# SOAR/Goodman Spectroscopic Assessment of Candidate Counterparts of the
LIGO–Virgo Event GW190814

D. L. Tucker Fermi National Accelerator Laboratory, P. O. Box 500, Batavia, IL
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Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain  S. Desai Department of Physics, IIT Hyderabad, Kandi, Telangana 502285,
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Schubnell Department of Physics, University of Michigan, Ann Arbor, MI 48109,
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O. Box 500, Batavia, IL 60510, USA

###### Abstract

On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected
a possible neutron star-black hole merger (NSBH), the first ever identified.
An extensive search for an optical counterpart of this event, designated
GW190814, was undertaken using the Dark Energy Camera (DECam) on the 4m Victor
M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of
Opportunity interrupts were issued on 8 separate nights to observe 11
candidates using the 4.1m Southern Astrophysical Research (SOAR) telescope’s
Goodman High Throughput Spectrograph in order to assess whether any of these
transients was likely to be an optical counterpart of the possible NSBH
merger. Here, we describe the process of observing with SOAR, the analysis of
our spectra, our spectroscopic typing methodology, and our resultant
conclusion that none of the candidates corresponded to the gravitational wave
merger event but were all instead other transients. Finally, we describe the
lessons learned from this effort. Application of these lessons will be
critical for a successful community spectroscopic follow-up program for LVC
observing run 4 (O4) and beyond.

gravitational waves, kilonovae, spectroscopic typing, neutron star, black hole

0.75in

††thanks: Based on observations obtained at the Southern Astrophysical
Research (SOAR) telescope, which is a joint project of the Ministério da
Ciência, Tecnologia, Inovações e Comunicações (MCTIC) do Brasil, the US
National Science Foundation’s National Optical-Infrared Astronomy Research
Laboratory (NOIRLab), the University of North Carolina at Chapel Hill (UNC),
and Michigan State University (MSU).

DES-2020-601 FERMILAB-PUB-21-454-AE-E-SCD

##  1 Introduction

The 2017 discovery of the optical counterpart of a binary neutron star (BNS)
merger –- a kilonova (KN) –- was one of the highlights of observational
astrophysics of the early 21st Century. This discovery, following on the 2015
discovery of the first ever detected gravitational wave (GW) event, GW150914
(Abbott, 2016), was a significant leap forward for astrophysics. The detection
of GW170817 in coincidence with a short gamma-ray burst by Fermi-GBM during
the second observing run (O2) of the Advanced LIGO (The LIGO Scientific
Collaboration et al., 2015) and Virgo (Acernese et al., 2015) network
inaugurated the era of multi-messenger astronomy with GWs (Abbott et al.,
2017a, c). The optical counterpart was discovered 12 hours after the merger by
several independent teams, including our own team, the Dark Energy Survey
Gravitational Wave Search and Discovery Team (DESGW). DESGW utilizes the Dark
Energy Camera (DECam) (Flaugher et al., 2015) on the Victor M. Blanco
Telescope at Cerro Tololo Interamerican Observatory (CTIO) in Chile (Soares-
Santos et al., 2017) This discovery enabled panchromatic imaging and
spectroscopy, which galvanized the astronomical community.

While this single event captured the focus of the entire astronomical
community, the breadth and number of scientific analyses stemming from it are
perhaps more astounding. Standard siren techniques enabled a direct
measurement of the expansion rate of the Universe today (Abbott et al. 2017b;
Soares-Santos & Palmese et al. 2019; Palmese et al. 2020) and in the future
they will also be a useful probe of the growth of structure (Palmese & Kim,
2020). Measuring elemental abundances in the merger ejecta using spectroscopic
instruments led to an understanding of the origin of heavy elements
synthesized during the merger (Chornock et al., 2017; Drout et al., 2017;
Tanaka et al., 2018), and we note the unique wavelength coverage of the VLT
X-Shooter in this task in particular (Pian et al., 2017; Smartt et al., 2017;
Watson et al., 2019). X-ray and radio observations characterized the geometry
of the explosion to be best described by a jet plus cocoon structure
(Alexander et al., 2017; Hallinan et al., 2017; Margutti et al., 2017; Troja
et al., 2017; Mooley et al., 2018; Ghirlanda et al., 2019). The gravitational
waveforms tested and further bolstered the validity of the theory of General
Relativity, as verified by numerical relativity simulations (Shibata et al.,
2017; Abbott et al., 2019), and several other studies explored the connection
between BNS mergers and short Gamma Ray Bursts (sGRBs) (e.g., Fermi-LAT
Collaboration 2017; Fong et al. 2017; Savchenko et al. 2017; Xiao et al. 2017;
Lyman et al. 2018; Ascenzi et al. 2020). These analyses, and many not listed,
were enabled by the association of the GW signal with its electromagnetic (EM)
signal. Given that these events are such a rich source of astrophysical
knowledge, finding counterparts to GW events related to compact object mergers
remains a primary goal of the multimessenger-focused astronomical community.

On 2019 August 14 at 21:10:39 UTC, during its observing run 3 (O3), the LVC
detected a binary merger initially designated as S190814bv and later given a
final designation of GW190814. This was one of 56 event alerts from LVC during
O3 and was particularly interesting: GW190814 was at the time classified as a
neutron star-black hole (NSBH) merger, the first high significance event of
this kind ever observed (LVC, 2019a, b; Abbott et al., 2020). The LIGO-VIRGO
analysis found that this merger event occurred at a distance of $267\pm 52$
Mpc. It had a 90% localization region of 23 deg2 and a probability of being a
NSBH merger of greater than 99%. Further, taking as an assumption that the
GW170817 BNS KN (at a distance of 43 Mpc) had a typical luminosity for such an
event and scaling by the inverse-square law, one could estimate that the
optical counterpart to GW190814 could conceivably peak at a brightness of
$i$$\sim$21 ($\approx$4 mag fainter than that of GW170817) – well within the
range of DECam, as well as still within the range of medium resolution
spectrographs on 4m-class optical telescopes – simplifying the effort of
following up any likely optical counterpart candidates. Thus, the DESGW team
undertook an extensive search for a KN event that would form the optical
counterpart to this potential NSBH merger event, making use of DECam
observations within the high-probability region of the GW event. This search
is described in detail in Morgan et al. (2020).

A number of other groups also searched for an EM counterpart to GW190814.
Kilpatrick et al. (2021) (many of whom are also members of the DESGW
Collaboration) discuss searches for KN candidates using several 0.7-1 meter
class telescopes as well as Keck/MOSFIRE and also present spectroscopy of a
number of candidates (including in their Figure 4 a copy of many of the
spectra described here in the current paper). They also present limits on EM
counterparts to GW190814 and consider scenarios in which an EM counterpart of
a NSBH would be detected. The Australian Square Kilometre Array Pathfinder
(ASKAP) imaged 30 deg2 at 2, 9 and 33 days after the event at a frequency of
944 MHz (Dobie et al., 2019). The Magellan Baade 6.5 m telescope was used to
search on a selection of galaxies within the localization area out to limiting
magnitude of $i=22.2$ and found no counterparts (Gomez, 2019). The MegaCam
instrument on the Canada-France-Hawaii Telescope (CFHT) was used to search
much of the localization region. Although the CFHT team reached a depth of
$i>23.9$ at 8.7 days post-merger, no KN was found (Vieira et al., 2020). The
GROWTH Collaboration used imaging from DECam along with other facilities for
imaging and spectroscopy of possible KN candidates. Using simulations, they
constrained possible ejecta mass from the merger to be $M_{\rm ejecta}<0.04$
$M_{\sun}$ at polar viewing angles (Andreoni et al., 2020). Watson et al.
(2020) described limits on an EM counterpart to GW190814 using observations
with optical imager DDOTI (at the Observatorio Astronómico Nacional in Mexico)
and Swift/BAT observations. They showed that Swift/BAT should have detected an
associated gamma ray burst at the 98% level. Ackley (2020) described the
ENGRAVE team search using the Very Large Telescope as well as involvement with
the ATLAS, GOTO, GRAWITA-VST, Pan-STARRS and VINROUGE projects. Their
observations covered the localization region to depths as faint as $r\approx
22$. Their limits suggest that it is likely the neutron star was not disrupted
during the merger. DDOTI wide-field observations were also used along with the
Lowell Discovery Telescope, the Reionization and Transients InfraRed and
spectroscopy from the Gran Telescopio Canarias to locate EM counterparts
(Thakur et al., 2020). Their data suggest that there was no gamma ray burst
along the jet’s axis.

While searching for an optical counterpart to GW190814, the DESGW pipeline
began with 33,596 events in the likelihood regions. Using the analysis
pipeline we produced a final list of 11 candidates that passed our cuts and
were bright enough for spectroscopy using a 4-m class telescope (Morgan et al.
2020; also § 4.2 below). For these candidates we proceeded to conduct
spectroscopic typing at the Southern Astrophysical Research (SOAR) 4.1 m
telescope111https://noirlab.edu/science/programs/ctio/telescopes/soar-
telescope using the Goodman High Throughput Spectrograph (HTS; Clemens et al.
2004). (Spectroscopic typing is facilitated by the fact that, due to the fast
ejecta velocities expected of kilonovae — 0.03-0.30$c$ — their spectra are
expected to be featureless or only have very broad, smooth spectral features,
especially in the optical during the first few days after the merger event,
which distinguishes their spectra from supernovae [SNe] and other optical
transients; see, e.g, the KN models of Kasen et al. 2017.) The spectroscopic
follow-up team submitted Target of Opportunity (ToO) observing requests to the
SOAR telescope on 8 separate nights in order to use the Goodman HTS on SOAR
for spectroscopic typing of these 11 candidates.

After taking spectra for 8 candidates (plus the host galaxies of 3 additional
candidates which had faded beyond the straightforward capabilities of SOAR –
i.e. $i\sim 21.5$), no optical counterpart was discovered for GW190814.
Despite this null result, this paper serves several important functions.
First, it serves as a companion paper to our other two papers (Morgan et al.,
2020; Kilpatrick et al., 2021), providing a deep dive into the methodology and
detailed results of a coordinated spectroscopic campaign of the first possible
NSBH event ever detected, including the finding charts, light curves, and KN
spectral fitting not covered in detail by the other two companion papers.
Further, it describes and provides previously unpublished open source tools
that can be of use to similar future spectroscopic campaigns. Also, by
comparing results from two separate SN spectrum fitters and a KN spectrum
fitter, this paper goes into some detail into the subtleties associated with
spectroscopic classification of relatively faint SNe and KNe. Finally,
although it does not change the conclusions of the companion papers, some of
the final classifications of the candidate counterparts here are updates from
what what was seen in the previous papers.

In summary, we describe in this paper the DESGW collaboration’s spectroscopic
follow-up campaign for the GW190814 gravitational merger event. We also
describe our overall spectroscopic follow-up methods and strategy, how we
employed them in this particular follow-up campaign, the lessons learned, and
the prospects for the future. The paper is organized as follows: In §2 we
describe the LIGO/Virgo observations of GW190814. In §3 we describe the DESGW
search for candidate optical counterparts. In §4 we describe the selection and
filtering of the candidates. In §5 we describe the SOAR observing strategy and
the observations of counterpart candidates for GW190814. In §6 we discuss our
results and address the population of objects we found. In §7 we summarize our
conclusions. In addition, we provide in § 8 a list of software packages used
throughout our analysis.

In this paper we follow the cosmology given by Bennett et al. (2014), with
flat $\Lambda$CDM cosmology with $\Omega_{\rm M}=0.286\pm 0.008$ and $H_{\rm
0}=69.6\pm 0.7$ km s-1 Mpc-1.

##  2 LIGO/Virgo Observations

As noted above, on 2019 August 14 UTC, the LVC observed gravitational
radiation at high statistical significance. The event, initially named
S190814bv, occurred during a time that all three detectors (LIGO Hanford, LIGO
Livingston, and Virgo) were operating normally, which enabled both a good
angular localization of the source and more precise estimate of the source
parameters. The false alarm probability was calculated at $2.0\times 10^{-33}$
Hz — or once per $10^{15}$ Hubble times — suggesting a very high signal-to-
noise event (LVC, 2019b). Using the bayestar pipeline (Singer & Price, 2016),
the LVC team localized the source of the GW signal to a $38$ $(7)$ sq. degree
area at the $90\%$ ($50\%$) confidence level in the Southern Hemisphere on the
night of the merger. The initial luminosity distance estimate was $276\pm 56$
Mpc (LVC, 2019a). Preliminary source classification via a machine-learning-
based tool (Kapadia et al., 2020) identified the event as a “mass-gap” binary
merger – i.e., a merger event in which at least one of the compact objects has
a mass falling within the hypothetical mass gap between neutron stars (NSs)
and black holes (BHs) (i.e., in the mass range 3-5 $M_{\sun}$; LVC 2020a;
Abbott et al. 2020). The small localization area and the potential of
identifying an optical counterpart made this event interesting from the
perspective of follow-up projects.

The following day, the LVC LALInference pipeline (LIGO Scientific
Collaboration, 2018) localized the source to $23(5)$ sq. degrees at the $90\%$
($50\%$) confidence level, refined the classification to an NSBH merger, and
estimated the luminosity distance of the event to be $267\pm 52$ Mpc
($z=0.059\pm 0.011$ for a standard $\Lambda$CDM cosmology; Bennett et al.
2014, Wright 2006). S190814bv thus became the first possible NSBH system
observed by a GW observatory and a prime target for follow-up by the EM
astronomical community. However, the LVC parameter estimation indicated that
the parameter HasRemnant was $<1\%$. (HasRemnant is the probability that a
nonzero mass was ejected during the collision and remains outside the final
remnant object [Foucart et al. 2018; LVC 2020b]). This suggested that there
was a low probability that any ejecta was preserved outside the BH and thus
that there was a small chance of there being an observable KN.

Well after searches for an EM counterpart were completed, the LVC published
results from an updated offline analysis (Abbott et al., 2020), where the
final luminosity distance was estimated to be $239^{+41}_{-45}$ Mpc (median
and 90% credible interval), the 90% localization area was updated to 18.5
square degrees, and the masses of the two merging objects was updated to 23.2
$M_{\sun}$ (a BH) and 2.6 $M_{\sun}$ (a mass-gap object – i.e., either an
underweight BH or an excessively massive NS). It was also at this time that
this GW event was re-named from its initial designation, S190814bv, to
GW190814.

The nature of this GW190814 was recently debated and summarized by Abbott et
al. (2020), and, since its discovery, only a couple more GW merger events with
comparable properties have been identified (see The LIGO Scientific
Collaboration et al. 2021) and the interactive plot at
https://ligo.northwestern.edu/media/mass-plot/index.html). Particularly
striking is the mass ratio of the GW190814 merger components — a value of
0.112 — whereas the average mass ratio of more typical LIGO BBH events is
$\sim$1\. As noted above, one of the components of the GS190814 merger was a
23.2 $M_{\sun}$ BH, but the other was a 2.6 $M_{\sun}$ “mass-gap” object. If
this mass-gap object is an NS, this has ramifications for the NS equation of
state, which is a determining factor in the maximum allowable mass of NS’s
(currently estimated to be $\lesssim$2.6$M_{\sun}$). Independent of whether
the mass-gap object is a NS or a BH, if these types of mergers are more common
than expected, there may be consequences for stellar population synthesis
models, since these models tend to favor the merger of systems with components
that are less asymmetric in mass, although stellar environment may also play a
role: merger rates between NS’s and BH’s are low in globular clusters ($\sim$
10-2-10-1 Gpc-3 yr-1; e.g., Ye et al., 2020), but likely higher in young
stellar clusters ($<$ 10-1 Gpc-3 yr-1; Ziosi et al., 2014); thus, star
clusters with young stellar populations might be the preferrred location for
mergers similar to GW198014. For the purposes of this paper, we will assume
that GW190814 is a possible NSBH merger, as it was classified during the SOAR
follow-up observing runs.

In the next section we describe the efforts of the DESGW Collaboration to
identify transients that were possible KN candidates.

##  3 DECam Search Campaign

In searching for an optical counterpart to GW190814, the DESGW collaboration
triggered ToO observations with the 570-mega pixel DECam optical imager on the
CTIO Blanco 4-m telescope. Together, the Blanco and DECam reach a $5\sigma$
limiting $r$-band magnitude of $\sim 23.5$ in a 90 second exposure in a 3
square degree field of view (FoV) (Neilsen et al., 2019). The combination of
deep imaging and a wide FoV make Blanco/DECam the ideal instrument for
efficiently detecting optical transients localized to tens of square degrees.

Our follow-up efforts for GW190814 utilized the resources of the Dark Energy
Survey (DES), which is a wide-field optical survey that covered a 5,000 square
degree region of the southern sky from 2013 to 2019 using Blanco/DECam (Diehl
et al., 2019). DES imaging of the DES footprint reaches a $10\sigma$ depth for
point sources of $grizY$ $=$ 25.2, 24.8, 24.0, 23.4, 21.7 mag (Mohr et al.,
2012). The LVC 90% containment region for GW190814 is entirely within the DES
footprint, enabling the use of high-quality DES images during difference
imaging.

We performed DECam ToO follow-up observations of GW190814 for six nights
following the LVC alert, namely nights 0, 1, 2, 3, 6, and 16. The early nights
were chosen to look for rapidly evolving transients immediately following the
merger. KNe from either BNS (Arcavi et al., 2017) or NSBH (Kawaguchi et al.,
2016) events are expected to vary by about a magnitude over the course of a
single night in the first days after the event. Observations 16 nights after
the merger were used to exclude persisting SNe. Due to moon brightness,
especially during the first nights of DECam follow-up, we opted to use the
redder $i$ and $z$ bands to minimize the effect of sky brightness on our
imaging depth.

The DECam images were processed by the DES Difference Imaging Pipeline (Herner
et al., 2020), an updated version of the DES SN Program’s Pipeline described
in Kessler et al. (2015), using coadded DES wide-field survey images (Abbott
et al., 2018) as templates.

After image processing, candidate KNe were identified and then selected for
spectroscopic follow-up. The selection process included eliminating moving
objects (e.g., asteroids), known transients (e.g., variable stars and active
galactic nuclei [AGN]), and transients with colors and/or light curves
characteristic of SNe. Visual inspection of the images was also important,
especially in the first nights of DECam follow-up, when light curves for the
candidates consisted of only one or two epochs. For GW190814 in particular,
there were 33,596 candidates immediately after the image processing. KN
candidates were found in DECam images after running them through the reduction
pipeline. Objects were found by SExtractor (Bertin & Arnouts, 1996). Objects
that had good detections in SExtractor, showed evidence of being transients by
comparison to known object templates and passed visual inspection checks were
considered. Other candidates were identified in alert notifications from the
Gamma-ray Coordinates Network (GCN)222https://gcn.gsfc.nasa.gov/ put out by
other groups searching for kilonova KN candidates. A more rigorous process of
object assessment was done later, described in more detail in Morgan et al.
(2020) and summarized in § 4.2. In the end, spectroscopic follow-up was
performed using the SOAR Goodman HTS for 11 candidates (or their host
galaxies).

In Table 1 we present candidates found and spectroscopically targeted by the
DESGW team during DECam follow-up of GW190814. In this table we provide both
the DESGW ID and the Transient Name Server name, which we continue to use in
this work. In the final two columns, we present the localization probability
enclosed within the GW sky-map including each object location. For further
details of the processing of the DECam data and the subsequent identification
of possible candidates, please refer to our companion paper (Morgan et al.,
2020).

DESGW | TNS | RA(2000) | Dec(2000) | GCN / ID | Mag at | band | Prob reg | Prob reg
---|---|---|---|---|---|---|---|---
ID | Name | (deg) | (deg) |  | discovery |  | initial | final
624921 | 2019nqq | 20.95506 | -33.034762 | 25373 / c | 20.76 | i | 90% | o
624609 | 2019nqr | 23.573539 | -32.741781 | 25373 / d | 18.34 | i | 80% | 90%
624690 | 2019noq | 12.199493 | -25.30652 | 25356 (Pan-STARRS) | 19.93 | i | 30% | 30%
624157 | 2019ntn${\dagger}$ | 23.722184 | -31.380451 | 25393 (GROWTH) | 20.8 | i | 90% | o
626761 | 2019npw | 13.968327 | -25.783283 | 25362 / e | 20.5 | i | 40% | 60%
631360 | 2019num | 13.881714 | -22.968887 | 25393 (GROWTH) | 21.3 | i | 90% | o
661833 | 2019ntr | 15.007796 | -26.714266 | 25393 (GROWTH) | 21.2 | z | 80% | o
625839 | 2019omx | 24.18436 | -33.302678 | 25486 / z | 22.1 | z | 90% | o
626956 | 2019ntp | 12.550247 | -26.197878 | 25393 (GROWTH) | 21.0 | i | 50% | 60%
631484 | 2019nte | 23.557358 | -31.721700 | 25398 / f | 20.95 | i | 80% | o
635566 | 2019omw | 12.234396 | -23.170137 | 25486 / y | 22.8 | i | 50% | 80%

Table 1: Candidates found by the DESGW team during the DECam Follow-up of
GW190814 that were then followed up with SOAR ToO observations. The DESGW ID
is the internal identification number while the TNS name comes from the
Transient Name Server (https://wis-tns.weizmann.ac.il). The coordinates are
given here in degrees, along with the GCN announcing discovery of the
transient. Magnitude at discovery is given in the band listed. The confidence
probability enclosed within the GW sky-map including the object position is
given both for the initial map issued by LVC used during observing and for the
final, smaller map. (The “o” means outside the the 90% sky-localization
probability region.)

††footnotetext: AT2019mbq was accidentally targeted for SOAR spectroscopy
instead of the intended target AT2019ntn, and this accident was not discovered
until much later. This mistake has been traced to a copying error during the
handoff of this target from the DECam processing & analysis team to the SOAR
observing team. Candidate AT2019mbq is at RA=10.835384 deg DEC=-25.883880 deg,
with a magnitude at discovery of $i=18.75$. We note that AT2019mbq was not
originally considered for spectroscopic follow-up since its host galaxy had a
too high estimated photo-$z$ ($z_{\rm photo}=0.17\pm 0.05$) and since there
was evidence of a pre-merger detection for this candidate. As for AT2019ntn,
although no spectrum was taken of it, the fact that it brightened in $z$-band
about 4 days after the merger and the fact that it lay outside the 90%
confidence contour of the LVC final map (Fig. 1) make it unlikely that
AT2019ntn was the optical counterpart.

Figure 1: LVC sky-localization maps for GW190814; colors indicate confidence
probability contours. The top figure is the initial sky-map, released shortly
after event discovery on 2019 August 14. The bottom figure is the final sky-
map, released after further analysis by the LVC collaboration. The locations
of each of the 11 objects we describe in this paper are also given.

In Figure 1 we show both the initial and the final sky-localization maps
issued by the LVC along with the locations of each of the 11 objects we
observed. Note that in the smaller final probability regions, some of the
objects we observed are outside the 90% probability area, but all were
included within this area in the initial map.

##  4 SOAR Spectroscopic Candidate Selection

To achieve the maximum science, rapid spectroscopic follow-up of candidate KNe
is a necessity: first to discover the optical counterpart from among the list
of potential candidates, and then, if discovered, to permit the longest
possible timeline for optical monitoring of the evolution of the potential
KN’s light curve and spectral energy distribution before it fades to
obscurity. The constraints for our SOAR spectroscopic program, however, were
two-fold: (1) to preserve each night’s main program as much as possible, as
SOAR ToO interrupts are limited to 2.5 hours per night (including overheads);
and (2) to achieve reasonable $S/N$ ($\gtrsim$5-10) of a medium-resolution
spectrum on SOAR within a reasonable amount of time. Due to these constraints,
each observation is limited to objects with brightnesses of $i<21$. (We pushed
the limits for GW190814, relaxing this constraint to $i\lesssim 21.5$.) In
§4.1 we present our baseline strategy for SOAR/Goodman spectroscopy in LVC O3.
Then in §4.2 we describe our strategy for filtering transients found with
DECam observing to find the candidates that should be followed up with
spectroscopy.

###  4.1 SOAR Program Baseline Strategy for LVC O3

We designed our SOAR ToO program for rapid and robust identification and
subsequent nightly follow-up of KN candidates to be coupled with the DECam
wide-field search & discovery program (Soares-Santos et al., 2017; Herner et
al., 2020; Morgan et al., 2020; DES Collaboration et al., 2020), which would
be providing a selection of candidates for spectroscopy. This project was
awarded time at the SOAR/Goodman HTS to observe GW optical candidates
discovered during the entire year-long O3 run of the LIGO/Virgo campaign. Due
to the transient nature of GW optical counterparts (KNe), SOAR spectroscopy
must be carried out in ToO mode. We requested SOAR/Goodman HTS ToO time in
instant activation mode for a total of 10 h or at least 4 ToO activations per
semester. This way we took advantage of the fast survey confirmations from the
DECam search & discovery program, which could be available within 1 h, if the
merger happened during the Chilean night. The LVC predicted that there would
likely be roughly 8 BNS mergers and 1 NSBH mergers – the events most likely to
yield an optical counterpart – over the course of the LVC O3 run (Abbott et
al., 2017a; Chen et al., 2017). Thus we planned to use SOAR to follow up the
2–3 of these events likely visible from the Southern Hemisphere each observing
semester.

The KN for the GW170817 BNS merger was exceptionally bright and easy to
identify. It was expected that future events would on average be much farther
away and thus likely to be much fainter and harder to distinguish from other
transients (e.g. SNe Ia) in the larger volume encompassed by LVC O3 detection
thresholds. We planned to use the SOAR Goodman HTS (1) to spectroscopically
identify the optical counterpart to the GW event from among a small list of
candidates provided by an initial DECam search & discovery program; (2) once
identified, to obtain a higher-S/N optical spectrum of the counterpart,
suitable for detailed modeling; and (3) to obtain additional high-S/N spectra
of the potential KN on successive nights until it was effectively too faint
for useful follow-up on SOAR. We would employ an instrument setup almost
identical to that of Nicholl et al. (2017), who were able to follow the
GW170817 KN event at reasonable $S/N$ using the Goodman HTS from day 1.5 to
day 7.5 after the GW trigger. In that case the kilonova faded from magnitude
$i\approx 18$ to 21 over 6 days; they used an integration time (IT) of
$3\times 900$ s with the 400 l/mm grating. Based on their Goodman spectra, we
anticipated that we could achieve the S/N necessary to classify whether a
given candidate was a true KN or just another transient using a single 900 s
exposure for $i\leq 19$ candidates, a single 1200 s exposure for $i\approx 20$
candidates, and a single 1800 s exposure for $i\approx 21$ candidates. We
would leave fainter candidates to programs on larger telescopes, like programs
on VLT and Gemini-South.

We planned following up the list of candidates until we either finished the
list (finding no KN) or identified the optical counterpart. For an identified
KN, two additional exposures of the same integration time would allow us to
build $S/N$ suitable for model fitting. We planned continued SOAR follow-up if
a confirmed KN was brighter than $i=20$ mag, requesting interrupts on all
successive nights until it faded below that value. We ran 100,000 simulations
of the SOAR search program. An average of 8.79 DECam candidates per LIGO event
in the magnitude range $i=16$–24 was assumed, where magnitudes were drawn
randomly from the expected candidate distribution (see the LC_SHAPE row of
Fig. 2, where the numbers add up to 8.79). To estimate the time needed, we
included not only the expected exposure times, but also all relevant overheads
(e.g., slewing, target acquisition, readout, standard star observations,
etc.). To compensate for possibly worse sky transparencies (Nicholl et al.
2017 found clear skies), the science integration times were multiplied by a
factor of 1.25. The simulations showed that, for a single GW event, 50% of the
time a SOAR follow-up would be completed in 4.3 h (2 ToO interrupts), 95% of
the time in 6.7 h (3 interrupts), and 100% of the time in 9.5 h (4
interrupts). Note that follow-up completion does not necessarily mean a
guaranteed identification of the optical counterpart: it may just mean that
the list of candidates bright enough to be observed by SOAR was exhausted
without identifying the optical counterpart or even that the optical
counterpart (if any) was too faint to be detected by the DECam imaging.
Nonetheless, in our time requests, we estimated approximately 10 h per GW
event to optimize our chances of spectroscopically identifying and monitoring
a KN with SOAR during the LVC O3 run.

For spectroscopic classification, it was anticipated SOAR could go as faint as
$i=21$. In Figure 2 we visually represent the process for DECam search &
candidate selection for spectroscopic follow-up. This figure shows the
expected number of DECam candidates per magnitude per square degree in LVC O3,
for a typical localization area of 60 sq deg. The columns are arranged in
order of magnitude, with magnitude getting dimmer to the right.

For continued monitoring of the evolution for the optical spectrum of an
identified KN, it was thought that a higher $S/N$ would be required; so
additional monitoring was planned to be constrained to KNe brighter than
$i=20$. Candidates fainter than $i=21$ and confirmed KNe fainter than $i=20$
would be handed over for larger telescopes for spectroscopic follow-up. Via
simple timing simulations, we estimated the amount of time to obtain SOAR
spectra for typical KN candidates from a given LVC O3 event to take no more
than $\approx$10 hours over the course of $\lesssim$5 nights (recalling the
maximum ToO “interrupt” time per night is 2.5 hours) The SOAR team would meet
with the DECam team once the DECam team had a set of candidates.

To elaborate, in Figure 3, panel A, we present a simplified flow chart for a
simulated SOAR follow-up for the optical counterpart of a single LVC O3 event.
$N_{\rm cand}$ is the total number of candidates from an imaging search and
discovery program – i.e. the expected number of objects for which we would
need to take spectroscopy from SOAR or, for fainter candidates, from other
telescopes. If we run this flowchart over 100,000 realizations and compile the
results, we get the histograms in panels B & C of Figure 3. Panel B shows the
distribution – over 100,000 simulated realizations – of the total duration (in
hours) of SOAR ToO interrupt time expected for a single LVC O3 event.
Likewise, panel shows the distribution over 100,000 simulated realizations of
the total number of SOAR interrupts expected for a single LVC O3 event.

###  4.2 Candidate Filtering for GW190814

For GW190814, we selected targets for SOAR spectroscopy by reducing the DECam
images in real-time and monitoring the GCN for objects of interest detected by
other follow-up teams. In both approaches, one important constraint is the
brightness of the candidates. For accurate spectroscopic classification, we
wanted a minimum SNR of 5–10 in the collected spectra. Therefore in typical
observing conditions, with 45 minute to 1 hour exposure times, objects fainter
than $21.5$ $i$-band mag are excluded. However, if the candidate’s host galaxy
was brighter than the magnitude threshold, we targeted the host to obtain a
precise redshift of the candidate.333We note that the host galaxy for each
candidate was identified by matching the candidate’s coordinates with the DES
Y3 galaxy catalog using both angular and galaxy photo-$z$ information. Details
can be found in § 3.3 of Morgan et al. (2020).

The candidate selection performed in real-time for the SOAR targets differs
from the offline candidate selection presented in Morgan et al. (2020). One
important difference is that all potential SOAR targets were selected before
we began co-adding the DECam images within the same night and filter. The cuts
applied to select spectroscopic targets were:

  1. 1.

ALL. Detected in DECam images by the DESGW Search and Discovery Pipeline;

  2. 2.

DETECTED 2x. At least two detections by SExtractor with no errors and with an
autoscan score of at least 0.7 separated by at least one hour (autoscan is a
machine learning-based tool for differentiating between image artifacts and
real objects (Goldstein & D’Andrea, 2015));

  3. 3.

PHOTO z. If a host-galaxy exists in the DES Catalog, the estimated photometric
redshift and its error must be consistent with the LVC distance mean within
three standard deviations;

  4. 4.

INSPECTION. Pass visual inspection by the DESGW team.

Whether an object was first reported to the GCN by the DESGW team or by
another follow-up team, it was still required to pass the same set of
selection criteria prior to being targeted with SOAR. Technical details and
motivations for these criteria are presented in Morgan et al. (2020).
Remaining objects after the above selection criteria were sorted by their
single-band average rate of change in flux to look for rapidly evolving
transients. Finally, we triggered SOAR on objects passing the criteria and
that had not already been ruled out by other teams in order of largest flux
change to smallest flux change444Those candidates ruled out by other teams
included candidates observed on the The Gran Telescopio Canarias (GTC; Lopez-
Cruz et al. 2019b; Castro-Tirado et al. 2019; Lopez-Cruz et al. 2019a; Hu et
al. 2019), The Southern African Large Telescope (SALT; Morgan et al. 2020),
and The Giant Magellan Telescope (GMT; Morgan et al. 2020), and in general
were too faint for SOAR ToO follow-up.. The selection process for the specific
case of GW190814 is illustrated in Figure 4.

In total, 11 objects were targeted with SOAR for either spectroscopic
classification of the transient or to obtain a spectroscopic redshift of the
host-galaxy. These objects are cataloged in Table 2 and their times of
photometric discovery and spectroscopic follow-up are shown visually in Figure
5. We note that the observed rate (11 candidates within 48 sq deg) well
matches the anticipated rate (9 candidates within 60 sq deg), and are in fact
identical within the Poisson errors.

Figure 2: The baseline DECam search & discovery candidate selection for
spectroscopic follow-up for LVC O3. The need for a robust classification
pipeline to find KNe in O3 — as was uniquely done for GW170817 in Soares-
Santos et al. (2017) — is shown here in the ($i$-band) magnitude distribution
of all transient candidates expected to be found by a DECam search & discovery
imaging sequence for a typical BNS GW trigger in LVC O3, assuming a typical
search area of 60 sq deg (e.g., see Scolnic et al. 2018). The first row
(“ALL’’), which corresponds to the magenta histogram, is the distribution of
candidates expected to be output from the DECam Difference Imaging Pipeline.
In these simulations, we rejected moving objects and artifacts by requiring
$>$2 observations (“N_OBS’’) and machine learning classification score $>$0.7
(“ML_SCORE’’), rejected candidates with host galaxies at $z>0.2$ (“HOST’’),
and performed a color cut using the fact that, unlike SNe, the early evolution
of a KN is black body-like (“COLOR’’); as detection of a rising light curve
would immediately pin-point the target, we applied a reduction of 25% assuming
that, given DECam scheduling constraints, we would be able to get 2 epochs at
$<$24h from merger for 1 in 4 events (“LC_SHAPE”). Thus, this last row
(“LC_SHAPE”), which corresponds to the cyan histogram, is the expected
distribution of candidates remaining after all the image-level culling
procedures have been run. (Note: the numbers listed below the plot are the
total per magnitude bin for the full 60 sq deg search area; the $y$-axis of
the plot, however, is the number per magnitude bin per square degree. Also
note: the results shown in the above plot and histogram are based on multiple
simulations covering areas larger than 60 sq deg; scaling to a 60 sq deg
localization area and averaging over the multiple simulations means that the
numbers in these bins are not integers [e.g., why the number of candidates in
the $i=21$ bin in the “ALL” row is 875.68 and not, say, exactly 875].) Figure
3: (A) A simplified flow-chart for a single realization of a simulated SOAR
follow-up of a single GW event, where $N_{\rm cand}$ is the total number of
candidates from an imaging search & discovery program. For the simulations
here, $N_{\rm cand}$ is either 8 or 9, but averages overall to 8.79. The
distribution of $i$-band magnitudes for the candidates is drawn from the
“LC_SHAPE” row in Fig. 2, and the overall average number of candidates (8.79)
is just the sum of the entries in the “LC_SHAPE” row. (B) Results of the
simulation (using 100,000 realizations): histogram of the total durations of
SOAR ToO interrupt time [in hours] for a single LVC O3 event. (C) Results of
the simulation (using 100,000 realizations): histogram of the total number of
SOAR ToO interrupts for a single LVC O3 event. (Note that the number of
interrupts does not scale exactly as the total duration of interrupt time,
since the number of hours per interrupt will vary between the “search &
discovery” phase and the follow-up phase of the observations for a given KN
event.) Figure 4: The DECam search & discovery candidate selection for
spectroscopic follow-up for GW190814. Whereas Fig. 2 provided the typical
distribution of DECam candidates expected for a typical LVC O3 BNS merger,
here we show the corresponding $i$-band magnitude distribution of all
transient candidates observed and visually inspected and identified within the
observed area by DECam across the selection criteria of §4.2 specifically for
the GW event GW190814. The final 11 candidates targeted with SOAR compose the
cyan histogram and the ‘‘INSPECTION’’ row; 4 other candidates, which were in
the $i=21-22$ range, were observed by other telescopes and are omitted from
the cyan histogram and ‘‘INSPECTION’’ row. Note that at the time of SOAR
follow-up on three of these transients, their magnitudes had faded below the
SOAR detection limit, so we observed their host galaxies to measure their
redshifts. (Note: the numbers listed below the plot are the total per
magnitude bin for the full 48.0 sq deg search area; the $y$-axis of the plot,
however, is the number per magnitude bin per square degree.)

In Figure 6 we show the expected incidence of each of several types of SN
during a search for a KN. These data come from simulated full light curves
using the SuperNova ANAlysis software (SNANA; see § 8). The models are the
same as in the Photometric LSST Astronomical Time-series classification
challenge (PLAsTiCC, Kessler et al. 2019). We start with $\approx$3300 SNe
with a distribution of SN types at random points in their light curves – what
one might net in a typical transient search by DECam covering several tens of
square degrees – and then apply the selection (culling) steps detailed above,
in the end yielding about a dozen SNe whose imaging and photometric properties
closely enough mimic that of a KN that they would require follow-up
spectroscopy (and/or a more robust photometry-based technique) to eliminate
them as candidates in a KN search. This could be viewed as an estimate of the
rough contamination rate by SNe in a real-time imaging search using similar
candidate selection criteria. Finally, it is interesting to note that the
distribution of SN types is very similar between the sample of 3346 SNe that
were rejected by the above selection steps and the sample of a dozen SNe that
successfully passed through all these steps. In other words, the selection
steps do not seem to favor or disfavor any particular SN type.

Figure 5:  Observational timelines for each KN candidate. All dates are shown
as number of days ($\Delta$MJD) since 58709.00, MJD corresponding to August
14, 2019, the day GW190814 was detected. The time of the NSBH merger event at
MJD 58709.88 is shown (using a red circle) on each. The date of transient
discovery is shown as a blue square. The date of SOAR spectroscopy is shown as
a green triangle for each KN candidate (open triangles indicate that
spectroscopy was only done for the host galaxy). Vertical lines show beginning
time of DECam observations. Figure 6: Predictions of the relative incidence of
each of several types of SN within a spectroscopic follow-up KN candidate
sample post DECam processing & analysis. The predictions are based on
simulated data using SNANA light-curves and PLAsTiCC models and run through
the selection steps of Morgan et al. (2020). The blue histogram shows the
relative distribution of SNe that were rejected by the selection steps; the
orange histogram, the relative distribution of SNe that survived (i.e. were
selected by) all the selection steps. Similar relative sizes of bars indicates
no bias towards any particular SN type. The error largely comes from the
Poisson counting statistics.

##  5 SOAR Observations

In the following section (§5.1) we provide details of our ToO triggers and
real-time (not final) classifications in search of the optical counterpart of
GW190814. We explain how the methods described in §4 were executed when our
SOAR 2019B ToO program was triggered to observe candidates for an optical
counterpart of GW190814.

###  5.1 GW190814 candidate observations

Based on input from the DECam search & discovery program, we developed a list
of candidates for spectroscopy as described in the previous section. For the
objects possible to observe each night we developed nightly webpages with
information on object airmasses, finding charts and other information that
would be required once our ToO time began. On each night we issued a ToO
interrupt, there were several possible kilonova candidates that could be
observed. The selection of which ones were to be targeted for the night was
based on observing conditions (e.g. low airmass) and brightest magnitude.

In order to complete data processing in real time, we employed a custom-made
reduction pipeline that we developed, a Jupyter notebook we call the SOAR
Goodman Quick Reduce ( see § 8), to obtain quick results immediately after the
data are transferred from the SOAR telescope machines. The preliminary
processing consists of a quick reduction of the spectra using an arc-lamp
wavelength calibration frame and a calibration from a standard star taken at
the start of ToO observing. This publicly available Jupyter notebook takes the
2D spectrum, extracts the 1D spectrum, and performs basic wavelength and
spectrophotometric calibration with relatively simple and straightforward
inputs. With a little practice, it is time-competitive with just using the
IRAF implot task – but with the added advantage of providing a quick
calibrated spectrum. Generally, a “by eye” check of the calibrated spectrum
indicates whether or not a candidate is a KN – usually due to the
disqualifying presence of one or more relatively sharp emission lines or the
spectral features of an SN – but, even so, each calibrated spectrum was also
sent that same night to one of our SN-fitting experts, who would fit the
spectrum to SN model spectra. The resulting spectra were intended to be
analyzed with fast classification tools (see below) and the spectroscopic
class and redshift of the transient to be published promptly to the community
via a GCN circular. The list of objects for which spectra were taken, along
with initial redshift and SN classifications and the GCNs the DESGW SOAR
observing team issued, is given in Table 2.

To avoid fatigue, the DESGW SOAR spectroscopy task force was divided into four
teams – a team based in Brazil (PI M. Makler), a team based in Chile (PI F.
Olivares), a team based at UC-Santa Cruz (PI C. Kilpatrick), and a team based
at Fermilab (PI D. Tucker) – each team signing up for multiple 2-week shifts
throughout the course of LVC O3. Our default plan was to use the Goodman HTS
Blue camera, the 400 l/mm grating in its M1 configuration, and a slit width of
1 arcsec, to yield a wavelength range of roughly 3000Å to 7050Å at a
resolution of $R\sim 930$ (e.g., see Nicholl et al. 2017), but, if the night’s
main program that our ToO was interrupting was using a roughly similar
configuration, we could also use that instead, minimizing issues with switch-
overs from and to the main program.

####  5.1.1 Observations

We issued ToO interrupts on 2019 August 16, 20, 26, 28, and 31 (start dates,
based on local time). On several other nights we attempted to conduct ToO
observations, but found skies to be too cloudy to effectively observe and so
we canceled the ToO interrupts. During the course of the August 2019
observations, the Fermilab and Chilean teams were on shift. In addition,
spectra were taken for us by SOAR scientific staff during the SOAR engineering
nights of September 13 (host galaxy for AT2019nte) and October 17 (host galaxy
of AT2019omw). This information and the GCNs issued are summarized in Table 2.

Candidate | Night | GCN | Classification Source | Classification | Redshift
---|---|---|---|---|---
AT2019nqq | Aug 16 | 25379 | Astrodash | Type Ic-broad SN | 0.3257
AT2019nqr | Aug 16 | 25379 | Astrodash | Type IIb SN | 0.0888
AT2019noq | Aug 20 | 25423 | SNID | Type IIP SN | 0.07
AT2019mbq | Aug 20 | 25423 | SNID | Type Ia-CSM SN | 0.10
AT2019npw | Aug 26 | 25484 | Astrodash | Type IIb SN | 0.163
AT2019num | Aug 26 | 25484 | Astrodash | Type IIP SN | 0.113
AT2019ntr | Aug 28 | 25540 | Astrodash | Type II-L SN | 0.2
AT2019omx | Aug 28 | 25540 | H$\alpha$ emission line | host galaxy | 0.275⋆
AT2019ntp | Aug 31 | 25596 | Astrodash | Type Ic-BL SN | 0.3284
AT2019nte | Sep 13 | 25784 | H$\alpha$/[NII] emission lines | host galaxy | 0.0704⋆
AT2019omw | Oct 17 | N/A | H$\alpha$ emission line | host galaxy | 0.0467⋆

Table 2: Initially reported data for the 11 candidates described in this
paper. Data include candidate name as assigned by the Transient Name Server,
night of observation, GCN in which spectral results were reported, source of
initial classification and redshift, initial classification and initial
redshift. These are the values reported in the GCNs. (No GCN was submitted for
AT2019omw.) These values were updated after full reduction and processing of
data. Updated values are given in Table 3. (Astrodash and SNID are SN spectrum
fitting codes; see § 6.2 and § 8. Which fitting code was used in this initial
classification for a given candidate depended heavily on which team member was
available on that night to perform the classification, and the team member’s
preference.)

**footnotetext: Redshift of the host galaxy.

Note. — Night=civil date of the start of the night of observation, the NOAO
convention of designating an observing night. The asterisk to the right of
several $z$ values indicates that this is redshift for the host galaxy, as the
transient was too dim to observe.

In Figure 5 we graphically summarize our sequence of observations. In this
figure we show a set of timelines indicating the dates of discovery and SOAR
spectroscopy of each of the candidates we observed, using a log scale for the
x-axis. The first mark (red circle) on each timeline is the MJD of the
GW190814 merger event. The second mark (blue square) is the date of discovery
in DECam observations. The third mark (green triangle) indicates the date of
SOAR spectroscopy. Vertical lines are also included that show the date of
DECam observations, as described in Morgan et al. (2020). The marks denoting
SOAR spectroscopy of AT2019nte, AT2019omw, and AT2019omx, are unfilled,
indicating that we did not take spectroscopy of the transient but of the host
galaxy only. We report redshifts of these host galaxies in Table 2. The
horizontal axis is given in $\Delta$MJD, time in days since MJD 58709.

Table 3: Final results for the 8 transients and the 3 host galaxies for which
we took spectra. Results include name from the Transient Name Server and the
S/N of the spectrum calculated using the 6000-6100 $\AA$ region. Then we
report the outputs from AstroDash and SNID, respectively, including SN type,
$\tt{rlap}$ values, redshift, and absolute magnitude (at DECam discovery; see
Table 1). For spectra with $S/N<5$ and for fits with $\tt{rlap}<6.0$
(AstroDash) or $\tt{rlap}<5.0$ (SNID), the classification may be unreliable.

|  | AstroDash | SNID | Comments
---|---|---|---|---
Name / ID | $S/N$ | Type | rlap | $z$ | $M_{\text{abs}}$ | Type | rlap | $z$ | $M_{\text{abs}}$ |
AT2019nqq† | 2.4 | Ia-csm | 0.14 | 0.071 | $-16.8$ | IIn | 5.3 | 0.070 | $-16.8$ | SNID preferred
AT2019nqr | 32.6 | Ia-csm | 9.97 | 0.086 | $-19.6$ | Ia | 4.36 | 0.101 | $-20.0$ | Seyfert 2 AGN @ $z=0.083$
AT2019noq | 7.7 | IIn | 19.55 | 0.074 | $-17.7$ | IIP | 13.11 | 0.072 | $-17.6$ | AstroDash preferred
AT2019mbq† | 23.1 | IIn | 15.96 | 0.102 | $-17.6$ | Ia | 12.09 | 0.110 | $-17.8$ | AstroDash preferred
AT2019npw | 6.4 | IIP | 4.76 | 0.148 | $-18.7$ | IIP | 6.44 | 0.148 | $-18.7$ | SNID preferred
AT2019num† | 7.5 | IIL | 7.95 | 0.123 | $-17.5$ | IIb | 6.96 | 0.149 | $-18.0$ | AstroDash preferred
AT2019ntr† | 1.8 | Ic-broad | 0.81 | 0.224 | $-19.0$ | Ia | 4.01 | 0.861 | $-22.5$ | None preferred; unknown
AT2019omx∗† | 2.3 | … | … | … | … | … | … | … | … | host galaxy @ $z=0.275$ ($M_{\rm abs}=-18.7$)
AT2019ntp | 11.8 | Ia-pec | 6.44 | 0.116 | $-17.7$ | Ia | 12.22 | 0.114 | $-17.6$ | SNID preferred
AT2019nte∗† | 5.8 | … | … | … | … | … | … | … | … | host galaxy @ $z=0.0704$ ($M_{\rm abs}=-16.6$)
AT2019omw∗ | 1.8 | … | … | … | … | … | … | … | … | host galaxy @ $z=0.0467$ ($M_{\rm abs}=-13.8$)

**footnotetext: Only the spectrum of the host galaxy was obtained; so it was
not fit by either AstroDash or SNID.

††footnotetext: This candidate lies outside the 90% confidence probability
contours of the final LVC map for GW190814; see Fig. 1.

Figure 7: Histograms of the redshifts of the eleven candidates, using final
preferred results from Table 3. The top panel is for the 8 transient targets
alone, the middle panel is for for the 3 host galaxy targets alone, and the
bottom panel is for all 11 SOAR targets combined (transients and host galaxies
together).

Even though none of these 11 candidates were determined to be the optical
counterpart of GW190814, these results will permit important upper limits to
be established in preparation for future searches for the optical counterparts
of these types of mergers (see next section).

##  6 Results & Discussion

AT2019noq
---
Finding Chart | DECam light curve
|
Fit to SN spectrum templates | Fit to KN spectrum models
|
Figure 8: Top Left: The thumbnail finding chart (using the DECam imaging) for
the AT2019noq KN candidate; the location of the candidate is marked by a small
yellow circle. Top Right: the candidate’s $i$\- and $z$-band light curves from
DECam photometry; the vertical dashed green indicates when SOAR spectroscopy
was obtained. Bottom Left: Observed and best-fit SN model spectrum for the
candidate object. Light blue is the processed, calibrated, and continuum-
subtracted observed spectrum; dark grey is the best-fit SN model from
AstroDash; and light grey is the best-fit SN model from SNID. In the panel we
provide the best-fit SN type and redshift from the two codes. Bottom Right:
Observed and best-fit model KN spectra for the candidate objects. Light blue
is the processed and calibrated observed spectrum; black is the best fit Kasen
et al. (2017) KN model. In the panel we provide the best-fit value of the
redshift, $z_{\rm best}$. Unlike in AstroDash/SNID fits plot, the continuum
has not been subtracted. Also, a slightly different smoothing technique is
used for the SN fits and for the KN fits. AT2019mbq
---
Finding Chart | DECam light curve
|
Fit to SN spectrum templates | Fit to KN spectrum models
|
Figure 9: Same as Fig. 8 except for the AT2019mbq KN candidate. AT2019npw
---
Finding Chart | DECam light curve
|
Fit to SN spectrum templates | Fit to KN spectrum models
|
Figure 10: Same as Fig. 8 except for the AT2019npw KN candidate. AT2019num
---
Finding Chart | DECam light curve
|
Fit to SN spectrum templates | Fit to KN spectrum models
|
Figure 11: Same as Fig. 8 except for the AT2019num KN candidate. AT2019ntr
---
Finding Chart | DECam light curve
|
Fit to SN spectrum templates | Fit to KN spectrum models
|
Figure 12: Same as Fig. 8 except for the AT2019ntr KN candidate. AT2019ntp
---
Finding Chart | DECam light curve
|
Fit to SN spectrum templates | Fit to KN spectrum models
|
Figure 13: Same as Fig. 8 except for the AT2019ntp KN candidate. (Due to the
additional smoothing in the SN-fitting plot, the strong narrow emission line
seen in the KN-fitting plot is mostly washed out.) AT2019nqr
---
Finding Chart | DECam light curve
|
Fit to SN spectrum templates | Fit to KN spectrum models
|
Fit to an AGN spectrum template |
|
Figure 14: Same as Fig. 8 except for the AT2019nqr KN candidate. We also show
the best fit to AGN template spectra, which is that of a Seyfert 2. AT2019nqq
---
Finding Chart | DECam light curve
|
Fit to SN spectrum templates | Fit to KN spectrum models
|
Fit to an AGN spectrum template |
|
Figure 15: Same as Fig. 8 except for the AT2019nqq KN candidate. We also show
the best fit to AGN template spectra, which is that of a Seyfert 2. AT2019omx
---
Finding Chart | DECam light curve
|
Host galaxy spectrum |
|
Figure 16:  Top Left and Top Right: Same as Fig. 8 except for the AT2019omx KN
candidate. Bottom Left : The spectrum of the host galaxy. AT2019nte
---
Finding Chart | DECam light curve
|
Host galaxy spectrum |
|
Figure 17: Same as Fig. 16 but for the AT2019nte KN candidate. (The vertical
purple line in the light-curve plot is just a very large error bar for the
$z$-band observation.) AT2019omw
---
Finding Chart | DECam light curve
|
Host galaxy spectrum |
|
Figure 18: Same as Fig. 16 except for the AT2019omw KN candidate.

In this section, we cover our final results from our SOAR observations of the
GW190814 candidates. In § 6.1 we describe the full reduction and analysis of
spectra and present the spectra themselves. In § 6.2 we present
classifications of the supernovae and consider our methods of analysis. In §
6.3 we fit each spectrum with Kasen et al. (2017) KN models; as nearly all
were found to be an SN, the KN models are generally not good fits. In § 6.4,
we discuss the 3 candidates for which we only obtained spectra for the host
galaxy and the likelihood that either of these 3 candidates could be the
optical counterpart for GW190814. Finally, in § 6.5 we consider lessons
learned in LVC O3 that can be applied as we prepare for LVC observing season
O4.

###  6.1 Spectral data from SOAR Telescope

For the final reduced spectra (shown in Figs. 8 – 18) — unless otherwise
noted555For the final reduced spectra for the host galaxies of AT2019nte and
AT2019omw, we made use of standard IRAF reductions provided by the SOAR
science staff. — we employed the UCSC spectral pipeline (link to Github
repository in § 8). This pipeline consists of the standard steps for the
processing of optical spectroscopic data: bias subtraction, flat fielding,
extraction of the 1D spectrum and flux and wavelength calibration against a
standard star, typically a Hamuy Tertiary Standard Star (Hamuy et al., 1992,
1994). These more careful reductions, performed later, are the same as those
used in the recent GW190914 omnibus paper by Kilpatrick et al. (2021).

###  6.2  SN Classifications

Offline analysis of the spectra we obtained was performed using the public
codes Super Nova IDentification (SNID; Blondin & Tonry, 2007) and Deep
Automated Supernova and Host classifier (DASH, a.k.a., AstroDash; Muthukrishna
et al., 2019) (see § 8). SNID is a template fitting method based on the
correlation techniques by Tonry & Davis (1979). AstroDash is a deep
convolutional neural network used to train a matching algorithm. These
analysis tools provide spectral matching, which allowed us to classify our
spectra by means of a comparison against a spectral library of transients and
other astrophysical sources. We chose these codes as SNID has been used
extensively by the community and AstroDash makes use of a powerful deep
learning technique. We discuss below the importance of using more than one SN
typing package to check results.

For our AstroDash fits of the spectrum of each candidate, we applied an
AstroDash smoothing length of 3 (unless otherwise stated), and we left the
redshift a free parameter. We then visually inspected the 20 best SN template
fits for that candidate, choosing the top two for further consideration. (The
top two fits based on visual inspection also typically had among the highest
rlap values of the 20 best fits.666rlap is a measure of the quality of the fit
that combines the correlation between the observed and the template spectrum
with the amount of overlap in $\ln{\lambda}$-space between the observed and
the template spectrum. The higher the value of rlap, the higher the quality of
the fit. For the detailed definition, see Blondin & Tonry (2007).) Unless
there were other relevant considerations (e.g., the putative epoch in the
light curve at which the spectrum was obtained), the SN template spectrum with
the higher of the two rlap values was chosen as the final best fit.

For our SNID fits of the spectrum of each candidate, we applied the default
SNID smoothing length of 1 pixel, and, as with our AstroDash fits, we also fit
for the redshift. We visually inspected the top 5 SN template fits for each
candidate, but in the end chose the one with the highest rlap as our SNID
classification.

In Table 3 we present final measurements from AstroDash and from SNID for the
8 transients of which we took spectra. (For completeness, we also include
information on the 3 candidates for which we only obtained host galaxy
spectra: AT2019omx, AT2019nte, and AT2019omw). These results are based on the
final reduced spectra. This table includes classification, the redshift, and a
measure of the goodness of fit ($\tt{rlap}$) from these two SN spectrum
fitting codes. We kept redshifts as free parameters in the fitting; the
photometric redshifts of the host galaxies were used during the selection
process of candidate objects discussed in § 3.

The distribution of the redshifts from the preferred fits in Table 3 is given
in Figure 7; as expected, transients were found over a range of redshifts with
a predominance of lower-$z$ objects.

In Figures 8–18, we provide the following information for each candidate: a
thumbnail finding chart containing the host galaxy and marking the location of
the transient; the DECam-based $i$\- and $z$-band light curves for the
transients; and the final reduced observed spectrum. For the candidates for
which we only obtained the host galaxy spectrum,777Note that, within the 2.5
hour time constraint of a SOAR ToO interrupt, we were basically confined to
observing targets that were $i\lesssim$21.5; so, in some cases – especially
for the later targets – we instead obtained spectra of the candidate’s host
galaxy as a means of excluding the target by its redshift: i.e., if the
redshift of the candidate’s host galaxy is substantially discrepant from the
redshift expected for the luminosity distance of the GW event ($z_{\rm
GW}=0.059\pm 0.011$), we can exclude that candidate. that is the sum of what
we show in these figures. For candidates for which we took a spectrum of the
transient candidate itself, we also include the best-fit SN templates from
AstroDash and SNID and the best-fit KN model from Kasen et al. (2017)
overplotted on the final reduced observed spectrum. As shown below, the
interplay of these different types of data often helped in the final
classification of a given candidate.

####  6.2.1 AT2019noq

For AstroDash, our two best fits were a $z=0.074$ SN IIn 42–46 days past
maximum light ($\texttt{rlap}=19.55$) and a $z=0.079$ SN IIP 2-6 days past
maximum light ($\texttt{rlap}=19.31$). The DECam light curve was relatively
flat over the period it was observed (Fig. 8); so we chose the SN IIn
classification as more likely. For SNID, our best fit was a $z=0.072$ SN IIP
9.8 days past maximum light ($\texttt{rlap}=13.11$). Due to its higher rlap
value, the AstroDash fit is preferred; see Figure 8.

####  6.2.2 AT2019mbq

Recall that a spectrum of AT2019mbq was mistakenly observed by SOAR (the
original target was AT2019ntn), and that there was evidence of a detection of
AT2019mbq before the GW190814 merger event, making it highly unlikely that
AT2019mbq is the optical counterpart.

For AstroDash, our two best fits were a $z=0.102$ SN IIn 46–50 days past
maximum light ($\texttt{rlap}=15.96$) and a $z=0.103$ SN IIn 42–46 days past
maximum light ($\texttt{rlap}=14.92$). The difference between the two
classifications was small, and the DECam light curve provided no strong
motivation to choose one over the other (Fig. 9); so we chose the template
with the higher rlap (a $z=0.102$ SN IIn 46–50 days past maximum light) as
more likely. For SNID, our best fit was a $z=0.110$ SN Ia 45.9 days past
maximum light ($\texttt{rlap}=12.09$). Despite the SNID fit’s relatively high
rlap value, a visual inspection of both the AstroDash and the SNID spectral
fits (Fig. 9) leads us to prefer the AstroDash fit.

####  6.2.3 AT2019npw

For AstroDash, our two best fits were a $z=0.148$ SN IIP 18–22 days past
maximum light ($\texttt{rlap}=4.76$) and a $z=0.147$ SN IIP 22–26 days past
maximum light ($\texttt{rlap}=4.72$). The difference between the two
classifications was small, and the DECam light curve provided no strong
motivation to choose one over the other; so we chose the template with the
higher rlap (a $z=0.148$ SN IIP 18–22 days past maximum light) as more likely.
The relatively low rlap values ($\texttt{rlap}<6$), however, are of some
concern. For SNID, our best fit was a $z=0.148$ SN IIP 44.3 days past maximum
light ($\texttt{rlap}=6.44$). Due to its higher rlap value, the SNID fit is
preferred, see Fig. 10.

####  6.2.4 AT2019num

For AstroDash, our two best fits were a $z=0.123$ SN IIL 6–10 days past
maximum light ($\texttt{rlap}=7.95$) and a $z=0.239$ SN Ibn 22–26 days past
maximum light ($\texttt{rlap}=0.4$). Since the DECam light curve for this
candidate is rising noticeably 10–6 days before the SOAR spectrum was obtained
(Fig. 11), it appears that this candidate is a likely a young SN; that,
combined with the substantial difference in rlap values led us to choose the
$z=0.123$ SN IIL 6–10 days past maximum light template as the more likely
classification. (We note that, for AT2019num, we used a smoothing length of 6
instead of 3 for our AstroDash fits.) SNID, our best fit was a $z=0.149$ SN
IIb, 17.3 days before maximum light ($\texttt{rlap}=6.96$). Due to its higher
rlap value (and the relative rarity of catching a SN so early before maximum
light), the AstroDash fit is preferred; see Figure 11.

####  6.2.5 AT2019ntr

For AstroDash, our two best fits were a $z=0.224$ SN Ic-broad near maximum
light (between 2 days before and 2 days after peak; $\texttt{rlap}=0.81$) and
a $z=0.264$ SN Ia-csm 6–10 days past maximum light ($\texttt{rlap}=0.76$). The
DECam light curve seems to be slightly rising 11–8 days before the SOAR
spectrum was taken (Fig. 12), indicating a relatively young SN. Due to the low
$S/N$ of the spectrum (1.8) and the poor rlap values for the fits, we are
reluctant to assign a classification based on the AstroDash fits; that said,
the $z=0.224$ SN Ic-broad template near maximum light appears to be marginally
better.

For SNID, our best fit was a $z=0.861$ SN Ia 11.2 days before maximum light
($\texttt{rlap}=4.01$). Given a discovery $z$-band magnitude of $21.2$ (Table
1), a redshift of $z=0.861$ implies a $z$-band absolute magnitude of roughly
$M_{\rm abs}=-22.5$, or substantially more luminous than a typical SN Ia
(Richardson et al., 2014). We therefore view the SNID fit as unreliable.

Due to the noisiness of this spectrum and the problems with both the AstroDash
and the SNID fits, we prefer neither the AstroDash nor the SNID
classifications. We therefore view AT2019ntr’s spectral classification as
unknown; see Figure 12. In hindsight, AT2019ntr would have been a natural
candidate for additional spectroscopy with a larger telescope.

####  6.2.6 AT2019ntp

For AstroDash, our two best fits were a $z=0.116$ SN Ia-pec 34–38 days past
maximum light ($\texttt{rlap}=6.44$) and a $z=0.331$ SN Ic-Broad 26–30 days
past maximum light ($\texttt{rlap}=4.35$). The DECam light curve provided no
strong motivation to choose one over the other (Fig. 13); so we chose the
template with the higher rlap (a $z=0.116$ SN Ia-pec 34–38 days past maximum
light) as more likely. For SNID, our best fit was a $z=0.114$ SN Ia 45.8 days
past maximum light ($\texttt{rlap}=12.22$). Due to its higher rlap value, the
SNID fit is preferred; see Figure 13.

####  6.2.7 AT2019nqr

For AstroDash, our two best fits were a $z=0.086$ SN Ia-csm 46-50 days past
maximum light ($\texttt{rlap}=9.97$) and a $z=0.086$ SN IIn 46–50 days past
maximum light ($\texttt{rlap}=7.85$). We chose the template with the higher
rlap value as the better fit, despite that none of the SN templates did a
reasonable job at fitting the narrow-but-strong emission lines at the observed
wavelengths of 5371Å and 5422Å, and despite that the DECam light curve
indicated that the transient may have been near a maximum brightness when the
spectrum was observed. For SNID, our best fit was a $z=0.101$ SN Ia 5.7 days
past maximum light ($\texttt{rlap}=4.36$). In the end, due to this candidate’s
central location in a spiral galaxy and a spectrum that well fits that of a
Seyfert 2 at $z=0.083$, we classify AT2019nqr as a Seyfert 2 AGN; see Figure
14.

####  6.2.8 AT2019nqq

For AstroDash, our two best fits were a $z=0.071$ SN IIn 14–10 days before
maximum light ($\texttt{rlap}=0.57$) and a $z=0.071$ SN Ia-csm 6–10 days past
maximum light ($\texttt{rlap}=0.14$). The DECam light curve appears to show a
very slight fading over the short time it was monitored before the spectrum
was taken (about 1 day before SOAR spectrum was obtained; Fig. 14); so we
chose the second template (a $z=0.071$ SN Ia-csm 6–10 days past maximum light)
as more likely, even though it has a lower rlap. We note that the observed
spectrum contains a prominent H$\alpha$ emission line redshifted to 7028Å and
a less prominent [O III] 5007 emission line redshifted to 5362Å, and an even
less prominent H$\beta$ emission line redshifted to 5205Å. For SNID, our best
fit was a $z=0.070$ SN IIn 50.2 days past maximum light ($\texttt{rlap}=5.3$).
Due to its higher rlap value, the SNID fit is preferred; see Figure. 15.

We note that AT2019nqq was one system for which we could compare results from
another facility. It was also observed by the GTC 10.4m (GCN25419), classified
as a Type IIP SN at 4 days post maximum with $z_{host}$=0.071. Although the
type classification differs from our result for this system (Type IIn SN), the
redshift estimate is consistent with ours.

In closing, we found that some classifications from both AstroDash and SNID
might be inconclusive. For one case, AT2019ntr, this is probably related to
the low-$S/N$ spectrum, in which the low value of rlap from both SNID and
AstroDash points towards a poor fit. It is also worth re-iterating that our
methods of choosing the best fits differed for the two packages: for
AstroDash, we depended more on a visual inspection of the 20 models with the
highest rlap values; for SNID, we basically chose the model with the highest
rlap value. This can lead to different classifications for the same object. In
general, for a fit of a relatively high $S/N$ spectrum ($S/N\geq 5)$ and a
relatively high value for rlap ($\geq 6.0$ for AstroDash; $\geq 5.0$ for
SNID), we view the classification (AstroDash or SNID) with the higher the
value of rlap as the preferred classification; in cases of a low $S/N$
spectrum ($S/N<5$), we view neither AstroDash’s nor SNID’s classification as
particularly reliable. These results enhance the importance of using multiple
methods to perform spectral classification.

###  6.3 Spectral fitting with KN models

KNe are expected to produce quasi-blackbody radiation. They are expected to
have a rapidly changing lightcurve, a luminosity consistent with nuclear rapid
neutron capture (r-process) heating, and a long-lived infrared emission.
Analysis of the spectrum of AT2017gfo (the KN associated with GW170817) showed
emission from both light r-process and heavy r-process components which led to
a spectrum that appears as a superposition of two blackbodies at different
temperatures. At early times the spectra are mostly featureless, while at
later times there are distinct features in the infrared.

For our analysis, we used the set of synthetic kilonova spectra by Kasen et
al. (2017) ( see § 8). This set of Kasen et al. (2017) models covers a
regularly sampled grid in parameter space of ejecta mass
($M=0.001-0.1M_{\sun}$), ejecta velocity ($v_{\rm kin}=0.03-0.40c$), and
ejecta lanthanide mass fraction ($X_{\rm lan}=10^{-9}-10^{-1}$). At each of
these grid points in ($M$,$v_{\rm kin}$,$X_{\rm lan})$-space is a time series
of synthetic spectra spaced in units of 0.1 day from $\approx$2 days pre-
merger out to $\approx$25 days post-merger. Each of these synthetic spectra
covers a rest-frame wavelength range from the ultraviolet ($\approx$ 150Å)
through the infrared ($\approx$ 10$\mu$m).

We took the processed and calibrated observed spectrum for each of our KN
candidates and performed a least-squares fit to the Kasen et al. (2017) grid
of synthetic spectra for the appropriate time post-merger when the candidate’s
spectrum was observed. In this fit, the redshifts of the synthetic spectra
were also allowed to float within a 1$\sigma$ range centered on the estimated
redshift of the LVC source ($z=0.059\pm 0.011$), yielding a best-fit spectrum
at a best-fit redshift.

In Figure 8 – 15 we show the results of these fits for our sample of observed
KN candidate spectra. With the possible exception of AT2019ntr, none of these
candidates have an observed spectrum that is a particularly good fit to the
Kasen et al. (2017) models – mostly due to the appearance of one or more
strong emission features in the observed spectrum – which is consistent with
our conclusion that none of these objects is a KN, but rather each is an SN
from one of several types. What of AT2019ntr? For this object the best-fit
redshift ($z_{b}=0.049$) is on the low end, but still within the $1\sigma$
errors from the redshift based on the original LVC O3 distance estimate
($z=0.059\pm 0.011$). Furthermore, this is one of the cases where the
AstroDash and SNID fits are both poor (low rlap) and inconsistent with each
other (see Table 3). So, is AT2019ntr the optical counterpart to GW190814?
Unfortunately, we cannot provide a definite conclusion based on the SOAR data
alone. As it turns out, though, it is unlikely that AT2019ntr is the KN we
were seeking: first, its sky coordinates lie outside the final LVC 90%
confidence contour for GW190814 (see Fig. 1); secondly and more importantly,
in their analysis of the DECam data for these candidates, Morgan et al. (2020)
applied a light-curve-based machine (ML) classifier – a combination of Sako et
al. (2011)’s PSNID fitting code and a random forest classifier – to the
photometric time series data for AT2019ntr, and this yielded a 96% probability
that AT2019ntr is an SN.

Finally, it might be asked whether it would not be more efficient to add the
Kasen templates into AstroDash/SNID so one could directly compare the
likelihood that an object is a classical SN vs. a KN. One of the first things
AstroDash/SNID does is to fit the continuum of the spectrum and remove it. KN
spectra – especially early on in their light curves – are continuum dominated,
with few prominent emission/absorption features. Thus, there would be little
left to fit in the case of the KNe models. Maybe a version of AstroDash/SNID
that did not subtract off the continuum during the fit would work, but that
would be a future project.

###  6.4 Spectra of Host Galaxies

Finally, there were three candidates which were too faint for us to target
effectively with SOAR (AT2019nte, AT2019omw, AT2019omx). We instead targeted
the host galaxy, with the idea that, if the host galaxy’s redshift was
significantly discordant with that of the distance estimated from the GW
signal, that would rule out that candidate as a possible counterpart to
GW190814. We found that only one (AT2019omx) had a truly discordant redshift
($z=0.275$); see Figure 16. The host galaxies of the other two candidates,
AT2019nte ($z=0.070$; Fig. 17) and AT2019omw ($z=0.047$; Fig. 18) have
redshifts that are consistent with the redshift corresponding to the GW
distance at about the 1$\sigma$ level. As it turns out, in the end both
AT2019nte and AT2019omw failed the DESGW Search & Discovery offline imaging
pipeline criteria for a good candidate: AT2019nte because it did not meet a
sufficiently high detection threshold in the DECam imaging, and AT2019omw
because it did not survive the offline visual inspection of candidates (Morgan
et al., 2020). Thus, we consider all three of these candidates as being ruled
out.

###  6.5 Lessons Learned from DESGW Spectroscopy in O3

One of final results we would like to discuss are those of “lessons learned”
during the concerted effort by the DESGW imaging and spectroscopic follow-up
teams during the follow-up of GW190814 candidates, particularly as the
spectroscopic follow-up of this LVC event may be viewed as a template for
future spectrosopic follow-ups in LVC O4 and beyond, since, as the LVC becomes
increasingly more sensitive, the optical counterparts of future LVC events
will likely be relatively distant and faint, unlike the very nearby and bright
BNS KN GW170817.

First, we found that our SOAR spectroscopic follow-up effort benefited from
being a loose confederation of semi-independent teams that could operate the
telescope remotely: a team based at Fermilab, a team based at University of
California - Santa Cruz, a team based in Chile, and a team based in Brazil.
Each of these teams signed up to be “on-call” for 2-week blocks throughout LVC
O3. The team “on-call” when an LVC O3 alert went out would have the
responsibility for preparing and carrying out any SOAR spectroscopic follow-up
during their watch. That said, the “on-call” team could request help from the
other teams, and the other teams were welcome to follow along during the night
of a follow-up observation. In the case of GW190814, the Fermilab team was the
on-call team for most of the time of the spectroscopic follow-up, but other
teams also provided help during Fermilab’s time block (in particular, the
Chilean team took over a couple nights when the Fermilab team was unable to
observe). This relatively loose structure of our spectroscopic follow-up
effort seemed to work well, especially over the full course of LVC O3.

Second, especially as SOAR is primarily run as a remote observing facility, it
is vital to have good communications with the SOAR scientific and technical
staff. We were able to easily communicate with the SOAR staff and on several
occasions SOAR staff provided invaluable help to us in obtaining spectra of
dimmer objects that required a longer process for target acquisition. Further,
long after the optical signature of any expected KN should have faded, the
SOAR staff obtained the spectra of the host galaxies of two remaining
candidates (AT2019nte and AT2019omw) during engineering time, in order to
check if these candidates had redshifts that fell within the distance
estimates measured by LVC for the GW event.

Third, it became clear early on that it is very difficult to obtain
sufficiently high $S/N$ spectra with SOAR for candidate KNe fainter than about
$i\approx 21$ in the allotted time for a SOAR ToO interrupt. For spectroscopic
follow-up in LVC O4, candidates fainter than $i\approx 21$ should either be
pursued by 6-to-10-meter-class telescopes, or have their host galaxies
targeted as a means to qualify them or to rule them out.

Finally, we stress the importance of being able to reduce and analyze the data
at the telescope for quick classification of the candidate as a KN or not. If
there are obvious features in the spectrum indicating that a given candidate
is not a KN (e.g., sharp emission or absorption lines or features typical of
an SN spectrum), one can quickly move on to the next target in the candidate
list; if, however, the spectrum indicates that the candidate is indeed the KN,
the rest of the astronomical community can be quickly alerted. At the
telescope during the observations for this paper, we typically made use of our
SOAR Quick Reduce Pipeline or IRAF routines to process and calibrate the
spectra on the fly, and classified the spectra by eye or by running them
through the AstroDash and/or the SNID SN typing software that same night. A
later, more refined reduction and analysis were performed later offline, as
described in § 6.1 and § 6.2. We note that, however, whereas some of the
classifications changed between the real-time and off-line analysis, none of
the resulting spectra – with the possible exception of the very low-$S/N$
AT2019ntr spectrum – were ever seriously considered to be that of a KN: i.e.,
the quick reductions are sufficient for the purpose. One weakness during our
O3 observations of GW190814 candidates was the lack of an analog of our Quick
Reduce pipeline to fit a candidate’s spectrum to a grid of KN model spectra on
the fly at the telescope. Since then, we have developed an initial version of
own publicly available DESGW KN spectrum fitter (DLT_DESGW_KNfit; see § 8),
which can be run at the telescope with the output of our SOAR Quick Reduce
pipeline and should be useful for spectroscopic follow-up in LVC O4.

##  7 Conclusions

In the era of multi-messenger astronomy, we have demonstrated that we can
perform a deep, one-of-its-kind spectroscopic follow-up campaign for possible
NSBH events. We have reported on the SOAR/Goodman spectroscopy of 11 KN
candidates associated with the LIGO/VIRGO event GW190814. For 8 of these we
have reported the redshift and spectroscopic typing of the transient itself,
and for the other 3 we have reported the redshift of the host galaxy. We
concluded that none of these candidates were the optical counterpart
associated with the compact object binary merger. This SOAR/Goodman
spectroscopy was done through SOAR ToO observations on a series of nights
following the LVC discovery of gravitational waves from GW190814. These
targeted observations were performed after KN candidate identification and
culling by the DESGW collaboration following observations using DECam on the
Blanco telescope, and they have allowed us to place interesting constraints on
the properties of the binary (Morgan et al., 2020) and to use this event as a
dark standard siren (that is, as a constraint on $H_{0}$ using GWs) (Palmese
et al., 2020).

We have also described the DESGW spectroscopic pipeline, part of the DESGW KN
search process and candidate assessment, and our process and timeline for
creating a spectroscopic follow-up candidate list. In addition, we have
presented our QuickReduce software (for quick look spectroscopic reduction)
and the UCSC Reduction Pipeline software (for offline spectroscopic
reduction). Furthermore, we have shown our use of AstroDash, SNID, and a
least-square KN model fitting software for the process of candidate spectrum
classification. Finally, we have demonstrated the effectiveness of our program
and these tools within DESGW and are prepared for more extensive searches for
KNe in LVC O4.

##  8 Software

We present here links to the software packages mentioned in the text:

  1. 1.

Quick Reduce Pipeline, used for reduction and analysis of spectra immediately
after observing.
https://github.com/DouglasLeeTucker/SOAR_Goodman_QuickReduce/blob/master/notebooks/SOAR_Goodman_QR_Notebook.ipynb

  2. 2.

UCSC spectral pipeline, used for data reduction and analysis:
https://github.com/msiebert1/UCSC_spectral_pipeline

  3. 3.

AstroDash supernova typing software: https://github.com/daniel-
muthukrishna/astrodash

  4. 4.

Image Reduction and Analysis Facility (IRAF). IRAF had been distributed by the
National Optical Astronomy Observatory, which was operated by the Association
of Universities for Research in Astronomy (AURA) under a cooperative agreement
with the National Science Foundation. The software is currently maintained and
distributed by the IRAF Community: https://iraf-community.github.io/

  5. 5.

SNID supernova typing software:
https://people.lam.fr/blondin.stephane/software/snid/

  6. 6.

Kasen KN models: https://github.com/dnkasen/Kasen_Kilonova_Models_2017

  7. 7.

DESGW KN spectrum fitting software: https://github.com/cdebom/DLT_DESGW_KNfit

  8. 8.

SNANA SuperNova ANAlysis software https://snana.uchicago.edu/

  9. 9.

matplotlib (Hunter, 2007),

  10. 10.

numpy (Van Der Walt et al., 2011),

  11. 11.

scipy (Jones et al., 2001),

  12. 12.

astropy (Astropy Collaboration et al., 2013),

  13. 13.

TOPCAT (Taylor, 2005).

## Acknowledgments

Funding for the DES Projects has been provided by the DOE and NSF(USA),
MEC/MICINN/MINECO (Spain), STFC (UK), HEFCE(UK). NCSA (UIUC), KICP (U.
Chicago), CCAPP (Ohio State), MIFPA (Texas A&M), CNPQ, FAPERJ, FINEP (Brazil),
DFG (Germany) and the Collaborating Institutions in the Dark Energy Survey.

The Collaborating Institutions are Argonne Lab, UC Santa Cruz, University of
Cambridge, CIEMAT-Madrid, University of Chicago, University College London,
DES-Brazil Consortium, University of Edinburgh, ETH Zürich, Fermilab,
University of Illinois, ICE (IEEC-CSIC), IFAE Barcelona, Lawrence Berkeley
Lab, LMU München and the associated Excellence Cluster Universe, University of
Michigan, NOAO, University of Nottingham, Ohio State University, University of
Pennsylvania, University of Portsmouth, SLAC National Lab, Stanford
University, University of Sussex, Texas A&M University, and the OzDES
Membership Consortium.

Based in part on observations at Cerro Tololo Inter-American Observatory,
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.

The DES Data Management System is supported by the NSF under Grant Numbers
AST-1138766 and AST-1536171. The DES participants from Spanish institutions
are partially supported by MINECO under grants AYA2015-71825, ESP2015-88861,
FPA2015-68048, and Centro de Excelencia SEV-2016-0588, SEV-2016-0597 and
MDM-2015-0509. Research leading to these results has received funding from the
ERC under the EU’s 7th Framework Programme including grants ERC 240672, 291329
and 306478.

We acknowledge support from the Australian Research Council Centre of
Excellence for Gravitational Wave Discovery (OzGrav) project CE170100004.

The UCSC team is supported in part by NASA grant NNG17PX03C, NSF grant
AST-1815935, the Gordon & Betty Moore Foundation, the Heising-Simons
Foundation, and by a fellowship from the David and Lucile Packard Foundation
to R.J.F.

IA is a CIFAR Azrieli Global Scholar in the Gravity and the Extreme Universe
Program and acknowledges support from that program, from the European Research
Council (ERC) under the European Union’s Horizon 2020 research and innovation
program (grant agreement number 852097), from the Israel Science Foundation
(grant number 2752/19), from the United States - Israel Binational Science
Foundation (BSF), and from the Israeli Council for Higher Education Alon
Fellowship.

DAH is supported by NSF grant AST-1911151.

R. Morgan thanks the LSSTC Data Science Fellowship Program, which is funded by
LSSTC, NSF Cybertraining Grant #1829740, the Brinson Foundation, and the Moore
Foundation; his participation in the program has benefited this work.

FOE acknowledges support from FONDECYT grant 1201223.

L. Santana-Silva acknowledges the financial support from FAPESP through the
grant $\\#2020/03301-5$.

Based on observations obtained at the Southern Astrophysical Research (SOAR)
telescope, which is a joint project of the Ministério da Ciência, Tecnologia e
Inovação (MCTI) da República Federativa do Brasil, the U.S. National Optical
Astronomy Observatory (NOAO), the University of North Carolina at Chapel Hill
(UNC), and Michigan State University (MSU).

This research uses services or data provided by the NOAO Science Archive. NOAO
is operated by the Association of Universities for Research in Astronomy
(AURA), Inc. under a cooperative agreement with the National Science
Foundation.

This manuscript has been authored by Fermi Research Alliance, LLC under
Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of
Science, Office of High Energy Physics. The U.S. Government retains and the
publisher, by accepting the article for publication, acknowledges that the
U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide
license to publish or reproduce the published form of this manuscript, or
allow others to do so, for U.S. Government purposes.

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