# Early Warnings of Binary Neutron Star Coalescence using the SPIIR Search

Manoj Kovalam<EMAIL_ADDRESS>Australian Research Council
Centre of Excellence for Gravitational Wave Discovery (OzGrav)  Department of
Physics, University of Western Australia, Crawley WA 6009, Australia  Md
Anwarul Kaium Patwary Australian Research Council Centre of Excellence for
Gravitational Wave Discovery (OzGrav)  Department of Computer Science and
Software Engineering, University of Western Australia, Crawley WA 6009,
Australia  Anala K. Sreekumar  Australian Research Council Centre of
Excellence for Gravitational Wave Discovery (OzGrav)  Department of Physics,
University of Western Australia, Crawley WA 6009, Australia  Linqing Wen
<EMAIL_ADDRESS>Australian Research Council Centre of Excellence for
Gravitational Wave Discovery (OzGrav)  Department of Physics, University of
Western Australia, Crawley WA 6009, Australia  Fiona H. Panther Australian
Research Council Centre of Excellence for Gravitational Wave Discovery
(OzGrav)  Department of Physics, University of Western Australia, Crawley WA
6009, Australia  Qi Chu  Australian Research Council Centre of Excellence for
Gravitational Wave Discovery (OzGrav)  Department of Physics, University of
Western Australia, Crawley WA 6009, Australia

###### Abstract

Gravitational waves from binary neutron star mergers can be used as alerts to
enable prompt follow-up observations. In particular, capturing prompt
electromagnetic and astroparticle emissions from the moment of a binary merger
presents unique constraints on the time scale and sky localization for online
gravitational wave detection. Here we present the expected performance of the
SPIIR online detection pipeline that is designed for this purpose in the
upcoming international LIGO-Virgo’s 4th Science Run (O4). Using simulated
Gaussian data for the two LIGO observatories with expected O4 sensitivity, we
demonstrate that there is a non-negligible opportunity to deliver pre-merger
warnings at least $10\,\mathrm{s}$ before the final plunge. These alerts are
expected to be issued at a nominal rate of one binary neutron star coalescence
per year and localized within a median searched area of
$300\,\mathrm{deg^{2}}$. We envision such a detection to be extremely useful
for follow-up observatories with a large field of view such as the Murchison
Widefield Array radio facility in Western Australia.

Gravitational wave astronomy (675), Compact binary stars (283), Gravitational
wave sources (677), Neutron stars (1108)

##  1 Introduction

The era of gravitational wave (GW) astronomy began with its first ever
detection from a compact binary coalescence (CBC) in 2015 (Abbott et al.,
2016), during the first advanced LIGO observing run. This was achieved using
the two LIGO observatories at Hanford (H1) and Livingston (L1), (Aasi et al.,
2015). In 2017, Virgo (V1, Acernese et al. (2015)) joined the duo in the
second observing run. The iconic detection of a binary neutron star (BNS)
merger (Abbott et al., 2017a) and associated electromagnetic (EM) emission
signaled the beginning of a new era of GW multi-messenger astronomy (MMA).
This was a joint detection of GWs and a short gamma-ray burst (sGRB) detected
$\sim 2\,\mathrm{s}$ after the binary merger, by the Fermi-GBM and INTEGRAL
space telescopes (Abbott et al., 2017b). This event was followed by numerous
other EM observations spanning the entire EM spectrum. This detection has had
a tremendous impact in astronomy with several studies emerging from it,
including estimation of the Hubble constant (Abbott et al., 2017c),
constraints on the neutron star equation of state (Abbott et al., 2019), and
also connecting sGRBs and kilonovae to BNS mergers (Abbott et al., 2017b).

GW detections were reported publicly in real-time for the first time during
the third LIGO-Virgo observing run O3 (Buikema et al., 2020; Acernese et al.,
2019), leading to the search for EM counterparts of GW observations. However,
the typical delay between a GW detection and the associated GCN alert was on
the order of at least minutes (Magee et al., 2021), and hence the discovery of
prompt EM emissions, which are expected during a binary merger (Rezzolla et
al., 2011), continued to rely on serendipitous discovery. For example, in the
case of GW170817, the sGRB was observed only two seconds after the GW
detection. Thus, an advance warning of such events is crucial to alert
conventional EM telescopes for prompt follow up observations.

Five gravitational wave detection pipelines have processed GW data in real-
time for the past LIGO-Virgo-Kagra collaboration (LVK) science runs. Four
modelled search pipelines - SPIIR (Luan et al., 2012; Hooper et al., 2012; Chu
et al., 2020), GstLAL (Messick et al., 2017; Sachdev et al., 2019), PyCBC
(Nitz et al., 2018; Dal Canton et al., 2020) and MBTA (Adams et al., 2016;
Aubin et al., 2021) \- use known CBC waveforms to identify a signal within the
detector data, while burst signals are recovered by the cWB (Klimenko et al.,
2016) pipeline via a coherent analysis. SPIIR (Summed Parallel Infinite
Impulse Response) uses a time-domain filtering method equivalent to matched
filtering to detect GWs. The SPIIR pipeline uses GPU acceleration for parallel
processing to reduce latency and improve computational efficiency (Liu et al.,
2012; Guo et al., 2018). Out of the 38 detections reported by SPIIR through
public alerts in O3 (Chu et al., 2020), $90\%\,(35)$ of them were confirmed by
the offline searches and later added to the GW catalogs (Abbott et al., 2021a,
b, c). The overall latency for the SPIIR pipeline, defined as the time delay
between a GW event merger and its detection, is $\sim 9\,\mathrm{s}$,111not
encompassing latencies associated with data transfer from detectors and
skymap/alert generation which corresponds to the H1L1 two detector analysis.
This paper describes a new feature for the SPIIR search, targeting ’early
warning’ to detect GWs and generate alerts before the merger or at negative
latencies. Note that a binary coalescence involving at least one neutron star,
tends to spend a few minutes of its time during the inspiral phase within the
LIGO-Virgo sensitive frequency band $(15-2000\,\mathrm{Hz})$.

Early warning alert systems associated with other matched filtering pipelines
have been tested for their localization accuracy and detection efficiency1
(Sachdev et al., 2020; Nitz et al., 2020). In 2020, a mock data challenge
(Magee et al., 2021) was conducted within the LVK to test the capability of
the low-latency infrastructure to send EW alerts. SPIIR participated in this
test sending out one of the five EW mock GCNs and successfully demonstrated
the feasibility for it to send pre-merger
alerts.222https://gcn.gsfc.nasa.gov/gcn3/27989.gcn3 This study also estimated
the expected rate of BNS mergers and their localization areas using Bayestar
rapid localization (Singer & Price, 2016), by simulating a four-detector
network H1, L1, V1 and Kagra (K1, Akutsu et al. (2020)) in O4.

In this work, we analyse the performance of the SPIIR EW pipeline in a
simulated O4 environment. We, in particular, study the performance of the two
LIGO detector network (H1L1) which has the minimum expected overall latency
caused by data transfer among all available detectors. The main motivation
behind the choice of this network is to make detection as early as possible,
thereby assisting EM telescopes to observe possible short transients
$(\sim{1}\,\mathrm{s})$ right at the merger (Rezzolla et al., 2011). Since
most follow-up telescopes need time to orient themselves to the source
direction, saving an additional $4\,\mathrm{s}$ of the delay from Virgo is
extremely useful. We wish to take advantage of this faster network even though
it results in a possibly worse localization, which can still be beneficial to
observatories with large fields of view (FOV). It should be noted that Virgo
will be added to the full-bandwidth search which will produce an enhanced
localization within tens of seconds. We then demonstrate the accuracy of
parameters including signal-to-noise ratio (SNR) and the chirp mass, which are
estimated internally and used to classify the source type via p_astro (Kapadia
et al., 2020) and infer properties via hasNS (Foucart et al., 2018). We also
ensure the reliability of localization areas at various latencies, which are
published via SPIIR EW alerts.

This paper is organized as follows. In section 2, we show details about the
SPIIR EW method, and provide information about the simulated O4 data and GW
signal injections. Section 3 discusses the results of this simulation run.
Finally, in section 4, we discuss the results in the context of EM follow-up
by ground and space-based facilities.

##  2 Method

Early warning alerts are expected to be issued publicly for the first time by
LVK in O4. Through this study we demonstrate the expected performance of the
SPIIR online pipeline to deliver EW alerts. We only use the LIGO detectors in
this work because this two detector network has a very high coincident duty-
cycle time ($62\%$ of total) reported in O3 (Davis et al., 2021), high
sensitivity with nearly aligned antenna beam patterns, and most importantly,
saves an additional four second data transfer latency caused by Virgo (Magee
et al., 2021), making it the most viable combination to promptly report a pre-
merger alert in O4, at the cost of a poorer localization for some of the
bright events at early detections as compared to other combinations.

###  2.1 SPIIR Early Warning pipeline

Matched filtering is the optimal method used by CBC pipelines to detect the
presence of a GW signal amidst noise. This method involves cross correlating
known waveforms, also known as templates, with detector data to output SNR
(Finn, 1992; Cutler & Flanagan, 1994). SPIIR filtering is a time-domain
equivalent to matched filtering (Luan et al., 2012; Hooper et al., 2012) which
uses first order IIR filters to approximate GW templates to a high accuracy,
which are then used in a time-domain convolution with the detector data,
constructing the SPIIR SNR. The best matched template would maximize the SNR
in the presence of a signal, assuming that the noise is stationary Gaussian.

SPIIR pipeline has the capability to process GW data in extremely low latency.
Since it is a time-domain convolution, it takes SPIIR theoretically close to
$0\,\mathrm{s}$ to produce SNRs (Luan et al. (2012); Hooper et al. (2012)).
However, in our actual implementation, the design costs SPIIR $\sim
1\,\mathrm{s}$ to compute the SNR and $\sim 4\,\mathrm{s}$ to identify
candidates (Chu et al., 2020). A coherent network SNR across all active
detectors is calculated for any single-detector event above a particular SNR
threshold (set to $\rho>4$ for real-time searches). Trigger candidates are
then ranked based on two quantities: (a) the reduced chi-square ($\xi^{2}$),
indicating a goodness-of-fit of the SNR time series against expectation and
(b) the coherent network SNR ($\rho_{\mathrm{c}}$). A false alarm rate (FAR)
is calculated based on the probability of an event associated with the
background.

In this work, we use a template bank consisting of $100,000$ binary mass and
projected spin pairs. These waveform templates have component masses ranging
from 1.1 $\mathrm{M_{\odot}}$ < $\mathrm{m_{1},m_{2}}$ < 3.0
$\mathrm{M_{\odot}}$, focusing on binaries with two neutron stars with
promising opportunities to observe EM emissions (Rezzolla et al., 2011).
Waveforms for these masses (based on the SpinTaylorT4 (Buonanno et al., 2009)
time domain approximation) are truncated at specific time intervals before
merger and used to construct the SPIIR EW filters. The SPIIR filters used in
the work are set to have a high ($>97\%$) overlap, which is the inner product
of the original waveform and the approximant waveform.

We conduct $7$ parallel EW searches on the simulated data using the SPIIR
pipeline, with the searches having their templates truncated between
$10\,\mathrm{s}-70\,\mathrm{s}$, at $10$ second intervals respectively. For
simplicity, we label these configurations with their pre-merger truncation
time. For example, the EW search with its templates truncated $10\,\mathrm{s}$
pre-merger is simply addressed as the $-10\,\mathrm{s}$ run. It should be
noted that this number only represents the pre-merger template truncation time
and does not correspond to the overall detection latency, which will be
addressed in section 3.3. The EW searches are processed at a sampling rate of
$256\,\mathrm{Hz}$ in conjunction to a full-bandwidth simulation (non-
truncated templates), which is processed at $2048\,\mathrm{Hz}$ for
comparison. This is because EW detections are recovered at a frequency $<128$
Hz at $10\,\mathrm{s}$ before the merger and earlier, in the inspiral
phase.333The downsampling also reduces the computational complexity of EW
simulations, thereby decreasing the amount of resources used. We label the
full-bandwidth simulation as the $0\,\mathrm{s}$ run to stay consistent
across. For each of these searches, the FARs associated with candidate
triggers are computed independent from one another.

###  2.2 Simulated data

We inject signals into stationary Gaussian noise simulating the strain data
produced by the LIGO detectors in O4. The estimated power spectral density
(PSD) of LIGO in O4 is given in Abbott et al. (2018), with an expected BNS
range of $190\,\mathrm{Mpc}$. We used the gstlal_fake_frames package (Messick
et al., 2017) to generate this strain data using the given PSD. Both LIGO-
Hanford and LIGO-Livingston are expected to reach a similar sensitivity in O4
and hence the output strain is expected to be similar in both. The whole data
segment spans three weeks and is generated in the
gwf444https://dcc.ligo.org/LIGO-T970130/public format.

The injected signals used in this analysis are generated using the lalsuite
package (LIGO Scientific Collaboration, 2018). The component masses of the
injections are sampled between 1.0 $\mathrm{M_{\odot}}$ <
$\mathrm{m_{1},m_{2}}$ < 2.3 $\mathrm{M_{\odot}}$, with a uniform
distribution. The spin is restricted to be below $0.4$ for both components and
sampled with an isotropic distribution. The injected signals are distributed
uniformly within a comoving volume of redshift $z=0.2$. The final population
set, using the constraints mentioned above, has $\mathcal{O}$($10^{4}$) number
of injections.

Expected SNRs for GW signals can be theoretically calculated using their mass,
spin and distance parameters. By truncating the waveforms, we can also
estimate the SNRs at different latencies before the merger. Fig. 1 shows the
evolution of SNRs at different pre-merger latencies for a fiducial BNS
reference $1.4\,\mathrm{M_{\odot}}+1.4\,\mathrm{M_{\odot}}$ source at
$100\,\mathrm{Mpc}$ in O4 sensitivity. We also show that the SPIIR SNRs at the
truncated intervals indicate consistency with the expected SNRs. Similarly,
expected SNRs at different pre-merger latencies are theoretically calculated
for all the injected signals in our simulation.

Figure 1: Evolution of SNRs at different pre-merger latencies in simulated
LIGO O4 data for a $1.4$$\mathrm{M_{\odot}}$$-1.4$$\mathrm{M_{\odot}}$BNS
system at a distance of $100$ Mpc. The dotted line represents the theoretical
SNRs calculated using matched filtering while the symbols show SNRs from the
SPIIR method. A strong event with a full-bandwidth SNR of $30$ can be detected
at sub-minute pre-merger latencies.

##  3 Results

Recovered signals from the searches are considered to be a detection only if
the FAR reported by the associated search is less than one per month (or
$3.85\times 10^{-7}\,\mathrm{Hz}$), which is also the threshold used for
reporting EW candidates in the MDC (Magee et al., 2021). We find that $61$% of
all injected signals were detected by the $0\,\mathrm{s}$ run. Using
$320^{+490}_{-240}\,\mathrm{Gpc}^{-3}\text{yr}^{-1}$ as the local BNS merger
rate, estimated in Abbott et al. (2021a), we expect a median detection rate of
$\sim 18$ BNSs per year in O4 using the full-bandwidth configuration. It
should be noted that our choice of the FAR threshold also includes sub-
threshold events, when compared to the threshold of one per two months used to
select the open public alerts during O3. In comparison, Magee et al. (2021)
uses a threshold of $\mathrm{SNR}>12$ and anticipate the detection rate to be
$\sim 9$ BNSs per year for a H1L1V1K1 four detector network. If we apply the
same criterion, we also see a similar rate but this is for the H1L1 two
detector network. This is probably due to the fact that Magee et al. (2021)
uses a Gaussian distribution for their mass model, while we use a uniform
distribution, which leads to a larger detection range for our search and hence
a comparatively larger rate.

Figure 2: Accuracy of chirp mass and SNR recovery for the $0\,$s, $-30\,$s and
$-60\,$s configurations. Top panel: Distributions for fractional differences
in recovered and injected chirp masses. The error margin is within $2\%$ for
all the runs. Bottom panel: Distributions for fractional differences in
recovered and expected SNRs. About half of all events fall within $10\%$ error
margin (in the bottom panel), of which $3\%$ can be attributed to the SPIIR GW
waveform approximation.

###  3.1 Accuracy

Online estimations of parameters like chirp mass and SNRs are calculated
internally by the pipeline and not published directly to the public. However,
these parameters are essential to compute the published values of FARs,
p_astro (Kapadia et al., 2020) and hasNS (Foucart et al., 2018), which are
used to infer source properties and classify detections. Thus, it is very
important for pipelines to determine the accuracy of these parameters to
ensure the reliability of the published alerts. Fig. 2 compares the SNRs and
chirp masses recovered from the searches to the injection set. We demonstrate
the performance of the SPIIR EW pipelines by first comparing the chirp mass of
the recovered signals to the expected values (Fig. 2, upper panel). We find
the fractional difference between the two to be less than $2\%$ for all runs,
indicating an accurate recovery. We see that the chirp mass recovered from the
$0\,\mathrm{s}$ run is within an error margin of $0.5\%$, with subsequently
wider margins for other EW configuration. Next we find that, at an average,
the recovered SNRs are within an error margin of $\sim 10\%$ with the expected
SNRs (Fig. 2, lower panel). It should be noted that a $3\%$ deviation is
expected in SNRs because of the SPIIR waveform approximation. The rest of the
discrepancy could be attributed to factors like the discreteness in template
banks leading to a mismatch with injections, noise influences in low-SNR
events and relatively minor PSD mismatches while calculating the SNRs using
moving PSD estimations. The accuracy of chirp mass and SNR estimates for the
full-bandwidth run has been well studied in Chu et al. (2020) with similar
results reported. Fig. 2 shows a histogram of the fractional differences in
injected and recovered parameters for $0\,\mathrm{s}$, $30\,\mathrm{s}$ and
$60\,\mathrm{s}$ runs.555$-60$ s simulation has a lower number of statistics
as compared to others.

Figure 3: Searched areas vs number of detected events in each EW configuration
as a cumulative fraction of the total number of full bandwidth detection (left
y-axis). The secondary y-axis shows the same as function of expected number of
BNS detections per year in O4. Each color represents one EW configuration.
Using a simulated Gaussian environment for O4, we expect to recover at least
$1(6\%)$ pre-merger event per year with a localization area
$<300\,\mathrm{deg^{2}}$ with the H1L1 configuration in O4.

###  3.2 Localization

Accurate and rapid localization of GW signals is crucial to enable prompt
followup observations. Providing source direction to EM observatories via pre-
merger alerts would help enable a prompt follow-up observation. For this
study, we use LVK’s rapid localization software, Bayestar (Singer & Price,
2016), to construct sky maps. As we go down in latency, EW simulations trigger
at lower frequencies, so only a partial information of the detected signal is
recovered. This lowers the estimated SNRs and also the accuracy with which the
temporal and phase information is recovered, thus resulting in larger
localization areas (Fairhurst, 2018). Fig. 3 shows the cumulative histogram of
the searched areas (defined as the smallest area needed to be searched to
encompass an event) normalized w.r.t. the total number of detection in the
full-bandwidth run ($18$).

Based on our Gaussian noise simulation for O4, we expect to deliver pre-merger
BNS alerts at a rate of $1\,(2)$ per year in O4 with a searched area below
$300\,(1000)\,\mathrm{deg^{2}}$ using the LIGO network alone. We also expect
to detect at least one pre-merger detection in two years localized within a
searched area of $100\,\mathrm{deg^{2}}$ in O4. However, the chance to have a
sub-minute pre-merger detection is seen to be less than $0.2\%$. We also
compare the searched areas of the candidates with their $90\%$ credible region
areas in Fig. 4. Table 1 shows the median values for the searched and $90\%$
credible areas, and also the expected detection rate at all EW latencies in
O4. We find that the median values of searched areas are several times smaller
than $90\%$ credible areas and the difference becomes more prominent as we go
down in latency for the EW runs.

Figure 4: Box and whiskers plot comparing searched and $90\%$ credible areas
for all the runs. The y-axis represents the localization areas and the x-axis
represents the truncation latency for each EW run. The boxes encompass $95\%$
of the events and the whiskers extend up to the rest. The white lines within
the boxes represent the median values of the respective data sets. Using
simulated Gaussian data, we demonstrate that the median searched areas are
several times lower than the $90\%$ credible areas, implying that a lot less
area (as compared to the $90\%$ credible region) can be searched to localize
the GW event in O4.

$\mathrm{EW}$ | $\mathrm{N_{events}(yr^{-1})}$ | $\mathrm{Area_{se}(\text{deg}^{2})}$ | $\mathrm{Area_{90}(\text{deg}^{2})}$ | $\mathrm{Latency(s)}$
---|---|---|---|---
$0\,$s | $18$ | $245$ | $911$ | $9.46^{+1.42}_{-0.76}$
$-10\,$s | $3.51$ | $876$ | $4912$ | $-2.30^{+1.15}_{-1.20}$
$-20\,$s | $1.64$ | $886$ | $5673$ | $-12.36^{+1.31}_{-1.15}$
$-30\,$s | $0.83$ | $902$ | $6243$ | $-22.35^{+1.33}_{-1.18}$
$-40\,$s | $0.42$ | $995$ | $6299$ | $-32.32^{+1.29}_{-1.17}$
$-50\,$s | $0.23$ | $1178$ | $7187$ | $-42.27^{+1.19}_{-1.18}$
$-60\,$s | $0.12$ | $1104$ | $6803$ | $-52.25^{+1.13}_{-1.14}$
$-70\,$s | $0.04$ | $1136$ | $7683$ | $-61.86^{+0.81}_{-0.82}$

Table 1: Expected BNS detection rate ($\mathrm{N_{events}}$), median searched
area ($\mathrm{Area_{se}}$), median $90\%$ credible area
($\mathrm{Area_{90}}$) and the median latency (with $90\%$ error margins)
associated with different EW simulations for O4.

###  3.3 Latency

The overall pipeline latency for each configuration is measured on an online
data streaming platform and the median values are recorded in Table 1. These
values correspond to the internal latency of the pipeline, which includes
delays in the SPIIR filtering algorithm and calculating the FARs. We find that
the full-bandwidth configuration has a median intrinsic latency of
$9-10\,\mathrm{s},$666with the potential to go below $5s$. consistent with
results from Chu et al. (2020). Additional latencies including data-transfer,
localization and alert-generation depend on the online infrastructure, which
are believed to be significantly lower in O4 as compared to O3 (Magee et al.,
2021). The EW runs have a negative median latency indicating pre-merger
detection.

##  4 Discussion

In this work, we investigated the prospects of using the SPIIR EW search, on
the two-detector LIGO network, to detect GWs from binary neutron stars before
their merger in O4. This configuration saves an additional $4\,\mathrm{s}$ of
data transfer latency from Virgo making it the most probable combination to
issue negative latency alerts in O4. However, the major drawback of using this
network is that the source localization areas for bright events are poorer as
compared to three or four detector networks. This is evident from the full-
bandwidth detection of GW170817 during O2, where the localization area
improved by a factor of $\sim{6}$ with the inclusion of an additional detector
(Abbott et al., 2017a). The main motivation behind our choice of a two
detector network in O4 is to facilitate the rapid follow-up detection of short
transient signals (only $\sim{1}\,\mathrm{s}$ long) within seconds of a BNS
merger (Rezzolla et al., 2011) that was not possible before. Given the
following facts that: (a) most major follow-up telescopes require a response
time of $\textgreater\,\sim{10}\,\mathrm{s}$ to reorient themselves to a
particular sky direction, (b) the anticipated event rate is small for an early
detection of BNS events in O4 (even just $10\,\mathrm{s}$ before their final
merger), and (c) the latencies of the existing pipelines are larger than
$10\,\mathrm{s}$, it is extremely challenging to observe short transients
right at the merger. Thus, saving a few seconds of latency might prove to be
extremely useful to capture such events using instruments with larger viewing
areas and fast response times. Configurations including other detectors can
also be run in parallel for better localizations but with a slight delay.

Using a Gaussian noise simulation, we show that EW pipelines have an
exceptional recovery of chirp mass, which is helpful in classifying the source
properties such as p_astro and embright. We also compare the $90\%$ credible
areas and the searched areas associated with the EW triggers, and demonstrate
that the actual searched area for a detection is about one order of magnitude
better. These areas are still a few times larger than the three and four
detector network localizations reported in Magee et al. (2021); Sachdev et al.
(2020); Nitz et al. (2020), but this trade-off to several to tens of seconds
in latency is something we hope to take in O4. It is important to note that a
more accurate three-detector and four-detector localization will be provided
by the full-bandwidth SPIIR search.

We demonstrate the possibility of issuing alerts for atleast one BNS merger
per year $\sim 12\,\mathrm{s}$ before the merger in table 1. Adding an
additional $8\,\mathrm{s}$ for data transfer and localization, this would
still be a prospect for a negative latency alert. The best localized EW alert
is likely to be from the $-10\,\mathrm{s}$ search, and we expect to deliver
alerts localized within $300\,\mathrm{deg^{2}}$ at a nominal rate of one
detection per year from this search.

These alerts are especially useful for follow-up observatories which have a
large FOVs. Radio telescopes like the Murchison Widefield Arrray (MWA, Tingay
et al. (2013)) is one such benefactor, which has the capability of viewing a
quarter of the sky. James et al. (2019) has investigated the response time of
this MWA observational mode to be about $10\,\mathrm{s}$. In the future, EW
alerts produced by SPIIR can also be ingested by x-ray and gamma-ray missions
with large FoVs. For example, the Neil Geherels Swift Observatory (FoV $\sim
4600\,\mathrm{sq.\,degrees}$) has developed a fully-autonomous, extremely low-
latency on-board commanding pipeline (GUANO, Tohuvavohu et al. (2020)) capable
of recovering sub-threshold Burst Alert Telescope (BAT) triggers. Similarly,
the large fields of view of the FERMI gamma-ray telescope’s gamma-ray burst
monitor (FoV $\sim 3.2E4\,\mathrm{sq.\,degrees}$, Meegan et al. (2009)) and
INTEGRAL’s gamma-ray burst detection sub-systems (FoV all-sky for SPI-ACS,
except for regions occulted by the Earth, Rau et al. (2005)) are also well
suited to following up SPIIR triggers. Thus, the SPIIR EW pipeline will
contribute significantly to the possibility for targeted followup of sGRB
signals by the global astronomy community.

##  5 Acknowledgements

This research was supported by the University of Western Australia and funded
by the Australian Research Council (ARC) Centre of Excellence for
Gravitational Wave Discovery OzGrav under grant CE170100004. This work relied
on the computational resources provided by the LIGO Laboratory at California
Institute of Technology and the OzStar super-computing cluster at Swinburne
University of Technology. The authors are grateful to them. The LIGO
Laboratory cluster is funded by National Science Foundation Grants PHY-0757058
and PHY-0823459 and the OzStar program receives funding in part from the
Astronomy National Collaborative Research Infrastructure Strategy (NCRIS)
allocation provided by the Australian Government. We thank Jarrod Hurley and
Stuart Anderson for the resources provided on the computing clusters and
acknowledge the efforts by Alex Codoreanu and Patrick Clearwater in
maintaining the SPIIR repository.

## References

  * Aasi et al. (2015) Aasi, J., et al. 2015, Class. Quant. Grav., 32, 074001, doi: 10.1088/0264-9381/32/7/074001
  * Abbott et al. (2016) Abbott, B. P., et al. 2016, Phys. Rev. Lett., 116, 061102, doi: 10.1103/PhysRevLett.116.061102
  * Abbott et al. (2017a) —. 2017a, Phys. Rev. Lett., 119, 161101, doi: 10.1103/PhysRevLett.119.161101
  * Abbott et al. (2017b) —. 2017b, Astrophys. J. Lett., 848, L12, doi: 10.3847/2041-8213/aa91c9
  * Abbott et al. (2017c) —. 2017c, Nature, 551, 85, doi: 10.1038/nature24471
  * Abbott et al. (2018) —. 2018, Living Rev. Rel., 21, 3, doi: 10.1007/s41114-020-00026-9
  * Abbott et al. (2019) —. 2019, Phys. Rev. X, 9, 011001, doi: 10.1103/PhysRevX.9.011001
  * Abbott et al. (2021a) Abbott, R., et al. 2021a, Phys. Rev. X, 11, 021053, doi: 10.1103/PhysRevX.11.021053
  * Abbott et al. (2021b) —. 2021b.  https://arxiv.org/abs/2108.01045
  * Abbott et al. (2021c) —. 2021c.  https://arxiv.org/abs/2111.03606
  * Acernese et al. (2015) Acernese, F., et al. 2015, Class. Quant. Grav., 32, 024001, doi: 10.1088/0264-9381/32/2/024001
  * Acernese et al. (2019) —. 2019, Phys. Rev. Lett., 123, 231108, doi: 10.1103/PhysRevLett.123.231108
  * Adams et al. (2016) Adams, T., Buskulic, D., Germain, V., et al. 2016, Class. Quant. Grav., 33, 175012, doi: 10.1088/0264-9381/33/17/175012
  * Akutsu et al. (2020) Akutsu, T., et al. 2020.  https://arxiv.org/abs/2005.05574
  * Aubin et al. (2021) Aubin, F., et al. 2021, Class. Quant. Grav., 38, 095004, doi: 10.1088/1361-6382/abe913
  * Buikema et al. (2020) Buikema, A., et al. 2020, Phys. Rev. D, 102, 062003, doi: 10.1103/PhysRevD.102.062003
  * Buonanno et al. (2009) Buonanno, A., Iyer, B., Ochsner, E., Pan, Y., & Sathyaprakash, B. S. 2009, Phys. Rev. D, 80, 084043, doi: 10.1103/PhysRevD.80.084043
  * Chu et al. (2020) Chu, Q., et al. 2020.  https://arxiv.org/abs/2011.06787
  * Cutler & Flanagan (1994) Cutler, C., & Flanagan, E. E. 1994, Phys. Rev. D, 49, 2658, doi: 10.1103/PhysRevD.49.2658
  * Dal Canton et al. (2020) Dal Canton, T., Nitz, A. H., Gadre, B., et al. 2020.  https://arxiv.org/abs/2008.07494
  * Davis et al. (2021) Davis, D., et al. 2021, Class. Quant. Grav., 38, 135014, doi: 10.1088/1361-6382/abfd85
  * Fairhurst (2018) Fairhurst, S. 2018, Class. Quant. Grav., 35, 105002, doi: 10.1088/1361-6382/aab675
  * Finn (1992) Finn, L. S. 1992, Phys. Rev. D, 46, 5236, doi: 10.1103/PhysRevD.46.5236
  * Foucart et al. (2018) Foucart, F., Hinderer, T., & Nissanke, S. 2018, Phys. Rev. D, 98, 081501, doi: 10.1103/PhysRevD.98.081501
  * Guo et al. (2018) Guo, X., Chu, Q., Chung, S. K., et al. 2018, Comput. Phys. Commun., 231, 62, doi: 10.1016/j.cpc.2018.05.002
  * Hooper et al. (2012) Hooper, S., Chung, S. K., Luan, J., et al. 2012, Phys. Rev. D, 86, 024012, doi: 10.1103/PhysRevD.86.024012
  * James et al. (2019) James, C. W., Anderson, G. E., Wen, L., et al. 2019, Mon. Not. Roy. Astron. Soc., 489, L75, doi: 10.1093/mnrasl/slz129
  * Kapadia et al. (2020) Kapadia, S. J., et al. 2020, Class. Quant. Grav., 37, 045007, doi: 10.1088/1361-6382/ab5f2d
  * Klimenko et al. (2016) Klimenko, S., et al. 2016, Phys. Rev. D, 93, 042004, doi: 10.1103/PhysRevD.93.042004
  * LIGO Scientific Collaboration (2018) LIGO Scientific Collaboration. 2018, LIGO Algorithm Library - LALSuite, free software (GPL), doi: 10.7935/GT1W-FZ16
  * Liu et al. (2012) Liu, Y., Du, Z., Chung, S. K., et al. 2012, Class. Quant. Grav., 29, 235018, doi: 10.1088/0264-9381/29/23/235018
  * Luan et al. (2012) Luan, J., Hooper, S., Wen, L., & Chen, Y. 2012, Phys. Rev. D, 85, 102002, doi: 10.1103/PhysRevD.85.102002
  * Magee et al. (2021) Magee, R., et al. 2021, Astrophys. J. Lett., 910, L21, doi: 10.3847/2041-8213/abed54
  * Meegan et al. (2009) Meegan, C., et al. 2009, Astrophys. J., 702, 791, doi: 10.1088/0004-637X/702/1/791
  * Messick et al. (2017) Messick, C., et al. 2017, Phys. Rev. D, 95, 042001, doi: 10.1103/PhysRevD.95.042001
  * Nitz et al. (2018) Nitz, A. H., Dal Canton, T., Davis, D., & Reyes, S. 2018, Phys. Rev. D, 98, 024050, doi: 10.1103/PhysRevD.98.024050
  * Nitz et al. (2020) Nitz, A. H., Schäfer, M., & Dal Canton, T. 2020, Astrophys. J. Lett., 902, L29, doi: 10.3847/2041-8213/abbc10
  * Rau et al. (2005) Rau, A., Kienlin, A. V., Hurley, K., & Lichti, G. G. 2005, A&A, 438, 1175, doi: 10.1051/0004-6361:20053159
  * Rezzolla et al. (2011) Rezzolla, L., Giacomazzo, B., Baiotti, L., et al. 2011, Astrophys. J. Lett., 732, L6, doi: 10.1088/2041-8205/732/1/L6
  * Sachdev et al. (2019) Sachdev, S., et al. 2019.  https://arxiv.org/abs/1901.08580
  * Sachdev et al. (2020) —. 2020, Astrophys. J. Lett., 905, L25, doi: 10.3847/2041-8213/abc753
  * Singer & Price (2016) Singer, L. P., & Price, L. R. 2016, Phys. Rev. D, 93, 024013, doi: 10.1103/PhysRevD.93.024013
  * Tingay et al. (2013) Tingay, S. J., et al. 2013, Publ. Astron. Soc. Austral., 30, 7, doi: 10.1017/pasa.2012.007
  * Tohuvavohu et al. (2020) Tohuvavohu, A., Kennea, J. A., DeLaunay, J., et al. 2020, Astrophys. J., 900, 35, doi: 10.3847/1538-4357/aba94f