# The LIGO HET Response (LIGHETR) Project to Discover and Spectroscopically
Follow Optical Transients Associated with Neutron Star Mergers

M. J. Bustamante-Rosell Department of Physics, University of Texas at Austin,
Austin, TX  Department of Astronomy, University of California, Santa Cruz, CA
Greg Zeimann Hobby Eberly Telescope, University of Texas, Austin, Austin, TX,
78712, USA  J. Craig Wheeler Department of Astronomy, University of Texas at
Austin, Austin, TX  Karl Gebhardt Department of Astronomy, University of Texas
at Austin, Austin, TX  Aaron Zimmerman Department of Physics, University of
Texas at Austin, Austin, TX  Chris Fryer Los Alamos National Laboratory, Los
Alamos, NM  Oleg Korobkin Los Alamos National Laboratory, Los Alamos, NM
Richard Matzner  Department of Physics, University of Texas at Austin, Austin,
TX  V. Ashley Villar Department of Astronomy and Astrophysics, Penn State
University, College Park, PA  S. Karthik Yadavalli Department of Astronomy and
Astrophysics, Penn State University, College Park, PA  Kaylee M. de Soto
Department of Astronomy and Astrophysics, Penn State University, College Park,
PA  Matthew Shetrone University of California Observatories, Santa Cruz, CA
95064  Steven Janowiecki McDonald Observatory, University of Texas at Austin,
Austin, TX  Pawan Kumar  Department of Astronomy, University of Texas at
Austin, Austin, TX  David Pooley Department of Physics and Astronomy, Trinity
University, San Antonio, TX  Eureka Scientific, Inc.  Benjamin P. Thomas
Department of Astronomy, University of Texas at Austin, Austin, TX  Hsin-Yu
Chen Department of Physics, University of Texas at Austin, Austin, TX  Lifan
Wang Department of Astronomy, Texas A&M University, College Station, Texas
Jozsef Vinkó Department of Astronomy, University of Texas at Austin, Austin,
TX  Konkoly Observatory, Budapest, Hungary  Department of Optics and Quantum
Electronics, University of Szeged, Hungary  David J. Sand Department of
Astronomy, University of Arizona, Tucson, AZ  Ryan Wollaeger Los Alamos
National Laboratory, Los Alamos, NM  Frederic V. Hessman Institut für
Astrophysik und Geophysik, University of Göttingen, Germany  Kristen B.
McQuinn Department of Physics and Astronomy, Rutgers University, Piscataway,
NJ M. J. B. Rosell<EMAIL_ADDRESS>
(Received 2022 October 25)

###### Abstract

The LIGO HET Response (LIGHETR) project is an enterprise to follow up optical
transients (OT) discovered as gravitational wave merger sources by the
LIGO/Virgo collaboration (LVC). Early spectroscopy has the potential to
constrain crucial parameters such as the aspect angle. The LIGHETR
collaboration also includes the capacity to model the spectroscopic evolution
of mergers to facilitate a real-time direct comparison of models with our
data. The principal facility is the Hobby-Eberly Telescope. LIGHETR uses the
massively-replicated VIRUS array of spectrographs to search for associated OTs
and obtain early blue spectra and in a complementary role, the low-resolution
LRS-2 spectrograph is used to obtain spectra of viable candidates as well as a
densely-sampled series of spectra of true counterparts. Once an OT is
identified, the anticipated cadence of spectra would match or considerably
exceed anything achieved for GW170817 = AT2017gfo for which there were no
spectra in the first 12 hours and thereafter only roughly once daily. We
describe special HET-specific software written to facilitate the program and
attempts to determine the flux limits to undetected sources. We also describe
our campaign to follow up OT candidates during the third observational
campaign of the LIGO and Virgo Scientific Collaborations. We obtained VIRUS
spectroscopy of candidate galaxy hosts for 5 LVC gravitational wave events and
LRS-2 spectra of one candidate for the OT associated with S190901ap. We
identified that candidate, ZTF19abvionh = AT2019pip, as a possible Wolf-Rayet
star in an otherwise unrecognized nearby dwarf galaxy.

††journal: ApJ††facilities:  This study employs observations obtained with the
Hobby-Eberly Telescope, which is a joint project of the University of Texas at
Austin, the Pennsylvania State University, Ludwig-Maximilians-Universität
München, and Georg-August-Universität Göttingen. The HET is named in honor of
its principal benefactors, William P. Hobby and Robert E. Eberly. The Low
Resolution Spectrograph 2 (LRS2) was developed and funded by the University of
Texas at Austin McDonald Observatory and Department of Astronomy and by
Pennsylvania State University. We thank the Leibniz-Institut für Astrophysik
Potsdam (AIP) and the Institut für Astrophysik Göttingen (IAG) for their
contributions to the construction of the integral field units. This study also
utilized X-ray data from the Neil Gehrels Swift Observatory, and
Chandra.††software:  astropy (Astropy Collaboration et al., 2013, 2018), emcee
(Foreman-Mackey et al., 2013), numpy (Harris et al., 2020), scipy (Virtanen et
al., 2020), matplotlib (Hunter, 2007), pandas (pandas development team, 2020;
Wes McKinney, 2010). Swift data were reduced with XRTDAS (v0.13.5), CALDB
(v20190910), XRTPIPELINE and XSELECT. Chandra data were processed with
SPECEXTRACT.

##  1 Introduction

The first gravitational-wave event discovered by LIGO, the merger of two black
holes in a binary system (BBH), opened an exciting new vista in multi-
messenger astronomy (Abbott et al., 2016)

Utilizing this new window into the Universe is one of the most exciting
prospects in astrophysics. Optical astronomers and observers in other
electromagnetic bands were invited to this arena with the advent of GW170817,
the result of the inspiral and merger of two neutrons stars (BNS), and its
gamma-ray (GW170817A) and optical (AT2017gfo) counterparts. (Abbott et al.,
2017a, b; Coulter et al., 2017; Kilpatrick et al., 2017; Valenti et al., 2017;
Haggard et al., 2017; Kasen et al., 2017; Kasliwal et al., 2017; Murguia-
Berthier et al., 2017; Margutti et al., 2017; Nicholl et al., 2017; Tanvir et
al., 2017; Villar et al., 2017; Troja et al., 2017; Hallinan et al., 2017;
Evans et al., 2017; Lyman et al., 2018; Mooley et al., 2018; Margutti et al.,
2018; Pooley et al., 2018; Piro et al., 2019; Troja et al., 2019; Lamb et al.,
2019; Hajela et al., 2019; Gillanders et al., 2022; Kilpatrick et al., 2022;
Hajela et al., 2022). AT2017gfo corresponded to a kilonova, a transient about
1000 times brighter than a classical nova (Li & Paczyński, 1998; Metzger et
al., 2010).

Many groups have reported their techniques for searching for optical
transients (OT) associated with gravitational wave events in the third LVC run
that began on April 1, 2019 (O3; Gomez et al., 2019; Hosseinzadeh et al.,
2019; Lundquist et al., 2019; Yang et al., 2019; Gompertz et al., 2020; Ackley
et al., 2020; Kasliwal et al., 2020; Chang et al., 2021; Kim et al., 2021;
Paterson et al., 2021; Becerra et al., 2021; Oates et al., 2021; de Jaeger et
al., 2022; Rastinejad et al., 2022). Here we report on the LIGO HET Response
(LIGHETR) project that employed spectrographs on the Hobby-Eberly Telescope
(HET) at McDonald Observatory to conduct a spectroscopic search and follow-up
during O3.

The first spectra were taken 0.88 and 1.84 hours after the detection of the OT
of AT2017gfo (Shappee et al., 2017), fully 12 hours after the original LVC GW
signal. The next few spectra were obtained at $\sim$ 1.18 and 1.5 days
(McCully et al., 2017; Pian et al., 2017; Nicholl et al., 2017; Buckley et
al., 2018) and then a few at daily intervals after that, including
VLT/X-shooter data extending into the infrared (Pian et al., 2017). The target
varied rapidly over that timescale and was unobservable spectroscopically
after about 10 days. Pian et al. (2017) argue for the detection of shallow,
broad features at about 8100 Å and 12,300 Å in a spectrum obtained at 1.5 days
and several subsequent spectra. Watson et al. (2019) suggest that the former
may be evidence of the neutron-capture element strontium. Perego et al. (2022)
and Tarumi et al. (2023) explore the possibility that the feature is He I
$\lambda 10830$ and stress the possible role of non-LTE effects in
differentiating strontium from helium.

Although it was fortuitously nearby, practical factors prevented a higher
cadence of spectroscopy of AT2017gfo. The southern location at a declination
of -17∘51’ meant that it was primarily visible in the south and only for about
2 hours per night. A delay in the release of the refined location meant that
it was too late to observe from Africa and too early for Chile. Once the OT
was accessible in Chile, discovery came quickly and spectroscopy shortly
thereafter. The result was an inadequate cadence for such a rapidly-changing
event. We were left to wonder about the nature of earlier spectra.

LIGHETR is a program designed to complement the global effort to obtain
spectroscopy of merger optical transient (OT) components.

The Hobby-Eberly Telescope (HET) with the massively-replicated wide-field
VIRUS IFU spectrographs (Hill et al., 2018a, b; Hill et al., 2021; Gebhardt et
al., 2021) and the Marcario Low-Resolution Spectrograph (LRS2) (Chonis et al.,
2016; Hill et al., 1998, Hill, et al. 2023, in preparation) can play a
significant role in multi-messenger astronomy since 1) the queue-scheduled HET
is designed to respond quickly to events, 2) the blue sensitivity of VIRUS
that extends to 3500 Å can be an important discriminant for various models of
the mergers, 3) VIRUS provides the largest sky area coverage of any
spectrograph by a factor of 70, 4) the location of GW170817 at the edge of a
nearby galaxy highlights the advantage of using an array like VIRUS and 5)
LRS2 can provide frequent wide wavelength spectroscopic coverage of these
rapidly-evolving events.

With the HET, we can, in principle, discover the OT, get the first spectrum,
and then a dense time series of spectra. Ideally, we could obtain several
spectra per night, depending on the declination, and maintain this schedule
until the OT fades below detectability, in roughly a week. In this time, under
ideal circumstances, we could obtain of order 20 spectra and capture in detail
the structure and evolution of these incredible events. O3 was a resounding
success for the detection of gravitational wave signals from compact star
mergers, but disappointing in the lack of observable OTs. Here we describe our
attempts to spectroscopically detect six merger events. We failed to see any
OT associated with a GW event, but did reveal an interesting object,
ZTF19abvionh, that is of intrinsic interest for its own sake.

Section 2 gives an outline of the LIGHETR observational program. Section 3
presents some results of the program in the first portion of O3 that spanned
April 1 to September 31, 2019. Section 3.5 presents our follow up of ZTF
candidate ZTF19abvionh that proved to be interesting, but not a merger
counterpart. Section 4 explores attempts to quantify our ability to assess the
upper limits of undetected sources. Section 5 presents models that illustrate
the potential power of early, frequent, spectral observations. Section 6
summarizes the results to date and our future plans. Appendix A gives a table
of compositions used in kilonovae light curve and spectral simulations,
Appendix B gives some details of LIGHETR operating procedures, and Appendix C
presents a notional timetable for responding to an LVC event and initiating a
spectroscopic search and followup.

##  2 Observational Program

LIGHETR was designed to observe about one LVC event per trimester during O3
using the VIRUS IFU array spectroscopically to search for and perhaps discover
the OT and to use the LRS2 low-resolution spectrograph to do intensive, high-
cadence spectroscopic followup. LIGHETR was prepared rapidly to generate the
appropriate target information from a trigger and work with the HET staff in
order to implement the search and subsequent spectroscopic monitoring as
rapidly as possible. Our observational strategy involved digesting public
alerts with our custom software diagnosis and assessing if the candidate is
visible to HET. After further human vetting of the candidate, the VIRUS array
was utilized to take spectra over a wide field of view centered on the
candidate. We have the capacity to carry out further intensive spectroscopic
followup with the LRS2 instrument. LIGHETR has the capability to be on target
20 minutes after an alert, achieving dense spectral coverage of the target.
LIGHETR can, in principle, observe in bright time since the spectrographs are
always mounted.

The LVC alerts give approximate locations, distances, and estimates of the
nature of the merger: BNS, BBH, or NSBH. The LIGHETR program was triggered if
an appropriate event, BNS or NSBH, fell within the RA and DEC attainable with
HET at that epoch and promised a reasonably bright OT.

Details of how our program was implemented are given below. A proposed
timetable for the LIGHETR response in O3 is given in Appendix C

###  2.1 The Instruments

The Hobby-Ebberly Telescope111 HET is a collaboration of the University of
Texas at Austin, Pennsylvania State University, Stanford University, Georg-
AugustUniversität, Göttingen, and Ludwig-Maximillians-Universität, Munich
(HET) is equipped with a 11 m spherical primary mirror made of hexagonal
segments fixed at a zenith angle of 35∘(Hill et al., 2021). It can be moved in
azimuth, in such way that over a day, it can cover $70\%$ of the sky visible
at McDonald Observatory. The pupil, which is 10.0 m in diameter at the center
of the track, can be moved to follow an object for different lengths of time
depending on the declination. This tracking varies from 40 minutes at the
lowest point (at $\delta=-10.3$∘) to 2.8 hours at the highest (at
$\delta=+71.6$∘) with a maximum of 5 hours at 65∘.

Mounted on the HET are two low-resolution integral field unit (IFU)
instruments: VIRUS and LRS2. VIRUS is made up of 156 spectrographs, each fed
by a fiber-integral unit, having an overall total fill factor of 1/4.6. Each
unit consists of 248 fibers with an individual 1/3 fill-factor, filled out by
a three-point dither pattern, moving approximately 1.5″ between each position.
While this dither fills in the gaps between fibers in each IFU, it does not
fill in the gaps between IFUs. Each fiber has a diameter of 1.5″ and each
spectrograph covers an area of 50″ $\times$ 50″. The VIRUS wavelength coverage
goes deeply in the blue, ranging from $350<\lambda\>(\mathrm{nm})<500$ at a
resolving power of $R=\lambda/\delta\lambda\approx 700$. LRS2, on the other
hand, has a much smaller field-of-view (FoV) of 12″$\times$ 6″, with a higher
resolution and broader wavelength coverage, spread between two independent
fiber-fed dual-channel spectrographs. The blue spectrograph, LRS2-B, with
$R=\lambda/\delta\lambda$ of 1900 and 1100 (for the respective channels)
covering $370<\lambda\>(\mathrm{nm})<700$ and the red spectrograph, LRS2-R,
with $R=\lambda/\delta\lambda$ of 1800 covering
$650<\lambda\>(\mathrm{nm})<1050$. The response functions of LRS2 will be
presented in Hill et al. (2023, in prep).

Typical observing conditions at HET have a median seeing of 1.7″. Under such
conditions, a baseline 20 minute observation with three dithered exposures of
360 s has a line sensitivity at 5 $\sigma$ of $\sim 6\times 10^{-17}$ erg cm-2
s-1. The fill factor of VIRUS of 1/4.6 limits its capacity to get several
galaxies in one dither pattern. This is why we do targeted galaxy follow-up,
placing individual galaxies at the center of an individual IFU.

###  2.2 Alert

In O3, if automated vetting detected a potential merger event, LVC then sent
an automated public GCN Preliminary Alert within about 10 seconds to 1 minute
of the detection. Each alert was subject to human vetting and either an
Initial Alert or a retraction was sent on a timescale of tens of minutes to an
hour. The GCN Preliminary Alert pointed to the LVC sky map that gave the 3D
probability distribution of the source.

Both due to the rapidly varying nature of kilonovae and the fact that HET is a
fixed zenith telescope, which gives it limited tracking capability, quick
knowledge of when the region of highest probability for a given event would be
visible required a custom alert system. With this in mind, we created
diagnosis222https://github.com/Majoburo/Diagnosis, a low-latency alert system
and observation planner for HET. diagnosis takes into account the geometry of
the HET pupil and its position at any given point in time to submit an
observation request to the HET Resident Astronomers and to alert the LIGHETR
team.

diagnosis continuously listened to the Gamma-ray Coordinates Network/
Transient Astronomy Network (GCN/TAN)333https://gcn.gsfc.nasa.gov/ for alerts
on gravitational wave events. When triggered, if the event was likely to have
an electromagnetic counterpart444If it was likely to be a BNS, NSBH, or in the
mass gap (one or more of the binary objects is in the range of 3 to 5
$\mathrm{M}_{\odot}$). We did not pursue any BBH candidates in O3., diagnosis
downloaded the associated skymap, identified if and when the 90% confidence
region fell within the HET pupil, and if so, informed the observers. As an
example, Figure 1 shows the sky map produced by diagnosis for the merger event
S190425z, which turned out to be the second confident BNS detection by the LVC
(Antier et al., 2020; Coughlin et al., 2019). diagnosis also queried a galaxy
catalog for galaxies within the observable 90% probability region, organized
them by probability, gave their local sidereal times (LSTs) to start
observations and made a submission file for HET observations. An example of a
graphic representation of the notice sent to the observers is in Figure B.1 in
Appendix B.

Figure 1:  The sky map produced by using LVC localization data for the merger
event S190425z and the HET track corresponding to the epoch of the alert. The
LVC location of this alert is given by the green bands. The region accessible
to the HET above DEC = -10 degrees and below +71 degrees is given by the dark
blue band on the right. The lighter blue represents air mass less than 2.5.
The location within 18 degrees of the Sun is given by the large yellow patch.
The lighter brown area is air mass greater than 2.5 and the dark brown region
on the left represents the portion of the sky below the horizon. The HET track
overlapped with the large LVC sky map at the time of this alert. This figure
provides a visual aid for observers and illustrates the technique by which we
decided whether to trigger on a given LVC alert and to construct a prioritized
list of galaxies to be searched for a new optical transient.

###  2.3 Galaxy Catalog

We used version 2.3 of the GLADE galaxy catalog (Dálya et al., 2018). The
GLADE catalogue contains around 3.26 million objects (149 globular clusters,
297014 quasars, and 2965717 galaxies). It was constructed combining data from
5 astronomical catalogues: GWGC (White et al., 2011), 2MPZ (Bilicki et al.,
2013), 2MASS XSC (Skrutskie et al., 2006), HyperLEDA (Makarov et al., 2014),
and SDSS-DR12Q (Pâris et al., 2017). The catalog is complete up to
$d_{L}=37^{+3}_{-4}$ Mpc in terms of cumulative B-band luminosity out to such
distance, decreasing to 50% completeness at $d_{L}$ of 91 Mpc. Figure 2 shows
the catalog’s completeness in both B and K bands as a function of luminosity
for different redshift bins. The expected detection depth for LVC merger
events in O3 was 100 - 140 Mpc. This substantially exceeds the completeness
depth of the Glade catalog, but the catalog contains some galaxies extending
to the LVC limit.

Figure 2: Completeness of the GLADE catalog in the B and K bands for different
redshift ranges. The solid lines are luminosity histograms of GLADE galaxies
within different distance shells in terms of their measured B-band and K-band
luminosities. Dotted lines are our expectations for complete catalogues based
on Schechter function measurements for each band. The GLADE catalog is
designed to be more complete in the B-band, but we use the K-band since we aim
to use the luminosity as an indicator of mass.

We constrained the selection of galaxies in the catalog to those visible by
HET and those for which we could estimate their mass, that is, galaxies within
declinations of 71.6o to -10.3o and with luminosity distances and K-band
magnitude measurements. This cut our list of objects to 19% of the original
list, leaving only galaxies. Figure 3 shows the number density in the sky of
our selection of GLADE galaxies.

Figure 3: Galaxies contained within the GLADE catalog that are visible from
HET.

###  2.4 Galaxy Sorting

Gehrels et al. (2016) estimated that for bright galaxies contributing $\sim
50\%$ of the light in a given area of the sky, there will be $\sim 20$
galaxies inside a typical LVC error box that are also consistent with LVC
distance estimates. In order to rank the most probable galaxies, we weighed
the localization probability given by the LVC (Singer et al., 2016) by the
galaxy mass, which has been shown to be linearly proportional to the K-band
luminosity (Kauffmann & Charlot, 1998). Previous work took B-band luminosity
as a weight factor on the galaxy selection process (Arcavi et al., 2017; Yang
et al., 2019). Even though the GLADE catalog we used is more complete in terms
of galaxies with B-band luminosities, these luminosities have the problem of
being affected by star formation history and dust extinction, which makes them
an unreliable indicator for mass. Using the B-band without such considerations
would effectively establish a preference towards star-forming galaxies. Both
simulations and measurements have shown that short soft gamma-ray bursts
(SGRBs) are produced by BNS mergers (Wiggins et al., 2018). GW170817 confirmed
this expectation. A recent kilonova candidate was associated with a smaller
galaxy (Troja et al., 2022; Yang et al., 2022) and SGRBs are found in a
variety of galaxy types (O’Connor et al., 2022). Using a galaxy ranking based
on K-band luminosity might aid in the identification of events like GW170817
but might discriminate other candidates in other host galaxy types. Despite
significantly reducing the completeness of our galaxy sample by constraining
it to those with K-band luminosities as shown in Figure 2, we chose galaxy
mass and hence K-band luminosity as our weight factor in the localization
probability.

The LVC provided a localization probability, $\rho_{i}$, as a 2D HEALpix
skymap. For each pixel, i, within the skymap, the LVC also included Gaussian
parameters ($\sigma_{i}$; $\mu_{i}$) for the distance probability. Thus, we
took the galaxies within our catalog falling in the 90% confidence region in
terms of the 2D skymap, calculated their distance probability in terms of
their catalogued luminosity distances, $r_{g}$, and multiplied this by their
K-band luminosity, $L^{g}_{K}$, and 2D localization probability, $\rho_{i}$,
to get their final ranking,

$R(r_{g},L_{K}^{g})\propto
L_{K}^{g}\rho_{i}\frac{1}{\sigma_{i}}\exp\left[-\frac{\left(r_{g}-\mu_{i}\right)^{2}}{2{\sigma_{i}}^{2}}\right]r_{g}^{2}\,.$
(1)

This ranking was normalized to add to unity when summing over the most
probable 100 galaxies.

###  2.5 Search for the Optical Transient

As described above, the probability distributions of the LVC sky maps and HET
track maps illustrated in Figure 1 were automatically employed by diagnosis to
query the glade catalog to produce a prioritized list of galaxies to search
for the OT. Such galaxy lists were the basis for the VIRUS search. diagnosis
also generated the Phase II Target Submission List (TSL) for the prioritized
list of galaxies. The result was a prioritized list of galaxies in the HET
overlap region and within the distance window provided in the LVC alert. We
then systematically observed that sample of galaxies with the VIRUS array.
Examples of the LIGO alert as processed by diagnosis for local redistribution,
the galaxy priority list, and the selection of galaxy priorities are given in
Appendix B. Within O3, our observations all fell within the $\sim 10$ most
likely galaxies according to our prioritized list.

Once we start the search, a key capacity is to rapidly reduce the data to
decide whether there is an OT in a given galaxy or to continue to the next
galaxy on the priority list. VIRUS observations always produce a zeroth-moment
image as one of the first steps. The VIRUS IFU units do not physically touch
one another. The wavelength region of 3500-5500 Å in a selected aperture is
used to make a collapsed image, as illustrated in Figure 4. This initial
collapsed image was compared to archival imaging data (specifically from Pan-
STARRS), a step which could (but did not in O3) promptly reveal the OT.

Figure 4:  A sample VIRUS (Gebhardt et al., 2021; Hill et al., 2021) image
that would be used in our proposed search for the optical transient associated
with a gravitational-wave event. Each IFU unit covers an area of 50″ on a
side. The VIRUS array automatically produces a zeroth-moment image using the
wavelength region 3500-5500 Å and an adopted aperture to make a collapsed
image. The whole array is 21′ on a side. This image butts the IFUs together
preserving their relative position. Each of the 20,000 fibers is positioned to
an accuracy of 0.1″. The faintest objects in the image are g$\sim$22-23. The
streak in the upper right is a satellite or asteroid artifact.

During O3, VIRUS spectrograph units were steadily being added to the array. At
the time, the array observed about 80 % of its field that is 21′ on a side
(Figure 4). Each IFU unit covers an area of 50″ on a side. Normal VIRUS
operation is to do a three-point dither to fill the fiber spaces. We employed
this operation since it enhanced our ability to calibrate the spectrum.

For rapid data reduction, we used remedy v0.1, a stripped-down version of the
LRS2 reduction code, panacea (Zeimann & et al., 2021)555Panacea v0.1
documentation can be found at:
https://github.com/grzeimann/Panacea/blob/master/READMEv0.1.md. The initial
success of remedy encouraged an evolved version of the code to become the
HET’s default pipeline for VIRUS observations. A full description of remedy
can be found in Zeimann et al. (2023, in prep). remedy has the capability of
reducing the full frame or just the data from a dithered IFU exposure of the
host galaxy and its vicinity. We then produced a collapsed image from the IFU
and a reduced spectrum, all in 30 seconds.

We pointed a single VIRUS IFU at a target host galaxy while getting spectra of
all the other galaxies in the VIRUS field simultaneously. Depending on the
target density, circumstances might have allowed us to multiplex, acquiring
multiple target galaxies in a single VIRUS field. We note that the merger
event GW170817 was about 10″ away from its host galaxy. With the 50″ square
VIRUS IFU field of view, had we pointed at the host, we would easily have
picked up the OT and acquired its spectrum (Figure 4).

Each VIRUS set of three dithered exposures of a target galaxy required a total
of $\sim 15$ minutes, including shuffling between galaxies. The total time to
observe 20 galaxies was thus $\sim$ 5 hours. We were not able to reach this
limit in O3 because our targets only became accessible to HET a few hours
before sunrise.

Table 1: Estimated Number of Single Track Spectra Per Night as a Function of Declination and OT Magnitude. Declination | Track Length | Magnitude | Exposure Time | Number of Single Spectra | Number of B + R Spectra
---|---|---|---|---|---
degrees | minutes | mag | minutes |  |
20 | 72 | 17.5 | 20 | 3 | 1
45 | 96 | 17.5 | 20 | 4 | 2
70 | 156 | 17.5 | 20 | 7 | 3
20 | 72 | 21.0 | 60 | 1 | 0
45 | 96 | 21.0 | 60 | 1 | 0
70 | 156 | 21.0 | 60 | 2 | 1

###  2.6 Dense Spectral Followup

Once an OT candidate was identified by our search or others, we planned to
implement follow-up spectroscopy with LRS2. The spectra of a kilonova are
expected at first to change on timescales of hours and later on time scales of
days. We could, in principle, take spectra for about a week’s span with as
rapid a cadence as the HET would allow using all the viewable time.

Table 1 presents a short summary of track lengths and number of spectra
expected as a function of the brightness and declination of the target. We
assume that the total setup time is 10 minutes for a single track following a
single object. The actual setup time is often less than 10 minutes, and there
is very little setup time, perhaps one or two minutes, to switch from LRS2-B
and LRS2-R if both observe the same object. For an OT as bright as AT 2017gfo,
$\sim 17.5$ mag in the i band at discovery, we assume 20 minute exposures for
each early observation and hence that a single spectrum would require 10+20 =
30 minutes and that a full LRS2-B and LRS2-R spectrum requires 10+20+20 = 50
minutes. For long tracks allowing multiple exposures of the same object, the
total exposure would be 10 minutes plus the number of 20 minute intervals that
sum to less than the track length. If we elect to get only LRS2-B in the early
phases, then we would get roughly twice as many spectra per track than
obtaining LRS2-B and LRS2-R spectra, with the caveat that we could only get an
even number of B + R spectra utilizing a single track.

Thus, in an ideal northern declination case $\sim+70^{o}$ with a track length
of $\sim 156$ min and target of brightness $\sim 17.5$ mag, we can expect no
more than overhead plus seven consecutive 20 minute shots per track. We could
get three full LRS2-B + LRS2-R spectra per track. If the object is at lower
declination, $\sim 20^{o}$, then we can still get three single spectra or two
full spectra per track.

Events discovered in O3 were on the average more distant and fainter than
AT2017gfo, requiring longer exposures. AT2017gfo was at only $\sim 40$ Mpc.
LVK will reach to 160 to 190 Mpc in O4, implying a factor of 16 to 23 in
luminosity and 3 to 3.4 mag in brightness. A target in this distance range
would be about r = 20.5 to 21, requiring an exposure of about an hour on the
HET. A single spectrum including overhead would require 70 minutes. To get
both LRS2-B and LRS2-R would require 130 minutes. At this brightness at a
declination of $\sim+70^{o}$ with a track length of $\sim 156$ min and target
of brightness $\sim 21$ mag, we might acquire two single spectra per track and
one complete LRS2-B + LRS2-R spectrum per track. At declinations $\sim 20^{o}$
and $\sim 45^{o}$, we could expect only a single spectrum per track.

If we assume the target is visible throughout the whole night, once on an East
track and once on a West track, a somewhat unlikely circumstance in practice,
then we could get about twice as many spectra per night. For a declination of
$\sim 70^{o}$ at $\sim 17.5$ mag, we could get 14 single spectra per night or
six complete LRS2-B + LRS2-R spectra. At lower declinations of $\sim 20^{o}$
and $\sim 45^{o}$ and $\sim 21$ mag, we would need this special circumstance
to acquire LRS2-B on one track and LRS2-R on the other to get even a single
complete LRS2-B + LRS2-R spectrum per night.

While somewhat optimistic, this cadence of spectra would match or considerably
exceed anything achieved for AT2017gfo. At later epochs, the OT will be
dimmer, also calling for longer exposures, but the cadence can be more
relaxed. A Timetable for Alert, Search, Dense Spectral Sampling, and Analysis
is given in Appendix B

##  3 Results

Details on the number of exposures on all the events we followed in O3 are
summarized in Table 2.

Table 2: Summary of LIGHETR Observations

Date (GMT) | Event | Exposures | Instruments | Searched | Follow-up
---|---|---|---|---|---
19/04/12 | S190412m | 21 | VIRUS | 7 | 0
19/04/25 | S190425z | 18 | VIRUS | 6 | 0
19/04/27 | S190426c | 15 | VIRUS | 5 | 0
19/08/22 | S190822caaRetracted event | 9 | VIRUS | 3 | 0
19/08/29 | S190829uaaRetracted event | 6 | VIRUS | 2 | 0
19/09/03 | S190901ap | 3 | VIRUS | 1 | 0
19/09/03 | S190901ap | 3 | VIRUS | 0 | 1
19/09/06 | S190901ap | 5 | VIRUS, LRS2 | 0 | 1

Note. — Summary of all events we followed. Retracted events are shown in red.
1) Date searched; 2) GCN/TAN event name; 3) Number of Exposures; 4) Number of
searched Galaxies; 5) Number of external EM triggers followed.

With all the elements in place for a successful program, we recognized that
this is a complex process that had never been employed on the HET. For this
reason, we did a dry run during the day with a practice alert trigger.

No event in O3 revealed an OT to any facility, so we did not employ LRS2 (but
see Section 3.4).

###  3.1 Dress Rehearsal: GW190412m

To complement the dry run, we also planned a full dress rehearsal initial run.
For this, we proposed to trigger on the first LVC alert of O3 that was
accessible to the HET. The probability was that this first alert would be a
BBH merger event and hence most likely not accompanied by an electromagnetic
signal. There was a small possibility that the first alert of O3 might
correspond to a BNS or NSBH merger and an associated OT. Theoretical modeling
and observations hinted that a BBH merger within the gas-dense environment of
a supermassive black hole accretion disk might generate an OT (Cheng & Wang,
1999; Bartos et al., 2017; Graham et al., 2020; Perna et al., 2022). In any
case, responding to this alert would give us critical feedback that all our
interlocking components, hardware, software and human, would function as
needed for a merger with an OT.

For our dress rehearsal, we responded to BBH candidate GW5190412m, only the
second event of O3 and the first in range of the HET. The LVC alert for
GW190412m was ingested by our diagnosis pipeline, which created a prioritized
list of target galaxies, a corresponding TSL, and alerted our team. We were
awakened at 4 am local Texas time and were on the sky and taking data within
about 20 minutes. We observed 7 of the top priority list of 13 target galaxies
in the overlapped LVC/HET field. We drafted and sent a GCN notice about our
observations in real time and submitted it as the Sun rose on the Observatory
(Rosell et al., 2019a). In hindsight, the LVC alert had been issued about two
hours earlier than our local alert. This did not impede our observations in
practice, and the problem leading to the local delay was addressed and
eliminated. We also found that a vestigial instruction in the TSL requested
shorter exposures than ideal. This allowed us to make more observations, but
with less S/N ratio than preferable. We corrected this in subsequent
observations. Despite the expected absence of an OT for S5190412m, the dress
rehearsal served its purpose.

###  3.2 GW190425z

The next event with the potential to produce an optical signal was GW190425z
with a probability greater than $99\%$ of being a BNS merger. This was the
sort of event for which our program was designed. The local alert was released
at 4:01 am local time. Our target list of high priority galaxies was loaded
into the HET queue at 4:07 am. Unfortunately, the LVC sky map was very large
since only LVC Livingston and Virgo were operating. The distance was estimated
by LVC to be 155 $\pm$ 44 Mpc, much more distant than GW170817 at 40 $\pm$ 8
Mpc.

Pan-STARRS images of our target galaxies were shared at 4:48 am. We sampled a
prioritized list of 4 galaxies from the GLADE catalog that overlapped with the
LIGO probability map and the observable pupil of the HET. The effective
limiting magnitude in the B band was $\sim 22$ magnitudes. A GCN was submitted
at 6:51 am (Rosell et al., 2019b). We did not detect an OT, and neither did
any other team. Some transient candidates were reported, but they all proved
to be more prosaic, mostly supernovae. We elected not to continue our search
of the LVC sky map on a second night. LANL team members (CF, OK, RW) worked
through this period making models corresponding to the LVC distance (see §5)
and even of some of the transient candidates. LIGHETR group member JV and
collaborators observed the five galaxies targeted by HET at Konkoly
Obervatory. CCD frames were taken with the 0.6/0.9m Schmidt telescope (FoV
70x70 arcmin2, unfiltered, limiting mag 21.5) and the 0.8m RC telescope (FoV
18x18 arcmin2, g- and r-band, limiting r magnitude $\sim 20.4$).

###  3.3 GW190426c

The LVC announced an alert on GW190426c at 10:47 am local time the next
morning. This event had a 49% chance of being a BNS but a 13% chance of being
a NSBH merger and a 14% chance of being noise. The estimated distance was very
large, 375 $\pm$ 108 Mpc. The LANL group produced new light curve models based
on the estimated distance. Half the HET track was lost to solar occlusion.
Target galaxies in the useable track became observable about 4:30 am. After an
extensive discussion of the distance, the expected S/N ratio, the Moon
location, the expected rarity of targets, the competition from other groups,
and the likelihood of tentative (but ultimately irrelevant) OT candidates, we
elected to do our VIRUS search on this event. We sampled a prioritized list of
5 galaxies from the GLADE catalog and submitted a GCN at 6:31 am (Rosell et
al., 2019c).

###  3.4 S190510g

The next potentially interesting LIGO event, S190510g, had a probability of
42% of being a BNS but 48% of being noise. The distance was also estimated to
be rather large, 280 Mpc. After some debate, we passed on this event.

###  3.5 GW190901ap

There were no other alerts that required a response from us until GW190901ap,
which had an 86% chance of being a BNS merger. We observed one target galaxy
with VIRUS and sent a GCN (Rosell et al., 2019d). The next morning, the Zwicky
Transient Factory (ZTF) announced four possible OT candidates consistent with
the redshift estimated by LIGO (Kool et al., 2019). Three were ruled out by
other groups.

Figure 5: Spectra of ZTF19abvionh. The narrow
[$\textrm{O}\,\scriptstyle\mathrm{II}$] $\lambda 3727$ is from a galaxy at
redshift $\sim$0.1 that is near to ZTF19abvionh on the sky, but unrelated. The
figure shows all the data from VIRUS and LRS2-B overlaid with a spectrum of a
WN type Wolf-Rayet star (25%) plus an O7I star (75 %) at zero redshift. The WR
components corresponding to [$\textrm{He}\,\scriptstyle\mathrm{II}$] $\lambda
4686$ and H$\alpha$ align with features in the spectra of ZTF19abvionh.

We elected to observe the fourth ZTF candidate, ZTF19abvionh, with VIRUS on
9/2/19. We detected a nearly featureless continuum with narrow emission lines
corresponding to [O II] $\lambda$3727 and $\lambda$5370 and H$\beta$ at a
redshift of 0.1 from a galaxy near to ZTF19abvionh on the sky. That galaxy,
GALEXASC J165500.03+140301.3, was about 2.5$\sigma$ more distant than the
estimated distance of the merger candidate. We submitted a GCN (Rosell et al.,
2019e) at 12:05 am and another (Rosell et al., 2019f) at 12:39 am after
estimating a black body temperature of about 10,500 K.

The next night, 9/3/19, we observed ZTF19abvionh with both VIRUS and LRS2-B.
These data showed broadened emission features at about 4686Å and 6560Å as
shown in Figure 5 that were compatible with He II $\lambda 4686$ and H$\alpha$
(or maybe He II $\lambda$6560) at a negligible redshift. This spectrum is
compatible with a Wolf-Rayet star of type WN at $\sim 1$ Mpc. Since we saw no
host galaxy, the host would have to be a previously unknown dwarf galaxy
(e.g., McQuinn et al., 2020) accidentally along the line of sight, but
unrelated to GALEXASC J165500.03+140301.3.

We checked an alternative hypothesis that ZTF19abvionh was an Ultra-luminous
X-ray Source (ULX) that can have similar spectra, but could be at much greater
distances, $\sim 10$ Mpc, by requesting a Swift observation. Swift/XRT
observed the field on 2019-09-20 with an exposure of 3921 s and on 2019-09-23
with an exposure of 435 s. The source was not detected in either observation.
We combined the two exposures and used a source region of 45″ radius centered
on 16:55:00.212, +14:03:04.67. In this region, there are six counts in the
0.2-10 keV band. In a nearby source-free region of 5′ radius, there are 251
counts in 0.2-10 keV, for an average of 0.000888 background counts per square
arcsecond, or 5.65 in our source region. Using the Bayesian method of Kraft et
al. (1991), we calculated a 90% confidence upper limit of 5.67 counts, for a
countrate of 0.0013 c/s. We assumed an absorbed power-law spectrum with photon
index of two and column density of $nH=4.81\times 10^{20}$ cm-2 (HI4PI
Collaboration et al., 2016). We used WebPIMMS to calculate a 90% upper limit
of $5.6\times 10^{-14}$ erg cm-2 s-1 in the 0.2-10 keV band.

The lack of detection could be because the source is a WN star at 1 Mpc, but
could also be because ULX are variable, and the source had evolved to an X-ray
minimum. ZTF19abvionh then underwent solar occlusion. We submitted a third GCN
specifically concerning ZTF19abvionh (Rosell et al., 2019g).

##  4 Quantitative Assessment of Null Results

While a careful visual inspection of all the data cubes obtained by the
LIGHETR program has been performed and no obvious electromagnetic counterpart
has been found, the need for a quantitative error on our assessment is still
necessary. For this purpose, we performed two different subsequent sets of
analysis on our data, one using the standard continuum grid search and point
source extraction routines provided by HETDEX (described in full in Gebhardt
et al., 2021) and one using an adaptation of scarlet (Melchior et al., 2018),
a package that performs source separation on multiband images designed for the
Legacy Survey of Space and Time (LSST) Science Pipeline. Our preliminary
investigation of both methods highlights the challenges of making quantitative
estimates to our sensitivity to OTs. We now discuss these analyses and provide
a prescription for a path forward.

###  4.1 Point Source Detection and Extraction via HETDEX’s Standard Pipeline

Most of LIGHTER’s target fields are centered, by the nature of the search, on
extended sources (galaxies), with the occasional centering on an externally
detected transient. Although HETDEX’s tools are not designed to deal with
extended sources and deblending, they do routinely perform point source
detection and extraction. Under optimal conditions, they can detect point
sources up to magnitude 23 in the g-band (Gebhardt et al., 2021). While the
one datapoint at hand, AT2017gfo, did appear as a point source in the
outskirts of its host galaxy, uncertainties in the intensity of supernova
kicks and the orbital energy dissipated in the explosion debris leave unclear
whether kilonovae are to be preferentially found in the outskirts of galaxies.
Even if that were to be the case, it is likely that many will be placed within
the line of sight of the continuum of the galaxy. With this caveat in mind, we
briefy present HETDEX’s standard continuum grid search and point source
extraction techniques and the results of applying them to our dataset to
assess the existence or absence of a transient within our fibers.

For the continuum search, spectra from a VIRUS data cube are flagged as
possible continuum sources if they have at least 0.5 ergs s-1 cm-2 Å-1 either
in the blue (from 3700 to 3900 Å) or in the red (from 5100 to 5300 Å). Each
possible continuum source then gets searched around a 1.5″ $\times$ 1.5″ grid
of 0.1″ $\times$ 0.1″ resolution, and the spatial location with the maximum
flux is selected as the location of the source. Around each location, a point-
spread function (PSF) extraction of 3″ radius in aperture is subsequently
performed. The particular aperture and specifics of the weights that go into
the PSF are determined both by typical seeing conditions and known systematics
in the design of the instrument (particularly, regarding lack of an
atmospheric dispersion corrector and the fiber dithering pattern by which we
fill in our collected area). With a set of possible continuum sources in hand
for the fields around all of our targets, we compare them with archival
sources from the Pan-STARRS catalogue.

Figure 6: Target galaxy GALEXASC J165500.03+140301.3. On the left is Pan-
STARRS imaging of the field overlaid with Pan-STARRS catalogued sources (blue)
and VIRUS point sources (red). On the right is the collapsed spectral image of
a VIRUS shot of the same field cropped around a 25″ radius. The streaks across
the IFU field correspond to CCD artifacts at the time of the observations and
affected point source identification. This artifact are primarily hot pixels
and charge traps that have been subsequently identified and removed from the
latest reductions.

Figure 6 shows, for reference, one of our target galaxies, GALEXASC
J165500.03+140301.3. As can be seen in the image, the CCDs had artifacts
across a large fraction of the IFU’s footprint. These artifacts made for
spurious point source detections and hid some catalogued sources from
detection. The problem has since been fixed with improved reductions. Extended
sources like galaxies or effectively extended sources like bright stars were
also tagged as more than one source, due to oversaturation and to the point
source algorithm failing to identify them as single sources. Figure 7 shows a
binned histogram of our point source detections for all of our targets. Point
sources detected by VIRUS that are less than 2″ from a Pan-STARRS source, with
a g-band magnitude difference of less than 1 magnitude, are considered
coincident. The histogram shows over-identification of bright sources and
under-identification of low brightness ones. Visual inspection of our targets
shows this is mostly due to saturation and spurious point source
identification in extended sources both from Pan-STARRS and VIRUS. While we
expected the identification of points sources within the continuum of the
galaxy to have significant difficulties, the large number of
misidentifications outside of that continuum both for bright and dim sources
makes it impossible for us to place limiting magnitudes on point sources for
the fields observed. We thus turn to an altogether different detection
algorithm for our search.

Figure 7: Point source magnitudes for all of our target fields. Transparent
bins correspond to Pan-STARRS catalogued sources. Blue and orange are stacked
and correspond, respectively, to VIRUS sources matched with Pan-STARRS or only
detected by VIRUS. The point source identification algorithm fails with the
brightest targets, where saturation leads it to identify more than one source
per target. For lower brightness, the mismatch can be explained by spurious
continuum point source identification both from Pan-STARRS and VIRUS.

###  4.2 Non-parametric extended source identification with scarlet

Many of the difficulties of our search with VIRUS data cubes and the standard
pipeline are due to the intrinsic spatial unevenness of a dithering IFU. The
fibers fall in different places of the CCD and have inherent flux variations
that can be as large as a factor of $\sim$ 2\. While remedy does a superb job
on the weighting of this variation, software can only do so much and will
never be comparable with direct imaging in terms of spatial evenness. The
power of spectral data cubes is in that extra spectral dimension and a tool
that fully uses this advantage should be employed.

It is in the context of these difficulties that scarlet, a software tool
designed for multiband source separation for the LSST Science Pipeline,
appears to be a promising alternative (Melchior et al., 2018). scarlet makes
use of a constrained matrix factorization in which each source is modeled with
a Spectral Energy Distribution (a spectrum) and a non-parametric morphology
(the equivalent of a PSF). The code also allows the imposition of priors on
the shape of each source, allowing further constraints. scarlet seems
particularly suitable for our problem. The algorithm can make use of the
spectral information to identify neighboring fibers contributing to the same
flux, so systematics in the PSF extraction could be significantly mitigated.
In addition, artifacts could also be removed from the CCD, since again the
bleeding into adjacent pixels should show similar spectral features.

Figure 8: Preliminary results of an adaptation of scarlet to work on VIRUS
spectral cubes. Images are on the same scale but with inherent different
resolutions and displayed in pixel space. Upper left shows one of the extended
sources modeled by scarlet. Upper middle shows the source, rendered to the
given PSF. Upper right shows the actual observation compressed along spectra.
The colors have been mapped to the corresponding wavelengths of maximum
intensity. In the bottom is a picture of the overall collected spectra.
Results are promising but need to be combined with a customized continuum grid
search routine to be usable.

Figure 8 shows an individual source from a VIRUS observation extracted by
scarlet. The package treats point sources, on which a symmetry prior is
enforced, or multiple overlapping sources with non-parametric morphologies.
Constraints on the shape of the spectra can also be enforced. The ability to
use analytic priors to fit against, say, a black-body spectrum, were
implemented recently in scarlet due to a suggestion from MJRB. One of the
major limitations on the immediate use of scarlet for our purposes is that it
requires approximate positions for the sources to be given beforehand. As
shown in Figure 6, in its current state the standard continuum grid search
when applied to our data suffers from too high a degree of mismatch to be
usable as a source of positions.

Given these difficulties, our final approach was to simulate a set of point
sources with a black body spectrum and place them randomly within a VIRUS data
cube to assess under what conditions could they be recovered. We performed a
maximum likelihood analysis fit on the data cube, with which we matched a PSF-
weighted 3D source (2D for space, 1D for spectra) to all positions in the data
cube that hosted the injected sources. We observed that we could only detect
sources down to a few magnitudes brighter than the nominal value quoted for
HETDEX’s pipeline, but we suspect flux calibration issues were at the heart of
this difference. Proper knowledge of the PSF weights accessible to HETDEX
point source extraction algorithms will be of extreme value for this method to
be usable, and further investigations are ongoing. A combination of the
continuum grid-search and this method might also be of great value, since the
overidentification of continuum sources could be mended if the non-parametric
PSF of scarlet engulfs them all.

##  5 Models Guiding Observations and Motivation for Spectral Followup

Observations of GW170817/AT2017gfo closely confirmed expectations of
theoretical models. The merging neutron stars create a tidal tail of material
ripped from their surfaces and ejected in the orbital plane. This material is
expected to be rich in lanthanides, with very large opacity, strong absorption
in the blue, and hence characterized by a red continuum (Kasen et al., 2017;
Tanaka et al., 2017). Orthogonal to the orbital plane, ejecta are expected to
be expelled at about 0.3c in a jet or cocoon of somewhat lower opacity
material radiating in the blue. AT2017gfo first showed a blue continuum
evolving over the course of a few days into a red continuum (Shappee et al.,
2017; McCully et al., 2017; Pian et al., 2017; Nicholl et al., 2017; Buckley
et al., 2018). This was interpreted as first seeing the lower opacity material
ejected along the orbital axis and then the high opacity, lanthanide-dominated
matter from the orbital plane. Even if other events are very similar, the
relative proportions of these components may be different and the orientation
will surely be different, so spectral observations promise a rich new haul of
insight. A neutron star + black hole (NSBH) merger event will be
quantitatively and perhaps even qualitatively different (Bhattacharya et al.,
2019; Desai et al., 2019; Zhu et al., 2021; Darbha et al., 2021).

Since we expect any lines to be broadened by the fraction of the speed of
light velocities at which the ejecta expand, there could be shallow, broad
features in the early spectra as well as fast, transient emission lines. There
is a slim chance that one portion of the ejecta, a jet, could irradiate
another portion of the ejecta, a tidal stream, and yield a photo-ionized
region that would briefy produce emission lines. A fortuitous orientation
might mean that broadening is minimized, but even the transverse Doppler shift
might be an issue in broadening and obscuring lines. Models of kilonovae show
that blue emission is sensitive to the radii of the neutron stars, and that
particular aspect angles can show broad yet distinct spectral features as well
as a very strong dependance of the flux on angle in the optical band. Early
spectral observations in the blue, like the ones LIGHETR is particularly
designed to perform, promise strong constraints on the nature of the ejecta.

The LIGHETR program sought to integrate modeling constraints by coordinating
with the LANL group simulating merger models, especially their spectra. The
observational properties of kilonovae are subject to many variables, including
the morphology of winds, disks, and jets, the composition on a given line of
sight, and, crucially, the aspect angle. To illustrate how early and densely-
sampled spectra can serve to constrain these variables, we leverage a large,
and continually growing, LANL database of kilonova spectra and light-curve
calculations. To demonstrate the importance of the spectra, we present the
results from two separate studies: a morphology study (Korobkin et al., 2021)
and a new study designed for this paper varying the composition based on
yields from detailed post-merger disk calculations (Miller et al., 2020). The
ultimate goal is to be able to query a data base of models in real time as
spectra of candidate OTs are obtained.

Expected kilonova emission is subject to a broad range of uncertainties, both
in the ejecta properties (that depend on aspects of both merger and disk-wind
calculations) and in the physics and its numerical implementation. Examples of
these uncertainties span all facets of kilonova emission modeling efforts:
e.g. the amount of matter that is ejected in the dynamical phase of the
explosion and distributed in a disk still depends sensitively on the numerics
and physics (Henkel et al., 2022) and on the composition; the ejecta angular
and velocity distributions are sensitive to physics modeling such as neutrino
transport (Miller et al., 2020); the emission depends on the nuclear network
simulations and the model for energy deposition (Barnes et al., 2021). The
model database is built on a large suite of models using the the SuperNu code
(Wollaeger et al., 2013; van Rossum et al., 2016), adapting initial conditions
to study a range of uncertainties in the ejecta composition. This work
includes a suite of models varying the ejecta mass (Wollaeger et al., 2021),
composition (Wollaeger et al., 2018; Even et al., 2020), morphology (Wollaeger
et al., 2018; Korobkin et al., 2021), energy sources (Wollaeger et al., 2019),
and atomic (Fontes et al., 2020) and nuclear (Zhu et al., 2018) physics666Many
of these models are available at
https://ccsweb.lanl.gov/astro/transient/transients_astro.html.

The significant modeling uncertainties currently make it difficult to quickly
and unambiguously identify which transients in the LVC localization maps to
follow up with LIGHETR observations. We hope to use our growing suite of
models to help identify the transients to follow-up with LIGHETR and then use
LIGHETR observations to further constrain the properties of the kilonova
ejecta. Figure 9 shows g- and r-band light curves from models produced by
Korobkin et al. (2021). This work varied the morphology, mass, and velocity of
a “wind” (modest electron fraction) and “dynamical” (neutron rich) component
for the ejecta. The morphologies follow the nomenclature described in the
paper including a Toroidal (“T”), Peanut-shaped (“P”) and spherical (“S”)
morphology. In this figure, we limit ourselves to models with
$0.01\,M_{\odot}$ in each component, but we vary the morphology and average
ejecta velocity of each component. All of the models are characterized by a
fast rise (fraction of a day) and decay time (from a fraction of a day to 2
days) for the light-curves.

Figure 9: g-band (top) and r-band (bottom) magnitudes of three different two-
component models varying morphologies and average ejecta velocities (see
Korobkin et al. (2021) for details). Despite the fact that ejecta masses are
the same, the different morphologies and velocities of the components produce
different lgiht-curves. Because of the complex morphologies, the light-curves
can also vary with viewing angle.

With 2-3 days of observations, the fast evolution in the optical would be a
strong indicator of a kilonova observation. To guide follow-up observations,
we need observational indicators already on day 1. In an attempt to quickly
differentiate these models, we are investigating a range of discriminating
light-curve features. For example, in Figure 10 we calculate the change in
magnitude with time of the models presented in Figure 9. Most of our light
curves are characterized by an initial rapid rise followed by a slower decay.
The rate of this decay depends on the model. In most cases, the rapid
evolution all occurs within the first day. After the first 6 hours, some
models are already decaying. Others continue to rise for 12-24 hours. In all
cases, the variability is high in kilonova models and variation of a few
tenths of a magnitude in a few hours is an indication of a potential kilonova.

Figure 10:  The rage of change (magnitudes/h) in the g- and r-bands for the
models in Figure 9. All of the models are characterized by a sharp rise in the
emission. But if we miss this initial rise, the evolution can vary
dramatically depending on the model. Within the first 12 hours, some models
decline, others continue to rise, but at a slower pace. All are characterized
by rapid variability and, although other transients may exhibit this
variability, this rapid variability is a strong indicator of a potential
kilonova.

Once we identify potential kilonovae and obtain observations, we plan to use
these same simulations to help interpret the observations. A variety of ejecta
yields were predicted from different analyses of AT2017gfo (Côté et al.,
2018). The results of studies of potential kilonovae associated with gamma-ray
bursts have also shown a variety of interpretations for a single data set
(Fong et al., 2021; O’Connor et al., 2021). Constraining this range of
interpretations of a single data set requires a broad set of observations and
the HET can play a critical role in differentiating model interpretations.
Differentiating the models can be done with light-curve bands, but spectral
observations also provide insight into the properties of the ejecta. For
example, although the fast ejecta velocities and dense forest of lines produce
continuum-like spectra, both observations of AT2017gfo and a series of models
suggest that line features might be observed across a broad spectral range
(Watson et al., 2019; Korobkin et al., 2021; Domoto et al., 2022).

Many of these spectral features are difficult to detect, requiring high
signal-to-noise spectra, and often-times these features are line blends from
multiple elements. Despite these difficulties, broad line features and
spectral slopes can help us constrain the ejecta composition. Broad spectral
features can also probe the morphology and viewing angle of the merger. Figure
11 shows the spectra at four epochs in time in a series of as-yet unpublished
spherically-symmetric wind models using ejecta compositions spanning a range
of distributions. The compositions in Figure 11 are listed in Table A.1.
Compositions $C_{1}$ and $C_{2}$ have high mass fractions of heavier elements
and display relatively few features beyond some broad features above $8000$ Å
at two days. As we decrease the fraction of these heavy elements ($C_{4}$ and
$C_{5}$ have the lowest mass fractions), a number of spectral features from
elements up to the first r-process peak are visible. The wide variation in
this spherically-symmetric wind model, particularly in the optical bands,
demonstrates just how sensitive the HET spectra could be to the ejecta
characteristics. We can also use these broad spectral features to help us
confirm an electromagnetic counterpart, allowing us to notify the community of
a true kilonova detection.

Figure 11: Spectra from a set of compositions produced by a high-fidelity
spherical wind-ejecta model at a variety of times for a range of angles using
the wind disk model from Miller et al. (2020) at 0.05, 1, 2, and 4 days. Even
though these results are focused on a single wind profile, varying the
abundances produce a range of spectral fluxes, especially in the optical bands
that vary by over an order of magnitude. The compositions studied in this plot
are listed in Table A.1.

Composition is just one of the properties of model kilonovae that we have
studied. Figure 12 shows the spectra in the 3000–7000 Å wavelength range
focusing on one of the models from the morphology study (Korobkin et al.,
2021) with a spherical wind of steady velocity 0.5 c and a toroidal disk
component with a velocity of 0.2 c (TSvw0.5vd2), comparing spectra at
different times and different viewing angles using the standard “wind 1”
composition from Korobkin et al. (2021). The variation with respect to viewing
angle for this particular morphology is dramatic. Without constraining the
viewing angle, we have yet to identify distinct model spectral features in the
early blue kilonova spectra. We do expect any line features to be broad and,
as the LANL team improves its opacities, we will continue to look for specific
spectral features. These features are essential to help us distinguish between
the morphology, composition, and viewing angle effects. Even so, extensive
observations are required to successfully disentangle these effects, and HET
spectra can be a critical part of this effort.

Figure 12:  Model spectra at 0.5, 1.0, and 2.0 days for a model TSvw0.5vd2
from Korobkin et al. (2021) along 2 different viewing angles (along the
angular momentum axis and perpendicular to this axis).

Our model database continues to grow as we incorporate new physics and initial
conditions into our models and import the simulations of other groups into our
studies. The kilonova community is conducting comparison projects to better
constrain issues in physics and numerics. These studies also will ultimately
allow us to do better uncertainty quantification as well.

##  6 Future Observations

The LVC third observing run brought two new factors into play. The greater
sensitivity increased the search volume thus increasing the likelihood of
detecting a BNS event. This also meant, however, that the average merger was
more distant and the OT more difficult to detect. It is also true that the
factors influencing the intrinsic luminosity and color of a BNS kilonova are
many, especially including the aspect angle, so it may not be reliable to
scale expectations with the observations of AT2017gfo. The bottom line is that
no group anywhere detected an OT in O3, despite massive deployment of
resources. That said, we are confident that our original analysis of the
capabilities of the HET stands, and that the program merits continuing with
the same commitment of HET resources.

For the O4 observing run, diagnosis will listen to alerts sent by LVK on Kafka
Notices via
SCiMMA777https://rtd.igwn.org/projects/userguide/en/stable/quickstart.html,
which is the method by which LVK alerts will be sent in O4. In addition,
galaxies within the region observable by HET will be weighted more accurately
by mass using the updated GLADE catalog.

LIGHETR collected a considerable amount of ancillary data during O3. We will
make these data available upon request. We will make our O3 pointings
available to the Treasure Map888http://treasuremap.space/ record of O3 data
and intend to implement deposition of our real time pointing record in O4.

The O3 science run drove home the difficulty in identifying the kilonova
resulting from a compact object merger among the extensive lists compiled by
optical astronomers of contemporary transients that fell within the
gravitational wave localization errors. To help us focus on the most-likely
candidates to follow-up with our telescope time, we plan to leverage a large,
and continually growing, database of model kilonova spectra and light-curve
calculations from our LANL collaborators. These simulations can be used both
to guide our observations by helping us determine which transients to follow-
up and to better interpret our results once observations are made.

More work remains to be done to strengthen the connection between models and
the observational data. Kilonovae templates make use of a broad suite of
models from the LANL simulation effort, with physics uncertainty studies
covering opacity, composition, power source and morphology. Template matching
to these models could improve our sensitivity to low brightness kilonovae and
also help in the effort of distinguishing kilonovae from other transients in
real time, which could significantly reduce the amount of telescope time the
community devotes to LVC alerts. When and if a source were to be detected by
LIGHETR, we could also wield the templates to make inferences on the kilonova
properties and thus provide the community with a more detailed picture of
physics behind the transient.

The pipelines we are developing for LIGHETR are designed to incorporate the
growing data base of LANL models. We have recently incorporated a neural
network-based classifier that scans each pixel in an IFU data cube and can
identify kilonova candidates by comparison to theoretical models. This
classifier was trained on the LANL suite of models and will be updated with
any new theoretical developments. During an active search, the software can
spectroscopically identify a kilonova candidate and then rapidly compare to
the LANL model grid to constrain model parameters such as the critical aspect
angle. We will thus have a substantial theoretical and simulation effort to
complement our observational program.

The challenges faced by both the standard HETDEX pipeline and scarlet in
extracting candidate OTs from the VIRUS data illustrate the value of repeated
observations of our target galaxies. With repeated observations we could
perform data cube differentiation and any transients, be they systematic or
real, would be identified and characterised. This would also help assessing
the performance of our methods, since we could compare our deblended
extraction of some of the transients we followed-up with the differentiated
data.

While it is clear much needs to be done to obtain a quantitative figure of
merit for non-detections, we should also highlight the great success of the
LIGHETR program. The LIGHETR Collaboration achieved the production of an
effective alert system for possible kilonovae transients, diagnosis,
customized to the specifics of our instrument. The creation of an extremely
quick and powerful reduction pipeline, remedy, has also been successfully
achieved for the purposes of this endeavour. These tools allow members of the
collaboration to have, within minutes of a gravitational wave alert, access to
spectral data cubes of galaxies that have a finite probability of hosting the
transient. During O3, these data cubes were obtained, reduced, and immediately
inspected for possible continuum sources but no evident kilonovae transients
were found.

The data and tools collected during this campaign are of great scientific
value regardless of the identification of a transient. Kinematic maps for
several galaxies have been collected and already remedy has become a standard
reduction tool for the HETDEX consortium. Some of the transients we followed
up motivated new observing proposals. The Wolf-Rayet star we suspect to be
hosted by a low-brightness dwarf galaxy remains a puzzle we hope to solve.

For O4, the VIRUS array is fully implemented with 78 VIRUS units with a total
of 34,944 fibers spanning an area 21′ on a side (Figure 4). Previous VIRUS
operations have shown that typical set up time is about 6 minutes. An exposure
of 9 minutes will get down to about 19th magnitude. Although in this project
the average time for set up was only 2.3 minutes, we still plan on a single
exposure on a target galaxy to require a total time of 15 minutes. There will
be a great deal of competition from wide-field robotic photometric facilities
to discover the OT, but if we find the OT first, we will automatically acquire
the first spectrum. Even if we do not find the OT first, by concentrating on a
high confidence region of the candidate skymap HET will be roughly positioned
quickly to get early spectra. If we see an early blue component, as widely
expected, that will already be interesting. If we do not see an early blue
component, that will also be interesting. In any case, the blue response of
the HET VIRUS array is well designed to get this early data in the blue.

## Acknowledgments

We thank Gary Hill for discussions of the response functions of VIRUS and
LRS-2. We thank Chad Hanna and David Radice for discussions in the initial
stages of forming the LIGHETR program.

BPT and JCW are supported in part by NSF grant 1813825, by a DOE grant to the
Wooten Center for Astrophysical Plasma Properties (WCAPP; PI Don Winget), and
by grant G09-20065C from the Chandra Observatory. KG acknowledges support for
this work from NSF-2008793. AZ was supported by NSF Grant Numbers PHY-1912578
and PHY-2207594 during this work. RM acknowledges the intellectual support of
The Weinberg Institute, The Center for Gravitational Physics, and the
Department of Physics at The University of Texas at Austin. DP is supported in
part by the National Aeronautics and Space Administration through Chandra
Award Numbers GO0-11007A and GO GO9-20065A issued by the Chandra X-ray Center,
which is operated by the Smithsonian Astrophysical Observatory for and on
behalf of the National Aeronautics Space Administration under contract
NAS8-03060. JV is supported by the project “Transient Astrophysical Objects”
GINOP 2.3.2-15-2016-00033 of the National Research, Development and Innovation
Office (NKFIH), Hungary, funded by the European Union. Time domain research by
the University of Arizona team and D.J.S. is supported by NSF grants
AST-1821987, 1813466, 1908972, & 2108032, and by the Heising-Simons Foundation
under grant #2020-1864. The work by CLF, OK, and RW was supported by the US
Department of Energy through the Los Alamos National Laboratory. Los Alamos
National Laboratory is operated by Triad National Security, LLC, for the
National Nuclear Security Administration of U.S. Department of Energy
(Contract No. 89233218CNA000001).

The University of Texas at Austin and McDonald Observatory sit 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.

This research was made possible by the open release of gravitational wave
candidates detected by the Advanced LIGO and Virgo observatories. LIGO
Laboratory and Advanced LIGO are funded by the United States National Science
Foundation (NSF) as well as the Science and Technology Facilities Council
(STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of
Niedersachsen/Germany for support of the construction of Advanced LIGO and
construction and operation of the GEO600 detector. Additional support for
Advanced LIGO was provided by the Australian Research Council. Virgo is
funded, through the European Gravitational Observatory (EGO), by the French
Centre National de Recherche Scientifique (CNRS), the Italian Istituto
Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions
by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan,
Monaco, Poland, Portugal, Spain. The construction and operation of KAGRA are
funded by Ministry of Education, Culture, Sports, Science and Technology
(MEXT), and Japan Society for the Promotion of Science (JSPS), National
Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea,
Academia Sinica (AS) and the Ministry of Science and Technology (MoST) in
Taiwan.

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##  Appendix A Model Lightcurve Compositions

Table A.1 gives the compositions employed in Figure 11.

Z | $C_{1}$ | $C_{2}$ | $C_{3}$ | $C_{4}$ | $C_{5}$ | $C_{6}$
---|---|---|---|---|---|---
24 | 0.0 | 7.49370e-04 | 3.84341e-03 | 9.12667e-03 | 1.08047e-02 | 1.50548e-04
26 | 1.09998e-04 | 2.94116e-03 | 1.60909e-02 | 2.93123e-02 | 4.22312e-02 | 5.53921e-03
34 | 1.20896e-01 | 1.99367e-01 | 2.86043e-01 | 2.92805e-01 | 2.35532e-01 | 1.85846e-01
35 | 3.69738e-02 | 5.20294e-02 | 1.11589e-01 | 2.27298e-01 | 3.27268e-01 | 6.73422e-02
40 | 1.09691e-02 | 2.26226e-02 | 6.74217e-02 | 1.32521e-01 | 1.65283e-01 | 1.69854e-01
46 | 1.47586e-01 | 1.39364e-01 | 1.05772e-01 | 9.16517e-02 | 7.82567e-02 | 1.68748e-01
52 | 6.04172e-01 | 5.22899e-01 | 3.86228e-01 | 2.14772e-01 | 1.38436e-01 | 3.76756e-01
57 | 1.96323e-03 | 1.39742e-03 | 7.12728e-04 | 1.26050e-04 | 1.12861e-04 | 9.23346e-04
58 | 2.95243e-03 | 2.23794e-03 | 1.14957e-03 | 1.81696e-04 | 1.91118e-04 | 1.54047e-03
59 | 7.70276e-04 | 5.68369e-04 | 3.06602e-04 | 1.19901e-04 | 0.0 | 3.27700e-04
60 | 1.92511e-03 | 1.32451e-03 | 5.91964e-04 | 1.19743e-04 | 1.26560e-04 | 1.09535e-03
62 | 2.38889e-03 | 1.58250e-03 | 7.01276e-04 | 1.19743e-04 | 1.15225e-04 | 9.87945e-04
63 | 5.97838e-04 | 3.86548e-04 | 1.78528e-04 | 0.0 | 0.0 | 2.69349e-04
64 | 5.82899e-03 | 3.87802e-03 | 1.72436e-03 | 1.91844e-04 | 1.31481e-04 | 1.05073e-03
65 | 1.54890e-03 | 1.02126e-03 | 4.51920e-04 | 0.0 | 0.0 | 3.63695e-04
66 | 1.31050e-02 | 8.49339e-03 | 3.89174e-03 | 4.32027e-04 | 2.87312e-04 | 2.10961e-03
67 | 2.90705e-03 | 1.87583e-03 | 8.73047e-04 | 0.0 | 0.0 | 4.56880e-04
68 | 9.80968e-03 | 6.47393e-03 | 2.92234e-03 | 3.28006e-04 | 2.32171e-04 | 1.71006e-03
69 | 1.44669e-03 | 9.58414e-04 | 4.32732e-04 | 0.0 | 0.0 | 2.95809e-04
70 | 1.84161e-02 | 1.24477e-02 | 5.52962e-03 | 6.75113e-04 | 5.96579e-04 | 4.89435e-03
92 | 1.56322e-02 | 1.73818e-02 | 3.54577e-03 | 3.39432e-04 | 3.94817e-04 | 9.73867e-03
Table A.1: Composition of the models presented in Figure 11.

##  Appendix B LIGHETR Alert Components

Here we give some illustrations of how the internal LIGHETR alert system
works. Figure B.1 gives an example of an LVC alert as processed by diagnosis
for local redistribution. Figure B.2 gives the Target Submission List (TSL)
subsequently sent to the Resident Astronomers at the HET. Figure B.3 presents
a graphical representation of the galaxy target priority list used in the
original search for an OT.

Figure B.1: Trial LIGO alert as processed by diagnosis for local distribution.
Figure B.2: Phase II TSL generated by diagnosis giving the prioritized list of
target galaxies that will be employed to trigger the search for the OT with
the VIRUS IFU array. The galaxies in this list are also presented in Figure
B.3 Figure B.3: Sample output from combining localization information for LIGO
event S190425z as illustrated in Figure 1 with the GLADE galaxy catalog to
produce a list of 20 galaxies prioritized by the likelihood of being the host
of a LIGO alert burst of gravitational waves. The vertical scale gives the log
of the probability, and the horizontal scale gives the position in hour of
right ascension. Blue (single bands between the East and West extremes) shows
galaxies available on a single track, red (right bands) and green (left bands)
show the time when a given galaxy can be observed on the West track and the
East track, respectively.

##  Appendix C Timetable for Alert, Search, Dense Spectral Sampling, and
Analysis

VIRUS
00:00 + 01:00 LIGO alert, precursor or normal, sent to team, RAs
01:00 + 00:30 automatically run diagnosis tool in response to the alert
01:30 + 00:30 diagnosis generates VIRUS Phase II TSL with priority list of
galaxy targets
02:00 + 10:00 scientists in consultation with RAs make decision on whether to
trigger telescope
12:00 + 02:00 RAs ingest galaxy Phase II into HET queue and decide when to
implement, depending on current observation
14:00 + 01:00 select first galaxy target in HET queue
15:00 + 05:00 slew telescope and setup
20:00 + 06:00 start first 6 minute dither
26:00 + 01:00 run remedy code to produce collapsed image and reduced spectrum.
Start second dither
27:00 + 05:00 inspect remedy output. If OT, send alert, submit LRS2 Phase II
32:00 + 06:00 2nd dither out, proceed as above with remedy, start 3rd dither
38:00 + 05:00 3rd dither out, either move to next target or switch to LRS2 if
OT found
43:00 – proceed as above; start next VIRUS exposure or first LRS2 exposure if
OT found
Total 180 minutes

LRS2

First Night
00:00 + 05:00 halt VIRUS observations, submit LRS2 Phase II
05:00 + 08:00 setup on LRS2-B
13:00 + 20:00 first LRS2-B exposure
33:00 + 02:00 set up on LRS2-R
35:00 + 20:00 first LRS2-R exposure. Reduce and examine first LRS2-B spectrum
55:00 + 02:00 setup again on LRS2-B
57:00 + 20:00 second LRS2-B exposure. Reduce and examine first LRS2-R spectrum
77:00 + 02:00 setup again on LRS2-R
79:00 – continue until target no longer visible for the HET
Total 300 minutes

Second Night
00:00 + 08:00 setup on LRS2-B
08:00 + 20:00 first LRS2-B exposure
28:00 + 02:00 set up on LRS2-R
30:00 + 20:00 first LRS2-R exposure. Reduce and examine first LRS2-B spectrum
50:00 + 02:00 setup again on LRS2-B
52:00 + 20:00 second LRS2-B exposure. Reduce and examine first LRS2-R spectrum
72:00 + 02:00 setup again on LRS2-R
74:00 + 20:00 second LRS2-R exposure. Reduce and examine second LRS2-B
spectrum
94:00 + 02:00 setup again on LRS2-B
96:00 + 20:00 third LRS2-B exposure. Reduce and examine second LRS2-R spectrum
116:00 + 02:00 setup again on LRS2-R
118:00 + 20:00 third LRS2-R exposure. Reduce and examine third LRS2-B spectrum
138:00 +12:00 Reduce and examine third LRS2-R spectrum
Total 150 minutes

Third, Fourth, Fifth Nights
00:00 + 08:00 setup on LRS2-B
08:00 + 30:00 first LRS2-B exposure
38:00 + 02:00 set up on LRS2-R
40:00 + 30:00 first LRS2-R exposure. Reduce and examine first LRS2-B spectrum
70:00 + 02:00 setup again on LRS2-B
72:00 + 30:00 second LRS2-B exposure. Reduce and examine first LRS2-R spectrum
102:00 + 02:00 setup again on LRS2-R
104:00 + 30:00 second LRS2-R exposure. Reduce and examine second LRS2-B
spectrum
134:00 + 06:00 Reduce and examine second LRS2-R spectrum
Total 140 minutes each night

Day 1 – begin analysis of data, comparison to models
Day 5 – finish analysis of data, comparison to models
Day 7 – submit paper