Exoplanet Research Group, Szent Imre h.
u. 112, Szombathely, 9700, Hungary  Wolfram Kollatschny Institut für
Astrophysik, Universität Göttingen, Friedrich-Hund Platz 1, 37077 Göttingen,
Germany  Chenxu Liu South-Western Institute for Astronomy Research, Yunnan
University, Kunming, Yunnan, 650500, People’s Republic of China  Department of
Astronomy, University of Texas at Austin, 2515 Speedway, Stop C1400 Austin,
Texas 78712-1205, USA  Enikő Regős Konkoly Observatory, CSFK, Konkoly-Thege M.
út 15-17, Budapest, 1121, Hungary  Krisztián Sárneczky Konkoly Observatory,
CSFK, Konkoly-Thege M. út 15-17, Budapest, 1121, Hungary József Vinkó
<EMAIL_ADDRESS>
###### Abstract

We have extracted 636 spectra taken at the positions of 583 transient sources
from the third Data Release of the Hobby-Eberly Telescope Dark Energy
eXperiment (HETDEX). The transients were discovered by the Zwicky Transient
Facility (ZTF) during 2018 - 2022. The HETDEX spectra have potential to
classify a large number of objects found by photometric surveys for free. We
attempt to explore and classify the spectra by utilizing machine learning (ML)
and template matching techniques. We have identified one transient source,
ZTF20aatpoos = AT 2020fiz, as a likely supernova candidate, and another
object, ZTF19abdkelq, as a possible supernova. We classify AT 2020fiz as a
Type IIP supernova observed $\sim 10$ days after explosion, and we propose
ZTF19abdkelq as a likely Type Ia SN caught $\sim 40$ days after maximum light.
ZTF photometry of these two sources are consistent with their classification
as supernovae. Beside these two objects, we have confirmed several ZTF
transients as variable AGNs based on their spectral appearance, and determined
the host galaxy types of several other ZTF transients.

supernovae: general - supernovae: individual (SN 2020fiz)

††journal: ApJ††facilities: Hobby-Eberly Telescope (HET); Zwicky Transient
Facility (ZTF), Texas Advanced Computing Center (TACC)††software:  ALeRCE:
http://alerce.science/;
IRAF: https://iraf-community.github.io/ (Tody, 1986, 1993);
Astropy: http://www.astropy.org (Astropy Collaboration et al., 2013, 2018);
numpy: https://numpy.org (Harris et al., 2020);
scipy: https://scipy.org  (Virtanen et al., 2020);
matplotlib: https://matplotlib.org (Hunter, 2007);
scikit-learn: https://scikit-learn.org/stable/ (Pedregosa et al., 2011);
pandas: https://pandas.pydata.org/ (McKinney, 2010);
SNID: https://people.lam.fr/
blondin.stephane/software/snid/index.html (Blondin & Tonry, 2007).

##  1 Introduction

The advent of deep, all-sky, untargeted surveys, such as the Zwicky Transient
Facility (ZTF, Bellm et al. 2019), the Dark Energy Survey (DES, Bernstein et
al. 2012; Kessler et al. 2015) and the upcoming Legacy Survey in Space and
Time (LSST, Ivezić et al. 2019) has ushered in a new age for the study of
transient astronomy, and it is now one of the most popular fields in astronomy
and astrophysics. The thousands of new transients discovered each day offer
the potential to build statistical samples of otherwise rare and poorly known
phenomena, such as tidal disruption events (Gezari, 2021) and superluminous
supernovae (Gal-Yam, 2019), as well as find new types of transient objects
that currently remain unknown.

Supernovae (SNe) are among the most well-known and frequently studied
transient objects for several reasons. First, due to their outstanding peak
brightness, they can be discovered and studied even at high redshifts, i.e.,
$z>1$. Moreover, the $\sim$month timescale of their evolution is neither too
fast nor too slow compared to human timescales, which makes them ideal targets
for astrophysical studies. Also, supernovae are among the best distance
indicators in extragalactic astronomy, and thus, are useful from not only an
astrophysical, but also a cosmological point of view: for example, the
discovery of the accelerated expansion of the Universe was at first based on
distance measurements from Type Ia SNe (Riess et al., 1998; Perlmutter et al.,
1999). Finally, the James Webb Space Telescope (JWST) and future space
observatories will extend our cosmic horizon considerably, thus, enabling the
discovery of transients beyond $z>2$, in the very early Universe (e.g. Regős &
Vinkó, 2019; Regős et al., 2020, 2021). The recently discovered very high
redshift galaxies at $z>12$ – including one at $z=16.7$ – by JWST (Donnan et
al., 2022) improve our understanding of the cosmic star formation rate at
$z>8$ and the epoch of reionization. Studies of these high redshift supernovae
will ultimately constrain the (true) initial mass function of the first stars.

The unprecedented number of new transients in the data stream makes their
analysis very challenging. Since most of the surveys mentioned above are based
on imaging and photometry, the possibility for a prompt revealing of the
physical nature of new transients is limited. As most classification schemes
require (mostly optical) spectroscopy, it is not currently possible to
definitively classify the bulk of these new sources. Instead, the current
strategy is concentrated on identifying potentially interesting sources based
on either their position (e.g., being in a nearby galaxy or galaxy cluster) or
their photometric properties (e.g., unusually bright, blue/red, fast/slow
evolution, etc.). This still leaves most transients unclassified and they fade
away with minimal spectroscopic attention.

In such circumstances, survey programs that produce a massive number of
spectroscopic observations can be useful. One of them is the Hobby-Eberly
Telescope Dark Energy eXperiement (HETDEX, Gebhardt et al. 2021; Hill et al.
2021). HETDEX is a blind spectroscopic survey that, once completed, will use
observations of 1.2 million Ly$\alpha$ emitting galaxies to measure the
baryonic acoustic oscillations (BAO) and constrain the fundamental parameters
that describe the time evolution of the dark energy equation-of-state. HETDEX
aims to obtain spectral coverage of 540 deg2 of the sky with a filling factor
of 1 in 4.6; a spectrum is obtained at every position that is covered by a
fiber in the field-of-view, including all objects that are brighter than the
HETDEX $5\sigma$ limiting magnitude of $m_{\rm lim}(\mathrm{AB})\sim 22.5$ in
$g$-band (Gebhardt et al., 2021).

The HETDEX survey is unique as it contains untargeted, deep, wide-field
spectroscopic data. While HETDEX is at heart a high-redshift galaxy survey
(Davis et al., 2021; Lujan Niemeyer et al., 2022), it also obtains spectra of
all other objects in its field-of-view, including stars of all types (Hawkins
et al., 2021), early and late type galaxies (Indahl et al., 2021), high-
redshift quasars (Zhang et al., 2021; Liu et al., 2022), and, in principle,
supernovae. The unbiased nature of the HETDEX spectroscopic selection function
implies that there must exist spectra of anomalous, perhaps unprecedented,
objects within the HETDEX continuum-source catalog. The HETDEX data can also
be used, at least potentially, for the classification of all types of
transient objects that appear within the survey footprint.

Another advantage of HETDEX is that it uses integral field units (IFUs), thus,
not only single objects but also their immediate environment can be observed
spectroscopically. This could be promising, for example, for studying the
hosts of extragalactic transients (see Section 4.6).

In this paper we present the first attempt to use HETDEX data for
spectroscopic classification and characterization of transients that were
discovered within the survey’s footprint. While our primary purpose was
finding supernovae, it turned out that the HETDEX spectra are also useful for
identifying variable active galactic nuclei (AGN), and studying the host
galaxies of supernovae and other transients.

This paper is organized as follows. Data extraction is outlined in Section 2,
then the applied classification methods are detailed in Section 3. Results are
discussed in Section 4, while the conclusions are summarized in Section 5.

##  2 Data

HETDEX uses the Visible Integral-field Replicable Unit Spectrograph (VIRUS,
Hill et al. 2021) instrument at the focal surface of the Hobby-Eberly
Telescope (HET, Ramsey et al. 1998; Hill et al. 2021). VIRUS comprises up to
78 integral field units, each with 448 $1\farcs 5$-diameter fibers, such that
33,152 individual spectra covering 54 arcmin2 are obtained with each visit.
For more details about the survey design concepts and specifications, see
Gebhardt et al. (2021). For more details about the instrumentation, see Hill
et al. (2021).

The HETDEX Data Release 2.1 (HDR2.1) continuum-source catalog contains spectra
for ${\sim}62$k unique objects (Mentuch Cooper, in prep), and includes a vast
amount of information that is too large to be visually classified by human
experts. Thus, the very nature of the HETDEX survey design motivates machine
learning efforts not only for anomaly detection, but also for object
classification. In the HETDEX Data Release 3 (HDR3), the size of the
continuum-source catalog has been extended further, reaching ${\sim}230$k
sources.

Supernovae identifications in the HETDEX catalog are challenging, not least
because they are not the ultimate target of the survey. As a result, the
various reduction pipelines that produce the survey’s data products are not
optimized to find extragalactic transients. For example, one such issue is
that, within the continuum detection process, sources are rejected if they are
within $1\farcs 5$ of another galaxy; in these cases only the brightest
continuum object remains in the catalog. This cut may discard supernovae and
similar astronomical transients that are associated with a bright host galaxy,
but are actually separate point sources near their host galaxy’s core. We
therefore opted not to use the default HETDEX data products for our initial
search for supernovae.

Instead, for this work we chose to perform forced spectral extractions at the
known locations of transient sources. There are many astronomical surveys that
can provide auxillary information to HETDEX. One of these is the Zwicky
Transient Facility (ZTF, Bellm et al. 2019; Graham et al. 2019; Masci et al.
2019; Dekany et al. 2020), which covers the full northern sky in two filters
every three nights. This survey typically produces between $10^{5}$ and
$10^{6}$ alerts per night (Förster et al., 2021), 80% of which are associated
with real astronomical sources (Carrasco-Davis et al., 2021). Thus, we aimed
to extract spectra at the positions of public ZTF transients announced between
2018 and 2022 from the HETDEX spectroscopic dataset, in the hopes of finding
SN-like signals (broad features) in those spectra. Details are given in the
rest of this paper.

###  2.1 Source extraction

We used the Automatic Learning for the Rapid Classification of Events (ALeRCe;
Förster et al. 2021) Application Programming Interface
(API)111https://alerce.readthedocs.io/en/latest/tutorials/ztf_api.html to
query the ZTF servers to find all detected ZTF transients that were within 12
arcminutes of the position of a HETDEX pointing. (This is roughly the size of
the HET’s usuable focal plane, which is 20 arcminutes in diameter.) We then
determined which of the resulting 4585 ZTF sources fell within 3$\farcs$5 of
any of the $\sim$34,000 fibers that are part of VIRUS. This additional
downsampling resulted in 636 spectra of 583 objects matched between ZTF and
HETDEX. (Although HETDEX is a single epoch survey, some science verification
fields have been observed multiple times.) Using HETDEX-
API/get_spec.py222https://github.com/HETDEX/hetdex_api/blob/master/hetdex_tools/get_spec.py,
we were then able to extract HETDEX spectra that are closely matched to each
of these transient locations. We used a point spread function (PSF)-weighted
1D extraction at the exact position that the ZTF source was detected and
required that at least 7 fibers be contained within the 3$\farcs$5 circular
aperture that is needed to fully capture the VIRUS PSF. Thus, the final
spectrum is produced from a PSF-weighted sum over many fibers combined within
a circular aperture with a radius of 3$\farcs$5 (Gebhardt et al., 2021).

It is worth noting here that the HETDEX spectra associated with these sources
may not have been taken while the prospective transient was active. Indeed, in
the majority of cases they were not. We can, however, still search for the
nebular spectra of supernovae that have faded beyond the ZTF detection limit.
While searches for pre-SN progenitor stars may also be possible, it is likely
that those progenitors will be too faint for detection.

##  3 Methods

This section summarizes the various methods that we applied to explore and
characterize the extracted HETDEX spectra.

###  3.1 Exploration: machine learning (ML)

A large number of discoveries in astronomy, and science more generally, have
been catalyzed due to some degree of serendipity. The use of machine learning
for anomaly detection has the potential to automate serendipity, and
dramatically widen the potential discovery pool.

One method that facilitates the visual exploration of large datasets is
t-distributed stochastic neighbor embedding (t-SNE; van der Maaten et al.
2008). The t-SNE program is fundamentally a dimensionality-reduction technique
that preserves the pairwise similarity between points while producing a
$d$-dimensional map of those points (usually $d=2$). We use the version of
t-SNE implemented in the scikit-learn Python package (Pedregosa et al., 2011).

Here the dimensionality of the high-order space is set by the number of flux
measurements (1036) in a single HETDEX spectrum. The t-SNE program works by
first computing pairwise similarities $p_{ij}$ between these high-dimensional
data points ${\bf x}_{i}$ and ${\bf x}_{j}$,

$p_{j|i}=\frac{{\rm exp}(-||{\bf x}_{i}-{\bf
x}_{j}||^{2}/2\sigma^{2}_{i})}{\sum_{k\neq i}{\rm exp}(-||{\bf x}_{i}-{\bf
x}_{k}||^{2}/2\sigma^{2}_{i})},$ (1)

where $\sigma_{i}$ is the variance of a Gaussian distribution centered on
point ${\bf x}_{i}$. This parameter is related to a user-defined
hyperparameter called the perplexity. We choose a perplexity value of
$\sqrt{N}$, where $N$ is our number of data points.

To mitigate problems associated with outliers, the symmetrical pairwise
similarity is defined as:

$p_{ij}=\frac{p_{j|i}+p_{i|j}}{2N},$ (2)

where $p_{j|i}$ is the probability that point ${\bf x}_{i}$ would choose point
${\bf x}_{j}$ as its nearest neighbor under the assumption of a Gaussian
probability distribution centered at point ${\bf x}_{i}$.

The next step is for t-SNE to attempt to learn a low-dimensional ($d=2$)
representation of the points, ${\bf y}_{1},...,{\bf y}_{N}$, that preserves
the high-dimensional similarities $p_{ij}$ as analogous low-dimensional
similarities $q_{ij}$. In this low dimensional map,

$q_{ij}=\frac{(1+||{\bf y}_{i}-{\bf
y}_{j}||^{2})^{-1}}{\displaystyle\sum_{k\neq m}(1+||{\bf y}_{k}-{\bf
y}_{m}||^{2})^{-1}}.$ (3)

This metric is related to the Student’s t-distribution (e.g., Press et al.,
2002), and its primary purpose is to avoid over-crowding in the low-
dimensional embedded space. Also, the Student’s t-distribution is faster to
compute compared to a Gaussian as it does not contain any exponential terms.

The key process of t-SNE is to determine the low-dimensional embeddings of the
points ${\bf y}_{i}$ by minimizing (via gradient-descent) the Kullback-Leibler
divergence between the low- and high-dimensional similarity distributions $P$
and $Q$,

$KL(P||Q)=\sum_{i\neq j}p_{ij}{\rm log}\frac{p_{ij}}{q_{ij}}.$ (4)

This will result in an optimized $d=2$ dimensional map that preserves the
similarities of points from the high-dimensional distribution.

###  3.2 Classification: template matching via $\chi^{2}$-minimization

The traditional method for automated classification of astronomical objects is
through comparisons with a list of template spectra using a mathematical
criterion to judge the quality of matching; the classification then follows
from the best-matched template. We employed this template-matching scheme
using as templates the Pickles Stellar Spectral Flux Library (Pickles, 1998)
for stars and the Kinney synthetic spectra (Kinney et al., 1996) for galaxies.
The former contains 131 stellar spectra covering the Harvard classes from O5
through M4 and the Morgan-Keenan luminosity classes from I to V; the latter
includes spectra for 11 galaxies, including an elliptical (E), a lenticular
(S0), three spirals (Sa, Sb, Sc) and six star-bursting (SB1 - SB6) systems.
The star-forming templates are dominated by emission features of varying
strength, while the elliptical and lenticular templates represent the spectra
of most passive galaxies.

We adopted $\chi^{2}$ statistics for measuring the quality of the match
between the observed spectrum and a template. The normalized $\chi^{2}$
function was defined as

$\chi_{N}^{2}~{}=~{}\frac{1}{N}\sum_{k=0}^{N}\frac{1}{\sigma_{k}^{2}}(f_{c}^{\rm
obs}(\lambda_{k})-f_{c}^{\rm templ}(\lambda_{k}))^{2},$ (5)

where $N$ is the number of sampled wavelengths in an observed spectrum, the
functions $f_{c}^{\rm obs}$ and $f_{c}^{\rm templ}$ are the continuum-
normalized flux of the object and the template as a function of wavelength
($\lambda$), and $\sigma_{k}$ is the uncertainty of the observed fluxes. Note
that, as written, Equation 5 does not contain any terms for the the possible
redshift mismatch between the observed and the template spectrum, and thus is
only applicable for Milky Way objects with $z=0$. Before using the equation
for galaxies, the templates were therefore redshifted, and a value of
$\chi^{2}_{N}$ was computed for each $z$ between $0<z<0.3$ range, using
$dz=0.001$ (see Section 4.2 for further information).

Before applying Equation 5, simple linear interpolation was applied to the
template spectra to correct for the different wavelength sampling between the
template and the observed spectrum. In addition, both spectra were normalized
to the continuum by iteratively fitting an 8th order polynomial to both the
observed and the template spectrum. After each iteration, the standard
deviation (stddev) of the residuals were calculated and data that exceeded the
residual by -2*stddev and +3*stddev were rejected from the sample. After $\sim
10$ such iterations the fitted polynomial was found to represent the continuum
reasonably well.

The uncertainties of the observed fluxes were estimated empirically by
calculating the mean ($\langle f_{c}\rangle$) and standard deviation
($\sigma$) of the continuum-normalized data in the red part of the spectrum,
between 4600 and 5400 Å. The same region was used to derive an empirical
signal-to-noise (S/N) parameter, defined as s/n$=\langle f_{c}\rangle/\sigma$.
Because of the presence of spectral features in the fitted wavelength region,
this method clearly overestimates the noise in the case of spectra with
moderate to high S/N, while giving more realistic results for noise-dominated
data. We chose this simple approach of adopting a single $\sigma$ for the
whole spectrum in Eq. 5, i.e., $\sigma_{k}=\sigma$ for all $k$, because the
majority of our sources did not reach high S/N, and the $\chi^{2}_{N}$
function was used only for measuring the quality of fitting between the
observed and the template spectra. We do not treat the value as a proper
statistical quantity.

###  3.3 Classification: template matching via cross-correlation

Figure 1: The series of supernova spectral templates for SNe Ia (left) and SNe
IIP (right) applied in the CCF-method. The spectral phase in days for each
template is indicated in the legend.

The other widely used algorithm for template matching is cross-correlation. A
code that is based on cross-correlation and applied very often in the
supernova community is SNID (SuperNova IDentification; Blondin & Tonry, 2007),
which uses the observed spectra of different types of SNe as templates. Such
codes compute the cross-correlation function (CCF) between the observed
($f(\lambda)$) and the template spectrum ($g(\lambda$)) via the following
definition:

$CCF(\Delta x)=\int_{-\infty}^{+\infty}f(x)\cdot g(x-\Delta x)dx,$ (6)

where $x=\ln\lambda$, $\Delta x=\ln(\lambda/\lambda_{0})=\ln(1+z)$ and $z$ is
the redshift of the object. In short, the CCF measures the overlap, i.e., the
similarity, between the object and the template spectrum: the stronger the
overlap, the greater than similarity of the two spectra. Thus, the height of
the $CCF$ peak is proportional to the overlap, while the horizontal shift
($\Delta x$) gives the redshift of the observed spectrum with respect to the
template.

Because the public template libraries of SNID are based on published observed
spectra, Type Ia SNe are overrepresented in them. In addition, most of these
template libraries do not fully cover the HETDEX wavelength range of 3500 to
5500 Å. Thus, instead of applying SNID for our sample, we developed our own
pipeline based on the fxcor task in IRAF333IRAF was distributed by the
National Optical Astronomy Observatory, which is operated by the Association
of Universities for Research in Astronomy (AURA) under a cooperative agreement
with the National Science Foundation..

Since our primary intention was finding supernovae, we adopted the Type Ia
supernova templates compiled by Eric Hsiao (Hsiao et al., 2007) for our
analysis. These spectra span from $-15$ days to $+91$ days in phase with
respect to the moment of $B$-band maximum, with a 1-day cadence. Since our
single epoch data do not allow precise temporal resolution, we pre-selected 9
spectra from the library (having epochs at $-15$, $-5$, 0, +5, +10, +20, +30,
+40, and +60 days with respect to maximum light) and used only those data in
the cross-correlation. The selected SN Ia templates are shown in the left
panel of Figure 1. Each template spectrum has $\Delta\lambda=10$ Å resolution
(which is much lower than the $\sim 2$ Å resolution of the HETDEX spectra) and
covers the wavelength interval between 2500 and 6000 Å.

Because the spectra of hydrogen-poor (Type Ib/c) core collapse SNe are often
similar to those of Type Ia SNe, at least around maximum light when the SN
appears to be the brightest, we did not define a separate set of templates for
those objects. Instead, the Hsiao-templates were utilized for the SN Ib/c-type
objects.

For hydrogen-rich (Type II) core collapse SNe, we adopted the published
spectra of the archetypal Type IIP SN 1999em (Hamuy et al., 2001; Elmhamdi et
al., 2003), downloaded from the Weizmann Interactive Supernova Data Repository
(WiseRep)444https://www.wiserep.org/. The selected spectra are corrected for
the redshift of SN 1999em ($z=0.002392$) and shown in the right panel of
Figure 1. We chose only the spectra that were taken during the plateau phase
(lasting $\sim 100$ days after the explosion), because the SN is brighter by
at least 1.5 - 2 magnitudes during this period than in the subsequent tail-
and nebular phase. Thus, it is more probable to detect a Type II at this time.

##  4 Results and Discussion

The results of our analysis are presented and discussed in this Section.

###  4.1 Machine learning

After having extracted spectra from the HETDEX database at the positions of
the known ZTF transients (see Section 2.1), we normalized the spectra by their
uncertainty (produced by the HETDEX reduction pipeline) to account for edge
defects and other minor issues, and fed them directly into the t-SNE
algorithm555Some other studies have used a principle component analysis to
reduce the dimensionality of the data before feeding it into t-SNE. We found
that this was not necessary in our case due to the relatively small size of
the data set.. This resulted in a two-dimensional map shown in Figure 2, which
we could then explore and visually inspect. We created a point-and-click tool
to quickly and efficiently examine the spectra represented by each t-SNE data
point, and by this visual inspection process, we identified the clusters that
were predominantly AGN, star-forming galaxies, and low S/N sources. We also
flagged several objects for further analysis. These objects of special
interest were analyzed using SNID in order to determine their astronomical
type. Amongst these was ZTF20aatpoos, which we classified as a Type II-P SN
about 10 days after peak (see Section 4.4). This classification represents our
first success in searching for SNe in the HETDEX database, and motivates
efforts for further discovery.

Figure 2: Top panel: The output embedding of our 636 HETDEX-ZTF spatially-
matched spectra from t-SNE. Each point represents a HETDEX spectrum, and
spectra that are similar are clustered together. The classes that dominate
each region of the map are marked in the text. ZTF20aatpoos is shown as a red
square, and is found in a map region that is otherwise primarily inhabited by
passive galaxies. Bottom panels: Example spectra of AGN, pipeline artifacts,
and low signal-to-noise objects are shown for the three categories marked on
the top panel.

###  4.2 Template fitting via $\chi^{2}$

The $\chi^{2}_{N}$ value (Equation 5) was computed for all 636 observed
spectra using all templates in both the stellar and the galaxy library. We
defined three groups of objects, named star, galaxy and low, as the initial
classification categories for the observed spectra. Spectra that were noise-
dominated and did not reach s/n=3 were moved into the low category. Sources
that had lower $\chi^{2}_{N}$ values for fits to $z=0$ stellar templates than
fits to galaxies at $z>0$ were classified as stars, and the remaining spectra
were, at first, labeled as galaxies.

In the next step, all spectra were inspected visually, and peculiar objects
were moved into additional categories. We identified broad-lined AGNs
(category agn) in the galaxy group using the measured width of their emission
features. Since many of these sources showed only a single broad line in the
relatively narrow wavelength interval observed by HETDEX (3500 to 5500 Å),
their redshifts, as derived from the best-fitting galaxy template may differ
substantially from the true value. These objects are analyzed further in
Section 4.5. Finally, spectra that did not resemble any other members in the
above categories were moved to the uncertain category.

Table 1: Statistics of classified objects by their ALeRCE categories Sp. class | total | VS | AGN | SN | AST | UNC
---|---|---|---|---|---|---
star | 149 | 114 | 18 | 6 | 0 | 11
galaxy | 122 | 2 | 61 | 50 | 0 | 9
agn | 52 | 4 | 38 | 7 | 0 | 3
low | 64 | 0 | 6 | 30 | 4 | 32
Figure 3: Sources classified via $\chi^{2}$-minimization as a function of
their ALeRCE categories (Variable Star, AGN, SN, ASTeroid, UNClassified; see
text). Each panel shows the histogram from sources belonging to the same
HETDEX spectral type, namely stars (top left), galaxies (top right), AGNs
(bottom left) and low S/N (bottom right panel).

It is an interesting question whether the results from our simple
classification scheme have any correlation with the classifications performed
by the ALeRCE pipeline. To test for this, we cross-compared our classification
results for each transient (star, galaxy, agn, and low) to their most probable
ALeRCE classification. From the various algorithms available in ALeRCE, we
selected the one that had the highest probability value from the so-called
Stamp Classification that uses the following categories: variable star (VS),
active galactic nucleus (AGN), supernova (SN), asteroid (AST), and artifact
(BOGUS). In addition, we introduced a 6th category, named unclassified (UNC),
for those ZTF objects that were announced but had no ALeRCE classification
available.

As an initial step, all sources belonging to the BOGUS category were rejected
from the sample. Such “transients” usually appear in the vicinity of bright,
saturated objects, e.g., near their diffraction spikes, and are artifacts of
the ZTF image subtraction process. A total of 251 such artifacts were removed
from the sample, and only the remaining 385 spectra were analyzed further.

The summary of our cross-comparison can be found in Table 1 and is shown
graphically in Figure 3. It is seen that the results from the $\chi^{2}$
template matching correlate very well with those from the ALeRCE Stamp
Classification. For example, of the 149 sources with stellar-like HETDEX
spectra, 114 were classified as variable stars by ALeRCE. Similarly, the 122
galaxy-like HETDEX spectra belong to ZTF transients that were classified by
ALeRCE as either AGN or SN. It is especially encouraging that out of the 52
spectra that we classify as belonging to AGN, ALeRCE found 38 to be likely
AGN.

As expected, many of the low S/N spectra, i.e., those that belong to the low
category in our scheme, have no ALeRCE classification. It is also interesting
that most of the transients that ALeRCE identified as most likely SNe are
associated with either galaxies or noise-dominated sources. This is also an
expected result, because many of the HETDEX spectra were taken outside the
visibility window of an active transient, resulting in either a low S/N
spectrum or that of the likely host galaxy.

Figure 4: ZTF frame stamps (size $78"\times 78"$) taken in the $r$-band for the active ALeRCE SNe. North is up and East is to the left in each stamp. The white squares indicate masks placed onto saturated pixels. Figure 5: Normalized HETDEX spectra of the active transients classified as SNe by ALeRCE. Table 2: Active ZTF transients classified as SNe by ALeRCE  Name | R.A.(deg) | Dec.(deg) |  MJDDEX |  $\Delta t$ (d) | AL type | AL prob. | Sp.type | S/N | $\chi^{2}$ | z | Class.
---|---|---|---|---|---|---|---|---|---|---|---
ZTF18aagstka | 226.937577 | 51.452847 | 58660 | 405 | SN | 0.4370 | SB4 | 3.1275 | 0.2609 | 0.045 | galaxy
ZTF18aamlgvn | 230.134233 | 51.274517 | 58314 | 38 | SN | 0.4128 | – | 1.9978 | 2.3363 | 0 | low
ZTF18aaqmidj | 185.676093 | 51.375824 | 58495 | 195 | SN | 0.4317 | – | 0.001 | 0.001 | 0 | low
ZTF18aaxqqxd | 185.675409 | 51.375815 | 58495 | 223 | SN | 0.3498 | – | 3.0660 | 2.4245 | 0 | low
ZTF19aavnhbd | 267.924007 | 65.042444 | 58720 | 87 | SN | 0.4321 | G0V | 10.4723 | 1.1244 | 0 | star
ZTF19abuxhqd | 15.680562 | 0.720323 | 58788 | 62 | SN | 0.3881 | S0 | 13.9679 | 3.1949 | 0.080 | galaxy
ZTF19aceckmm | 172.320355 | 51.525159 | 58907 | 141 | SN | 0.7280 | SB6 | 7.4568 | 2.5238 | 0.032 | galaxy
ZTF20aaoykbt | 181.108375 | 55.606322 | 58952 | -2 | SN | 0.5260 | SB6 | 11.3571 | 3.1397 | 0.056 | agn
ZTF20aatpoos | 186.512456 | 55.702264 | 58954 | 18 | SN | 0.6277 | SB6 | 9.1188 | 2.1196 | 0.033 | uncertain

Although our HETDEX spectral extractions were all at the announced coordinates
of the ZTF transients, many of the spectra are not actually associated with a
transient object, due to the object’s finite visibility window. In order to
overcome this issue, we identified the “active” transients that were stamp-
classified as SN by ALeRCE and were observed by HETDEX when their ZTF followup
was still active. Nine such objects, collected in Table 2, were found. Their
ZTF subframes, downloaded from the ALeRCE website, are shown in Figure 4,
while their HETDEX spectra and the results of their $\chi^{2}$-classifications
are plotted in Figure 5.

It is seen that three of the objects that were stamp-classified as SN by
ALeRCE (ZTF18aamlgvn, ZTF18aaqmidj, and ZTF18aaxqqxd) have low S/N spectra
that prevented a reliable classification. (In fact, the latter two appear to
be artifacts from the same very bright star.) Of the remaining 6 objects, 3
are galaxies, 1 is a star and another is a broad-lined AGN. Of the set,
ZTF19aceckmm is the most interesting, because it is located near a bright
galaxy (see Figure 4), and has an ZTF light curve (shown on the ALeRCE
website) that resembles that of a Type I supernova. Unfortunately, the HETDEX
spectrum was taken 141 days after the ZTF discovery, when the SN had already
faded below the HETDEX detection limit.

The last object, ZTF20aatpoos, which we classified as uncertain, is the only
object in the sample that shows neither a stellar- nor galaxy-like spectrum
(see Figure 5). In fact, the spectrum is that of a Type IIP supernova, which
was also found as a SN candidate in Section 4.1 and in Section 4.3 below. More
details on this object are presented in Section 4.4.

###  4.3 Cross-correlation

Figure 6: The maximum cross-correlation coefficient plotted against spectrum
ID (left panel) and redshift (right panel) for the HETDEX sample. The dotted
horizontal line marks the chosen threshold value for SN candidates (see text).

In the left panel of Figure 6, the peak height of the CCF is shown against the
object index of the HETDEX spectra. It is seen that for the majority of
objects, the CCF is below 0.6, which we set as an initial threshold: each
spectrum exceeding this threshold was inspected visually to check whether the
similarity with the SN template was real.

In the right panel of Figure 6 the same quantity is plotted against the
redshift provided by fxcor. This diagram is useful because objects with
negative redshifts can immediately be rejected from the list of SN candidates,
even though they may reach higher CCF peaks. As a first approximation, any
object above the 0.6 threshold level and having positive redshift was
considered a real candidate.

In both diagrams of Figure 6, the object that has the highest CCF peak (by
far) is ZTF20aatpoos; this source was also identified as a good SN candidate
in the previous sections. All other candidates having $z>0$ and
$\mathit{CCF}>0.6$ turned out to be broad-line AGNs with strong emission
lines, thus showing that their spectral overlap with SN templates is
artificial.

After the visual inspection of all HETDEX spectra of “real” (i.e., not BOGUS,
see Section 3.2) sources, we also identified ZTF19abdkelq as a potential SN
candidate. This spectrum was classified as low by the $\chi^{2}$ template
matching algorithm because of its low signal-to-noise parameter, but the CCF
method indicated some resemblance with the spectrum of Type Ia SN. In this
case, the peak of the CCF is lower than the empirical threshold (0.6) due to
the dominance of observational noise.

More details on these two SN candidates, ZTF20aatpoos and ZTF19abdkelq, are
presented in the next subsection.

###  4.4 Supernovae

Figure 7: The spectrum of ZTF20aatpoos, a Type IIP SN at $\sim 10$ days after
explosion (plotted as a black curve), compared to a Type IIP SN template (red
curve). The left panel shows the result from our cross-correlation analysis
(see Section 4.3), while the right panel displays the output from SNID.

The strongest candidate for being a SN in our HETDEX sample is ZTF20aatpoos.
This object was originally classified as a SN by ALeRCE, and announced on the
Transient Name Server (TNS) website666https://www.wis-tns.org/ as AT 2020fiz
on 2020-03-29 (see Table 3). HETDEX observed this target serendipitiously on
2020-04-15, $\sim 2$ weeks after discovery.

ZTF20aatpoos was identified as a SN candidate by all of our SN finding
methods. In Figure 7 we plot its observed spectrum (black curve) together with
the best-matching SN templates: our SN template (red curve) is shown in the
bottom left panel, while a template found by SNID is plotted with red on the
right panel. It is seen that both CCF-based codes found approximately the same
SN template – that for a relatively young (10 - 20 days after explosion) Type
IIP SN.

Figure 8: ZTF photometry on ZTF20aatpoos ($r$-band: red dots, $g$-band: green
dots) compared to SN IIP template light curves, shifted vertically to match
the observations. $g$-band synthetic photometry from the HETDEX spectrum is
plotted with an orange square.

ZTF20aatpoos was followed up by ZTF for 109 days after discovery, and its
light curve (plotted in Figure 8) fully confirms that it shows the $\sim 100$
day-long plateau, characteristic of Type II-P SNe. Template light curves from
the data of the Type IIP SN 2005cs (Pastorello et al., 2009) in $V$\- and
$R$-bands are shown for comparison.

We also computed a synthetic $g$-band AB-magnitude for ZTF20aatpoos directly
from the HETDEX spectrum. The value, $g=19.180\pm 0.002$ mag, is plotted as an
orange square in Figure 8. It is seen that it agrees very well with the
$g$-band ZTF photometry taken at similar epochs.

Figure 9: The same as Figure 7 but for the Type Ia SN ZTF19abdkelq, observed at $\sim 40$ days after maximum light. Figure 10: $r$-band ZTF photometry for ZTF19abdkelq, plotted together with Ia template light curves in rest-frame $B$ and $V$-bands. The $g$-band synthetic photometry from the HETDEX spectrum is shown by the orange square.  Table 3: Parameters of the SNe found in HETDEX ZTF name | IAU name | R.A.(deg) | Decl.(deg) | Date |  SNID type |  SNID $z$ |  SNID phase (day)
---|---|---|---|---|---|---|---
ZTF19abdkelq | – | 23.086249 | 0.658040 | 2019-08-08 | Ia | 0.125 $\pm 0.011$ | 40.4 $\pm$20.1
ZTF20aatpoos | AT 2020fiz | 186.512456 | 55.702264 | 2020-04-15 | IIP | 0.026 $\pm$0.013 | 10.7 $\pm$6.3

Our other SN candidate, ZTF19abdkelq, was originally classified as an asteroid
(AST) by ALeRCE, but ZTF also recorded $r$-band magnitudes at 4 later epochs,
with the last ZTF detection occurring $\sim 30$ days after discovery. Thus, it
is unlikely that this source is a moving object. Since it was not flagged as a
transient, there is no available TNS record for the object.

The HETDEX spectrum of ZTF19abdkelq is plotted in Figure 9. In the left panel,
we show the continuum-normalized spectrum (plotted with red) together with the
best-matching Hsiao SN Ia template (black). Even though the observed spectrum
is noisy, the pseudo-emission feature around 5200 Å is a good match to the
template spectrum. The resemblance is strengthened by the analysis with SNID
(right panel), which suggests SN 2000cx, a peculiar Type Ia SN, as the best-
matching template. Both our CCF code and SNID provided a consistent redshift
for ZTF19abdkelq of $z\sim 0.12$.

In Figure 10, the light curve of ZTF19abdkelq is plotted from ZTF $r$-band
photometry. Green and blue curves show the expected brightness decline of a
Type Ia SN, as inferred from rest-frame $V$\- and $B$-band template light
curves from MLCS2k2 (Jha et al., 2007). At $z\sim 0.12$, the rest-frame
$V$-band template light curve represents the observer-frame $r$-band data
well. The result of $g$-band synthetic photometry from the HETDEX spectrum
($g=22.144\pm 0.098$ mag) is also shown as an orange square. It is seen that
both the ZTF and the HETDEX photometry is entirely consistent with the
proposed Type Ia SN classification of ZTF19abdkelq.

The parameters for the two SNe found in HETDEX are summarized in Table 3.

###  4.5 Active Galactic Nuclei

Figure 11: The s/n parameter of the AGN spectra as a function of redshift.
Figure 12: Observed spectra of a subsample of AGNs compared to the rest-frame
SDSS QSO template.

Members of the AGN group were identified via the $\chi^{2}$-minimization
method (see Section 3.2), although some were also found by cross-correlating
the spectra with supernova templates (Section 3.3). We identified 52 spectra
(Table 1) that had a galaxy spectrum as their best-matching template, but also
showed broader emission features than those of the star-forming galaxy
templates. After visual inspection, 3 of them turned out to be saturated
stars, which were then removed from the sample. The remaining 49 were analyzed
further.

Redshifts were derived by fitting the “high luminosity QSO” template spectrum
taken from the SDSS
site777https://http://classic.sdss.org/dr5/algorithms/spectemplates/ to the
AGN spectra. The fitting parameters for the AGN sample are collected in Table
A2 in the Appendix.

Figure 11 shows the s/n parameter of each spectrum (see Section 3.2) against
redshift. It is seen that most of the ZTF transients that turned out to be
AGNs are at low redshifts, i.e., at $z\lesssim 0.5$. As a comparison, the full
HETDEX AGN sample (Liu et al., 2022) extends to at least $z\sim 3.5$ and has a
median redshift of $z_{\rm med}\sim 2.1$; in other words, the AGNs identified
here are more local than the majority of the HETDEX AGNs. This is most likely
due to the sensitivity limit of the ZTF public stream, which is
$m_{AB}\lesssim 20$ mag. This limit is significantly brighter than the HETDEX
detection threshold for AGNs ($m_{AB}\sim 26$ mag; Liu et al., 2022).

In Figure 12, spectra of some of the ZTF AGN sources are plotted against rest-
frame wavelengths compared to the SDSS QSO template. One object, ZTF20aappepk
($z\sim 2.29$) has a HETDEX spectrum that extends beyond the blue edge of the
SDSS QSO template, but the overlapping part shows good agreement with the
template, just like the other, lower redshifted AGNs in this small sample.

###  4.6 Host galaxies of supernovae

Figure 13: Distribution of host galaxy types for SNe with known light curve
classification (green, blue and magenta bars) as well as that of the whole
sample (red bars). Figure 14: Histogram of host galaxy redshifts for the whole
sample (red) and those having known ALeRCE light curve classification (blue).

Information on the host galaxies of transients can be as important as the
transients themselves. HETDEX presents a unique opportunity to gather spectra
of transient host galaxies with a minimal observational cost: if a SN exploded
within a HETDEX field, then it should be possible to extract the host galaxy
spectrum from the HETDEX data products. While such a dedicated study of
transient host galaxies is beyond the scope of the present study, it may be a
fruitful endeavor for future work.

Here we present galaxy types and redshifts for those HETDEX galaxies that were
extracted at the position of ZTF transients that were classified as SNe by
ALeRCE. We found 50 such objects according to Table 1, of which 21 had a
refined ALeRCE supernova classification based on their ZTF light curves.
ALeRCE uses the following categories from its lc_classification_transient
pipeline: SNIa, SNIbc, SNII and SLSN. This subsample is listed in Table A3 in
the Appendix, together with the host galaxy properties found by the
$\chi^{2}$-classification method (Section 3.2).

The distribution of the host galaxy types for this subsample is shown in
Figure 13. Here we added the numbers of SNIbc SNe to that of the SNII objects,
and use the SNcc category for those core-collapse SNe. Note that the numbers
for the SLSN category are probably overestimated by ALeRCE. The same
distribution for the whole sample (i.e., with those objects that do not have
an ALeRCE light curve classification) is also plotted via the red bars. It is
clear that even though the numbers are too low for any robust conclusions,
most of the SNe occurred in starburst galaxies showing strong emission
features. Since core collapse SNe originating from massive stars likely
dominate our low-redshift sample, the distribution of host galaxy types as
shown in Figure 13 is consistent with expectations.

Note that ZTF19abuxhqd, which was classified as a likely Type II SN by ALeRCE,
occurred in an early-type (S0) galaxy. This would be peculiar, since early-
type galaxies usually host Type Ia SNe that originate from older population of
stars; however, a close inspection of the noise-dominated ZTF light curve of
ZTF19abuxhqd provides no convincing evidence that the presence of a core
collapse SN. Specifically, the transient showed random-like brightness
fluctuations for $\sim 400$ days, instead of any sign of a decline.

The distribution of the redshifts for the whole sample, as well as for those
SNe that have refined ALeRCE types is plotted in Figure 14. This figure
confirms that the ZTF SNe whose host galaxies could be measured by HETDEX are
distributed in the $z<0.3$ low-redshift space.

###  4.7 The expected number of supernovae in HETDEX

Even though HETDEX is not an ideal survey for finding extragalactic transients
(see Section 1), the number of supernovae that are expected to show up within
the survey footprint may be of interest. Since the classification of new
transients generally needs spectroscopy, the future HETDEX data releases could
be useful for classifying targets that might otherwise escape attention.

The expected number of supernovae within the survey area ($\Omega$) covered by
observations during the survey time ($T_{S}$) in redshift interval $\Delta z$
at redshift $z$ can be expressed as

$N_{SN}(z,\Delta z)=\Omega T_{s}\int_{z-\Delta
z}^{z}\frac{R_{SN}}{1+z}\frac{dV}{dz}dz,$ (7)

where $R_{SN}$ is the measured supernova volumetric rate (the number of SNe
per Mpc3 per year) and $dV/dz$ is the differential comoving volume. The factor
$1+z$ accounts for cosmological time dilation.

The HETDEX survey field consists primarily of two parts: a Spring field of 390
deg2 and a Fall field of 150 deg2. Due to gaps between the IFUs, the effective
survey field of view is $\sim 94$ deg2 (Gebhardt et al., 2021). The area
covered by a single shot with VIRUS is $\sim 0.015$ deg2. To estimate the
number of SNe found during a year, we assume that, on average, $\sim 3$ such
fields are taken on each night in a year for either the Spring or the Fall
field. For the volumetric SN rates, we adopt $R_{Ia}=2.5\times
10^{-5}\cdot(1+z)^{1.5}$ SNe Mpc-3 yr-1 for SNe Ia (Hounsell et al., 2018) and
$R_{CC}(z=0.028)=9.1\times 10^{-5}$ SNe Mpc-3 yr-1 for core-collapse SNe
(Frohmaier et al., 2021). The latter is scaled with the cosmic star formation
rate function by Hopkins & Beacom (2006) for different redshifts. Thus, using
$\Omega=3\cdot 0.015=0.045$ deg2, $T_{s}=1$ year and the volumetric SN rates
given above, we get $N_{Ia}\sim 0.24$ SN Ia in the $0<z<0.3$ redshift range.
Since core collapse SNe are fainter by $\lesssim 1$ magnitude than Type Ia
SNe, they are accessible by HETDEX only in the $0<z<0.2$ redshift range
(assuming $\sim 22$ mag as the detection limit of continuum sources in
HETDEX). Within this volume we get $N_{CC}\sim 0.38$ core collapse SN per
year. This optimistic calculation does not take into account detection
efficiencies (signal-to-noise, for example), thus, it is likely an
overestimate, but the final numbers are consistent with our results (one SN of
both types within $\sim 5$ years of HETDEX operation). Note that between 2017
and 2022 the HETDEX field-of-view was not completely filled up by IFUs, which
decreased our chances to find transients. Starting from 2022, having the
field-of-view fully populated by 78 IFUs, the probability of discoveries are
expected to increase. Overall, we conclude that we can expect to find a few
more ($N\lesssim 5$) supernovae during the upcoming years of HETDEX operation.

##  5 Conclusions

The conclusions of the present study are summarized as follows:

  * •

We identified the counterparts of 583 transient objects in the HETDEX spectral
database (but not necessarily at the same epoch) out of 4845 transient sources
found by ALeRCe in the ZTF public data stream. In total, 636 spectra covering
the 3500 - 5500 Å wavelength range were extracted from the HETDEX observing
archive and analyzed.

  * •

We applied the t-distributed stochastic neighbor embedding (t-SNE) machine-
learning method to explore the dataset and identify potentially interesting
sources. Our primary intention was to identify supernovae, which succeeded in
2 cases (see below).

  * •

All ZTF sources having HETDEX spectra were classified into star, galaxy, agn,
and low categories based on matching with template spectra (or, in the last
case, having low signal-to-noise). For most objects, these categories agreed
very well with their “Stamp-classification” made by the ALeRCe pipeline for
their ZTF counterparts.

  * •

We attempted to find supernovae among the HETDEX spectra in three different
ways: visual inspection of the t-SNE 2D output, looking for outliers in the
$\chi^{2}$ template matching classification, and cross-correlation with SN
templates. ZTF20aatpoos was flagged by all three methods as a potential SN,
which was successfully confirmed as a Type IIP event using SNID. We also
identified another SN candidate, ZTF19abdkelq, among the low S/N sources, and
SNID confirmed it to be a Type Ia SN taken $\sim 40$ days after maximum light.

  * •

As a by-product, we identified 49 AGN sources, most of them at low ($z\lesssim
0.5$) redshifts; 38 of these were also classified as AGN by ALeRCe.

  * •

We obtained host galaxy spectra for 50 ZTF transients classified as supernovae
by ALeRCE. Most turned out to be starburst galaxies, which is consistent with
the expectation that core-collapse events dominate the supernova sample in the
low-redshift ($z\lesssim 0.3$) Universe.

We expect to find a few ($\lesssim 5$) more supernovae during the upcoming
years of the HETDEX survey.

HETDEX is led by the University of Texas at Austin McDonald Observatory and
Department of Astronomy with participation from the Ludwig-Maximilians
Universitat Munchen, Max-Planck-Institut fur Extraterrestrische Physik (MPE),
Leibniz-Institut fur Astrophysik Potsdam (AIP), Texas A&M University, The
Pennsylvania State University, Institut fur Astrophysik Gottingen, The
University of Oxford, Max-Planck Institut fur Astrophysik (MPA), The
University of Tokyo, and Missouri University of Science and Technology. In
addition to Institutional support, HETDEX is funded by the National Science
Foundation (grant AST0926815), the State of Texas, the US Air Force (AFRL
FA9451-04-2-0355), and generous support from private individuals and
foundations. The Hobby-Eberly Telescope (HET) is a joint project of the
University of Texas at Austin, the Pennsylvania State University, Ludwig-
Maximilians-Universitat Munchen, and Georg-August-Universitat Gottingen. The
HET is named in honor of its principal benefactors, William P. Hobby and
Robert E. Eberly. VIRUS is a joint project of the University of Texas at
Austin, Leibniz-Institut für Astrophysik Potsdam (AIP), Texas A&M University
(TAMU), Max-Planck-Institut für Extraterrestriche-Physik (MPE), Ludwig-
Maximilians-Universität München, Pennsylvania State University, Institut für
Astrophysik Göttingen, University of Oxford, and the Max-Planck-Institut für
Astrophysik (MPA). The authors acknowledge the Texas Advanced Computing Center
(TACC) at The University of Texas at Austin for providing high performance
computing, visualization, and storage resources that have contributed to the
research results reported within this paper. URL: http://www.tacc.utexas.edu
The University of Texas at Austin sits on indigenous land. The Tonkawa lived
in central Texas and the Comanche and Apache moved through this area. The
Davis Mountains that host McDonald Observatory were originally husbanded by
Lipan Apache, Warm Springs Apache, Mescalero Apache, Comanche and various
tribes of the Jumanos. We acknowledge and pay our respects to all the
Indigenous Peoples and communities who are or have been a part of these lands
and territories in Texas. JV, BPT and JCW are supported by NSF grant
AST1813825. ZJ was supported by a PRODEX Experiment Agreement No. 4000137122
between the ELTE Eötvös Loránd University and the European Space Agency
(ESA-D/SCI-LE-2021-0025). The Institute for Gravitation and the Cosmos is
supported by the Eberly College of Science and the Office of the Senior Vice
President for Research at the Pennsylvania State University.

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##  6 Appendix

Table A1 lists the parameters of the ZTF transients classified via
$\chi^{2}$-minimization (Section 3.2). Only a few objects are given here to
illustrate the data structure. The full table in electronic form can be
accessed on GitHub888https://github.com/jozsefvinko/Transients-in-HETDEX.

Table A1: ZTF transients classified via $\chi^{2}$-minimization  Name | R.A.(deg) | Dec.(deg) |  MJDstart |  MJDstop |  MJDDEX | AL type | AL prob. | Sp.type | S/N | $\chi^{2}$ | z | Class.
---|---|---|---|---|---|---|---|---|---|---|---|---
ZTF17aaclhdu | 33.36269135 | -0.87615907 | 58781.3 | 59079.5 | 58862 | AGN | 0.46 | G0V | 16.44 | 0.9381 | 0 | star
ZTF18aagstka | 226.93757719 | 51.45284761 | 58255.3 | 59641.4 | 58660 | SN | 0.43 | SB4 | 3.12 | 0.2609 | 0.045 | galaxy
ZTF18aaguppa | 230.02485526 | 51.14954140 | 59067.2 | 59094.2 | 58173 | – | – | M2V | 0.96 | 1.3199 | 0 | low
ZTF18aaiwfgo | 197.17922553 | 55.52398578 | 59291.3 | 59316.3 | 58958 | AGN | 0.65 | Sa | 8.02 | 1.9890 | 0.079 | agn
$\vdots$ | $\vdots$ | $\vdots$ | $\vdots$ | $\vdots$ | $\vdots$ | $\vdots$ | $\vdots$ | $\vdots$ | $\vdots$ | $\vdots$ | $\vdots$ | $\vdots$
Table A2: Parameters of the AGN sample ZTF name | S/N | $\chi^{2}$ | $z$ | ZTF name | S/N | $\chi^{2}$ | $z$
---|---|---|---|---|---|---|---
ZTF18aaiwfgo | 8.01 | 1.2152 | 0.28 | ZTF20aarsdui | 4.37 | 8.0946 | 1.57
ZTF18aajlvsx | 10.02 | 0.6340 | 0.13 | ZTF20aasjgqr | 5.47 | 0.9711 | 0.79
ZTF18aakqshm | 16.24 | 1.2993 | 0.23 | ZTF20aaxalny | 11.57 | 0.9945 | 0.35
ZTF18aaktpmg | 9.21 | 11.2259 | 0.42 | ZTF20abakioq | 4.31 | 2.0508 | 0.45
ZTF18aaqkxzg | 10.05 | 0.4965 | 0.19 | ZTF20abbfimy | 4.82 | 2.3148 | 1.59
ZTF18aaxqtbj | 13.99 | 1.2383 | 0.24 | ZTF20abcamux | 9.19 | 0.4522 | 0.18
ZTF18aceyycp | 9.22 | 0.6280 | 0.21 | ZTF20abcjsvg | 2.53 | 0.3731 | 0.81
ZTF19aalbcut | 14.66 | 4.4769 | 0.42 | ZTF20abrigbg | 5.05 | 0.6352 | 0.35
ZTF19aarioyj | 11.27 | 1.3345 | 0.17 | ZTF20abwfqoj | 9.74 | 5.6799 | 0.57
ZTF19aasbotx | 11.42 | 1.2587 | 0.27 | ZTF20abxpvdk | 5.57 | 1.7046 | 0.60
ZTF19aasccum | 5.30 | 2.6832 | 0.52 | ZTF20abxrxbh | 17.89 | 3.9041 | 0.57
ZTF19acllhlh | 21.58 | 1.6776 | 0.41 | ZTF20ackoaaa | 5.54 | 0.9043 | 0.80
ZTF20aabqori | 18.72 | 1.0551 | 0.21 | ZTF20acpzigz | 7.76 | 0.7796 | 0.91
ZTF20aadbtju | 10.57 | 1.6565 | 0.31 | ZTF20acxtdub | 11.50 | 1.5784 | 0.21
ZTF20aahgivv | 8.96 | 1.5503 | 0.42 | ZTF21aandbyi | 4.36 | 2.0195 | 0.45
ZTF20aaicqat | 5.61 | 1.0932 | 0.82 | ZTF21aaplbpm | 5.02 | 3.9342 | 0.46
ZTF20aaivjyq | 32.11 | 6.4454 | 0.50 | ZTF21aavdhbj | 3.18 | 1.8110 | 0.80
ZTF20aajbuhw | 5.79 | 0.9001 | 0.28 | ZTF21aavdugs | 10.41 | 1.1423 | 1.57
ZTF20aajcmsc | 9.30 | 0.4392 | 0.18 | ZTF21aayehok | 6.74 | 3.4700 | 0.49
ZTF20aaliybg | 14.40 | 1.7369 | 0.26 | ZTF21aazcpmg | 3.77 | 2.0711 | 0.53
ZTF20aankago | 7.90 | 0.9189 | 0.86 | ZTF21aazrcye | 5.93 | 0.8802 | 0.31
ZTF20aaoyjbk | 10.92 | 1.9170 | 0.49 | ZTF21aazxsci | 8.80 | 1.2792 | 0.20
ZTF20aaoykbt | 11.35 | 1.6181 | 0.41 | ZTF21abatxlh | 7.27 | 3.7249 | 0.59
ZTF20aappepk | 4.93 | 2.3970 | 2.29 | ZTF21abiimfm | 20.44 | 2.4730 | 0.49
ZTF20aaqpzao | 18.37 | 1.2529 | 0.30 |  |  |  |
Table A3: Parameters of the host galaxies having ALeRCE SN light curve classification Name | SN type | SN type prob. | Host type | S/N | $\chi^{2}$ | z
---|---|---|---|---|---|---
ZTF18aagstka | SNII | 0.408 | SB4 | 3.12 | 0.2609 | 0.045
ZTF18aamsecj | SNII | 0.296 | SB6 | 9.27 | 2.3834 | 0.080
ZTF18aaovsji | SLSN | 0.320 | SB1 | 6.96 | 6.8908 | 0.131
ZTF18abkiqna | SNIa | 0.330 | SB4 | 5.10 | 4.3505 | 0.130
ZTF18abuatfp | SNII | 0.342 | SB6 | 11.81 | 5.3940 | 0.136
ZTF18acwtrfe | SNIbc | 0.336 | SB3 | 4.64 | 1.0119 | 0.049
ZTF18acwyxnp | SLSN | 0.364 | Sa | 12.32 | 3.3459 | 0.130
ZTF19aangier | SNIa | 0.472 | Sb | 14.44 | 2.5332 | 0.060
ZTF19abuxhqd | SNII | 0.346 | S0 | 13.96 | 3.1949 | 0.080
ZTF19aceckmm | SNII | 0.462 | SB6 | 7.45 | 2.5238 | 0.032
ZTF19acfxbki | SNIa | 0.596 | Sa | 21.03 | 2.3064 | 0.058
ZTF20aafcheh | SLSN | 0.418 | Sa | 12.44 | 3.3433 | 0.130
ZTF20aagnbdf | SLSN | 0.438 | SB3 | 5.86 | 3.0425 | 0.089
ZTF20aahhvci | SNIa | 0.382 | SB3 | 4.32 | 2.0259 | 0.104
ZTF20aaolglj | SLSN | 0.360 | SB3 | 9.39 | 1.6336 | 0.029
ZTF20aausyrp | SNIa | 0.296 | SB6 | 5.10 | 3.3010 | 0.026
ZTF20aayxapt | SLSN | 0.376 | SB1 | 8.35 | 4.8623 | 0.145
ZTF20abeiqzy | SLSN | 0.344 | Sa | 9.53 | 2.4443 | 0.171
ZTF21aaoblpm | SNIa | 0.384 | SB3 | 12.35 | 13.0117 | 0.117
ZTF21aaplbom | SNIbc | 0.360 | SB3 | 11.24 | 6.0530 | 0.121
ZTF21abfjcap | SNIa | 0.388 | SB6 | 12.76 | 3.6394 | 0.151