of 13
All-sky search for long-duration gravitational-wave transients in the second
Advanced LIGO observing run
B. P. Abbott
etal.
*
(LIGO Scientific Collaboration and Virgo Collaboration)
(Received 28 March 2019; published 14 May 2019)
We present the results of a search for long-duration gravitational-wave transients in the data from the
Advanced LIGO second observation run; we search for gravitational-wave transients of 2
500 s duration in
the 24
2048 Hz frequency band with minimal assumptions about signal properties such as waveform
morphologies, polarization, sky location or time of occurrence. Signal families covered by these search
algorithms include fallback accretion onto neutron stars, broadband chirps from innermost stable circular
orbit waves around rotating black holes, eccentric inspiral-merger-ringdown compact binary coalescence
waveforms, and other models. The second observation run totals about 118.3 days of coincident data
between November 2016 and August 2017. We find no significant events within the parameter space that
we searched, apart from the already-reported binary neutron star merger GW170817. We thus report
sensitivity limits on the root-sum-square strain amplitude
h
rss
at 50% efficiency. These sensitivity estimates
are an improvement relative to the first observing run and also done with an enlarged set of gravitational-
wave transient waveforms. Overall, the best search sensitivity is
h
50%
rss
¼
2
.
7
×
10
22
Hz
1
=
2
for a
millisecond magnetar model. For eccentric compact binary coalescence signals, the search sensitivity
reaches
h
50%
rss
¼
9
.
6
×
10
22
Hz
1
=
2
.
DOI:
10.1103/PhysRevD.99.104033
I. INTRODUCTION
The secondobservationrunoftheAdvancedLIGO
[1]
and
Advanced Virgo
[2]
detectors ushered in the era of multi-
messenger astronomy. In addition to the detection of further
binary black hole systems
[3
5]
, the first binary neutron star
system GW170817
[6]
, associated with GRB 170817A
[7]
and corresponding electromagnetic radiation AT 2017gfo
[8]
, was jointly detected. This led to searches for a post-
merger signal from the binary neutron star event, including
on the timescales presented in this paper
[9,10]
. In this paper,
we update the results of the unmodeled long-duration
transient search from the first Advanced LIGO observing
run
[11]
with the data from the second observing run.
We use four pipelines, described below, with different
responses across the parameter space, providing comple-
mentary coverage of the signal models we are interested in.
The search was motivated by a wide range of poorly
understood astrophysical phenomena for which predictive
models are not readily available; these include fallback
accretion, accretion disk instabilities and nonaxisymmetric
deformations in magnetars. Fallback accretion of ejected
mass in newborn neutron stars can lead to deformation,
causing the emission of gravitational waves until the star
collapses into a black hole
[12
14]
. Accretion disk insta-
bilities and fragmentation can cause stellar material to spiral
in a black hole, emitting relatively long-lived gravitational
waves
[15
17]
. Nonaxisymmetric deformations in magnet-
ars, proposed as progenitors of long and short gamma-ray
bursts
[18,19]
, can also emit gravitational waves
[20]
.
Moreover, we introduce new waveform families based on
astrophysical phenomena such as fallback accretion down to
the innermost stable circular orbit of a rapidly rotating black
hole
[21]
, highly eccentric binary black hole coalescences
[22]
, and gamma-ray burst and x-ray events
[20]
.
Although this analysis targets sources for which the
gravitational waveform is not well described, it is possible
for the long-duration searches to detect low-mass compact
binary coalescences, typically searched for with matched
filtering techniques. As discussed in other publications
[6]
,
the data containing the gravitational-wave signal resulting
from GW170817 are corrupted by the presence of a short-
duration (less than 5 ms), powerful transient noise event in
one of the detectors
[6]
. Using a dataset where this short
transient has been subtracted from the LIGO-Livingston
data stream, the GW170817 signal is the most significant
event of the search. As the searches reported in this paper
do not add significantly to the many other studies carried
out for this event
[6,10,23,24]
, it has been decided to keep
the original dataset, veto the large transient noise and focus
on any other long-duration gravitational-wave signals.
The paper is organized as follows. We describe the data
used in the analysis in Sec.
II
. The algorithms used to
analyze the data are outlined in Sec.
III
. The results of the
*
Full author list given at end of the article.
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=
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=
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© 2019 American Physical Society
analysis and their implications are discussed in Sec.
IV
.
Section
V
provides our conclusions and avenues for future
research.
II. DATA
The second observation run lasted from November 25,
2016 to August 25, 2017. Between the first and second
observingruns,aseriesoffixesandupgradesofthetwoLIGO
detectors in Hanford, Washington and Livingston, Louisiana,
allowed the run to begin with LIGO detectors
sensitivity
reaching a binary neutron star range of
80
Mpc
please see
[25]
for a discussion of the range metric. Thanks to com-
missioning break periods, Livingston
s sensitivity increased
steadily during the second observation run, finally reaching
100 Mpc. LIGO Hanford suffered from a 5.8 magnitude
earthquake in Montana on July 6, 2017, which induced a
10 Mpc drop in sensitivity, and this was not recovered during
thescience run. On August 1,theVirgo detector joined therun
with a binary neutron star range of 26 Mpc. It has been shown
that adding the one-month Virgo dataset does not improve the
search sensitivity mainly because of the sensitivity difference
between the detectors. We thus report the results of a two
LIGO detector coincident search. The overlap in time when
both detectors are taking in data suitable for analysis was
approximately 118.3 days. The effective coincident time
analyzed by each pipeline depends on the data segmentation
choice and lies in the range 114.7 to 118.3 days.
Coincident data contains a large number of non-
Gaussian transient noise events (glitches) of instrumental
or environmental origin that mimic the characteristic of the
targeted signals. For the first time, well-identified sources
of noise have been subtracted from the LIGO data
[26]
. Yet,
some glitches, typically lasting from a few milliseconds up
to few seconds and varying widely in frequency, remain.
Their presence, even the very short ones, may negatively
impact the sensitivity of the searches
[27]
. Time varying
spectral lines are also a source of noise events for the long-
duration transient searches. To veto these transient noise
events, each pipeline implements specific glitch rejection
criteria; because the search targets long-duration signals,
short-duration glitches, which are usually the most prob-
lematic sources of noise, are easily suppressed. The next
section provides more details about the noise rejection
procedures that also may include data quality vetoes based
on correlations with auxiliary channels
[28,29]
.
III. SEARCHES
As in the previous analysis, we use four pipelines to
search for transients that last between 2
500 s and span a
frequency band of 24
2048 Hz. The use of multiple
pipelines provides redundancy, and due to the differences
in the clustering algorithms, leads to different sensitivities
to different waveform morphologies or parts of the param-
eter space. Unmodeled searches for gravitational waves
typically cast the analysis as pattern recognition problems.
Gravitational-wave time series are Fourier transformed in
chunks of time, and spectrograms are created based on
statistics derived from these Fourier transforms. Then
pattern recognition algorithms are used to search for
patterns, corresponding to gravitational waves, within
spectrograms. In general, these consist of two classes.
The first is seed based
[30,31]
, where thresholds are placed
on pixel values in the spectrograms and pixels above this
threshold are clustered together. The second is seedless
[32,33]
, where tracks are constructed from a generic model
and integrated across the spectrograms; in this analysis, we
use B ́
ezier curves
[32
36]
.
The pipelines used are the long-duration configuration of
Coherent WaveBurst (cWB)
[37]
, two different versions of
the Stochastic Transient Analysis Multi-detector Pipeline
all sky (STAMP-AS)
[31,36]
, and the X-pipeline Spherical
Radiometer (X-SphRad)
[38]
. These pipelines are the same,
or slightly updated versions, of those used in the search for
long-duration transients in the first observation run and
fully described in
[39]
. cWB is based on a maximum-
likelihood-ratio statistic, built as a sum of excess power
coherent between multiple detectors in the time-frequency
representation of the interferometer responses
[37]
. The
search is performed in the frequency range 24
2048 Hz, on
data where all poor quality periods have been discarded.
The trigger events surviving the selection criteria to reject
glitches are ranked according to their detection statistic
η
c
,
which is related to the coherent signal-to-noise ratio (SNR).
The selection criteria require the coherence coefficient
c
c
to
be larger than 0.6, and the weighted duration of the
candidate to be larger than 1.5 s. The first measures the
degree of correlation between the detectors, while the latter
measures the duration weighted by the excess power
amplitude of the pixel on the time-frequency likelihood
map. The trigger events are then divided into two samples
according to their estimated mean frequency: 24
200 Hz
and 200
2048 Hz. This allows for the isolation of the
unexpected higher rate of glitches at low frequency during
the first half of the O2 observation run. STAMP-AS uses
the cross-correlation of data from two detectors to create
coherent time-frequency maps of cross-power SNR with a
pixel size of
1
1
Hz covering 24
2000 Hz in combi-
nation with a seed-based (Zebragard) and seedless
(Lonetrack) clustering algorithm. Significant spectral fea-
tures, including wandering lines, are masked in the creation
of the spectrograms. As in the search during the first
observing run, Zebragard eliminates the short duration
glitches by requesting that the fraction of SNR in each time
bin be smaller than 0.5 and that the SNR ratio between the
two detectors be smaller than 3. The X-SphRad uses an
X-pipeline
[40]
back end in combination with a fast cross-
correlator in the spherical harmonic domain
[41]
to search
for gravitational-wave transients in the 24
1000 Hz fre-
quency range. The method allows for the data to be
B. P. ABBOTT
et al.
PHYS. REV. D
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processed independently of sky position and avoids redun-
dant computations. A next-nearest-neighbor clustering
algorithm is applied on a time-frequency representation
of the data with a resolution of
1
1
Hz to form trigger
events, which are then ranked by the ratio of the sum of
power in all the l
>
0
spherical harmonic modes to that in
the l
¼
0
mode. Significant spectral features such as
standing power lines are removed using a zero-phase linear
predictor filter that estimates the power spectrum and
whitens the data
[42]
. Finally, X-SphRad eliminates trig-
gers that coincide with poor quality data periods that have
been identified using auxiliary channels. These periods are
excluded from the analysis time by cWB, and STAMP-AS
Zebragard analysis selects a subset of them according to a
procedure described in
[43]
.
The false alarm rate of each search is estimated as a
function of the pipeline
s ranking statistic. Each uses the
data to perform this estimate, as opposed to a Gaussian
approximation, because of the significant non-Gaussianity
of the data, transient noise, and the nonstationarity of some
of the spectral features. These glitches have a variety of
causes, both environmentally driven such as from seismic
events
[44,45]
or magnetic fields
[46,47]
, and instrumental
effects, such as test mass suspension glitches
[48]
and other
sources of spectral features
[49]
. For all of the pipelines in
this analysis, the correlation of data in different detectors is
used to exclude data transients which are unlikely to be of
astrophysical origin. To estimate the background for all
pipelines used in this analysis, the time-slide methodology
is applied
[50,51]
, each one implementing its own version.
The fundamental idea is to shift the detector data with
nonphysical relative time delays to eliminate any correla-
tion from gravitational waves and reanalyze the data. The
procedure is repeated until a total of 50 years of coincident
detector time has been analyzed, allowing us to estimate
false alarm rates at the level of 1 event in 50 years.
IV. RESULTS
None of the pipelines finds a significant excess of
coincident events. The most significant events found by
each pipeline are reported in Table
I
. Their false alarm rate is
in agreement with the expected background estimation.
Given the absence of a detection, we can derive upper limits
on long-duration gravitational-wave transients
strain ampli-
tude. A usual measure of gravitational-wave amplitude is the
root-sum-square strain amplitude at the Earth,
h
rss
,
h
rss
¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Z
−∞
ð
h
2
þ
ð
t
Þþ
h
2
×
ð
t
ÞÞ
d
t
s
;
ð
1
Þ
where
h
þ
and
h
×
are signal polarizations at Earth
s center
expressed in the source frame. We can relate this quantity to
the gravitational-wave energy radiated by a source emitting
isotropically at a given central frequency
f
0
[52]
,
E
iso
gw
¼
π
c
3
G
D
2
Z
d
ff
2
ðj
̃
h
þ
ð
f
Þj
2
þj
̃
h
×
ð
f
Þj
2
Þ
π
2
c
3
G
D
2
f
2
0
h
2
rss
;
ð
2
Þ
where
D
is the distance to thesource and
̃
h
indicates a Fourier
transform. To estimate the
h
rss
at 50% detection efficiency,
we add simulated waveforms coherently to detector data,
uniformly distributed in time and over sky locations. The
waveform polarization angle and the cosine of the inclination
are also varied uniformly. Waveforms are generated at a
variety of distances (or equivalently
h
rss
) such that the 50%
detection efficiency is well measured. The events recon-
structed are then
detected
if their false alarm rate is lower
than the chosen value of
1
=
50
years.
We use 13 families of simulated gravitational-wave
signals to estimate the sensitivity of each pipeline. The
waveform families include a variety of astrophysically
motivated waveforms and
ad hoc
waveform models. For
the astrophysical models, we include fallback accretion
onto neutron stars (FA)
[14]
, broadband chirps from
innermost stable circular orbit waves around rotating black
holes (ISCOchirp)
[21]
, inspiral-only compact binary
coalescence waveforms up to second post-Newtonian order
[53]
(CBC), eccentric inspiral-merger-ringdown compact
binary coalescence waveforms (ECBC)
[22]
, secular bar-
mode instabilities in postmerger remnants
[12,20]
,newly
formed magnetars powering a gamma-ray burst plateau
(GRBplateau)
[20]
, black hole accretion disk instabilities
(ADI)
[16]
, postmerger magnetars (magnetar)
[54]
, and
neutron star spin down waveforms (MSmagnetar)
[55,56]
.
For the
ad hoc
waveforms, we include monochromatic
waveforms (MONO), waveforms with a linear (LINE) or
quadratic (QUAD) frequency evolution, white noise band-
limited (WNB) and sine-Gaussian bursts (SG). The wave-
forms are designed to span a range of astrophysical models,
as well as a wide duration and frequency parameter space to
test the response of the algorithms across the parameter
space. Figure
1
shows the coverage of a representative
sample of the simulation set in the time-frequency space.
The frequency band 10
300 Hz is well covered with the
TABLE I. Properties of the most significant coincident triggers
found by each of the long-duration transient search pipelines
during the second observation run. FAR stands for false alarm
rate, while the p-value is the probability of observing at least 1
noise trigger at higher significance than the most significant
coincident trigger.
FAR
Frequency Duration
Pipeline
(Hz)
p-value
(Hz)
(s)
cWB
1
.
4
×
10
7
0.75
53
69
11
Zebragard
2
.
5
×
10
7
0.92
1649
1753
29
Lonetrack
7
.
9
×
10
8
0.80
608
1344
463
X-SphRad
9
.
7
×
10
8
0.60
435
443
3
ALL-SKY SEARCH FOR LONG-DURATION
...
PHYS. REV. D
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GRBplateau and ADI families. Astrophysical waveform
families such as ISCOchirp and magnetar are characterized
by a wide frequency coverage and populate the higher
frequency band 700
2000 Hz.
Ad hoc
waveform families
such as MONO, LINE, QUAD, WNB and SG span a wide
frequency range and cover the band 50
800 Hz, filling in
any potential gap in coverage from the other models.
In Fig.
2
, we show the best results among all pipelines for
almost all waveforms. We also compute the 90% confidence
level limit on the rate of long-duration gravitational-wave
transients assuming a Poissonian distribution of sources. To
do so, we use the loudest event statistic method
[57]
.We
fold in the systematic uncertainty that arises from the strain
amplitude calibration, which is 7% in amplitude and
3 degrees in phase, a conservative number used for both
instruments in the frequency band analyzed here
[58]
.
Figure
3
shows the rate as a function of distance for the
eccentric compact binary coalescence signals considered in
this analysis. For a
1
.
4
1
.
4
solar mass binary with an
eccentricity of 0.4, the 50% efficiency distance is 30 Mpc.
FIG. 1. Time-frequency representations of a few model signals
used in the search, showing a mix of chirp-up (FA, ECBC) and
chirp-down (Magnetar, ADI) astrophysical waveforms as well as
a linearly decreasing
ad hoc
waveform (LINE). Descriptions of
these waveforms and others are given in Sec.
IV
. The harmonics
of ECBC are also visible. The full set of waveforms (
70
) chosen
for this analysis fully covers the search frequency band of
24
2000 Hz. The waveforms are shifted in time to show how
they cover the parameter space in this axis as well.
FIG. 2. Upper limits on gravitational-wave strain vs frequency for sources detected with 50% efficiency and a false alarm rate of
1 event in 50 years. The lowest value among all four pipelines is represented on the plots. The left figure shows the
ad hoc
waveforms
results while the
physical
waveforms are represented on the right. The average amplitude spectral density curves for both Hanford and
Livingston are also shown.
FIG. 3. Upper limits (marginalizing over the second observa-
tion run amplitude calibration errors) on eccentric compact binary
coalescences as a function of the distance at a 90% confidence
level considering the best results for each waveform. The inset
shows the distance at 50% detection efficiency for the pipelines in
this analysis for comparison. ECBC_A, ECBC_B, and ECBC_C
are
1
.
4
1
.
4
solar mass binaries with eccentricities of 0.2, 0.4, and
0.6 respectively, while ECBC_D, ECBC_E, and ECBC_F are
3
.
0
3
.
0
solar mass binaries with eccentricities of 0.2, 0.4, and 0.6
respectively, where the masses are quoted in the detector frame.
B. P. ABBOTT
et al.
PHYS. REV. D
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For comparison, this is more than a factor 2 lower than what
matched filter searches could reach for
1
.
4
1
.
4
solar mass
binaries with no eccentricity during the second observation
run
[6]
. Due to the improved sensitivity and greater
duration of the second observation run above and beyond
the first observation run, the rate limits for models used in
previous analyses improved by a factor of
30%
. The
detection distances vary significantly from one signal to
another. For example, the ADI waveforms have distance
limits of tens of megaparsecs, while the magnetar wave-
forms have limits of tens of kiloparsecs. The difference in
ranges is due mainly to the energy budget of the system, but
also due to the overall signal morphologies, which can be
more or less difficult for the pipeline clustering techniques
to recover entirely.
V. CONCLUSIONS
We have performed an all-sky search for unmodeled
long-duration gravitational-wave transients in the second
observing run. This search did not lead to the detection of
any new gravitational waves. In addition to the intrinsic
gain due to detectors
sensitivity improvement and the
length of the observing run, we have increased significantly
the number of waveforms used to estimate the pipelines
sensitivity. The theoretical uncertainties of the models used
are rather large, including the mechanisms, their ampli-
tudes, and their potential rates, although it is likely we are
sensitive to relatively small amplitude emissions within the
Local Group.
With the recent arrival of Advanced Virgo to the
advanced gravitational-wave detector network, its future
improvements will merit its inclusion in analyses in the
next observing runs. Overall, the expectation is that the
design sensitivities for the gravitational-wave networks will
yield gains of up to a factor of 10, depending on the
frequency range considered
[25]
.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the support of the
United States National Science Foundation (NSF) for the
construction and operation of the LIGO Laboratory
and Advanced LIGO as well as the Science and
Technology Facilities Council (STFC) of the United
Kingdom, the Max-Planck-Society (MPS), and the State
of Niedersachsen/Germany for support of the construction
of Advanced LIGO and construction and operation of the
GEO600 detector. Additional support for Advanced LIGO
was provided by the Australian Research Council. The
authors gratefully acknowledge the Italian Istituto
Nazionale di Fisica Nucleare (INFN), the French Centre
National de la Recherche Scientifique (CNRS) and the
Foundation for Fundamental Research on Matter supported
by the Netherlands Organisation for Scientific Research,
for the construction and operation of the Virgo detector and
the creation and support of the EGO consortium. The
authors also gratefully acknowledge research support
from these agencies as well as by the Council of
Scientific and Industrial Research of India, the
Department of Science and Technology, India, the
Science & Engineering Research Board (SERB), India,
the Ministry of Human Resource Development, India,
the Spanish Agencia Estatal de Investigación, the
Vicepresid`
encia i Conselleria d
Innovació, Recerca i
Turisme and the Conselleria d
Educació i Universitat del
Govern de les Illes Balears, the Conselleria d
Educació,
Investigació, Cultura i Esport de la Generalitat Valenciana,
the National Science Centre of Poland, the Swiss National
Science Foundation (SNSF), the Russian Foundation
for Basic Research, the Russian Science Foundation,
the European Commission, the European Regional
Development Funds (ERDF), the Royal Society, the
Scottish Funding Council, the Scottish Universities
Physics Alliance, the Hungarian Scientific Research
Fund (OTKA), the Lyon Institute of Origins (LIO), the
National Research, Development and Innovation Office
Hungary (NKFI), the National Research Foundation of
Korea, Industry Canada and the Province of Ontario
through the Ministry of Economic Development and
Innovation, the Natural Science and Engineering
Research Council Canada, the Canadian Institute for
Advanced Research, the Brazilian Ministry of Science,
Technology, Innovations, and Communications, the
International Center for Theoretical Physics South
American Institute for Fundamental Research (ICTP-
SAIFR), the Research Grants Council of Hong Kong,
the National Natural Science Foundation of China
(NSFC), the Leverhulme Trust, the Research
Corporation, the Ministry of Science and Technology
(MOST), Taiwan and the Kavli Foundation. The authors
gratefully acknowledge the support of the NSF, STFC,
MPS, INFN, CNRS and the State of Niedersachsen/
Germany for provision of computational resources.
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156,157
N. Krupinski,
24
G. Kuehn,
9,10
A. Kumar,
143
P. Kumar,
158
Rahul Kumar,
46
Rakesh Kumar,
110
L. Kuo,
89
A. Kutynia,
156
S. Kwang,
24
B. D. Lackey,
75
D. Laghi,
20,21
K. H. Lai,
92
T. L. Lam,
92
M. Landry,
46
B. B. Lane,
14
R. N. Lang,
159
J. Lange,
61
B. Lantz,
50
R. K. Lanza,
14
A. Lartaux-Vollard,
28
P. D. Lasky,
6
M. Laxen,
7
A. Lazzarini,
1
C. Lazzaro,
53
P. Leaci,
118,32
S. Leavey,
9,10
Y. K. Lecoeuche,
46
C. H. Lee,
95
H. K. Lee,
160
H. M. Lee,
161
H. W. Lee,
150
J. Lee,
94
K. Lee,
45
J. Lehmann,
9,10
A. K. Lenon,
38
N. Leroy,
28
N. Letendre,
33
Y. Levin,
6
A. Li,
92
J. Li,
82
K. J. L. Li,
92
T. G. F. Li,
92
X. Li,
47
F. Lin,
6
F. Linde,
162,36
S. D. Linker,
132
T. B. Littenberg,
163
J. Liu,
65
X. Liu,
24
M. Llorens-Monteagudo,
22
R. K. L. Lo,
92,1
L. T. London,
14
A. Longo,
164,165
M. Lorenzini,
16,17
V. Loriette,
166
M. Lormand,
7
G. Losurdo,
21
J. D. Lough,
9,10
C. O. Lousto,
61
G. Lovelace,
27
M. E. Lower,
167
H. Lück,
10,9
D. Lumaca,
84,31
A. P. Lundgren,
139
R. Lynch,
14
Y. Ma,
47
R. Macas,
105
S. Macfoy,
25
M. MacInnis,
14
D. M. Macleod,
105
A. Macquet,
64
I. Magaña Hernandez,
24
F. Magaña-Sandoval,
30
R. M. Magee,
88
E. Majorana,
32
I. Maksimovic,
166
A. Malik,
59
N. Man,
64
V. Mandic,
42
V. Mangano,
45,118,32
G. L. Mansell,
46,14
M. Manske,
24
M. Mantovani,
29
M. Mapelli,
52,53
F. Marchesoni,
51,40
F. Marion,
33
S. Márka,
104
Z. Márka,
104
C. Markakis,
19
A. S. Markosyan,
50
A. Markowitz,
1
E. Maros,
1
A. Marquina,
103
S. Marsat,
26
F. Martelli,
62,63
I. W. Martin,
45
R. M. Martin,
35
V. Martinez,
78
D. V. Martynov,
13
H. Masalehdan,
133
K. Mason,
14
E. Massera,
111
A. Masserot,
33
T. J. Massinger,
1
M. Masso-Reid,
45
S. Mastrogiovanni,
26
A. Matas,
75
F. Matichard,
1,14
L. Matone,
104
N. Mavalvala,
14
J. J. McCann,
65
R. McCarthy,
46
D. E. McClelland,
8
S. McCormick,
7
L. McCuller,
14
S. C. McGuire,
168
C. McIsaac,
139
J. McIver,
1
D. J. McManus,
8
T. McRae,
8
S. T. McWilliams,
38
D. Meacher,
24
G. D. Meadors,
6
M. Mehmet,
9,10
A. K. Mehta,
18
J. Meidam,
36
E. Mejuto Villa,
115,68
A. Melatos,
99
G. Mendell,
46
R. A. Mercer,
24
L. Mereni,
23
K. Merfeld,
71
E. L. Merilh,
46
M. Merzougui,
64
S. Meshkov,
1
C. Messenger,
45
C. Messick,
88
F. Messina,
43,44
R. Metzdorff,
72
P. M. Meyers,
99
F. Meylahn,
9,10
A. Miani,
116,117
H. Miao,
13
C. Michel,
23
H. Middleton,
99
L. Milano,
79,5
A. L. Miller,
30,118,32
M. Millhouse,
99
J. C. Mills,
105
M. C. Milovich-Goff,
132
O. Minazzoli,
64,169
Y. Minenkov,
31
A. Mishkin,
30
C. Mishra,
170
T. Mistry,
111
S. Mitra,
3
V. P. Mitrofanov,
60
G. Mitselmakher,
30
R. Mittleman,
14
G. Mo,
96
D. Moffa,
120
K. Mogushi,
85
S. R. P. Mohapatra,
14
M. Molina-Ruiz,
140
M. Mondin,
132
M. Montani,
62,63
C. J. Moore,
13
D. Moraru,
46
F. Morawski,
55
G. Moreno,
46
S. Morisaki,
81
B. Mours,
33
C. M. Mow-Lowry,
13
F. Muciaccia,
118,32
Arunava Mukherjee,
9,10
D. Mukherjee,
24
S. Mukherjee,
107
Subroto Mukherjee,
110
N. Mukund,
9,10,3
A. Mullavey,
7
J. Munch,
56
E. A. Muñiz,
41
M. Muratore,
34
P. G. Murray,
45
A. Nagar,
87,126,171
I. Nardecchia,
84,31
L. Naticchioni,
118,32
R. K. Nayak,
172
B. F. Neil,
65
J. Neilson,
115,68
G. Nelemans,
66,36
T. J. N. Nelson,
7
M. Nery,
9,10
A. Neunzert,
136
L. Nevin,
1
K. Y. Ng,
14
S. Ng,
56
C. Nguyen,
26
P. Nguyen,
71
D. Nichols,
141,36
S. A. Nichols,
2
S. Nissanke,
141,36
F. Nocera,
29
C. North,
105
L. K. Nuttall,
139
M. Obergaulinger,
22,173
J. Oberling,
46
B. D. O
Brien,
30
G. Oganesyan,
16,17
G. H. Ogin,
174
J. J. Oh,
151
S. H. Oh,
151
F. Ohme,
9,10
H. Ohta,
81
M. A. Okada,
15
M. Oliver,
100
P. Oppermann,
9,10
Richard J. Oram,
7
B. O
Reilly,
7
R. G. Ormiston,
42
L. F. Ortega,
30
R. O
Shaughnessy,
61
S. Ossokine,
75
D. J. Ottaway,
56
H. Overmier,
7
B. J. Owen,
83
A. E. Pace,
88
G. Pagano,
20,21
M. A. Page,
65
G. Pagliaroli,
16,17
A. Pai,
129
S. A. Pai,
59
J. R. Palamos,
71
O. Palashov,
148
C. Palomba,
32
H. Pan,
89
P. K. Panda,
143
P. T. H. Pang,
92,36
C. Pankow,
57
F. Pannarale,
118,32
B. C. Pant,
59
F. Paoletti,
21
A. Paoli,
29
A. Parida,
3
W. Parker,
7,168
D. Pascucci,
45,36
A. Pasqualetti,
29
R. Passaquieti,
20,21
D. Passuello,
21
M. Patil,
157
B. Patricelli,
20,21
E. Payne,
6
B. L. Pearlstone,
45
T. C. Pechsiri,
30
A. J. Pedersen,
41
M. Pedraza,
1
R. Pedurand,
23,175
A. Pele,
7
S. Penn,
176
A. Perego,
116,117
C. J. Perez,
46
C. P ́
erigois,
33
A. Perreca,
116,117
J. Petermann,
133
H. P. Pfeiffer,
75
M. Phelps,
9,10
K. S. Phukon,
3
O. J. Piccinni,
118,32
M. Pichot,
64
F. Piergiovanni,
62,63
V. Pierro,
115,68
G. Pillant,
29
L. Pinard,
23
I. M. Pinto,
115,68,87
M. Pirello,
46
B. P. ABBOTT
et al.
PHYS. REV. D
99,
104033 (2019)
104033-8
M. Pitkin,
45
W. Plastino,
164,165
R. Poggiani,
20,21
D. Y. T. Pong,
92
S. Ponrathnam,
3
P. Popolizio,
29
E. K. Porter,
26
J. Powell,
167
A. K. Prajapati,
110
J. Prasad,
3
K. Prasai,
50
R. Prasanna,
143
G. Pratten,
100
T. Prestegard,
24
M. Principe,
115,87,68
G. A. Prodi,
116,117
L. Prokhorov,
13
M. Punturo,
40
P. Puppo,
32
M. Pürrer,
75
H. Qi,
105
V. Quetschke,
107
P. J. Quinonez,
34
F. J. Raab,
46
G. Raaijmakers,
141,36
H. Radkins,
46
N. Radulesco,
64
P. Raffai,
109
S. Raja,
59
C. Rajan,
59
B. Rajbhandari,
83
M. Rakhmanov,
107
K. E. Ramirez,
107
A. Ramos-Buades,
100
Javed Rana,
3
K. Rao,
57
P. Rapagnani,
118,32
V. Raymond,
105
M. Razzano,
20,21
J. Read,
27
T. Regimbau,
33
L. Rei,
58
S. Reid,
25
D. H. Reitze,
1,30
P. Rettegno,
126,177
F. Ricci,
118,32
C. J. Richardson,
34
J. W. Richardson,
1
P. M. Ricker,
19
G. Riemenschneider,
177,126
K. Riles,
136
M. Rizzo,
57
N. A. Robertson,
1,45
F. Robinet,
28
A. Rocchi,
31
L. Rolland,
33
J. G. Rollins,
1
V. J. Roma,
71
M. Romanelli,
70
R. Romano,
4,5
C. L. Romel,
46
J. H. Romie,
7
C. A. Rose,
24
D. Rose,
27
K. Rose,
120
D. Rosi
ń
ska,
73
S. G. Rosofsky,
19
M. P. Ross,
178
S. Rowan,
45
A. Rüdiger,
9,10
,
P. Ruggi,
29
G. Rutins,
131
K. Ryan,
46
S. Sachdev,
88
T. Sadecki,
46
M. Sakellariadou,
145
O. S. Salafia,
179,43,44
L. Salconi,
29
M. Saleem,
155
A. Samajdar,
36
L. Sammut,
6
E. J. Sanchez,
1
L. E. Sanchez,
1
N. Sanchis-Gual,
180
J. R. Sanders,
181
K. A. Santiago,
35
E. Santos,
64
N. Sarin,
6
B. Sassolas,
23
P. R. Saulson,
41
O. Sauter,
136,33
R. L. Savage,
46
P. Schale,
71
M. Scheel,
47
J. Scheuer,
57
P. Schmidt,
13,66
R. Schnabel,
133
R. M. S. Schofield,
71
A. Schönbeck,
133
E. Schreiber,
9,10
B. W. Schulte,
9,10
B. F. Schutz,
105
J. Scott,
45
S. M. Scott,
8
E. Seidel,
19
D. Sellers,
7
A. S. Sengupta,
182
N. Sennett,
75
D. Sentenac,
29
V. Sequino,
58
A. Sergeev,
148
Y. Setyawati,
9,10
D. A. Shaddock,
8
T. Shaffer,
46
M. S. Shahriar,
57
M. B. Shaner,
132
A. Sharma,
16,17
P. Sharma,
59
P. Shawhan,
76
H. Shen,
19
R. Shink,
183
D. H. Shoemaker,
14
D. M. Shoemaker,
77
K. Shukla,
140
S. ShyamSundar,
59
K. Siellez,
77
M. Sieniawska,
55
D. Sigg,
46
L. P. Singer,
80
D. Singh,
88
N. Singh,
73
A. Singhal,
16,32
A. M. Sintes,
100
S. Sitmukhambetov,
107
V. Skliris,
105
B. J. J. Slagmolen,
8
T. J. Slaven-Blair,
65
J. R. Smith,
27
R. J. E. Smith,
6
S. Somala,
184
E. J. Son,
151
S. Soni,
2
B. Sorazu,
45
F. Sorrentino,
58
T. Souradeep,
3
E. Sowell,
83
A. P. Spencer,
45
M. Spera,
52,53
A. K. Srivastava,
110
V. Srivastava,
41
K. Staats,
57
C. Stachie,
64
M. Standke,
9,10
D. A. Steer,
26
M. Steinke,
9,10
J. Steinlechner,
133,45
S. Steinlechner,
133
D. Steinmeyer,
9,10
S. P. Stevenson,
167
D. Stocks,
50
R. Stone,
107
D. J. Stops,
13
K. A. Strain,
45
G. Stratta,
185,63
S. E. Strigin,
60
A. Strunk,
46
R. Sturani,
186
A. L. Stuver,
187
V. Sudhir,
14
T. Z. Summerscales,
188
L. Sun,
1
S. Sunil,
110
A. Sur,
55
J. Suresh,
81
P. J. Sutton,
105
B. L. Swinkels,
36
M. J. Szczepa
ń
czyk,
34
M. Tacca,
36
S. C. Tait,
45
C. Talbot,
6
D. B. Tanner,
30
D. Tao,
1
M. Tápai,
130
A. Tapia,
27
J. D. Tasson,
96
R. Taylor,
1
R. Tenorio,
100
L. Terkowski,
133
M. Thomas,
7
P. Thomas,
46
S. R. Thondapu,
59
K. A. Thorne,
7
E. Thrane,
6
Shubhanshu Tiwari,
116,117
Srishti Tiwari,
134
V. Tiwari,
105
K. Toland,
45
M. Tonelli,
20,21
Z. Tornasi,
45
A. Torres-Forn ́
e,
189
C. I. Torrie,
1
D. Töyrä,
13
F. Travasso,
29,40
G. Traylor,
7
M. C. Tringali,
73
A. Tripathee,
136
A. Trovato,
26
L. Trozzo,
190,21
K. W. Tsang,
36
M. Tse,
14
R. Tso,
47
L. Tsukada,
81
D. Tsuna,
81
T. Tsutsui,
81
D. Tuyenbayev,
107
K. Ueno,
81
D. Ugolini,
191
C. S. Unnikrishnan,
134
A. L. Urban,
2
S. A. Usman,
91
H. Vahlbruch,
10
G. Vajente,
1
G. Valdes,
2
M. Valentini,
116,117
N. van Bakel,
36
M. van Beuzekom,
36
J. F. J. van den Brand,
74,36
C. Van Den Broeck,
36,192
D. C. Vander-Hyde,
41
L. van der Schaaf,
36
J. V. VanHeijningen,
65
A. A. van Veggel,
45
M. Vardaro,
52,53
V. Varma,
47
S. Vass,
1
M. Vasúth,
49
A. Vecchio,
13
G. Vedovato,
53
J. Veitch,
45
P. J. Veitch,
56
K. Venkateswara,
178
G. Venugopalan,
1
D. Verkindt,
33
F. Vetrano,
62,63
A. Vicer ́
e,
62,63
A. D. Viets,
24
S. Vinciguerra,
13
D. J. Vine,
131
J.-Y. Vinet,
64
S. Vitale,
14
T. Vo,
41
H. Vocca,
39,40
C. Vorvick,
46
S. P. Vyatchanin,
60
A. R. Wade,
1
L. E. Wade,
120
M. Wade,
120
R. Walet,
36
M. Walker,
27
L. Wallace,
1
S. Walsh,
24
H. Wang,
13
J. Z. Wang,
136
S. Wang,
19
W. H. Wang,
107
Y. F. Wang,
92
R. L. Ward,
8
Z. A. Warden,
34
J. Warner,
46
M. Was,
33
J. Watchi,
101
B. Weaver,
46
L.-W. Wei,
9,10
M. Weinert,
9,10
A. J. Weinstein,
1
R. Weiss,
14
F. Wellmann,
9,10
L. Wen,
65
E. K. Wessel,
19
P. Weßels,
9,10
J. W. Westhouse,
34
K. Wette,
8
J. T. Whelan,
61
B. F. Whiting,
30
C. Whittle,
14
D. M. Wilken,
9,10
D. Williams,
45
A. R. Williamson,
141,36
J. L. Willis,
1
B. Willke,
10,9
W. Winkler,
9,10
C. C. Wipf,
1
H. Wittel,
9,10
G. Woan,
45
J. Woehler,
9,10
J. K. Wofford,
61
J. L. Wright,
45
D. S. Wu,
9,10
D. M. Wysocki,
61
S. Xiao,
1
R. Xu,
108
H. Yamamoto,
1
C. C. Yancey,
76
L. Yang,
119
Y. Yang,
30
Z. Yang,
42
M. J. Yap,
8
M. Yazback,
30
D. W. Yeeles,
105
Hang Yu,
14
Haocun Yu,
14
S. H. R. Yuen,
92
A. K. Zadro
ż
ny,
107
A. Zadro
ż
ny,
156
M. Zanolin,
34
T. Zelenova,
29
J.-P. Zendri,
53
M. Zevin,
57
J. Zhang,
65
L. Zhang,
1
T. Zhang,
45
C. Zhao,
65
G. Zhao,
101
M. Zhou,
57
Z. Zhou,
57
X. J. Zhu,
6
A. B. Zimmerman,
193
M. E. Zucker,
1,14
and J. Zweizig
1
(LIGO Scientific Collaboration and Virgo Collaboration)
1
LIGO, California Institute of Technology, Pasadena, California 91125, USA
2
Louisiana State University, Baton Rouge, Louisiana 70803, USA
3
Inter-University Centre for Astronomy and Astrophysics, Pune 411007, India
4
Dipartimento di Farmacia, Universit`
a di Salerno, I-84084 Fisciano, Salerno, Italy
5
INFN, Sezione di Napoli, Complesso Universitario di Monte Sant
Angelo, I-80126 Napoli, Italy
6
OzGrav, School of Physics & Astronomy, Monash University, Clayton 3800, Victoria, Australia
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99,
104033 (2019)
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