Characterization of transient noise in Advanced LIGO relevant to
gravitational wave signal GW150914
A full list of authors and affiliations appears at the end of the article.
Abstract
On September 14, 2015, a gravitational wave signal from a coalescing black hole binary system
was observed by the Advanced LIGO detectors. This paper describes the transient noise
backgrounds used to determine the significance of the event (designated GW150914) and presents
the results of investigations into potential correlated or uncorrelated sources of transient noise in
the detectors around the time of the event. The detectors were operating nominally at the time of
GW150914. We have ruled out environmental influences and non-Gaussian instrument noise at
either LIGO detector as the cause of the observed gravitational wave signal.
1. Introduction
A gravitational wave signal, denoted GW150914, has been detected by the Advanced LIGO
detectors [1]. The recovered waveform indicated the source was a binary black hole system
with component masses and , which coalesced at a distance of Mpc away from Earth. The
significance of the GW150914 event was measured to be greater than 5.1
σ
, corresponding
to a false-alarm rate of less than 1 event per 203 000 years [
1
]. The event, lasting 0.2
seconds in Advanced LIGO’s sensitive frequency range, was detected in independent
searches for modeled compact binary coalescences (CBCs) and for unmodeled gravitational
wave bursts [
2
,
3
].
The US-based detectors, in Hanford, Washington (H1) and in Livingston, Louisiana (L1)
jointly comprise the Laser Interferometer Gravitational-wave Observatory (LIGO). The
detectors are designed to measure spacetime strain induced by passing gravitational waves
using a modified Michelson interferometer with 4 km length arms, as described in [
4
,
5
,
6
].
The detectors were operating in their nominal configuration at the time of GW150914. The
corresponding detector sensitivity is shown in Figure 1; both detectors achieved a best
sensitivity of ~ 10
−23
Hz
−1/2
between roughly 50 and 300 Hz. Peaks in the strain-equivalent
noise amplitude spectral density are due largely to mechanical resonances, mains power
harmonics, and injected signals used for calibration. Non-stationarity in the detector noise
manifests as variations in the level and shape of these sensitivity curves over time.
Even in their nominal state, the detectors’ data contain non-Gaussian noise transients
introduced by behavior of the instruments or complex interactions between the instruments
†
Deceased, May 2015.
‡
Deceased, March 2015.
#
Deceased, May 2012.
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and their environment. For LIGO, the fundamental signature of a transient gravitational
wave signal is a near-simultaneous signal with consistent waveforms in the two detectors.
The rate of coincident noise transients between the independent detector data sets is
estimated by the astrophysical searches using time-shift techniques [
2
,
3
]. A common time-
shift method is to shift the data of one detector relative to the other detector’s data by a time
interval significantly greater than 10 ms, the maximum difference in signal arrival time
between detectors. Coincident triggers in time-shifted data yield a distribution of
background triggers produced solely by the chance coincidence of transient noise. This time-
shifting of the data is performed many times to obtain a representative estimate of the
expected rate of background triggers, as detailed in [
2
,
7
]. The significance of a gravitational
wave event is a measure of the probability that it is a false detection due to coincident noise.
We study the characteristics of background triggers as well as correlations between the
gravitational wave strain data and instrument or environment signals to guide further
detector improvements and increase the sensitivity of the searches.
GW150914 occurred on September 14, 2015 09:50:45 UTC, 28 days into the eighth
engineering run (ER8)
‡
, 3 days into stable data collection with an accurate calibration, and 4
days preceding the scheduled start of the first observing run (O1).
After the event was identified as a highly significant candidate, the software and hardware
configuration of each LIGO detector was held fixed until enough coincident data had been
collected to set a sufficiently accurate upper bound on the false-alarm rate using the time-
shift technique described above. It took roughly six weeks to collect the required ~16 days
of coincident data because low noise operation of the detectors is disrupted by noisy
environmental conditions (such as storms, earthquakes, high ground motion, or
anthropogenic noise sources). During this six week period we only performed non-invasive
maintenance that was required for instrument stability.
The significance of GW150914 was calculated using data taken from September 12, 2015
00:00 through October 20, 2015 13:30 UTC. This data set was analyzed after removing time
segments during which an identified instrumental or environmental noise source coupled to
the gravitational wave strain signal. At these times, any triggered output of the astrophysical
searches would likely be due to noise. These
data quality vetoes
were built on detector
characterization efforts in earlier stages of testing and commissioning of the Advanced
LIGO detectors, as reported in [
8
].
This paper summarizes detector characterization techniques for identification of transient
noise (Section 2). We then present examples of transient noise couplings that can impact the
detectors (Section 3) and discuss techniques used to mitigate the impact of known noise
sources (Section 4). We show that the selected analysis period provides an accurate estimate
of the significance of GW150914 reported in [
1
] by discussing the stability of the search
backgrounds, and presenting the impact of applied data quality vetoes relevant to
GW150914 (Section 5). We also detail the specific checks performed to rule out an
‡
Engineering runs 1–7 served to test hardware and software infrastructure from the stability of instrument performance to the output
of the astrophysical searches. ER8 was the final engineering run, intended to provide a gradual transition between a test of the mature
instrument and search configurations and the continuous operation of an observing run.
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instrumental or environmental noise-transient origin for GW150914, including potentially
correlated noise sources such as global magnetic noise that would not be captured by time-
shift background estimation techniques (Section 6). Similar studies were also performed for
the second most significant event in the CBC search over the analysis period, designated
LVT151012
§
, observed with a false alarm probability of ~2% [
1
,
2
,
9
].
2. Identifying noise sources
In addition to the gravitational wave strain data,
h
(
t
), each of the LIGO detectors also
records over 200,000
auxiliary channels
that monitor instrument behavior and environmental
conditions. These channels witness a broad spectrum of potential coupling mechanisms,
useful for diagnosing instrument faults and identifying noise correlations. Examples of
instrument witness channels include measured angular drift of optics, light transmitted
through a mirror as detected by a set of photodiodes, and actuation signals used to control
optic position in order to maintain optical cavity resonance. In addition to candidate
gravitational wave events, we study background triggers for correlation with trends or
coincident transient noise in auxiliary channels on the broad scale of hours to days. We also
identify correlations on the order of the duration of transient astrophysical signals; a fraction
of a millisecond to a few seconds. Systematic correlations are used to generate data quality
vetoes used by the astrophysical searches to reduce the background, as described in
Appendix A.
An important set of auxiliary channels are the physical environment monitor (PEM) sensors,
which monitor the local surroundings for potential disturbances that may affect the
gravitational wave strain data, such as motion of the ground or optics tables, magnetic field
variations, acoustic disturbances, or potentially, cosmic ray showers [
10
]. A PEM sensor
array is distributed throughout each detector site such that external environmental
disturbances that could influence the detectors are witnessed with a significantly higher
signal-to-noise ratio (SNR) in the PEM sensors than in
h
(
t
). The PEM sensors are detailed in
Appendix B.
The relationship between environmental noise as witnessed by the PEM sensor array and the
gravitational wave strain signal
h
(
t
) is investigated using injection studies, where an
intentional stimulus is introduced and the responses of both PEM sensors and the instrument
are analyzed. These injections ensure that the environmental sensors are more sensitive to
environmental disturbances than the detector is, and also quantify the coupling between the
environment and
h
(
t
). Figure 2 illustrates a magnetic field injection test at the LIGO-
Hanford detector that measured magnetic field coupling to
h
(
t
) as well as the response of the
local magnetometer to the injected field. The frequency-dependent coupling between the
local magnetic field and
h
(
t
) can be calculated from these measurements and used to
accurately predict the response of
h
(
t
) to the presence of a magnetic field, as witnessed by
the local magnetometers. Figure 2 shows an injection performed at one of the strongest
coupling locations, in the building containing the beam splitter and most interferometer
optics. Other magnetic field injection measurements identical to this test were also
§
LIGO-Virgo Trigger (LVT) 151012 (October 12, 2015)
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conducted for other locations throughout the detector site. Similar injection studies were also
conducted for radio, acoustic, and mechanical vibration sources.
3. Potential noise sources
Transient noise in
h
(
t
) must occur within the frequency range targeted by the transient
astrophysical searches to affect the background. This range is dictated by the equivalent
strain noise of the detectors, as shown in Figure 1 for the Hanford and Livingston detectors
during the analysis period.
Motivated by this sensitivity curve, the transient astrophysical searches generally limit the
search frequency range to above 30 Hz and below 2–3 kHz, or roughly the human-audible
range. For example, a binary black hole signal like GW150914 is expected to have power
measurable by the Advanced LIGO detectors between roughly 35 and 250 Hz and sources of
short-duration noise with similar frequency content could impact the background estimation
of such events.
3.1. Uncorrelated noise
The following are examples of uncorrelated local noise features anticipated to be of
particular interest or known to have a significant impact on the gravitational wave search
backgrounds. The contribution of any uncorrelated noise sources is well estimated using
time shifts.
•
Some
anthropogenic noise
sources are likely to produce short duration
transients in
h
(
t
), such as human activity within one of the rooms that houses the
vacuum chambers or infrequent strong ground motion or noise from other nearby
locations. To reduce such vibrational or acoustic noise, detector staff do not enter
the rooms containing the optical components of the detectors when the detectors
are taking data. Any anthropogenic noise that could influence the detector is
monitored by an array of accelerometers, seismometers, and microphones.
•
Earthquakes
can produce ground motion at the detectors with frequencies from
approximately 0.03 to 0.1 Hz or higher if the epicenter is nearby [
10
]. R-waves,
the highest amplitude component of seismic waves from an earthquake [
11
], are
the most likely to adversely impact data quality by rendering the detectors
inoperable or inducing low frequency optic motion that up-converts to higher
frequencies in
h
(
t
) via mechanisms such as bilinear coupling of angular motion
or light scattering [
12
]. A network of seismometers installed at the LIGO
detectors can easily identify earthquake disturbances.
•
Radio Frequency (RF) modulation
sidebands are used to sense and control a
variety of optical cavities within the detector. Two modulations are applied to the
input laser field at 9 and 45 MHz [
6
]. Since the beginning of the analysis period,
sporadic periods of a high rate of loud noise transients have been observed at
LIGO-Hanford due to a fault in the 45 MHz electro-optic modulator driver
system, which then couples to the gravitational wave channel between 10 and
2000 Hz, covering the entire frequency range analyzed by the CBC searches.
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Data associated with this electronic fault were vetoed and not analyzed. The
engineering of this veto, as applied to the GW150914 analysis period, is detailed
in Appendix A.
•
Blip transients
are short noise transients that appear in the gravitational wave
strain channel
h
(
t
) as a symmetric ‘teardrop’ shape in time-frequency space,
typically between 30 and 250 Hz, with the majority of the power appearing at the
lowest frequencies, as seen in Figure 3. They appear in both detectors
independently with modest amplitude. The single detector burst identification
algorithm Omicron, which identifies excess power transients using a generic
sine-Gaussian time-frequency projection [
13
,
14
], will resolve such noise
transients with a signal-to-noise ratio of 10–100. No clear correlation to any
auxiliary channel has yet been identified. As a result, there is currently no veto
available to remove these noise transients from the astrophysical searches. Blip
transients contribute to some of the most significant background triggers in both
the unmodeled burst and modeled CBC searches. The noise transient shown in
Figure 3
‖
is one example.
The impact of noise sources on the astrophysical searches is discussed in Section 5.2.
3.2. Correlated noise
Noise sources that may affect both detectors almost simultaneously could potentially imitate
a gravitational wave event and would not be captured by time shifts in the search
background estimation.
Potential electromagnetic noise sources
include lightning, solar events and solar-wind
driven noise, as well as radio frequency (RF) communication. If electromagnetic noise were
strong enough to affect
h
(
t
), it would be witnessed with high SNR by radio receivers and
magnetometers.
Lightning strikes occur tens of times per second globally. They can excite magnetic
Schumann resonances
, a nearly harmonic series of peaks with a fundamental frequency near
8 Hz (governed by the light travel time around the earth) [
16
,
17
]. However, the magnetic
field amplitudes produced by Schumann resonances are of the order of a picoTesla; too
small to produce strong signals in
h
(
t
) (see Figure 2) [
18
].
Nearby individual lightning strikes can induce transient noise in
h
(
t
) via audio frequency
magnetic fields generated by the lightning currents. However, even large strikes do not
usually produce fields strong enough to be detected by the fluxgate magnetometers at both
detectors simultaneously.
Electromagnetic signals in the audio-frequency band are also produced by human and solar
sources, including solar radio flares and currents of charged particles associated with the
‖
The spectrograms shown in Figures 3, 10, and 13 are generated using a sine-Gaussian basis [
15
] instead of the sinusoidal basis of a
traditional Fast-Fourier Transform.
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solar wind. The strongest solar or geomagnetic events during the analysis period were
studied and no effect in
h
(
t
) was observed at either detector.
Electromagnetic fields that are outside the audio-frequency detection band are a potential
concern because the LIGO detectors use RF modulation and demodulation for optical cavity
control and because of the possibility of accidental demodulation with oscillators in the
electronics systems. RF coupling measured during injection tests indicated that background
RF fields were at least two orders of magnitude too small to influence the detector signal.
The strongest coupling was found to be at the 9 and 45 MHz modulation frequencies used
for control of optical cavities. These frequencies are monitored at both detectors with radio
receivers that were at least two orders of magnitude more sensitive to fluctuations than the
detector.
Cosmic ray showers
produce electromagnetic radiation and particle cascades when a highly
energetic cosmic ray enters the Earth’s atmosphere [
19
]. For even the most energetic
showers, the cosmic ray flux drops effectively to zero within roughly 10 km of the axis of
motion of the original collided particle [
20
], making coincident observation of a cosmic ray
shower between the two detectors highly unlikely. As a precaution, a cosmic-ray detector is
monitored at LIGO-Hanford; no coupling between cosmic ray particles and
h
(
t
) has been
observed.
4. Mitigating noise sources
Ideally, when a noise source is identified, the instrument hardware or software is modified to
reduce the coupling of the noise to
h
(
t
) such that it no longer impacts astrophysical searches.
If mitigating the noise source is not viable, as in the case of data collected prior to an
instrumental improvement, periods of time in which there are significant problems with the
quality of the data are omitted, or
vetoed
, from transient gravitational wave searches through
a procedure similar to those utilized in previous LIGO analyses [
21
].
There are two different types of data quality products that can be applied as vetoes. Data
quality
flags
typically exclude periods of data on the order of seconds to hours when some
reproducible criterion associated with known noise couplings is met [
21
,
22
,
23
,
24
]. For
example, a data quality flag might be defined for periods when any of the photodiodes used
to sense the laser field in the detector were overflowing their analog-to-digital converters.
Data quality
triggers
are short duration vetoes generated by algorithms that identify
significant statistical correlations between a transient in
h
(
t
) and transient noise in auxiliary
channels [
25
,
26
,
27
,
28
].
Data quality products are applied as vetoes in different categories that depend on the severity
of the problem or the impact of individual data quality products on a search’s background.
Data quality flags used in
category 1
collectively indicate times when data should not be
analyzed due to a critical issue with a key detector component not operating in its nominal
configuration. Since category-1-flagged times indicate major known problems with an
instrument they are identically defined across all transient searches. Data quality flags used
in
category 2
collectively indicate times when a noise source with known physical coupling
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to
h
(
t
) is active. Category 2 vetoes are typically applied after the initial processing of data for
a specific search. This approach renders more data useable by the searches because they
require unbroken strides of continuous data of up to 620 seconds for the coherent burst
search and up to 2064 seconds for the CBC searches. There are three considerations for
applying a data quality product as a category 2 veto to an astrophysical search: the physical
noise coupling mechanism must be understood, the associated veto must have a
demonstrated advantageous effect on the background of that search, and the veto must be
safe
.
The
safety
of a veto is a measure of the likelihood that the veto criteria would accidentally
remove a true gravitational wave signal. Veto safety is measured using hardware injection
tests, where a signal is injected into
h
(
t
) by inducing motion of the optics [
25
,
26
,
29
]. If any
auxiliary channels witness a corresponding response to a number of injected signals greater
than expected by chance, these channels are considered
unsafe
and are not used in the
definition of any applied veto.
The effectiveness of each data quality product in reducing the background is measured by
the ratio of its
efficiency
, or the fraction of background triggers it removes from a search, to
its introduced
deadtime
, or the fraction of time a particular flag will remove from the total
duration of the set of analyzable data. Data quality flags used as category 2 vetoes have an
efficiency-to-deadtime ratio for high SNR triggers significantly greater than 1, or the value
expected for random behavior. An example is described in Appendix A.
A third veto category (category 3), applied in the same way as category 2, is generally
reserved for data quality triggers, which are statistically generated, and data quality flags
where the coupling mechanism is not understood.
During the GW150914 analysis period, data quality triggers were applied as category 3 by
burst searches. Times during hardware injection tests were also flagged and removed from
the transient searches.
Modeled CBC searches, which use matched filtering techniques [
2
], apply additional
mitigation methods to target loud noise transients with a duration on the order of a second or
less that are particularly damaging. An accurate power spectral density (PSD) estimate is
required to calculate the amount of signal power that matches a template waveform.
Consequently, noise transients with a large amount of broadband power can corrupt the
analyzed data up to the duration of the strain-equivalent noise PSD estimate, ±8 seconds
from the time of the noise transient. Additionally, a loud, short-duration noise transient can
act as a delta function, which may imprint the impulse response of the matched filter on the
output data, generating triggers. As a result, before analyzing the data the CBC searches
apply a technique called
gating
that smoothly rolls the input data stream off to zero for short-
duration excursions identified as too loud to be consistent with an astrophysical signal [
2
].
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5. Transient search backgrounds
The data set used to calculate the significance of GW150914 is appropriate in both the
stability of the search backgrounds over the analysis period and the judicious application of
data quality vetoes.
5.1. Stability of the period analyzed for GW150914
To illustrate the level of variability of detector performance over the several weeks of data
collected for the analyzed time, Figure 4 shows the maximum sensitive distance of each of
the detectors for the coalescence of a binary black hole system with the same spin and mass
parameters as GW150914 in the detector frame (70 M
⊙
, 0.7). This is calculated as the
distance from Earth at which the coalescence of a binary object pair produces an SNR of 8
in a single detector using matched filtering, assuming optimal sky location and source
orientation. LIGO-Hanford had a mean maximum sensitive distance to GW150914-like
signals of 1906 Mpc during the analysis period, and LIGO-Livingston had a mean of 1697
Mpc.
LIGO-Hanford’s maximum sensitive distance exhibited a 90% range of ~1800–2000 Mpc,
and LIGO-Livingston’s a 90% range of ~1500–1900, which was sufficiently stable to
provide a reliable estimate of the CBC search background throughout the analysis period.
These small variations are due to a variety of fluctuations in the detectors and their
environment, such as optic alignment variations or changing low frequency ground motion.
Figure 5 shows the single-interferometer background trigger rate over time for the PyCBC
search [
7
] with two different thresholds on the detection statistic,
χ
2
-weighted SNR
¶
[
2
,
30
,
31
]. Triggers with a
χ
2
-weighted SNR ≥ 6.5 (shown in green) comprise the bulk of the
distribution and indicate the overall trigger rate from the search: ~1–10 Hz. Triggers with
χ
2
-weighted SNR ≥ 8 (shown in blue) are fairly rare, typically showing up at a rate
<
0.01
Hz during the analysis period.
The burst search background was also stable throughout the analysis containing GW150914.
Figure 6 shows the behavior of background triggers from the coherent all-sky burst search
cWB (coherent WaveBurst) [
32
,
33
] during the analysis period. In contrast to the single-
interferometer CBC triggers shown in Figure 5, the coherent burst search requires coherent
signal between multiple detectors to produce triggers, so the cWB background distribution is
generated using time-shifted data. The main features of the background remain constant
throughout the analyzed six weeks, particularly the domination of lower frequency triggers.
Week 6 shows a small excess of triggers, ~ 3% of total triggers, at lower than 60 Hz, which
is below the majority of the power in event GW150914.
Variations in the environmental conditions and instrumental state throughout the analysis
time, as captured in the range variation seen in Figure 4, did not have a significant impact on
the PyCBC or cWB background distributions.
¶
χ
2
-weighted SNR is the CBC detection statistic, where the SNR of a trigger is downweighted if there is excess power which does not
match the template waveform.
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5.2. The impact of data quality flags on the transient searches
Data quality flags were generated independently for each detector in response to
instrumental problems that demonstrated a well-defined, repeatable correlation with
transient noise in
h
(
t
). Figure 7 shows the CBC background trigger distributions from each
detector with and without data quality products applied. The LIGO-Hanford background
distribution was dramatically improved by the application of data quality vetoes, dominated
by the effect of a single data quality flag. This flag was designed to indicate a fault in the
phase modulation system used to create optical cavity control feedback signals, as discussed
in Appendix A. LIGO-Livingston exhibits a longer tail of unvetoed background events
which is largely composed of the blip noise transients discussed in Section 3. The total time
removed from the CBC search by vetoes is summarized for each detector by veto category in
Table 1.
For GW150914, the reported false-alarm probability was not significantly affected by these
data quality vetoes. GW150914 was the loudest recovered event during the analysis period –
significantly louder than every background event even without data quality products applied.
For less significant triggers, the application of data quality vetoes is more important [
34
]. As
an example, the false-alarm probability of the second most significant trigger (LVT151012)
was 2%. Without the inclusion of data quality vetoes, the false-alarm probability would have
been 14%, increased by roughly a factor of 7.
Figure 8 shows the impact of data-quality vetoes on the coherent burst search background, as
well as the signal-consistency cut that requires resolved signals to have a time-frequency
morphology consistent with expected astrophysical sources [
3
]. The data quality flag with
the highest efficiency-to-deadtime ratio for the coherent burst search background indicated
large excursions in
h
(
t
). This effective veto was defined using digital-to-analog overflows of
the optic motion actuation signal used to stabilize the differential arm motion of the
interferometer. This veto removed three of the loudest cWB background triggers during the
analysis period. The remaining outliers with vetoes applied are blip-like noise transients of
unknown instrumental origin.
The total coincident time removed by each veto category from the burst search is
summarized for each detector in Table 2. Category 1 was defined identically between the
burst and CBC searches, but there were some differences in the definition of category 2
largely due to differences in the observed impact of individual data quality products on the
searches. For example, the CBC search used a data quality flag indicating periods of excess
10–30 Hz ground motion at LIGO-Hanford at category 2, but it was not applied to the burst
search because it did not have a significant impact. The coherent burst search also applied a
set of data quality triggers [
25
] at category 3, whereas the CBC search did not find this data
quality product effective in reducing the background. A complete description of all data
quality vetoes applied to the transient searches during the analysis period is reported in [
35
].
Figure 9 shows the effect of data quality vetoes on Omicron triggers from each detector.
Since flags are tuned for specific problems at each detector, the impact on single-detector
Omicron triggers is much more apparent than on the coherent burst search background in
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Figure 8, where the search requirement of a high degree of signal correlation between
multiple detectors is effective in reducing the background.
Figure 9a shows that the same category 1 data quality veto that dominated the reduction in
the LIGO-Hanford CBC background distribution only impacted noise transients up to an
SNR of roughly 100. The higher SNR Omicron triggers vetoed at category 2 from both
detectors are mostly large excursions in
h
(
t
) that are witnessed by overflows in the digital-to-
analog conversion of the actuation signal controlling major optics, as mentioned for a data
quality flag used effectively at category 2 for the coherent burst search. Blip noise transients
are the main contributor to the unvetoed high SNR tail at both detectors along with 60–200
Hz nonstationarity that was persistent throughout the analysis period at LIGO-Livingston
with an undetermined instrumental coupling.
6. Transient noise around the time of GW150914
The GW150914 event produced a strong gravitational wave signal in the Advanced LIGO
detectors that shows the expected form of a binary black hole coalescence, as shown in
Figure 10 [
1
,
36
]. Immediately around the event the data are clean and stationary.
Even though the routine data quality checks did not indicate any problems with the data, in-
depth checks of potential noise sources were performed around the time of GW150914.
Potential noise couplings were considered from sources internal to the detector and local to
each site, as well as common, coincident sources external to the detectors. All checks
returned negative results for any pollution or interference large enough to have caused
GW150914. Activities of personnel at the detectors, both locally and via remote internet
connections, were confirmed to have no potential to induce transient noise in
h
(
t
). Because
GW150914 occurred during the early morning hours at both detectors, the only people on-
site were the control room operators. Signs of any anomalous activity nearby and the state of
signal hardware injections were also investigated. These checks came back conclusively
negative [
37
]. No data quality vetoes were active within an hour of the event. Rigorous
checks of the data calibration were also performed [
38
].
The results of a key subset of checks intended to demonstrate nominal detector performance,
quiet environment behavior, and clean data quality around the event are reported here.
For example, the U.S. Geological Survey (USGS) [
39
] reported two magnitude 2.1
earthquakes within 20 minutes of GW150914; one with an epicenter off the coast of Alaska
and another 70 miles south-west of Seattle. The earthquakes produced minimal vertical
ground motion at 0.03–0.1 Hz at the time of arrival; roughly 10 nm/s as measured by local
seismometers at both detectors, which is an order of magnitude too small to produce an
impact on the detector data.
6.1. Checks for potentially coincident noise sources
The primary means of detecting the rare electromagnetic events that could conceivably
produce coincident noise between the detectors are the array of magnetometers and radio
receivers at each detector. These and all other PEM sensors were checked for 1 second
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around the time of GW150914 independently of other coincident noise investigations. Any
PEM channel exhibiting power in the frequency band of GW150914 in excess of the
expected maximum of Gaussian noise in a 1000-second interval was further examined. Two
magnetometers at the Livingston detector sensitive to potential global coincident fields
exhibited excess power at least 40 times too small to produce an event with the amplitude of
GW150914. No excess power was observed in any radio receivers.
Given the global rate of lightning strikes, some coincidence with GW150914 is expected.
The VAISALA GLD360 Global Lightning Dataset reported approximately 60 strikes
globally during the second containing GW150914 [
40
,
41
]. One very strong lightning strike,
with a peak current of about 500 kA, occurred over Burkina Faso (roughly 9,200 km from
Livingston and 11,000 km from Hanford). Fluxgate magnetometers indicate that magnetic
disturbances at the LIGO detectors produced by coincident lightning strikes were at least 3
orders of magnitude too small to account for the amplitude of GW150914.
The PEM sensor network would easily detect any electromagnetic signal that would induce a
transient in
h
(
t
) with the same amplitude as GW150914. However, for redundancy, external
observatories were also checked for natural or human-generated electromagnetic signals [
42
,
43
,
44
,
45
,
46
,
47
,
48
,
49
,
50
] that coincided with GW150914. Geomagnetic signals at the
time of the strike were estimated to produce
h
(
t
) noise roughly 8 orders of magnitude
smaller than the GW150914 signal at 100 Hz.
Although cosmic ray events are not expected to produce coincidences between detectors, the
cosmic ray detector at LIGO-Hanford detected no events coincident with GW150914.
Additionally, cosmic ray rates at the LIGO-Hanford site and external detectors around the
world [
51
,
52
] were low and exhibited no unusual fluctuations at the time of the event.
6.2. Checks of auxiliary channels for noise coincident with GW150914
Three algorithms are used to statistically identify correlations between transient noise
identified in auxiliary channels and
h
(
t
) for each detector [
25
,
26
,
27
,
28
]. Implementation
details differ for each algorithm, but all work by defining a measure of correlation and
identifying auxiliary channels with significant correlation relative to chance.
All three algorithms were effective in identifying correlations between transients in
h
(
t
) and
auxiliary channels by systematically removing a larger fraction of noise transients than the
fraction of time removed for the week surrounding GW150914. Over the week surrounding
GW150914, these algorithms successfully removed an average of 6% of noise transients at
LIGO-Hanford and 2% at LIGO-Livingston for a deadtime of 0.1%, which is 20–60 times
greater than expected for chance coincidences.
None of the algorithms found a noise correlation within 180 seconds of the time of the event
for LIGO-Livingston or within 11 seconds of the event for LIGO-Hanford.
A comprehensive survey of transient excess power in all auxiliary channels was also
conducted for at least 8 seconds around GW150914. Although no channel was statistically
significant, a few of the transients nearby in time were followed up by hand in greater detail,
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as discussed in Section 6.3. None were found to contribute to
h
(
t
) in a way that might imitate
or impact GW150914.
As part of a related check, auxiliary channels monitoring the control signals for optic motion
actuation at both detectors were found to be well within their stable operating range at the
time of GW150914. Consequently, even if an environmental perturbation were present it
would not induce a transient in
h
(
t
) due to control loop instability.
6.3. Vetting of channels with identified excess power near the event time
A by-eye examination of spectrograms of every auxiliary channel identified a small subset
of auxiliary channels that exhibited excess power within one second of GW150914,
however, we found no evidence of noise that could generate GW150914 at either detector. In
addition to the magnetometer events discussed above in relation to potentially coincident
sources, there were 4 excess power events identified in magnetometers that monitor
electromagnetically noisy electronics rooms. The observed magnetic fields would have had
to have been at least 20 times stronger to account for the amplitude of GW150914 through
coupling to the electronics. Channels from a seismometer and an accelerometer at LIGO-
Hanford and two accelerometers at LIGO-Livingston also exhibited excess power. These
vibrational disturbances were at least 17 times too small to account for the amplitude of
GW150914. None of the environmental events matched GW150914 in time and frequency
behavior.
The excess power triggers in the seismometer channels at LIGO-Hanford were likely due to
a nearby air compressor with degraded vibration isolation that was running about 100m
away from optical components during the detection of GW150914. This excess ground
motion, shown in Figure 11, lasted for approximately three minutes at multiples of about 14
Hz (28, 42, 56 Hz). During the second containing GW150914, the largest disturbance
detected by the seismometer (at ~56 Hz) was at least 30 times too small to account for the
amplitude of GW150914.
There was also excess noise in the Livingston input mode cleaner [
6
] that was ruled out as a
potential indication of noise that might mimic GW150914. This noise had time-frequency
morphology that was inconsistent with any potential coupling mechanism. In particular, all
power was below 8 Hz and the noise duration was nearly one second. Such a long transient
would be unlikely to couple from the input mode cleaner to
h
(
t
) with duration comparable to
GW150914 (~ 200 ms).
6.4. Investigation of noise transients with similar morphology to CBC waveforms
Both detectors occasionally record short noise transients of unknown origin consisting of a
few cycles around 100 Hz, including blip noise transients, discussed in Section 3. None have
ever been observed to occur in coincidence between detectors and follow-up examination of
many of these transients confirmed an instrumental origin. While these transients are in the
same frequency band as the candidate event, they have a characteristic time-symmetric
waveform with significantly less frequency evolution, and are thus clearly distinct from the
candidate event.
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To illustrate this, Figure 12 shows a blip transient that produced one of the most significant
CBC background triggers associated with blip transients (
χ
2
-weighted SNR
≳
9; compare to
Figure 7) during the analysis period and the neutron-star-black-hole (NSBH) binary template
waveform it most closely matched. Although these noise transients do have significant
overlap with regions of the CBC parameter space that produce very short waveforms, such
as very high total mass binaries with extreme anti-aligned spins, they do not have a time
domain morphology that matches CBC templates with similar character to GW150914.
The potential impact of any accidental coincidence between such noise transients on the
sensitivity of the searches is accounted for in the reported background distribution. No noise
transients identified to have similar morphology elements to CBC signals [
53
], including
blip transients, produced nearly as high a
χ
2
-weighted SNR as GW150914.
6.5. LVT151012
GW150914 was by far the most significant event in all transient search results over the
sixteen days of analyzed data. The CBC search also identified the second most interesting
event on the 12th of October 2015. This trigger most closely matched the waveform of a
binary black hole system with masses and , producing a trigger with a false-alarm rate of 1
event per 2.3 years; far too high to be a strong detection candidate [
1
,
2
,
54
].
We performed similar in-depth checks of potential noise sources for this trigger. For LIGO-
Livingston data, LVT151012 is in coincidence with significant excess power at 10Hz lasting
roughly three seconds, a portion of which can be seen in Figure 13. There is no obvious
indication of upconversion to the frequency range analyzed by the transient searches, so the
low frequency noise is not thought to have caused the signal associated with LVT151012 in
the Livingston detector.
The data around this event were found to be significantly more non-stationary than those
around GW150914. The noise transient rate in the hours around LVT151012 was
significantly higher than usual at both LIGO detectors, seen in the Omicron trigger rate even
on a broad time scale for LIGO-Livingston in particular, as illustrated in Figure 14. This was
likely due to increased low frequency ground motion associated with ocean waves [
55
]. The
elevated noise transient rate at both sites induced a higher rate of background triggers around
the time of LVT151012.
No detector characterization studies to date indicate that LVT151012 was caused by a noise
artifact.
6.6. Noise transient rate
Figure 14 shows the rate of transient noise in the data as identified by the single-detector
burst algorithm Omicron for each of the two detectors over the analyzed period. GW150914
occurs during a period when the transient noise rate is low at both detectors, particularly for
louder transient noise. However, event LVT151012 occurs during a period when the rate of
transient noise is elevated, likely due to increased seismic noise, as described below.
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For LIGO-Hanford, major excursions from the normal noise transient rate of ~ 0.3 Hz can be
seen around 3 days into the analysis period due to an electronics failure in the instrumental
control system; similarly smaller problems are seen in the second and third weeks due to
problems with high seismic noise, and faulty radio frequency modulation electronics as
described in Appendix A. Periods with a significantly elevated noise transient rate at the
Hanford detector are largely removed from the analyzed period by the category 1 data
quality veto associated with these faulty electronics. For LIGO-Livingston, a high noise
transient rate is observed throughout weeks three and four, due in part to poor weather
conditions and elevated seismic noise. The instrumental coupling was not well enough
understood to generate an effective data quality veto for this elevated noise.
7. Conclusions
At the time of GW150914, the LIGO detectors were operating in a low-noise state with
nominal environmental and instrumental noise. Following the event, the detectors were
maintained in the same configuration to ensure that detector changes would not cause
unanticipated consequences which might bias the background estimation for the event. The
backgrounds measured by the transient searches were stable throughout this analyzed
period. Data quality vetoes were produced for each detector in response to instrumental or
environmental noise sources. We conclude that the selected analysis period provides an
accurate estimation of the significance of GW150914.
Additionally, thorough investigations found no evidence that environmental influences or
non-Gaussian detector noise at either LIGO site might have caused the observed
gravitational wave signal GW150914. A detailed study of environmental influences
conclusively ruled out all postulated potential sources of correlated detector output at the
time of the event, except for a binary black hole gravitational wave signal.
Characterization of the LIGO detectors via investigations of noise types that most impact the
astrophysical searches and mitigation of noise couplings will continue to play a critical role
in gravitational wave astronomy. Reducing the rate of high-significance background events
and increasing search sensitivity is particularly important for near-threshold events such as
LVT151012. Detector characterization will effectively expand the range of astrophysical
sources that the gravitational wave detectors are sensitive to, providing a significantly
greater number, and perhaps also variety, of events from which we can draw confident
physical inferences.
8. Acknowledgements
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
Abbott et al.
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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 &
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 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.
Authors
B P Abbott
1
, R Abbott
1
, T D Abbott
2
, M R Abernathy
1
, F Acernese
3,4
, K Ackley
5
, M
Adamo
4,21
, C Adams
6
, T Adams
7
, P Addesso
3
, R X Adhikari
1
, V B Adya
8
, C
Affeldt
8
, M Agathos
9
, K Agatsuma
9
, N Aggarwal
10
, O D Aguiar
11
, L Aiello
12,13
, A
Ain
14
, P Ajith
15
, B Allen
8,16,17
, A Allocca
18,19
, P A Altin
20
, S B Anderson
1
, W G
Anderson
16
, K Arai
1
, M C Araya
1
, C C Arceneaux
21
, J S Areeda
22
, N Arnaud
23
, K G
Arun
24
, S Ascenzi
25,13
, G Ashton
26
, M Ast
27
, S M Aston
6
, P Astone
28
, P Aufmuth
8
,
C Aulbert
8
, S Babak
29
, P Bacon
30
, M K M Bader
9
, P T Baker
31
, F Baldaccini
32,33
, G
Ballardin
34
, S W Ballmer
35
, J C Barayoga
1
, S E Barclay
36
, B C Barish
1
, D Barker
37
,
F Barone
3,4
, B Barr
36
, L Barsotti
10
, M Barsuglia
30
, D Barta
38
, J Bartlett
37
, I
Bartos
39
, R Bassiri
40
, A Basti
18,19
, J C Batch
37
, C Baune
8
, V Bavigadda
34
, M
Bazzan
41,42
, B Behnke
29
, M Bejger
43
, A S Bell
36
, C J Bell
36
, B K Berger
1
, J
Bergman
37
, G Bergmann
8
, C P L Berry
44
, D Bersanetti
45,46
, A Bertolini
9
, J
Betzwieser
6
, S Bhagwat
35
, R Bhandare
47
, I A Bilenko
48
, G Billingsley
1
, J Birch
6
, R
Birney
49
, S Biscans
10
, A Bisht
8,17
, M Bitossi
34
, C Biwer
35
, M A Bizouard
23
, J K
Blackburn
1
, L Blackburn
10
, C D Blair
50
, D G Blair
50
, R M Blair
37
, S Bloemen
51
, O
Bock
8
, T P Bodiya
10
, M Boer
52
, G Bogaert
52
, C Bogan
8
, A Bohe
29
, P Bojtos
53
, C
Bond
44
, F Bondu
54
, R Bonnand
7
, B A Boom
9
, R Bork
1
, V Boschi
18,19
, S Bose
55,14
,
Y Bouffanais
30
, A Bozzi
34
, C Bradaschia
19
, P R Brady
16
, V B Braginsky
48
, M
Branchesi
56,57
, J E Brau
58
, T Briant
59
, A Brillet
52
, M Brinkmann
8
, V Brisson
23
, P
Brockill
16
, A F Brooks
1
, D A Brown
35
, D D Brown
44
, N M Brown
10
, C C Buchanan
2
,
A Buikema
10
, T Bulik
60
, H J Bulten
61,9
, A Buonanno
29,62
, D Buskulic
7
, C Buy
30
, R L
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