of 12
Upper Limits on the Stochastic Gravitational-Wave Background
from Advanced LIGO
s First Observing Run
B. P. Abbott
etal.
*
(LIGO Scientific Collaboration and Virgo Collaboration)
(Received 7 December 2016; revised manuscript received 30 January 2017; published 24 March 2017)
A wide variety of astrophysical and cosmological sources are expected to contribute to a stochastic
gravitational-wave background. Following the observations of GW150914 and GW151226, the rate and
mass of coalescing binary black holes appear to be greater than many previous expectations. As a result, the
stochastic background from unresolved compact binary coalescences is expected to be particularly loud.
We perform a search for the isotropic stochastic gravitational-wave background using data from Advanced
Laser Interferometer Gravitational Wave Observatory
s (aLIGO) first observing run. The data display no
evidence of a stochastic gravitational-wave signal. We constrain the dimensionless energy density of
gravitational waves to be
Ω
0
<
1
.
7
×
10
7
with 95% confidence, assuming a flat energy density spectrum
in the most sensitive part of the LIGO band (20
86 Hz). This is a factor of
33
times more sensitive than
previous measurements. We also constrain arbitrary power-law spectra. Finally, we investigate the
implications of this search for the background of binary black holes using an astrophysical model for the
background.
DOI:
10.1103/PhysRevLett.118.121101
Introduction.
Many astrophysical and cosmological
phenomena are expected to contribute to a stochastic
gravitational-wave (GW) background, henceforth, simply
referred to as a
background.
These include unresolved
compact binary coalescences of both black holes and
neutron stars
[1
5]
, rotating neutron stars
[6
8]
,supernovae
[9
12]
,cosmicstrings
[13
16]
, inflationary models
[17
24]
,
phase transitions
[25
27]
, and the pre-big-bang scenario
[28
31]
. The variety of mechanisms potentially contributing
to the background provides the opportunity to study a
number of different environments within the Universe.
The recent detections of binary black hole (BBH)
coalescences by the Advanced Laser Interferometer
Gravitational Wave Observatory (aLIGO)
[32,33]
suggest
that the Universe may be rich with coalescing BBHs. While
events like GW150914 and GW151226 are loud enough
to be clearly detected, we expect there to be many more
events that are too far away to be individually resolved and
that contribute to the background. Since this BBH
population originates from sources that are too distant to
be individually detected, the stochastic search probes a
distinct population of binaries compared to nearby sources
[34]
. The background from these binaries provides com-
plementary information to individually resolved binary
coalescences
[35]
.
In this Letter, we report on the search for an isotropic
background using data from Advanced LIGO
s first
observing run O1. We search for the background by
cross-correlating data streams from the two separate
LIGO detectors and looking for a coherent signal. We find
no evidence for the background and place the best upper
limits to date on the energy density of the background in the
LIGO frequency band. We also update the implications for
a BBH background using all the data from O1.
Data.
Before this analysis, the best limits on the
background from Initial LIGO and Virgo data were
obtained using 2009
2010
[36]
and 2005
2007 data
[37]
. In this Letter, we use data from the upgraded
Advanced LIGO observatories in Hanford, Washington
(H1) and Livingston, Louisiana (L1)
[38]
. We analyze O1
data from September 18, 2015 15
00 UTC
January 12,
2016 16
00 UTC.
Method.
We define the background energy density
spectrum as
[39]
Ω
GW
ð
f
Þ¼
f
ρ
c
d
ρ
GW
df
;
ð
1
Þ
where
f
is the frequency,
ρ
c
¼
3
c
2
H
2
0
=
ð
8
π
G
Þ
is the critical
energy density to close the Universe (numerically,
ρ
c
¼
7
.
8
×
10
9
erg
=
cm
3
using the Hubble constant
H
0
¼
68
km s
1
Mpc
1
from
[40,41]
), and
d
ρ
GW
is the gravita-
tional-wave energy density in the frequency range from
f
to
f
þ
df
. For the LIGO frequency band, most theoretical
models for
Ω
GW
ð
f
Þ
can be approximated as a power law in
frequency
[39,42,43]
Ω
GW
ð
f
Þ¼
Ω
α

f
f
ref

α
:
ð
2
Þ
Following
[35]
, we assume a reference frequency of 25 Hz,
which corresponds to the most sensitive band of the LIGO
*
Full author list given at the end of the article.
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=
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© 2017 American Physical Society
stochastic search for a detector network operating at design
sensitivity. The variable
Ω
α
characterizes the background
amplitude across the sensitive frequency band. Past analy-
ses have used
α
¼
0
and
α
¼
3
to represent cosmologically
and astrophysically motivated background models, respec-
tively
[36,42
45]
. In this analysis, we use these two
spectral indices but, also, include limits on the background
spectrum assuming
α
¼
2
=
3
, which describes the back-
ground dominated by compact binary inspirals
[35,46]
.
This choice of spectral index is especially interesting given
the loud background from BBHs inferred from recent
Advanced LIGO detections in O1
[32,33,35,47]
.
Our search uses a cross-correlation method optimized to
search for the background using the pair of LIGO detectors
[39]
. As discussed, for instance, in
[48]
, cross-correlation is
preferred to autocorrelation methods because the noise
variances in each detector are not known sufficiently well to
perform subtraction of the noise autopower. We define the
estimator
ˆ
Y
α
¼
Z
−∞
df
Z
−∞
df
0
δ
T
ð
f
f
0
Þ
~
s

1
ð
f
Þ
~
s
2
ð
f
0
Þ
~
Q
α
ð
f
0
Þ
;
ð
3
Þ
with variance
σ
2
Y
T
2
Z
0
dfP
1
ð
f
Þ
P
2
ð
f
Þj
~
Q
α
ð
f
Þj
2
;
ð
4
Þ
where
~
s
1
;
2
ð
f
Þ
are the Fourier transforms of the strain time
series data from the two detectors,
δ
T
ð
f
f
0
Þ
is a finite-
time approximation to the Dirac delta function,
T
is the
observation time,
P
1
;
2
are the one-sided power spectral
densities for the detectors, and
~
Q
α
ð
f
Þ
is a filter function to
optimize the search
[49]
~
Q
α
ð
f
Þ¼
λ
α
γ
ð
f
Þ
H
2
0
f
3
P
1
ð
f
Þ
P
2
ð
f
Þ

f
f
ref

α
:
ð
5
Þ
The spatial separation and relative orientation of the two
detectors are accounted for in the overlap reduction
function,
γ
ð
f
Þ
[50]
, and the normalization constant
λ
α
is
chosen such that
h
ˆ
Y
α
Ω
α
.
Data Quality.
For this analysis, the strain time series
data are down-sampled to 4096 Hz from 16 384 Hz and
separated into 50%-overlapping 192 s segments, as in
[42]
.
The segments are Hann-windowed and high-pass filtered
with a 16th order Butterworth digital filter with knee
frequency of 11 Hz. The data are coarse grained to a
frequency resolution of 0.031 Hz. This is a finer frequency
resolution than was used in previous analyses due to
the need to remove many finely spaced lines at low
frequencies.
We apply cuts in the time and frequency domains,
following
[36]
. The total live time after all time domain
vetoes have been applied was 29.85 days. These cuts
remove 35% of the time-series data. The frequency
domain cuts remove 21% of the observing band. In the
Supplemental Material
[51]
, we discuss, in more detail, the
removed times and frequencies, the recovery of hardware
and software injections, and an analysis of correlated noise
due to geophysical Schumann resonances.
Results.
Our search finds no evidence of the back-
ground, and the data are consistent with statistical fluctua-
tions, assuming Gaussian noise. The integrand of Eq.
(3)
,
multiplied by
df
¼
0
.
031
Hz, gives an estimator for
Ω
0
in
each frequency bin. We plot this quantity, along with

2
σ
error bars, in Fig.
1
. To check for Gaussianity, we employ a
noise model that the estimator in each frequency bin is
drawn from a Gaussian distribution with zero mean with
the standard deviation of that frequency bin. We obtain a
χ
2
per degree of freedom of 0.92, indicating that the data are
consistent with Gaussian noise.
Consequently, we are able to place upper bounds on the
energy density present in the background. For
α
¼
0
,we
place the bound
Ω
0
<
1
.
7
×
10
7
at 95% confidence,
where 99% of the sensitivity comes in the frequency band
20
86 Hz. This is a factor of 33 times more sensitive than
the previous best limit at these frequencies
[36]
.
Following
[56]
, we show 95% confidence contours in the
Ω
α
-
α
plane in Fig.
2
by computing the joint posterior for
Ω
α
and
α
. In addition, in Table
I
, we report upper limits on the
energy density for specific fixed values of the spectral index,
marginalizing over amplitude calibration uncertainty
[57]
using the conservative estimates of 11.8% for H1 and 13.4%
for L1. Phase calibration uncertainties are negligible.
20
30
40
50
60
70
80
−3
−2
−1
0
1
2
3
x 10
−5
Frequency (Hz)
Ω
0
Ω
0
±
2
σ
Ω
0
FIG. 1. We show the estimator for
Ω
0
in each frequency bin,
along with

2
σ
error bars, in the frequency band that contains
99% of the sensitivity for
α
¼
0
. The loss of sensitivity at around
65 Hz is due to a zero in the overlap reduction function. There are
several lines associated with known instrumental artifacts which
do not lead to excess cross-correlation. The data are consistent
with Gaussian noise, as described in the Results section.
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We also compare our results with the limits placed at
high frequencies from the two colocated detectors at
the Hanford site (H1 and H2). In
[37]
, the limit
Ω
3
<
7
.
7
×
10
4
in the frequency band 460
1000 Hz was
obtained for the spectral index
α
¼
3
and
f
ref
¼
900
Hz.
Using this same frequency band, and using the cross-
correlated data between the Hanford and Livingston detec-
tors, we place a limit
Ω
3
<
1
.
7
×
10
2
for
f
ref
¼
900
Hz.
This is about a factor of 22 larger than the limit from
the colocated detectors, in part due to the loss in sensitivity
of a stochastic search from cross-correlating detectors at
different spatial locations.
In Fig.
3
, we show the constraints from this analysis and
from previous analyses using other detectors, theoretical
predictions, and the expected sensitivity of future mea-
surements by LIGO-Virgo and by the Laser Interferometer
Space Antenna (LISA). Where applicable, we show con-
straints using power-law integrated curves (PI curves)
[59]
,
which account for the broadband nature of the search by
integrating a range of power-law signals over the sensitive
frequency band of the detector. By construction, any
power-law spectrum which crosses a PI curve is detectable
with SNR
2
.
The blue curve labeled
aLIGO O1
in Fig.
3
shows the
measured O1 PI curve. We also display the PI curve for the
final science run of Initial LIGO and Virgo
[36]
,H1
H2
[37]
, as well as the projected design sensitivity for the
advanced detector network. The curve labeled
Design
assumes two years of coincident data taken with both
Advanced LIGO and Virgo operating at design sensitivity,
using the projections in
[58]
. For the sake of comparison,
the measured O1 PI curve at
α
¼
0
is 1.6 times larger than
the projected PI curve at
α
¼
0
using the projections in
[58]
and 29.85 days of live time, which is fairly good agreement
between predicted and achieved sensitivity. Finally, in red,
we present the projected sensitivity of a space-based
detector with similar sensitivity to LISA, using the PI
curve presented in
[59]
computed using the projections
in
[64,65]
.
We compare these constraints with direct limits from
the ringing of Earth
s normal modes
[63]
, indirect limits
from the cosmic microwave background (CMB) and big
bang nucleosynthesis
[61]
, and limits from pulsar timing
arrays
[62]
and CMB measurements at low multipole
moments
[60]
.
In addition, we give examples of several models which
can contribute to the background. We show the background
expected from slow-roll inflation with a tensor-to-scalar-ratio
r
¼
0
.
11
(the upper limit allowed by Planck
[40]
). We also
show examples of the BBH coalescence model, and the
binary neutron star (BNS) coalescence model, which we
describe below. As noted in
[66]
, LISA is likely to be able to
detect the BBH background of the size considered here.
Astrophysical Implications.
In order to model the
background from binary systems, we will follow the
approach of
[35]
. We divide the compact binary population
into classes labeled by
k
[67,68]
. Each class has distinct
values of source parameters (for example, the masses),
which we denote by
θ
k
. The total astrophysical background
is a sum over the contributions in each class. The
contribution of class
k
to the background may be written
in terms of an integral over the redshift
z
as
[1,5,69
74]
Ω
GW
ð
f
;
θ
k
Þ¼
f
ρ
c
H
0
Z
z
max
0
dz
R
m
ð
z
;
θ
k
Þ
dE
GW
df
ð
f
s
;
θ
k
Þ
ð
1
þ
z
Þ
E
ð
Ω
M
;
Ω
Λ
;z
Þ
;
ð
6
Þ
−5
−4
−3
−2
−1
0
1
2
3
4
5
10
−12
10
−11
10
−10
10
−9
10
−8
10
−7
10
−6
10
−5
10
−4
10
−3
α
Ω
α
Initial LIGO−Virgo
aLIGO O1
Design
FIG. 2. Following
[56]
, we present 95% confidence contours in
the
Ω
α
α
plane. The region above these curves is excluded at
95% confidence. We show the constraints coming from the final
science run of Initial LIGO-Virgo
[36]
and from O1 data. Finally,
we display the projected (not observed) design sensitivity to
Ω
α
and
α
for Advanced LIGO and Virgo
[58]
.
TABLE I. The frequency bands with 99% of the sensitivity are shown, along with the point estimate and standard
deviation for the amplitude of the background, and 95% confidence level upper limits using O1 data for three values
of the spectral index,
α
¼
0
;
2
=
3
;
3
. We also show the previous upper limits using Initial LIGO-Virgo data.
Spectral index
α
Frequency band
with 99% sensitivity
Amplitude
Ω
α
95% C.L.
upper limit
Previous limits
[36]
020
85.8 Hz
ð
4
.
4

5
.
9
Þ
×
10
8
1
.
7
×
10
7
5
.
6
×
10
6
2
=
3
20
98.2 Hz
ð
3
.
5

4
.
4
Þ
×
10
8
1
.
3
×
10
7
320
305 Hz
ð
3
.
7

6
.
5
Þ
×
10
9
1
.
7
×
10
8
7
.
6
×
10
8
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where
R
m
ð
z
;
θ
k
Þ
is the binary merger rate per unit comov-
ing volume per unit time,
dE
GW
=df
ð
f
s
;
θ
k
Þ
is the energy
spectrum emitted by a single binary evaluated in terms of
the source frequency
f
s
¼ð
1
þ
z
Þ
f
, and
E
ð
Ω
M
;
Ω
Λ
;z
Þ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Ω
M
ð
1
þ
z
Þ
3
þ
Ω
Λ
p
accounts for the dependence of
comoving volume on cosmology. We use cosmological
parameters from Planck
[40]
, and
Ω
M
¼
1
Ω
Λ
¼
0
.
308
.
The energy spectrum
dE
GW
=df
is determined from the
strain waveform of the binary system. The dominant
contribution to the background comes from the inspiral
phase, however for BBH, we include the merger and
ringdown phases using the waveforms from
[5,75]
with
the modifications from
[76]
. We choose to cut off the
redshift integral at
z
max
¼
10
. Redshifts larger than five
contribute little to the integral due to the small number of
stars formed at such high redshift
[1,5,34,69
74]
.
To compute the binary merger rate
R
m
ð
z
;
θ
k
Þ
, we use the
same assumptions as in
[35]
, unless stated otherwise.
For the BNS case, we assume that the minimal time
between the formation and the coalescence of the binary
is
t
min
¼
20
Myr, following, for instance,
[46]
. This is to be
compared to
t
min
¼
50
Myr for BBH
[35,77]
.
As was emphasized in
[78]
, heavy stellar mass black
holes are expected to form in regions of low metallicity,
which are associated with weaker stellar winds. To account
for this effect, following
[35]
, for binary systems with chirp
masses larger than
30
M
, we use only the fraction of stars
that form in an environment with metallicity
Z<Z
=
2
.
For BBH (and BNS) systems with smaller masses, we do
not use a cutoff. However, we note that it makes little
difference whether or not the cutoff is applied to high
masses.
With the model defined above, the free parameters are
the local merger rate
R
local
¼
R
m
ð
0;
θ
k
Þ
and the average
chirp mass
M
c
. The distribution of the chirp mass has little
effect on the spectrum for a fixed average chirp mass
[5]
.
We place upper limits at 95% confidence in the
M
c
-
R
local
plane, which are shown in Fig.
4
. Alongside the O1 results,
we show the limits using Initial LIGO-Virgo data, as well as
FIG. 3. Presented, here, are constraints on the background in PI form
[59]
, as well as some representative models, across many decades
in frequency. We compare the limits from ground-based interferometers from the final science run of Initial LIGO-Virgo, the colocated
detectors at Hanford (H1
H2), Advanced LIGO (aLIGO) O1, and the projected design sensitivity of the advanced detector network
assuming two years of coincident data, with constraints from other measurements: CMB measurements at low multipole moments
[60]
,
indirect limits from the cosmic microwave background (CMB) and big bang nucleosynthesis
[61,62]
, pulsar timing
[62]
, and from the
ringing of Earth
s normal modes
[63]
. We also show projected limits from a space-based detector such as LISA
[59,64,65]
, following
the assumptions of
[59]
. We extend the BNS and BBH distributions using an
f
2
=
3
power-law down to low frequencies, with a low-
frequency cutoff imposed where the inspiral time scale is of the order of the Hubble scale. In Fig.
5
, we show the region in the black box
in more detail.
10
0
10
1
10
2
10
0
10
2
10
4
10
6
M
c
(M
solar
)
R
local
(Gpc
−3
yr
−1
)
BNS
BBH
Flat
Power
Initial LIGO−VIRGO
aLIGO O1
Design
FIG. 4. Displayed, here, are the 95% confidence contours on
the local rate and average chirp mass parameters, using the model
described in the astrophysical implications section. In addition to
the constraint from aLIGO O1 data, we show the constraint from
the final science run of Initial LIGO-Virgo, and the projected
design sensitivity of Advanced LIGO-Virgo. We also show the
median rate with 90% uncertainty inferred from O1 data for
the power-law and flat-log mass distributions
[47]
, along with the
band containing 68% of the chirp mass for each distribution. The
gray band separates BNS from BBH backgrounds. The dip at
30
M
is due to the metallicity cutoff, as described in the
astrophysical implications section.
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projected sensitivity of the advanced detector network. The
limits presented here are about 10 times more sensitive than
those placed with Initial LIGO-Virgo data. Furthermore,
the future runs of the advanced detectors are expected to
yield another factor of 100 improvement in sensitivity in
R
local
for a given average chirp mass. We also show the
local rate and chirp mass inferred from direct detections of
BBH mergers during O1
[47,68]
. Comparing the projected
design sensitivity on
R
local
and
M
c
, with the values inferred
from BBH observations in O1, suggests that it may be
possible for the advanced detector network to detect the
astrophysical BBH background.
Finally, instead of treating the chirp mass and local merger
rate as free parameters, we can use the information from
individually observed BBHs to compute the corresponding
background, see Fig.
5
. To do this, we use the same model
described above, and we adopt the three rate models
described in
[47]
. Specifically, we consider the three-
events-based, power-law, and flat-log distributions of com-
ponent masses. In each case, the rate at redshift
z
¼
0
is
normalized to the local rate derived from the O1 detections.
With these assumptions, we compute the background, includ-
ing statistical uncertainty bands showing the 90% uncertainty
in the local rate. The three rate models agree well in the
sensitive frequency band of advanced detectors (10
100 Hz).
Note, also, that the final sensitivity of the advanced detectors
may be sufficient to detect this background.
Conclusions.
The search for the isotropic stochastic
gravitational-wave background presented in this Letter,
alongside the results described in Ref.
[79]
on the aniso-
tropic background, represent the first search for the
stochastic gravitational-wave background made with the
Advanced LIGO detectors. With no evidence of a
stochastic signal, we place an upper limit of
Ω
0
<
1
.
7
×
10
7
on the GW energy density, for a spectral index
α
¼
0
.
This is
33
times more sensitive than previous direct
measurements in this frequency band. We also constrain
the binary coalescence parameters of chirp mass and local
merger rate. For fixed chirp mass below the high mass
threshold of
30
M
, the constraint on the merger rate is
improved by a factor of
24
, while for fixed merger rate, the
constraint on the chirp mass is improved by a factor of
7
,as
can be seen from Fig.
4
. Finally, we update the background
predictions due to BBH coalescences using data from O1. In
this Letter, we have focused the implications ofour resultsfor
an astrophysical BBH background, as this provides the most
promising candidate for first detecting the background. The
implications of our search for other astrophysical and
cosmological models can be seen in Fig.
3
. There is also
an upcoming publication that will study implications for
cosmic string models in more detail.
These O1 results are a glimpse of the improvements in
sensitivity to be seen in upcoming years. As the advanced
detectors reach design sensitivity, there is a reasonable
possibility of detecting the background due to BBHs. Even
if no detection is made with these future searches, the
searches will be able to constrain important cosmological
and astrophysical background models.
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, Department of Science and
Technology, India, Science and Engineering Research
Board (SERB), India, Ministry of Human Resource
Development, India, the Spanish Ministerio de Economía
y Competitividad, the Conselleria d
Economia i
Competitivitat and Conselleria d
Educació, Cultura i
Universitats of the Govern de les Illes Balears, the
National Science Centre of Poland, the European
Commission, the Royal Society, the Scottish Funding
Council, the Scottish Universities Physics Alliance, the
Hungarian Scientific Research Fund (OTKA), the Lyon
FIG. 5. We present a range of potential spectra for a BBH
background, using the flat-log, power-law, and three-delta mass
distribution models described in
[47,78]
, with the local rate
inferred from the O1 detections
[47]
. For the flat-log and power-
law distributions, we show the 90% Poisson uncertainty band due
to the uncertainty in the local rate measurement. In addition, we
show the measured O1 PI curve and the projected PI curve for
Advanced LIGO-Virgo operating at design sensitivity.
PRL
118,
121101 (2017)
PHYSICAL REVIEW LETTERS
week ending
24 MARCH 2017
121101-5
Institute of Origins (LIO), 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, Canadian Institute for
Advanced Research, the Brazilian Ministry of Science,
Technology, and Innovation, Fundação de Amparo à
Pesquisa do Estado de São Paulo (FAPESP), Russian
Foundation for Basic Research, the Leverhulme Trust,
the Research Corporation, 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.This
article has been assigned the document number LIGO-
P1600258-v18.
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G. González,
2
J. M. Gonzalez Castro,
20,21
A. Gopakumar,
104
M. L. Gorodetsky,
49
S. E. Gossan,
1
M. Gosselin,
34
R. Gouaty,
8
A. Grado,
105,5
C. Graef,
36
M. Granata,
65
A. Grant,
36
S. Gras,
12
C. Gray,
37
G. Greco,
57,58
A. C. Green,
45
P. Groot,
53
H. Grote,
10
S. Grunewald,
29
G. M. Guidi,
57,58
X. Guo,
71
A. Gupta,
16
M. K. Gupta,
89
K. E. Gushwa,
1
E. K. Gustafson,
1
R. Gustafson,
106
J. J. Hacker,
23
B. R. Hall,
56
E. D. Hall,
1
G. Hammond,
36
M. Haney,
104
M. M. Hanke,
10
J. Hanks,
37
C. Hanna,
74
M. D. Hannam,
94
J. Hanson,
7
T. Hardwick,
2
J. Harms,
57,58
G. M. Harry,
3
I. W. Harry,
29
M. J. Hart,
36
M. T. Hartman,
6
C.-J. Haster,
45,97
K. Haughian,
36
J. Healy,
107
A. Heidmann,
60
M. C. Heintze,
7
H. Heitmann,
54
P. Hello,
24
G. Hemming,
34
M. Hendry,
36
I. S. Heng,
36
J. Hennig,
36
J. Henry,
107
A. W. Heptonstall,
1
M. Heurs,
10,19
S. Hild,
36
D. Hoak,
34
D. Hofman,
65
K. Holt,
7
D. E. Holz,
77
P. Hopkins,
94
J. Hough,
36
E. A. Houston,
36
E. J. Howell,
52
Y. M. Hu,
10
E. A. Huerta,
108
D. Huet,
24
B. Hughey,
103
S. Husa,
86
S. H. Huttner,
36
T. Huynh-Dinh,
7
N. Indik,
10
D. R. Ingram,
37
R. Inta,
72
H. N. Isa,
36
J.-M. Isac,
60
M. Isi,
1
T. Isogai,
12
B. R. Iyer,
17
K. Izumi,
37
T. Jacqmin,
60
K. Jani,
44
P. Jaranowski,
109
S. Jawahar,
110
F. Jiménez-Forteza,
86
W. W. Johnson,
2
D. I. Jones,
111
R. Jones,
36
R. J. G. Jonker,
11
L. Ju,
52
J. Junker,
10
C. V. Kalaghatgi,
94
V. Kalogera,
85
S. Kandhasamy,
73
G. Kang,
79
J. B. Kanner,
1
S. Karki,
59
K. S. Karvinen,
10
M. Kasprzack,
2
E. Katsavounidis,
12
W. Katzman,
7
S. Kaufer,
19
T. Kaur,
52
K. Kawabe,
37
F. Kéfélian,
54
D. Keitel,
86
D. B. Kelley,
35
R. Kennedy,
90
J. S. Key,
112
F. Y. Khalili,
49
I. Khan,
14
S. Khan,
94
Z. Khan,
89
E. A. Khazanov,
113
N. Kijbunchoo,
37
Chunglee Kim,
114
J. C. Kim,
115
Whansun Kim,
116
W. Kim,
70
Y.-M. Kim,
117,114
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44
E. J. King,
70
P. J. King,
37
R. Kirchhoff,
10
J. S. Kissel,
37
B. Klein,
85
L. Kleybolte,
27
S. Klimenko,
6
P. Koch,
10
S. M. Koehlenbeck,
10
S. Koley,
11
V. Kondrashov,
1
A. Kontos,
12
M. Korobko,
27
W. Z. Korth,
1
I. Kowalska,
62
D. B. Kozak,
1
C. Krämer,
10
V. Kringel,
10
A. Królak,
118,119
G. Kuehn,
10
P. Kumar,
97
R. Kumar,
89
L. Kuo,
75
A. Kutynia,
118
B. D. Lackey,
29,35
M. Landry,
37
R. N. Lang,
18
J. Lange,
107
B. Lantz,
40
R. K. Lanza,
12
A. Lartaux-Vollard,
24
P. D. Lasky,
120
M. Laxen,
7
A. Lazzarini,
1
C. Lazzaro,
42
P. Leaci,
81,28
S. Leavey,
36
E. O. Lebigot,
30
C. H. Lee,
117
H. K. Lee,
121
H. M. Lee,
114
K. Lee,
36
J. Lehmann,
10
A. Lenon,
31
M. Leonardi,
92,93
J. R. Leong,
10
N. Leroy,
24
N. Letendre,
8
Y. Levin,
120
T. G. F. Li,
122
A. Libson,
12
T. B. Littenberg,
123
J. Liu,
52
N. A. Lockerbie,
110
A. L. Lombardi,
44
L. T. London,
94
J. E. Lord,
35
M. Lorenzini,
14,15
V. Loriette,
124
M. Lormand,
7
G. Losurdo,
21
J. D. Lough,
10,19
G. Lovelace,
23
H. Lück,
19,10
A. P. Lundgren,
10
R. Lynch,
12
Y. Ma,
51
S. Macfoy,
50
B. Machenschalk,
10
M. MacInnis,
12
D. M. Macleod,
2
F. Magaña-Sandoval,
35
E. Majorana,
28
I. Maksimovic,
124
V. Malvezzi,
26,15
N. Man,
54
V. Mandic,
125
V. Mangano,
36
G. L. Mansell,
22
M. Manske,
18
M. Mantovani,
34
F. Marchesoni,
126,33
F. Marion,
8
S. Márka,
39
Z. Márka,
39
A. S. Markosyan,
40
E. Maros,
1
F. Martelli,
57,58
L. Martellini,
54
I. W. Martin,
36
D. V. Martynov,
12
K. Mason,
12
A. Masserot,
8
T. J. Massinger,
1
M. Masso-Reid,
36
S. Mastrogiovanni,
81,28
A. Matas,
125
F. Matichard,
12,1
L. Matone,
39
N. Mavalvala,
12
N. Mazumder,
56
R. McCarthy,
37
D. E. McClelland,
22
S. McCormick,
7
C. McGrath,
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S. C. McGuire,
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G. McIntyre,
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J. McIver,
1
D. J. McManus,
22
T. McRae,
22
S. T. McWilliams,
31
D. Meacher,
54,74
G. D. Meadors,
29,10
J. Meidam,
11
A. Melatos,
128
G. Mendell,
37
D. Mendoza-Gandara,
10
R. A. Mercer,
18
E. L. Merilh,
37
M. Merzougui,
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S. Meshkov,
1
C. Messenger,
36
C. Messick,
74
R. Metzdorff,
60
P. M. Meyers,
125
F. Mezzani,
28,81
H. Miao,
45
C. Michel,
65
H. Middleton,
45
E. E. Mikhailov,
129
L. Milano,
67,5
A. L. Miller,
6,81,28
A. Miller,
85
B. B. Miller,
85
J. Miller,
12
M. Millhouse,
84
Y. Minenkov,
15
J. Ming,
29
S. Mirshekari,
130
C. Mishra,
17
S. Mitra,
16
V. P. Mitrofanov,
49
G. Mitselmakher,
6
R. Mittleman,
12
A. Moggi,
21
M. Mohan,
34
S. R. P. Mohapatra,
12
M. Montani,
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B. C. Moore,
95
C. J. Moore,
80
D. Moraru,
37
G. Moreno,
37
S. R. Morriss,
87
B. Mours,
8
PRL
118,
121101 (2017)
PHYSICAL REVIEW LETTERS
week ending
24 MARCH 2017
121101-8
C. M. Mow-Lowry,
45
G. Mueller,
6
A. W. Muir,
94
Arunava Mukherjee,
17
D. Mukherjee,
18
S. Mukherjee,
87
N. Mukund,
16
A. Mullavey,
7
J. Munch,
70
E. A. M. Muniz,
23
P. G. Murray,
36
A. Mytidis,
6
K. Napier,
44
I. Nardecchia,
26,15
L. Naticchioni,
81,28
G. Nelemans,
53,11
T. J. N. Nelson,
7
M. Neri,
46,47
M. Nery,
10
A. Neunzert,
106
J. M. Newport,
3
G. Newton,
36
T. T. Nguyen,
22
A. B. Nielsen,
10
S. Nissanke,
53,11
A. Nitz,
10
A. Noack,
10
F. Nocera,
34
D. Nolting,
7
M. E. N. Normandin,
87
L. K. Nuttall,
35
J. Oberling,
37
E. Ochsner,
18
E. Oelker,
12
G. H. Ogin,
131
J. J. Oh,
116
S. H. Oh,
116
F. Ohme,
94,10
M. Oliver,
86
P. Oppermann,
10
Richard J. Oram,
7
B. O
Reilly,
7
R. O
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107
D. J. Ottaway,
70
H. Overmier,
7
B. J. Owen,
72
A. E. Pace,
74
J. Page,
123
A. Pai,
101
S. A. Pai,
48
J. R. Palamos,
59
O. Palashov,
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C. Palomba,
28
A. Pal-Singh,
27
H. Pan,
75
C. Pankow,
85
F. Pannarale,
94
B. C. Pant,
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F. Paoletti,
34,21
A. Paoli,
34
M. A. Papa,
29,18,10
H. R. Paris,
40
W. Parker,
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D. Pascucci,
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A. Pasqualetti,
34
R. Passaquieti,
20,21
D. Passuello,
21
B. Patricelli,
20,21
B. L. Pearlstone,
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M. Pedraza,
1
R. Pedurand,
65,132
L. Pekowsky,
35
A. Pele,
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S. Penn,
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37
A. Perreca,
1
L. M. Perri,
85
H. P. Pfeiffer,
97
M. Phelps,
36
O. J. Piccinni,
81,28
M. Pichot,
54
F. Piergiovanni,
57,58
V. Pierro,
9
G. Pillant,
34
L. Pinard,
65
I. M. Pinto,
9
M. Pitkin,
36
M. Poe,
18
R. Poggiani,
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P. Popolizio,
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A. Post,
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J. W. W. Pratt,
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T. Prestegard,
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M. Prijatelj,
10,34
M. Principe,
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S. Privitera,
29
G. A. Prodi,
92,93
L. G. Prokhorov,
49
O. Puncken,
10
M. Punturo,
33
P. Puppo,
28
M. Pürrer,
29
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52
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120
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37
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22
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37
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98
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48
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48
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P. Rapagnani,
81,28
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29
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20,21
V. Re,
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103
F. Ricci,
81,28
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106
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107
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24
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1
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ska,
134,43
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36
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10
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34
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37
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1
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37
L. Sadeghian,
18
M. Sakellariadou,
135
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34
M. Saleem,
101
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10
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74,94
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35
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106
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37
A. Sawadsky,
19
P. Schale,
59
J. Scheuer,
85
S. Schlassa,
61
E. Schmidt,
103
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10
P. Schmidt,
1,51
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27
R. M. S. Schofield,
59
A. Schönbeck,
27
E. Schreiber,
10
D. Schuette,
10,19
B. F. Schutz,
94,29
S. G. Schwalbe,
103
J. Scott,
36
S. M. Scott,
22
D. Sellers,
7
A. S. Sengupta,
137
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34
V. Sequino,
26,15
A. Sergeev,
113
Y. Setyawati,
53,11
D. A. Shaddock,
22
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37
M. S. Shahriar,
85
B. Shapiro,
40
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64
A. Sheperd,
18
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12
D. M. Shoemaker,
44
K. Siellez,
44
X. Siemens,
18
M. Sieniawska,
43
D. Sigg,
37
A. D. Silva,
13
A. Singer,
1
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29,10,19
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2
A. Singhal,
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A. M. Sintes,
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47
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16
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A. K. Srivastava,
89
A. Staley,
39
M. Steinke,
10
J. Steinlechner,
36
S. Steinlechner,
27,36
D. Steinmeyer,
10,19
B. C. Stephens,
18
S. P. Stevenson,
45
R. Stone,
87
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36
N. Straniero,
65
G. Stratta,
57,58
S. E. Strigin,
49
R. Sturani,
130
A. L. Stuver,
7
T. Z. Summerscales,
138
L. Sun,
128
S. Sunil,
89
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34
M. J. Szczepa
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czyk,
103
M. Tacca,
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6
D. Tao,
61
M. Tápai,
102
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29
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1
T. Theeg,
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M. Thomas,
7
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37
K. A. Thorne,
7
E. Thrane,
120
T. Tippens,
44
S. Tiwari,
14,93
V. Tiwari,
94
K. V. Tokmakov,
110
K. Toland,
36
C. Tomlinson,
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M. Tonelli,
20,21
Z. Tornasi,
36
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1
D. Töyrä,
45
F. Travasso,
32,33
G. Traylor,
7
D. Trifirò,
73
J. Trinastic,
6
M. C. Tringali,
92,93
L. Trozzo,
139,21
M. Tse,
12
R. Tso,
1
M. Turconi,
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D. Tuyenbayev,
87
D. Ugolini,
140
C. S. Unnikrishnan,
104
A. L. Urban,
1
S. A. Usman,
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H. Vahlbruch,
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G. Vajente,
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G. Valdes,
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J. F. J. van den Brand,
63,11
C. Van Den Broeck,
11
D. C. Vander-Hyde,
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L. van der Schaaf,
11
J. V. van Heijningen,
11
A. A. van Veggel,
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M. Vardaro,
41,42
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51
S. Vass,
1
M. Vasúth,
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A. Vecchio,
45
G. Vedovato,
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J. Veitch,
45
P. J. Veitch,
70
K. Venkateswara,
141
G. Venugopalan,
1
D. Verkindt,
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F. Vetrano,
57,58
A. Viceré,
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45
D. J. Vine,
50
J.-Y. Vinet,
54
S. Vitale,
12
T. Vo,
35
H. Vocca,
32,33
C. Vorvick,
37
D. V. Voss,
6
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45
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49
A. R. Wade,
1
L. E. Wade,
78
M. Wade,
78
M. Walker,
2
L. Wallace,
1
S. Walsh,
29,10
G. Wang,
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H. Wang,
45
M. Wang,
45
Y. Wang,
52
R. L. Ward,
22
J. Warner,
37
M. Was,
8
J. Watchi,
82
B. Weaver,
37
L.-W. Wei,
54
M. Weinert,
10
A. J. Weinstein,
1
R. Weiss,
12
L. Wen,
52
P. Weßels,
10
T. Westphal,
10
K. Wette,
10
J. T. Whelan,
107
B. F. Whiting,
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C. Whittle,
120
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B. Willke,
19,10
M. H. Wimmer,
10,19
W. Winkler,
10
C. C. Wipf,
1
H. Wittel,
10,19
G. Woan,
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J. Woehler,
10
J. Worden,
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J. L. Wright,
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D. S. Wu,
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G. Wu,
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W. Yam,
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H. Yamamoto,
1
C. C. Yancey,
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M. J. Yap,
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Hang Yu,
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Haocun Yu,
12
M. Yvert,
8
A. Zadro
ż
ny,
118
L. Zangrando,
42
M. Zanolin,
103
J.-P. Zendri,
42
M. Zevin,
85
L. Zhang,
1
M. Zhang,
129
T. Zhang,
36
Y. Zhang,
107
C. Zhao,
52
M. Zhou,
85
Z. Zhou,
85
S. J. Zhu,
29,10
X. J. Zhu,
52
M. E. Zucker,
1,12
and J. Zweizig
1
(LIGO Scientific Collaboration and Virgo Collaboration)
PRL
118,
121101 (2017)
PHYSICAL REVIEW LETTERS
week ending
24 MARCH 2017
121101-9