All-sky search for short gravitational-wave bursts
in the first Advanced LIGO run
B. P. Abbott
etal.
*
(LIGO Scientific Collaboration and Virgo Collaboration)
(Received 11 November 2016; published 16 February 2017)
We present the results from an all-sky search for short-duration gravitational waves in the data of the first
run of the Advanced LIGO detectors between September 2015 and January 2016. The search algorithms
use minimal assumptions on the signal morphology, so they are sensitive to a wide range of sources
emitting gravitational waves. The analyses target transient signals with duration ranging from milliseconds
to seconds over the frequency band of 32 to 4096 Hz. The first observed gravitational-wave event,
GW150914, has been detected with high confidence in this search; the other known gravitational-wave
event, GW151226, falls below the search
’
s sensitivity. Besides GW150914, all of the search results are
consistent with the expected rate of accidental noise coincidences. Finally, we estimate rate-density limits
for a broad range of non-binary-black-hole transient gravitational-wave sources as a function of their
gravitational radiation emission energy and their characteristic frequency. These rate-density upper limits
are stricter than those previously published by an order of magnitude.
DOI:
10.1103/PhysRevD.95.042003
I. INTRODUCTION
The first observing period of the Advanced LIGO
detectors
[1,2]
has been completed recently with the most
sensitive gravitational-wave (GW) detectors ever built.
The two LIGO observatories in Hanford, Washington,
and Livingston, Louisiana, achieved a major milestone
in gravitational-wave astronomy: the first direct detection
of gravitational waves on September 14, 2015, referred to
as GW150914
[3]
. Advanced LIGO is the first of a new
generation of instruments, including GEO 600
[4]
,
Advanced Virgo
[2]
, KAGRA
[5]
, and LIGO-India
[6]
.
This paper reports on a search for short-duration transient
gravitational-wave events, commonly referred to as GW
bursts, during the first observing run (O1) of the Advanced
LIGO detectors, from September 2015 to January 2016. The
first 16 days of coincident data have already been analyzed,
resulting in a high-significance detection statement for the
GW150914 event
[7]
. GW bursts can be generated by a wide
variety of astrophysical sources, such as merging compact
binary systems
[8,9]
, core-collapse supernovae of massive
stars
[10]
, neutron stars collapsing to form black holes,
pulsar glitches, and cosmic string cusps
[11]
. Some of these
sources have dedicated targeted searches such as optically
triggered core-collapse supernova
[12]
or Gamma-Ray
Bursts triggered searches
[13]
. To search broadly for these
phenomena, we employ searches with minimal assumptions
regarding the expected waveform characteristics and the
source direction. The search we report here is more sensitive
than the previous burst searches
[14]
because of both the
increased sensitivity of the Advanced detectors
[15]
and
improvements in the search algorithms in rejecting transient
non-Gaussian noise artifacts (glitches)
[16
–
19]
.
The described unmodeled all-sky search for GW bursts
consists of three different algorithms. This paper shows
the result of these algorithms and gives limits on the rate
density of transient GW events. All of these algorithms
have independently claimed high-significance detections
of GW150914
[7]
. The lower-mass GW event, GW151226
[20]
, and the LVT151012 candidate
[21,22]
were not
detected by these searches.
The paper is organized as follows. In Sec.
II
,wegive
an overview of the O1 data set. In Sec.
III
, we give a brief
overview of the three search algorithms. The sensitivity of
the search is described in Sec.
IV
. Finally, Secs.
V
and
VI
discuss the search results and their implications.
II. OBSERVING RUN 1
Our data set extends over 130 calendar days from
September 12, 2015, to January 19, 2016. This first
observing period (called O1) of Advanced LIGO began
after a series of major upgrades to both the Hanford and
Livingston detectors
[3]
.
In the most sensitive frequency band, 100
–
300 Hz, the
O1 LIGO detectors are three to five times more sensitive
than the initial LIGO detectors
[15]
. Future observing runs
are expected to increase sensitivity by an additional factor
of 3
[6]
.
As in the previous LIGO/Virgo searches
[23
–
25]
,
intervals of poor data quality are identified and excluded
from the analysis. To monitor environmental disturbances
and their influence on the detectors, each observatory is
equipped with an array of sensors: seismometers, acceler-
ometers, microphones, magnetometers, radio receivers,
*
Full author list given at the end of the article.
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weather sensors, ac-power line monitors, and a cosmic-ray
detector. Hundreds of thousands of auxiliary channels
within the instrument are also monitored. Characteri-
zation of the relationship of the strain data to this additional
information allows many non-GW transients to be removed
with high statistical confidence
[7,26]
.
The live time in which the two detectors were individu-
ally locked is about 79 days for H1 and 67 days for L1.
After data quality flags have been applied, the total
analyzable time is about 75 days for H1 and 65 days for
L1. The coincident live time between H1 and L1 is about
48 days. The 16 days of coincident data that have already
been analyzed in Ref.
[7]
are a subset of this coincident O1
live time. Finally, the estimated calibration uncertainty (
1
σ
)
below 2 kHz is less than 10% in amplitude and 10 deg
in phase
[27]
. The calibration uncertainty above 2 kHz is
less certain, although the limited data obtained at these
frequencies suggest upper bounds of 20% in amplitude and
10 deg in phase. These estimates will be further refined
through future measurements and analyses
[28]
.
III. SEARCHES
This search covers the most sensitive frequency band of
the involved detectors, i.e. 32
–
4096 Hz, and it consists
of the same three burst algorithms used to measure the
significance of GW150914
[7]
. They consist of two end-to-
end algorithms, coherent Waveburst (cWB)
[19,29]
and
omicron-LIB (oLIB)
[18]
, and a followup algorithm
applied to cWB events, BayesWave (BW)
[30,31]
. Using
multiple search algorithms has two advantages: it can
provide independent validation of results, and it can also
improve the search sensitivity in regions of parameter space
where a single algorithm outperforms the others.
The three algorithms ran over the 48 days of coincident
data. However, due to internal segmentation,
1
the cWB
and BW pipelines only actually analyzed 44 days of this
coincident data. The oLIB analysis loss time is negligible
and thus oLIB analyzed close to the full 48 days.
The three algorithms also ran in low-latency mode
during O1. This mode approximates the analysis presented
in this paper, but in real time so as to enable potential
electromagentic followup of gravitational-wave candidates.
In this mode, both cWB and oLIB produced independent
alerts of the GW150914 event, and the result was validated
by a BW followup
[32]
.
To characterize the statistical rate of transient noise
glitches occurring simultaneously at the two LIGO sites
by chance, this analysis uses the time-shift method: data
from one interferometer are shifted in time with respect to
the other interferometer by multiple delays much larger
than the maximum GW travel time between the interfer-
ometers. In this way, we can accumulate a significant
duration of estimated background that we use to estimate
the false-alarm rate (FAR) for each algorithm.
We set a FAR threshold of 1 in 100 yr for identifying a
detection candidate, which roughly corresponds to a 3
sigma detection statement for the duration of our obser-
vation. If an event in this search were to have a FAR less
than this threshold, a refined analysis (i.e., more time shifts)
would be performed to assign the appropriate significance
in the detection statement for this event.
A. Coherent WaveBurst
Coherent WaveBurst has been used in multiple searches
for transient GWs
[14,25]
. It calculates a maximum-
likelihood-ratio statistic for power excesses identified in
thetime-frequency domain. A primaryselectioncutisapplied
to the network correlation coefficient
c
c
, which measures
the degree of correlation between the detectors. Events with
c
c
<
0
.
7
are discarded from the analysis. Events are ranked
according to their coherent network signal-to-noise ratio
(SNR)
η
c
, which isrelatedto thematched-filter SNR,favoring
GW signals correlated in both detectors and suppressing
uncorrelated glitches. A detailed explanation of the algorithm
and the definition of these statistics are given in Ref.
[7]
.
The cWB analysis is divided in two frequency bands,
where the splitting frequency is 1024 Hz. For the low-
frequency band, the data are downsampled to reduce the
computational cost of the analysis.
Low-frequency cWB events are divided into three search
classes according to their morphology, as described in
Ref.
[7]
. The
C
1
class is based on cuts which primarily
select so-called
“
blip
”
glitches and nonstationary power-
spectrum lines. The former are non-Gaussian noise tran-
sients of unknown origin consisting of a few cycles around
100 Hz. The
C
3
class is based on cuts that select events of
which the frequency increases with time, i.e., those similar
in morphology to the merger of compact objects. The
C
2
class is composed of all the remaining events.
The FAR of each identified event is estimated using the
time-slide background distribution of similar class. Since
there are three independent classes, we apply a trials factor
of 3 to estimate the final significance. The high-frequency
analysis consists of only a single class.
About 1000 yr of coincident background data were
accumulated for the cWB analysis. Figures
1(a)
and
1(b)
report the cumulative FAR as a function of
η
c
for the
low-frequency and high-frequency analyses, respectively,
including the three different classes for the low-frequency
case.
B. Omicron-LIB
Omicron-LIB is a hierarchical search algorithm that first
analyzes the data streams of individual detectors, which
we refer to as an incoherent analysis. It then follows up
stretches of data that are potentially correlated across the
detector network, which we refer to as a coherent analysis.
1
The cWB algorithm requires at least 600 s of continuous data
to perform its analysis.
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The incoherent analysis (
“
Omicron
”
)
[33]
flags stretches
of coincident excess power. The coherent followup (
“
LIB
”
)
[18]
models gravitational-wave signals and noise transients
with a single sine-Gaussian, and it produces two different
Bayes factors. Each of these Bayes factors is expressed as
the natural logarithm of the evidence ratio of two hypoth-
eses: a GW signal vs Gaussian noise (Bayes factor coherent
Signal vs Gaussian Noise (BSN)) and a coherent GW signal
vs incoherent noise transients (Bayes factor Coherent signal
vs Incoherent glitch (BCI)). The joint likelihood ratio
Λ
of these two Bayes factors is used as a ranking statistic to
assign a significance to each event. See Ref.
[18]
for further
technical details on the implementation of these steps.
For this analysis, oLIB events are divided into two
classes, based on the inferred parameters of the best-fit
sine-Gaussian. The exact parameter ranges of these search
classes are chosen in order to group noise transients of
similar morphology together. Particularly noisy regions
of the parameter space are excluded from the analysis
entirely (e.g., events with median quality factor
Q>
108
).
Both classes contain only events of which the median
frequency
f
0
, as estimated by LIB, lies within the range of
48
–
1024 Hz. The first, analogous to cWB
’
s C1 class, is a
“
low-
Q
”
class that contains only events of which the
median quality factor
Q
lies within the range 0.1
–
2. The
second, analogous to the union of cWB
’
s C2 and C3
classes, is a
“
high-
Q
”
class that contains only events of
which the median
Q
lies within the range 2
–
108. In both
classes, event candidates were also required to have
positive Bayes factors, i.e., BSN
>
0
and BCI
>
0
,
(a)
(b)
(c)(d)
FIG. 1. Search results and backgrounds as a function of the detection statistic for the different searches. The FAR refers to the rate
(after trials factors are applied) at which events more significant than the corresponding detection statistic occur. If the results of a search
deviate from its background, we use the background FAR to characterize the significance of the deviation being caused by noise alone.
Apart from GW150914 (which is not reported in these figures), the search results are consistent with the expectations of accidental noise
coincidences.
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meaning the evidence for the signal model was greater than
the evidences for the noise models. A trials factor of 2
accounts for these independent search classes.
The oLIB background analysis is performed using 456 yr
of background data. We select single-detector events with
SNR
>
5
.
0
. This is lower than the threshold of 6.5 adopted
in Ref.
[7]
, and it is chosen to allow us to make a
significance estimation of low-SNR events. For this reason,
we cannot directly compare the two sets of results reported
in Ref.
[7]
and in this study using the likelihood ratios
Λ
,
but we have to consider the reported FAR. The results are
presented in Fig.
1(c)
.
C. BayesWave followup
BayesWave tests if the data in multiple detectors are best
explained by coincident glitches or a signal, and it is used
as a followup to events produced by cWB. It has been
shown that BW is able to increase the detection confidence
for GW signals of complex morphology
[16]
.
The BW algorithm uses a variable number of sine-
Gaussian wavelets to reconstruct the data independently
for the signal and glitch models, then computes the natural
logarithm of the Bayes factor between these two models,
ln
B
sg
. The number of wavelets used is determined by
using a reversible jump Markov chain Monte Carlo, with
more complex signals requiring more wavelets
[17]
. The
Bayes factor scales as ln
B
sg
∼
N
ln SNR, where
N
is
number of wavelets used. This means the detection
statistic depends on waveform complexity in addition to
the SNR. Full details of the algorithm can be found
in Ref.
[30]
.
In this search, BW followed up events produced by cWB
in any of the three low-frequency search classes with a
coherent network SNR of
η
c
≥
9
.
9
and correlation coef-
ficient of
c
c
>
0
.
7
. There are no additional cuts performed
on the data, and all of these events (C
1
þ
C
2
þ
C
3
) are
analyzed as a single class. The cumulative FAR as a
function of ln
B
sg
is shown in Fig.
1(d)
.
IV. SENSITIVITY
The detection efficiency of the search is measured by
adding simulated signals into the detectors
’
data and
evaluating whether or not they pass the selection cuts
explained in Sec.
III
for the different search algorithms.
This search deals with a wide range of GW sources that are
usually not well modeled. However, they can be repre-
sented with a variety of morphologies that were tested here,
spanning a wide range of amplitudes and duration, and with
characteristic frequencies within the sensitive bandwidth
of the detectors. We identify two different waveform sets:
a set of generic bursts and a set of simulated astrophysical
signals coming from the coalescence and merging of binary
black holes (BBH). All of the results in this section refer to
a FAR detection threshold of 1 in 100 yr.
A. Generic bursts
This family includes the waveform types described in
Ref.
[24]
, all with elliptical polarization:
Gaussian pulses
(GA), parametrized by their duration parameter
τ
;
sine-
Gaussian wavelets
(SG), sinusoids within a Gaussian
envelope, characterized by the frequency of the sinusoid
f
0
and a quality factor
Q
;
white-noise bursts
, white noise
bounded in frequency over a bandwidth
Δ
f
and with a
Gaussian envelope, described by the lower frequency
f
low
,
Δ
f
, and the duration
τ
. Table
I
lists the waveforms that have
been considered for this work.
The amplitudes of the test signals are chosen to cover a
wide range of values and are expressed in terms of the
root-mean-square strain amplitude at Earth (before
accounting for the detection response patterns), denoted
h
rss
[14]
.
Table
I
shows the
h
rss
value at which 50% of the
injections are detected for each signal morphology and
algorithm. There are some morphology-dependent features
that affect each of the different algorithms at the FAR
threshold of 1 in 100 yr. These features largely disappear,
and the different algorithms
’
results converge at detection
thesholds of higher FAR. For example, the detection
efficiencies are worse for cWB for low-
Q
morphologies
and high-
Q
morphologies because these injections are
classified as C1 events. As shown in Fig.
1(a)
, the C1
background extends to higher significances than in the
other bins, meaning these high-
Q
and low-
Q
events must
TABLE I. The
h
rss
values, in units of
10
−
22
Hz
−
1
=
2
, at which
50% detection efficiency is achieved at a FAR of 1 in 100 yr for
each of the algorithms, as a function of the injected signal
morphologies.
“
N/A
”
denotes that 50% detection efficiency was
not achieved.
“
-
”
denotes the waveform was not analyzed by
oLIB and BW because its characteristic frequency is higher than
1024 Hz.
Morphology
cWB oLIB BW
Gaussian pulses
τ
¼
0
.
1
ms
34 N/A N/A
τ
¼
2
.
5
ms
33
7.4 N/A
Sine-Gaussian wavelets
f
0
¼
70
Hz,
Q
¼
100
24 N/A N/A
f
0
¼
153
Hz,
Q
¼
8
.
9
1.6 1.7 5.4
f
0
¼
235
Hz,
Q
¼
100
14
1.9 N/A
f
0
¼
554
Hz,
Q
¼
8
.
9
2.6 2.7 3.6
f
0
¼
849
Hz,
Q
¼
3
27
3.3 5.4
f
0
¼
1615
Hz,
Q
¼
100
5.5
-
-
f
0
¼
2000
Hz,
Q
¼
3
8.7
-
-
f
0
¼
2477
Hz,
Q
¼
8
.
9
11
-
-
f
0
¼
3067
Hz,
Q
¼
3
15
-
-
White-noise bursts
f
low
¼
100
Hz,
Δ
f
¼
100
Hz,
τ
¼
0
.
1
s 2.0 N/A 3.0
f
low
¼
250
Hz,
Δ
f
¼
100
Hz,
τ
¼
0
.
1
s 2.2 N/A 9.2
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have large values of
η
c
to meet the FAR threshold of 1 in
100 yr. The oLIB detection efficiencies, while non-
negligible across all morphologies, never quite reach
50% for some non-sine-Gaussian morphologies because
the template mismatch residuals grow linearly with
h
rss
.
Finally, the detection efficiencies of BW suffers for high-
Q
events since its prior range only extends to
Q
¼
40
.
However, almost every morphology can be detected
efficiently by at least one of the algorithms.
Another way to interpret the search sensitivities is to
map them into the minimum amount of energy that needs
to be emitted through GWs for at least half of the sources
to be detected within a given search volume. Assuming a
fixed amount of energy is radiated isotropically away
from the source in GWs of a fixed frequency
f
0
,this
distance
r
0
can be converted into a value of
h
rss
via the
relationship
[14]
E
GW
¼
π
2
c
3
G
r
2
0
f
2
0
h
2
rss
:
ð
1
Þ
Here, we use the
h
rss
from Table
I
, the central frequency of
each morphology, and a fixed fiducial radius to calculate
this energy via Eq.
(1)
.Figure
2
shows this energy as a
function of characteristic frequency assuming a galactic
source at a distance of 10 kpc.
2
When taking into account
the results of all three algorithms, this emission energy is not
strongly dependent on the type of waveform (with excep-
tions on an algorithm-by-algorithm basis, as described
above). Figure
2
can easily be converted to other distances
by applying the scaling relation suggested by Eq.
(1)
.
Previous studies
[14]
have published similar emission-
energy-vs-frequency plots at a detection threshold of 1 in
8 yr. We note that the current results, when evaluated at this
higher-FAR threshold, are roughly an order of magnitude
more sensitive than these previous results, due mainly to the
improvement in detector sensitivites.
B. Binary black holes mergers
We also consider a set of astrophysical waveforms
using models of merging of binary black hole systems.
Specifically, we choose the SEOBNRv2 model as imple-
mented in the LAL software library
[34,35]
. The wave-
forms are generated with an initial frequency of 15 Hz. The
simulated binary systems are isotropically located in the
sky and isotropically oriented. The total redshifted mass of
the system in the detector frame
3
is distributed uniformly
between 10 and 150
M
⊙
, a range that encompasses the total
masses of both GW150914 and GW151226
[22]
. The black
hole spins are aligned with the binary angular momentum,
and the magnitude of the dimensionless spin vector,
a
1
;
2
,
is uniformly distributed between 0 and 0.99. We neglect
any cosmological corrections, such as normalizing our
spatial distribution to be constant in comoving volume.
We generate three different injection sets, each one with a
mass ratio
q
¼
m
2
=m
1
chosen from the set
f
0
.
25
;
0
.
5
;
1
.
0
g
(where
m
1
is by definition the more massive object).
In Fig.
3
, we compare the sensitive luminosity radius
[36]
as a function of the total redshifted mass in the detector
frame. While systems inside this distance may be missed
and systems outside of it may be detected depending on
their sky position and orientation, this sensitive radius
provides a
“
rule-of-thumb
”
determination on whether or
not this burst search will detect a system
’
s GW transients.
We can see that for systems like GW150914 (
∼
70
M
⊙
[37]
)
and GW151226 (
∼
20
M
⊙
[22]
), the search ranges at the
FAR of
1
=
100
yr are approximately 500
–
700 and 100
–
200 Mpc, respectively. These ranges demonstrates why
this search detects GW150914 (
∼
400
Mpc
[37]
) but not
GW151226 (
∼
400
Mpc
[22]
). Even though the two
sources are at a similar luminosity distance, this burst
search is less efficient at detecting low-mass BBH systems.
This behavior is true for two reasons: lower-mass systems
emit less energy into GWs than higher-mass systems, and
this energy is distributed over a longer duration of time.
These two features make it more difficult for nontemplated
FIG. 2. GW emission energy, in solar masses, at 50% detection
efficiency for standard-candle sources emitting at 10 kpc for the
non-GA waveforms listed in Table
I
. These results can be scaled
to any reference distance
r
0
using
E
GW
∝
r
2
0
.
2
Eq.
(1)
holds under the assumption that the source emits
gravitational waves monochromatically. For most of our injec-
tions, this conditional is well-approximated by the central
frequency of the injection. However, because Gaussians have a
central frequency of 0 Hz, they are only detectable due to their
broadband nature. Thus, Eq.
(1)
does not give physically mean-
ingful results for Gaussian injections, and we neglect them in
Fig.
2
.
3
Given the luminosity distance of the system, one can assume a
cosmology and calculate its redshift
z
. The system
’
s total mass in
the source frame can then be obtained by dividing the total
redshifted mass in the detector frame by (
1
þ
z
).
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algorithms to extract the GW signal from the detector noise
as compared to searches based on templates.
V. RESULTS
The most significant event and only detection established
in this search is GW150914
[3]
, which is independently
confirmed by all three algorithms. Specifically, it is found
by cWB in the C3 class of the low-frequency analysis with
an estimated FAR of less than 1 in 350 yr, by oLIB in the
“
high-
Q
”
class with an estimated FAR of less than 1 in
230 yr, and by BayesWave with an estimated FAR of less
than 1 in 1000 yr.
4
These results are less precise but
consistent with Ref.
[3]
.
All other events generated by the analyses are consistent
with the accidental noise coincidence rates. To be specific,
there are no other events found above the SNR thresholds in
either the low-
Q
class of oLIB or the entire BayesWave
analysis bin. The rate of other events in the oLIB high-
Q
bin are consistent with the accidental noise coincidence
rates within 1 sigma. The event in the cWB analysis with
the second-lowest FAR belongs to the high frequency
search, with a false-alarm probability of about 0.2.
These results set constraints on the population of
transient GW sources within the volume of the Universe
that the detectors were sensitive to during O1. Again, all of
the results in this section refer to a FAR detection threshold
of 1 in 100 yr.
We estimate the limits on the rate density of generic non-
BBH-like GW-burst sources in Fig.
4
by removing the
known BBH detections GW150914 and GW151226 from
our analysis. We emphasize that, although we remove
the resolved BBH detections from our analysis, these
upper limits may be contaminated by any unresolved
BBH signals still present in the data. We use the sine-
Gaussian injection set as a representative morphology and
present our cWB rate-density estimates as a function of
their characteristic frequencies. The bands represent the
90% confidence intervals on rate density
[14]
, calculated
using the Feldman-Cousins formalism for zero background
events
[38]
. The frequency-dependent variation among the
FIG. 3. Acomparisonofthesensitiveluminosityradii
[7]
in Mpc, as a function of the total redshifted masses in the detector frame, among the
three algorithms. The radii are binned according to mass ratio
q
(from left to right
q
¼
1
, 0.5, 0.25) and effective spin
χ
eff
, defined in Ref.
[7]
.
The three ranges of spin refer to aligned (
0
.
33
<
χ
eff
<
1
), nonspinning (
−
0
.
33
<
χ
eff
<
0
.
33
), and antialigned (
−
1
<
χ
eff
<
−
0
.
33
).
FIG. 4. The 90% confidence intervals of rate density given by
the cWB pipeline for the sine-Gaussian waveforms listed in
Table
I
. This plot assumes zero detections, zero background, and
that
1
M
⊙
c
2
of energy is emitted in gravitational waves. These
results can be scaled to any emission energy
E
GW
using
rate density
∝
E
−
3
2
GW
. The arrow markers signify that the con-
fidence intervals extend to zero.
4
Because GW150914 was louder than any of the background
events in this search, we can only provide the relatively unprecise
upper limits on FAR listed above.
B. P. ABBOTT
et al.
PHYSICAL REVIEW D
95,
042003 (2017)
042003-6
upper limits is due to the sine-Gaussians falling into
different cWB search classes as a result of their specific
value of
Q
. For a given value of
Q
, the results follow a
smoother frequency dependence. These results are not
directly comparable with those from previous runs
[14]
because of the different FAR detection thresholds.
However, we note that at the previously used FAR detection
threshold of 1 in 8 yr our search lowers these upper limits
by about an order of magnitude across all frequencies. The
sensitivity improvements of the detectors and pipelines
allow us to make these stricter rate statements even though
we analyzed less live time compared to Ref.
[14]
(less than
50 days compared to 1.7 yr). Figure
4
assumes
1
M
⊙
c
2
of
gravitational-wave energy has been emitted from the
source, but this can be scaled to any emission energy
E
GW
by using Eq.
(1)
. Note that the rate density scales
as
∝
E
−
3
2
GW
.
VI. DISCUSSION
This paper reports the results for the search for short-
duration GW in the first Advanced LIGO observing run,
with minimal assumptions on the signal waveform, direc-
tion, or arrival time. The two LIGO detectors, Livingston
and Hanford, were operating from mid-September 2015
to mid-January 2016, with a greater sensitivity to GWs
than any previous LIGO-Virgo run. This search has been
performed considering two end-to-end algorithms and a
followup algorithm.
The only detection established in this search is the
GW150914 event, a binary system consisting of two black
holes merging to form a single one
[3]
. The other known
black hole detection
[20]
falls below the sensitivity of this
search, and all other events in the search result are consistent
with accidental noise coincidences between the detectors.
We report the minimum GW emission energy needed to
detect at least half of the transient events emitted within
some fiducial distance. These energies depend primarily on
the signal frequency and are approximately constant over
the different models of GW emission morphology. We also
estimate rate-density limits on non-BBH transient sources
as a function of their frequency and their gravitational-wave
emission energy.
The interferometric detectors LIGO and Virgo are cur-
rently being upgraded for the next scientific run. LIGO
should improve its sensitivity over the next few years, Virgo
should soon come online, and the implementation of
KAGRA and LIGO India is also in progress. All of these
improvements will allow this type of unmodeled search to
achieve a better sensitivity in the future
[6]
.
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 European Gravitational
Observatory (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 &
Engineering Research Board, 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; the Lyon Institute of
Origins; 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,
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 the
provision of computational resources.
ALL-SKY SEARCH FOR SHORT GRAVITATIONAL-WAVE
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B. P. Abbott,
1
R. Abbott,
1
T. D. Abbott,
2
M. R. Abernathy,
3
F. Acernese,
4,5
K. Ackley,
6
C. Adams,
7
T. Adams,
8
P. Addesso,
9
R. X. Adhikari,
1
V. B. Adya,
10
C. Affeldt,
10
M. Agathos,
11
K. Agatsuma,
11
N. Aggarwal,
12
O. D. Aguiar,
13
L. Aiello,
14,15
A. Ain,
16
B. Allen,
10,17,18
A. Allocca,
19,20
P. A. Altin,
21
A. Ananyeva,
1
S. B. Anderson,
1
W. G. Anderson,
17
S. Appert,
1
K. Arai,
1
M. C. Araya,
1
J. S. Areeda,
22
N. Arnaud,
23
K. G. Arun,
24
S. Ascenzi,
25,15
G. Ashton,
10
M. Ast,
26
S. M. Aston,
7
P. Astone,
27
P. Aufmuth,
18
C. Aulbert,
10
A. Avila-Alvarez,
22
S. Babak,
28
P. Bacon,
29
M. K. M. Bader,
11
P. T. Baker,
30
F. Baldaccini,
31,32
G. Ballardin,
33
S. W. Ballmer,
34
J. C. Barayoga,
1
S. E. Barclay,
35
B. C. Barish,
1
D. Barker,
36
F. Barone,
4,5
B. Barr,
35
L. Barsotti,
12
M. Barsuglia,
29
D. Barta,
37
J. Bartlett,
36
I. Bartos,
38
R. Bassiri,
39
A. Basti,
19,20
J. C. Batch,
36
C. Baune,
10
V. Bavigadda,
33
M. Bazzan,
40,41
C. Beer,
10
M. Bejger,
42
I. Belahcene,
23
M. Belgin,
43
A. S. Bell,
35
B. K. Berger,
1
G. Bergmann,
10
C. P. L. Berry,
44
D. Bersanetti,
45,46
A. Bertolini,
11
J. Betzwieser,
7
S. Bhagwat,
34
R. Bhandare,
47
I. A. Bilenko,
48
G. Billingsley,
1
C. R. Billman,
6
J. Birch,
7
R. Birney,
49
O. Birnholtz,
10
S. Biscans,
12,1
A. Bisht,
18
M. Bitossi,
33
C. Biwer,
34
M. A. Bizouard,
23
J. K. Blackburn,
1
J. Blackman,
50
C. D. Blair,
51
D. G. Blair,
51
R. M. Blair,
36
S. Bloemen,
52
O. Bock,
10
M. Boer,
53
G. Bogaert,
53
A. Bohe,
28
F. Bondu,
54
R. Bonnand,
8
B. A. Boom,
11
R. Bork,
1
V. Boschi,
19,20
S. Bose,
55,16
Y. Bouffanais,
29
A. Bozzi,
33
C. Bradaschia,
20
P. R. Brady,
17
V. B. Braginsky,
48
,
†
M. Branchesi,
56,57
J. E. Brau,
58
T. Briant,
59
A. Brillet,
53
M. Brinkmann,
10
V. Brisson,
23
P. Brockill,
17
J. E. Broida,
60
A. F. Brooks,
1
D. A. Brown,
34
D. D. Brown,
44
N. M. Brown,
12
S. Brunett,
1
C. C. Buchanan,
2
A. Buikema,
12
T. Bulik,
61
H. J. Bulten,
62,11
A. Buonanno,
28,63
D. Buskulic,
8
C. Buy,
29
R. L. Byer,
39
M. Cabero,
10
L. Cadonati,
43
G. Cagnoli,
64,65
C. Cahillane,
1
J. Calderón Bustillo,
43
T. A. Callister,
1
E. Calloni,
66,5
J. B. Camp,
67
M. Canepa,
45,46
K. C. Cannon,
68
H. Cao,
69
J. Cao,
70
C. D. Capano,
10
E. Capocasa,
29
F. Carbognani,
33
S. Caride,
71
J. Casanueva Diaz,
23
C. Casentini,
25,15
S. Caudill,
17
M. Cavaglià,
72
F. Cavalier,
23
R. Cavalieri,
33
G. Cella,
20
C. B. Cepeda,
1
L. Cerboni Baiardi,
56,57
G. Cerretani,
19,20
E. Cesarini,
25,15
S. J. Chamberlin,
73
M. Chan,
35
S. Chao,
74
P. Charlton,
75
E. Chassande-Mottin,
29
B. D. Cheeseboro,
30
H. Y. Chen,
76
Y. Chen,
50
H.-P. Cheng,
6
A. Chincarini,
46
A. Chiummo,
33
T. Chmiel,
77
H. S. Cho,
78
M. Cho,
63
J. H. Chow,
21
N. Christensen,
60
Q. Chu,
51
A. J. K. Chua,
79
S. Chua,
59
S. Chung,
51
G. Ciani,
6
F. Clara,
36
J. A. Clark,
43
F. Cleva,
53
C. Cocchieri,
72
E. Coccia,
14,15
P.-F. Cohadon,
59
A. Colla,
80,27
C. G. Collette,
81
L. Cominsky,
82
M. Constancio Jr.,
13
L. Conti,
41
S. J. Cooper,
44
T. R. Corbitt,
2
N. Cornish,
83
A. Corsi,
71
S. Cortese,
33
C. A. Costa,
13
M. W. Coughlin,
60
S. B. Coughlin,
84
J.-P. Coulon,
53
S. T. Countryman,
38
P. Couvares,
1
P. B. Covas,
85
E. E. Cowan,
43
D. M. Coward,
51
M. J. Cowart,
7
D. C. Coyne,
1
R. Coyne,
71
J. D. E. Creighton,
17
T. D. Creighton,
86
J. Cripe,
2
S. G. Crowder,
87
T. J. Cullen,
22
A. Cumming,
35
L. Cunningham,
35
E. Cuoco,
33
T. Dal Canton,
67
S. L. Danilishin,
35
S. D
’
Antonio,
15
K. Danzmann,
18,10
A. Dasgupta,
88
C. F. Da Silva Costa,
6
V. Dattilo,
33
I. Dave,
47
M. Davier,
23
G. S. Davies,
35
D. Davis,
34
E. J. Daw,
89
B. Day,
43
R. Day,
33
S. De,
34
D. DeBra,
39
G. Debreczeni,
37
J. Degallaix,
64
M. De Laurentis,
66,5
S. Deléglise,
59
W. Del Pozzo,
44
T. Denker,
10
T. Dent,
10
V. Dergachev,
28
R. De Rosa,
66,5
R. T. DeRosa,
7
R. DeSalvo,
90
J. Devenson,
49
R. C. Devine,
30
S. Dhurandhar,
16
M. C. Díaz,
86
L. Di Fiore,
5
M. Di Giovanni,
91,92
T. Di Girolamo,
66,5
A. Di Lieto,
19,20
S. Di Pace,
80,27
I. Di Palma,
28,80,27
A. Di Virgilio,
20
Z. Doctor,
76
V. Dolique,
64
F. Donovan,
12
K. L. Dooley,
72
S. Doravari,
10
I. Dorrington,
93
R. Douglas,
35
M. Dovale Álvarez,
44
T. P. Downes,
17
M. Drago,
10
R. W. P. Drever,
1
J. C. Driggers,
36
Z. Du,
70
M. Ducrot,
8
S. E. Dwyer,
36
T. B. Edo,
89
M. C. Edwards,
60
A. Effler,
7
H.-B. Eggenstein,
10
P. Ehrens,
1
J. Eichholz,
1
S. S. Eikenberry,
6
R. A. Eisenstein,
12
R. C. Essick,
12
Z. Etienne,
30
T. Etzel,
1
M. Evans,
12
T. M. Evans,
7
R. Everett,
73
M. Factourovich,
38
V. Fafone,
25,15,14
H. Fair,
34
S. Fairhurst,
93
X. Fan,
70
S. Farinon,
46
B. Farr,
76
W. M. Farr,
44
E. J. Fauchon-Jones,
93
M. Favata,
94
M. Fays,
93
H. Fehrmann,
10
M. M. Fejer,
39
A. Fernández Galiana,
12
I. Ferrante,
19,20
E. C. Ferreira,
13
F. Ferrini,
33
F. Fidecaro,
19,20
I. Fiori,
33
D. Fiorucci,
29
R. P. Fisher,
34
R. Flaminio,
64,95
M. Fletcher,
35
H. Fong,
96
S. S. Forsyth,
43
J.-D. Fournier,
53
S. Frasca,
80,27
F. Frasconi,
20
Z. Frei,
97
A. Freise,
44
R. Frey,
58
V. Frey,
23
E. M. Fries,
1
P. Fritschel,
12
V. V. Frolov,
7
P. Fulda,
6,67
M. Fyffe,
7
H. Gabbard,
10
B. U. Gadre,
16
S. M. Gaebel,
44
J. R. Gair,
98
L. Gammaitoni,
31
S. G. Gaonkar,
16
F. Garufi,
66,5
G. Gaur,
99
V. Gayathri,
100
N. Gehrels,
67
G. Gemme,
46
E. Genin,
33
A. Gennai,
20
J. George,
47
L. Gergely,
101
V. Germain,
8
S. Ghonge,
102
Abhirup Ghosh,
102
Archisman Ghosh,
11,102
S. Ghosh,
52,11
J. A. Giaime,
2,7
ALL-SKY SEARCH FOR SHORT GRAVITATIONAL-WAVE
...
PHYSICAL REVIEW D
95,
042003 (2017)
042003-9
K. D. Giardina,
7
A. Giazotto,
20
K. Gill,
103
A. Glaefke,
35
E. Goetz,
10
R. Goetz,
6
L. Gondan,
97
G. González,
2
J. M. Gonzalez Castro,
19,20
A. Gopakumar,
104
M. L. Gorodetsky,
48
S. E. Gossan,
1
M. Gosselin,
33
R. Gouaty,
8
A. Grado,
105,5
C. Graef,
35
M. Granata,
64
A. Grant,
35
S. Gras,
12
C. Gray,
36
G. Greco,
56,57
A. C. Green,
44
P. Groot,
52
H. Grote,
10
S. Grunewald,
28
G. M. Guidi,
56,57
X. Guo,
70
A. Gupta,
16
M. K. Gupta,
88
K. E. Gushwa,
1
E. K. Gustafson,
1
R. Gustafson,
106
J. J. Hacker,
22
B. R. Hall,
55
E. D. Hall,
1
G. Hammond,
35
M. Haney,
104
M. M. Hanke,
10
J. Hanks,
36
C. Hanna,
73
J. Hanson,
7
T. Hardwick,
2
J. Harms,
56,57
G. M. Harry,
3
I. W. Harry,
28
M. J. Hart,
35
M. T. Hartman,
6
C.-J. Haster,
44,96
K. Haughian,
35
J. Healy,
107
A. Heidmann,
59
M. C. Heintze,
7
H. Heitmann,
53
P. Hello,
23
G. Hemming,
33
M. Hendry,
35
I. S. Heng,
35
J. Hennig,
35
J. Henry,
107
A. W. Heptonstall,
1
M. Heurs,
10,18
S. Hild,
35
D. Hoak,
33
D. Hofman,
64
K. Holt,
7
D. E. Holz,
76
P. Hopkins,
93
J. Hough,
35
E. A. Houston,
35
E. J. Howell,
51
Y. M. Hu,
10
E. A. Huerta,
108
D. Huet,
23
B. Hughey,
103
S. Husa,
85
S. H. Huttner,
35
T. Huynh-Dinh,
7
N. Indik,
10
D. R. Ingram,
36
R. Inta,
71
H. N. Isa,
35
J.-M. Isac,
59
M. Isi,
1
T. Isogai,
12
B. R. Iyer,
102
K. Izumi,
36
T. Jacqmin,
59
K. Jani,
43
P. Jaranowski,
109
S. Jawahar,
110
F. Jiménez-Forteza,
85
W. W. Johnson,
2
D. I. Jones,
111
R. Jones,
35
R. J. G. Jonker,
11
L. Ju,
51
J. Junker,
10
C. V. Kalaghatgi,
93
S. Kandhasamy,
72
G. Kang,
78
J. B. Kanner,
1
S. Karki,
58
K. S. Karvinen,
10
M. Kasprzack,
2
E. Katsavounidis,
12
W. Katzman,
7
S. Kaufer,
18
T. Kaur,
51
K. Kawabe,
36
F. Kéfélian,
53
D. Keitel,
85
D. B. Kelley,
34
R. Kennedy,
89
J. S. Key,
112
F. Y. Khalili,
48
I. Khan,
14
S. Khan,
93
Z. Khan,
88
E. A. Khazanov,
113
N. Kijbunchoo,
36
Chunglee Kim,
114
J. C. Kim,
115
Whansun Kim,
116
W. Kim,
69
Y.-M. Kim,
117,114
S. J. Kimbrell,
43
E. J. King,
69
P. J. King,
36
R. Kirchhoff,
10
J. S. Kissel,
36
B. Klein,
84
L. Kleybolte,
26
S. Klimenko,
6
P. Koch,
10
S. M. Koehlenbeck,
10
S. Koley,
11
V. Kondrashov,
1
A. Kontos,
12
M. Korobko,
26
W. Z. Korth,
1
I. Kowalska,
61
D. B. Kozak,
1
C. Krämer,
10
V. Kringel,
10
B. Krishnan,
10
A. Królak,
118,119
G. Kuehn,
10
P. Kumar,
96
R. Kumar,
88
L. Kuo,
74
A. Kutynia,
118
B. D. Lackey,
28,34
M. Landry,
36
R. N. Lang,
17
J. Lange,
107
B. Lantz,
39
R. K. Lanza,
12
A. Lartaux-Vollard,
23
P. D. Lasky,
120
M. Laxen,
7
A. Lazzarini,
1
C. Lazzaro,
41
P. Leaci,
80,27
S. Leavey,
35
E. O. Lebigot,
29
C. H. Lee,
117
H. K. Lee,
121
H. M. Lee,
114
K. Lee,
35
J. Lehmann,
10
A. Lenon,
30
M. Leonardi,
91,92
J. R. Leong,
10
N. Leroy,
23
N. Letendre,
8
Y. Levin,
120
T. G. F. Li,
122
A. Libson,
12
T. B. Littenberg,
123
J. Liu,
51
N. A. Lockerbie,
110
A. L. Lombardi,
43
L. T. London,
93
J. E. Lord,
34
M. Lorenzini,
14,15
V. Loriette,
124
M. Lormand,
7
G. Losurdo,
20
J. D. Lough,
10,18
G. Lovelace,
22
H. Lück,
18,10
A. P. Lundgren,
10
R. Lynch,
12
Y. Ma,
50
S. Macfoy,
49
B. Machenschalk,
10
M. MacInnis,
12
D. M. Macleod,
2
F. Magaña-Sandoval,
34
E. Majorana,
27
I. Maksimovic,
124
V. Malvezzi,
25,15
N. Man,
53
V. Mandic,
125
V. Mangano,
35
G. L. Mansell,
21
M. Manske,
17
M. Mantovani,
33
F. Marchesoni,
126,32
F. Marion,
8
S. Márka,
38
Z. Márka,
38
A. S. Markosyan,
39
E. Maros,
1
F. Martelli,
56,57
L. Martellini,
53
I. W. Martin,
35
D. V. Martynov,
12
K. Mason,
12
A. Masserot,
8
T. J. Massinger,
1
M. Masso-Reid,
35
S. Mastrogiovanni,
80,27
F. Matichard,
12,1
L. Matone,
38
N. Mavalvala,
12
N. Mazumder,
55
R. McCarthy,
36
D. E. McClelland,
21
S. McCormick,
7
C. McGrath,
17
S. C. McGuire,
127
G. McIntyre,
1
J. McIver,
1
D. J. McManus,
21
T. McRae,
21
S. T. McWilliams,
30
D. Meacher,
53,73
G. D. Meadors,
28,10
J. Meidam,
11
A. Melatos,
128
G. Mendell,
36
D. Mendoza-Gandara,
10
R. A. Mercer,
17
E. L. Merilh,
36
M. Merzougui,
53
S. Meshkov,
1
C. Messenger,
35
C. Messick,
73
R. Metzdorff,
59
P. M. Meyers,
125
F. Mezzani,
27,80
H. Miao,
44
C. Michel,
64
H. Middleton,
44
E. E. Mikhailov,
129
L. Milano,
66,5
A. L. Miller,
6,80,27
A. Miller,
84
B. B. Miller,
84
J. Miller,
12
M. Millhouse,
83
Y. Minenkov,
15
J. Ming,
28
S. Mirshekari,
130
C. Mishra,
102
S. Mitra,
16
V. P. Mitrofanov,
48
G. Mitselmakher,
6
R. Mittleman,
12
A. Moggi,
20
M. Mohan,
33
S. R. P. Mohapatra,
12
M. Montani,
56,57
B. C. Moore,
94
C. J. Moore,
79
D. Moraru,
36
G. Moreno,
36
S. R. Morriss,
86
B. Mours,
8
C. M. Mow-Lowry,
44
G. Mueller,
6
A. W. Muir,
93
Arunava Mukherjee,
102
D. Mukherjee,
17
S. Mukherjee,
86
N. Mukund,
16
A. Mullavey,
7
J. Munch,
69
E. A. M. Muniz,
22
P. G. Murray,
35
A. Mytidis,
6
K. Napier,
43
I. Nardecchia,
25,15
L. Naticchioni,
80,27
G. Nelemans,
52,11
T. J. N. Nelson,
7
M. Neri,
45,46
M. Nery,
10
A. Neunzert,
106
J. M. Newport,
3
G. Newton,
35
T. T. Nguyen,
21
S. Nissanke,
52,11
A. Nitz,
10
A. Noack,
10
F. Nocera,
33
D. Nolting,
7
M. E. N. Normandin,
86
L. K. Nuttall,
34
J. Oberling,
36
E. Ochsner,
17
E. Oelker,
12
G. H. Ogin,
131
J. J. Oh,
116
S. H. Oh,
116
F. Ohme,
93,10
M. Oliver,
85
P. Oppermann,
10
Richard J. Oram,
7
B. O
’
Reilly,
7
R. O
’
Shaughnessy,
107
D. J. Ottaway,
69
H. Overmier,
7
B. J. Owen,
71
A. E. Pace,
73
J. Page,
123
A. Pai,
100
S. A. Pai,
47
J. R. Palamos,
58
O. Palashov,
113
C. Palomba,
27
A. Pal-Singh,
26
H. Pan,
74
C. Pankow,
84
F. Pannarale,
93
B. C. Pant,
47
F. Paoletti,
33,20
A. Paoli,
33
M. A. Papa,
28,17,10
H. R. Paris,
39
W. Parker,
7
D. Pascucci,
35
A. Pasqualetti,
33
R. Passaquieti,
19,20
D. Passuello,
20
B. Patricelli,
19,20
B. L. Pearlstone,
35
M. Pedraza,
1
R. Pedurand,
64,132
L. Pekowsky,
34
A. Pele,
7
S. Penn,
133
C. J. Perez,
36
A. Perreca,
1
L. M. Perri,
84
H. P. Pfeiffer,
96
M. Phelps,
35
O. J. Piccinni,
80,27
M. Pichot,
53
F. Piergiovanni,
56,57
V. Pierro,
9
G. Pillant,
33
L. Pinard,
64
I. M. Pinto,
9
M. Pitkin,
35
M. Poe,
17
R. Poggiani,
19,20
P. Popolizio,
33
A. Post,
10
J. Powell,
35
J. Prasad,
16
J. W. W. Pratt,
103
V. Predoi,
93
T. Prestegard,
125,17
M. Prijatelj,
10,33
M. Principe,
9
S. Privitera,
28
G. A. Prodi,
91,92
L. G. Prokhorov,
48
O. Puncken,
10
M. Punturo,
32
P. Puppo,
27
M. Pürrer,
28
H. Qi,
17
J. Qin,
51
S. Qiu,
120
V. Quetschke,
86
E. A. Quintero,
1
R. Quitzow-James,
58
F. J. Raab,
36
D. S. Rabeling,
21
B. P. ABBOTT
et al.
PHYSICAL REVIEW D
95,
042003 (2017)
042003-10
H. Radkins,
36
P. Raffai,
97
S. Raja,
47
C. Rajan,
47
M. Rakhmanov,
86
P. Rapagnani,
80,27
V. Raymond,
28
M. Razzano,
19,20
V. Re,
25
J. Read,
22
T. Regimbau,
53
L. Rei,
46
S. Reid,
49
D. H. Reitze,
1,6
H. Rew,
129
S. D. Reyes,
34
E. Rhoades,
103
F. Ricci,
80,27
K. Riles,
106
M. Rizzo,
107
N. A. Robertson,
1,35
R. Robie,
35
F. Robinet,
23
A. Rocchi,
15
L. Rolland,
8
J. G. Rollins,
1
V. J. Roma,
58
R. Romano,
4,5
J. H. Romie,
7
D. Rosi
ń
ska,
134,42
S. Rowan,
35
A. Rüdiger,
10
P. Ruggi,
33
K. Ryan,
36
S. Sachdev,
1
T. Sadecki,
36
L. Sadeghian,
17
M. Sakellariadou,
135
L. Salconi,
33
M. Saleem,
100
F. Salemi,
10
A. Samajdar,
136
L. Sammut,
120
L. M. Sampson,
84
E. J. Sanchez,
1
V. Sandberg,
36
J. R. Sanders,
34
B. Sassolas,
64
B. S. Sathyaprakash,
73,93
P. R. Saulson,
34
O. Sauter,
106
R. L. Savage,
36
A. Sawadsky,
18
P. Schale,
58
J. Scheuer,
84
E. Schmidt,
103
J. Schmidt,
10
P. Schmidt,
1,50
R. Schnabel,
26
R. M. S. Schofield,
58
A. Schönbeck,
26
E. Schreiber,
10
D. Schuette,
10,18
B. F. Schutz,
93,28
S. G. Schwalbe,
103
J. Scott,
35
S. M. Scott,
21
D. Sellers,
7
A. S. Sengupta,
137
D. Sentenac,
33
V. Sequino,
25,15
A. Sergeev,
113
Y. Setyawati,
52,11
D. A. Shaddock,
21
T. J. Shaffer,
36
M. S. Shahriar,
84
B. Shapiro,
39
P. Shawhan,
63
A. Sheperd,
17
D. H. Shoemaker,
12
D. M. Shoemaker,
43
K. Siellez,
43
X. Siemens,
17
M. Sieniawska,
42
D. Sigg,
36
A. D. Silva,
13
A. Singer,
1
L. P. Singer,
67
A. Singh,
28,10,18
R. Singh,
2
A. Singhal,
14
A. M. Sintes,
85
B. J. J. Slagmolen,
21
B. Smith,
7
J. R. Smith,
22
R. J. E. Smith,
1
E. J. Son,
116
B. Sorazu,
35
F. Sorrentino,
46
T. Souradeep,
16
A. P. Spencer,
35
A. K. Srivastava,
88
A. Staley,
38
M. Steinke,
10
J. Steinlechner,
35
S. Steinlechner,
26,35
D. Steinmeyer,
10,18
B. C. Stephens,
17
S. P. Stevenson,
44
R. Stone,
86
K. A. Strain,
35
N. Straniero,
64
G. Stratta,
56,57
S. E. Strigin,
48
R. Sturani,
130
A. L. Stuver,
7
T. Z. Summerscales,
138
L. Sun,
128
S. Sunil,
88
P. J. Sutton,
93
B. L. Swinkels,
33
M. J. Szczepa
ń
czyk,
103
M. Tacca,
29
D. Talukder,
58
D. B. Tanner,
6
M. Tápai,
101
A. Taracchini,
28
R. Taylor,
1
T. Theeg,
10
E. G. Thomas,
44
M. Thomas,
7
P. Thomas,
36
K. A. Thorne,
7
E. Thrane,
120
T. Tippens,
43
S. Tiwari,
14,92
V. Tiwari,
93
K. V. Tokmakov,
110
K. Toland,
35
C. Tomlinson,
89
M. Tonelli,
19,20
Z. Tornasi,
35
C. I. Torrie,
1
D. Töyrä,
44
F. Travasso,
31,32
G. Traylor,
7
D. Trifirò,
72
J. Trinastic,
6
M. C. Tringali,
91,92
L. Trozzo,
139,20
M. Tse,
12
R. Tso,
1
M. Turconi,
53
D. Tuyenbayev,
86
D. Ugolini,
140
C. S. Unnikrishnan,
104
A. L. Urban,
1
S. A. Usman,
93
H. Vahlbruch,
18
G. Vajente,
1
G. Valdes,
86
N. van Bakel,
11
M. van Beuzekom,
11
J. F. J. van den Brand,
62,11
C. Van Den Broeck,
11
D. C. Vander-Hyde,
34
L. van der Schaaf,
11
J. V. van Heijningen,
11
A. A. van Veggel,
35
M. Vardaro,
40,41
V. Varma,
50
S. Vass,
1
M. Vasúth,
37
A. Vecchio,
44
G. Vedovato,
41
J. Veitch,
44
P. J. Veitch,
69
K. Venkateswara,
141
G. Venugopalan,
1
D. Verkindt,
8
F. Vetrano,
56,57
A. Viceré,
56,57
A. D. Viets,
17
S. Vinciguerra,
44
D. J. Vine,
49
J.-Y. Vinet,
53
S. Vitale,
12
T. Vo,
34
H. Vocca,
31,32
C. Vorvick,
36
D. V. Voss,
6
W. D. Vousden,
44
S. P. Vyatchanin,
48
A. R. Wade,
1
L. E. Wade,
77
M. Wade,
77
M. Walker,
2
L. Wallace,
1
S. Walsh,
28,10
G. Wang,
14,57
H. Wang,
44
M. Wang,
44
Y. Wang,
51
R. L. Ward,
21
J. Warner,
36
M. Was,
8
J. Watchi,
81
B. Weaver,
36
L.-W. Wei,
53
M. Weinert,
10
A. J. Weinstein,
1
R. Weiss,
12
L. Wen,
51
P. Weßels,
10
T. Westphal,
10
K. Wette,
10
J. T. Whelan,
107
B. F. Whiting,
6
C. Whittle,
120
D. Williams,
35
R. D. Williams,
1
A. R. Williamson,
93
J. L. Willis,
142
B. Willke,
18,10
M. H. Wimmer,
10,18
W. Winkler,
10
C. C. Wipf,
1
H. Wittel,
10,18
G. Woan,
35
J. Woehler,
10
J. Worden,
36
J. L. Wright,
35
D. S. Wu,
10
G. Wu,
7
W. Yam,
12
H. Yamamoto,
1
C. C. Yancey,
63
M. J. Yap,
21
Hang Yu,
12
Haocun Yu,
12
M. Yvert,
8
A. Zadro
ż
ny,
118
L. Zangrando,
41
M. Zanolin,
103
J.-P. Zendri,
41
M. Zevin,
84
L. Zhang,
1
M. Zhang,
129
T. Zhang,
35
Y. Zhang,
107
C. Zhao,
51
M. Zhou,
84
Z. Zhou,
84
S. J. Zhu,
28,10
X. J. Zhu,
51
M. E. Zucker,
1,12
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
American University, Washington, D.C. 20016, USA
4
Università di Salerno, Fisciano, I-84084 Salerno, Italy
5
INFN, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
6
University of Florida, Gainesville, Florida 32611, USA
7
LIGO Livingston Observatory, Livingston, Louisiana 70754, USA
8
Laboratoire d
’
Annecy-le-Vieux de Physique des Particules (LAPP), Université Savoie Mont Blanc,
CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
9
University of Sannio at Benevento, I-82100 Benevento, Italy
and INFN, Sezione di Napoli, I-80100 Napoli, Italy
10
Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
11
Nikhef, Science Park, 1098 XG Amsterdam, Netherlands
12
LIGO, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
13
Instituto Nacional de Pesquisas Espaciais, 12227-010 São José dos Campos, São Paulo, Brazil
14
INFN, Gran Sasso Science Institute, I-67100 L
’
Aquila, Italy
15
INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
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...
PHYSICAL REVIEW D
95,
042003 (2017)
042003-11
16
Inter-University Centre for Astronomy and Astrophysics, Pune 411007, India
17
University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53201, USA
18
Leibniz Universität Hannover, D-30167 Hannover, Germany
19
Università di Pisa, I-56127 Pisa, Italy
20
INFN, Sezione di Pisa, I-56127 Pisa, Italy
21
Australian National University, Canberra, Australian Capital Territory 0200, Australia
22
California State University Fullerton, Fullerton, California 92831, USA
23
LAL, Univ. Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, F-91898 Orsay, France
24
Chennai Mathematical Institute, Chennai 603103, India
25
Università di Roma Tor Vergata, I-00133 Roma, Italy
26
Universität Hamburg, D-22761 Hamburg, Germany
27
INFN, Sezione di Roma, I-00185 Roma, Italy
28
Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-14476 Potsdam-Golm, Germany
29
APC, AstroParticule et Cosmologie, Université Paris Diderot, CNRS/IN2P3, CEA/Irfu,
Observatoire de Paris, Sorbonne Paris Cité, F-75205 Paris Cedex 13, France
30
West Virginia University, Morgantown, West Virginia 26506, USA
31
Università di Perugia, I-06123 Perugia, Italy
32
INFN, Sezione di Perugia, I-06123 Perugia, Italy
33
European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
34
Syracuse University, Syracuse, New York 13244, USA
35
SUPA, University of Glasgow, Glasgow G12 8QQ, United Kingdom
36
LIGO Hanford Observatory, Richland, Washington 99352, USA
37
Wigner RCP, RMKI, H-1121 Budapest, Konkoly Thege Miklós út 29-33, Hungary
38
Columbia University, New York, New York 10027, USA
39
Stanford University, Stanford, California 94305, USA
40
Università di Padova, Dipartimento di Fisica e Astronomia, I-35131 Padova, Italy
41
INFN, Sezione di Padova, I-35131 Padova, Italy
42
Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, 00-716 Warsaw, Poland
43
Center for Relativistic Astrophysics and School of Physics, Georgia Institute of Technology,
Atlanta, Georgia 30332, USA
44
University of Birmingham, Birmingham B15 2TT, United Kingdom
45
Università degli Studi di Genova, I-16146 Genova, Italy
46
INFN, Sezione di Genova, I-16146 Genova, Italy
47
RRCAT, Indore, Madhya Pradesh 452013, India
48
Faculty of Physics, Lomonosov Moscow State University, Moscow 119991, Russia
49
SUPA, University of the West of Scotland, Paisley PA1 2BE, United Kingdom
50
Caltech CaRT, Pasadena, California 91125, USA
51
University of Western Australia, Crawley, Western Australia 6009, Australia
52
Department of Astrophysics/IMAPP, Radboud University Nijmegen,
P.O. Box 9010, 6500 GL Nijmegen, Netherlands
53
Artemis, Université Côte d
’
Azur, CNRS, Observatoire Côte d
’
Azur, CS 34229,
F-06304 Nice Cedex 4, France
54
Institut de Physique de Rennes, CNRS, Université de Rennes 1, F-35042 Rennes, France
55
Washington State University, Pullman, Washington 99164, USA
56
Università degli Studi di Urbino
’
Carlo Bo
’
, I-61029 Urbino, Italy
57
INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy
58
University of Oregon, Eugene, Oregon 97403, USA
59
Laboratoire Kastler Brossel, UPMC-Sorbonne Universités, CNRS, ENS-PSL Research University,
Collège de France, F-75005 Paris, France
60
Carleton College, Northfield, Minnesota 55057, USA
61
Astronomical Observatory Warsaw University, 00-478 Warsaw, Poland
62
VU University Amsterdam, 1081 HV Amsterdam, Netherlands
63
University of Maryland, College Park, Maryland 20742, USA
64
Laboratoire des Matériaux Avancés (LMA), CNRS/IN2P3, F-69622 Villeurbanne, France
65
Université Claude Bernard Lyon 1, F-69622 Villeurbanne, France
66
Università di Napoli
’
Federico II
’
, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
67
NASA/Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
68
RESCEU, University of Tokyo, Tokyo, 113-0033, Japan
69
University of Adelaide, Adelaide, South Australia 5005, Australia
70
Tsinghua University, Beijing 100084, China
B. P. ABBOTT
et al.
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Texas Tech University, Lubbock, Texas 79409, USA
72
The University of Mississippi, University, Mississippi 38677, USA
73
The Pennsylvania State University, University Park, Pennsylvania 16802, USA
74
National Tsing Hua University, Hsinchu City, 30013 Taiwan, Republic of China
75
Charles Sturt University, Wagga Wagga, New South Wales 2678, Australia
76
University of Chicago, Chicago, Illinois 60637, USA
77
Kenyon College, Gambier, Ohio 43022, USA
78
Korea Institute of Science and Technology Information, Daejeon 305-806, Korea
79
University of Cambridge, Cambridge CB2 1TN, United Kingdom
80
Università di Roma
’
La Sapienza
’
, I-00185 Roma, Italy
81
University of Brussels, Brussels 1050, Belgium
82
Sonoma State University, Rohnert Park, California 94928, USA
83
Montana State University, Bozeman, Montana 59717, USA
84
Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA),
Northwestern University, Evanston, Illinois 60208, USA
85
Universitat de les Illes Balears, IAC3
—
IEEC, E-07122 Palma de Mallorca, Spain
86
The University of Texas Rio Grande Valley, Brownsville, Texas 78520, USA
87
Bellevue College, Bellevue, Washington 98007, USA
88
Institute for Plasma Research, Bhat, Gandhinagar 382428, India
89
The University of Sheffield, Sheffield S10 2TN, United Kingdom
90
California State University, Los Angeles, 5154 State University Dr, Los Angeles, California 90032, USA
91
Università di Trento, Dipartimento di Fisica, I-38123 Povo, Trento, Italy
92
INFN, Trento Institute for Fundamental Physics and Applications, I-38123 Povo, Trento, Italy
93
Cardiff University, Cardiff CF24 3AA, United Kingdom
94
Montclair State University, Montclair, New Jersey 07043, USA
95
National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan
96
Canadian Institute for Theoretical Astrophysics, University of Toronto,
Toronto, Ontario M5S 3H8, Canada
97
MTA Eötvös University,
“
Lendulet
”
Astrophysics Research Group, Budapest 1117, Hungary
98
School of Mathematics, University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
99
University and Institute of Advanced Research, Gandhinagar, Gujarat 382007, India
100
IISER-TVM, CET Campus, Trivandrum Kerala 695016, India
101
University of Szeged, Dóm tér 9, Szeged 6720, Hungary
102
International Centre for Theoretical Sciences, Tata Institute of Fundamental Research,
Bengaluru 560089, India
103
Embry-Riddle Aeronautical University, Prescott, Arizona 86301, USA
104
Tata Institute of Fundamental Research, Mumbai 400005, India
105
INAF, Osservatorio Astronomico di Capodimonte, I-80131 Napoli, Italy
106
University of Michigan, Ann Arbor, Michigan 48109, USA
107
Rochester Institute of Technology, Rochester, New York 14623, USA
108
NCSA, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
109
University of Bia
ł
ystok, 15-424 Bia
ł
ystok, Poland
110
SUPA, University of Strathclyde, Glasgow G1 1XQ, United Kingdom
111
University of Southampton, Southampton SO17 1BJ, United Kingdom
112
University of Washington Bothell, 18115 Campus Way NE, Bothell, Washington 98011, USA
113
Institute of Applied Physics, Nizhny Novgorod 603950, Russia
114
Seoul National University, Seoul 151-742, Korea
115
Inje University Gimhae, 621-749 South Gyeongsang, Korea
116
National Institute for Mathematical Sciences, Daejeon 305-390, Korea
117
Pusan National University, Busan 609-735, Korea
118
NCBJ, 05-400
Ś
wierk-Otwock, Poland
119
Institute of Mathematics, Polish Academy of Sciences, 00656 Warsaw, Poland
120
Monash University, Victoria 3800, Australia
121
Hanyang University, Seoul 133-791, Korea
122
The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
123
University of Alabama in Huntsville, Huntsville, Alabama 35899, USA
124
ESPCI, CNRS, F-75005 Paris, France
125
University of Minnesota, Minneapolis, Minnesota 55455, USA
126
Università di Camerino, Dipartimento di Fisica, I-62032 Camerino, Italy
127
Southern University and A&M College, Baton Rouge, Louisiana 70813, USA
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