1 Introduction

First searches for optical counterparts to gravitational-wave candidate events

Abstract

During the LIGO and Virgo joint science runs in 2009-2010, gravitational wave (GW) data from three interferometer detectors were analyzed within minutes to select GW candidate events and infer their apparent sky positions. Target coordinates were transmitted to several telescopes for follow-up observations aimed at the detection of an associated optical transient. Images were obtained for eight such GW candidates. We present the methods used to analyze the image data as well as the transient search results. No optical transient was identified with a convincing association with any of these candidates, and none of the GW triggers showed strong evidence for being astrophysical in nature. We compare the sensitivities of these observations to several model light curves from possible sources of interest, and discuss prospects for future joint GW-optical observations of this type.

Subject headings:
gravitational waves – binaries: close – stars: neutron – surveys – catalogs
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1. Introduction

Transient gravitational-wave (GW) emission is expected from highly energetic astrophysical events such as stellar-core collapses and mergers of binary neutron stars. The Laser Interferometer Gravitational-wave Observatory (LIGO, Abbott et al., 2009; Harry et al., 2010) includes detectors located in the United States near Hanford, Washington (H1) and Livingston, LA (L1). A similarly designed Virgo (V1) (Accadia et al., 2012; Virgo Collaboration, 2009) detector is located in Italy near the city of Cascina. Each interferometer contains a pair of perpendicular arms, 4 km long in the LIGO detectors and 3 km in Virgo, whose effective optical path length is slightly altered by passing gravitational-wave signals. Since 2007, LIGO and Virgo have coordinated operations and shared data, so the three sites operate as a single network of detectors seeking direct measurements of gravitational-wave signals. A fourth site, GEO600 in Hannover, Germany (Grote et al., 2008), also shares data with LIGO and Virgo.

During the 2009-2010 science run of the LIGO/Virgo network (Abadie et al., 2012c) we implemented low-latency searches for GW transients. The analysis software identified GW event candidates (“triggers”), estimated their statistical significance, and reconstructed likely source positions. A collection of optical telescopes, as well as the Swift satellite, LOFAR, and the Expanded Very Large Array (EVLA) (Lazio et al., 2012), provided target of opportunity follow-up observations to the GW triggers. In earlier publications, we described the search method and likely sources of both GW and EM transients (Abadie et al., 2012b, a), as well as the results of the follow-up observations performed with the Swift satellite (Evans et al., 2012).

In this paper, we describe the data set collected with optical telescopes, detail the methods used to search the data for transients consistent with expected optical counterparts to GWs, and report the results of this analysis. In this first effort to use optical instruments to search for transients based on data from GW detectors, none of the GW triggers showed strong evidence for being astrophysical in nature. However, searching for transients in a large sky area is a challenging problem, and uncertainty in the expected light curve and spectrum of the sought optical counterpart makes the problem harder still. For this reason, we emphasize the methodologies used to identify transient phenomena in our data set and to separate objects consistent with our target models from those that are not. In addition, we discuss the results of Monte Carlo simulations used to test the efficiency of our pipelines in recovering various types of transients, and the implications for future searches of optical counterparts of GW events discovered with next generation observatories.

A variety of astrophysical processes are likely to be associated with both GW and EM emission. Among these, gamma-ray bursts (GRBs) are promising sources for joint GW and EM studies (e.g., Kochanek & Piran, 1993; Kobayashi & Mészáros, 2003; Abadie et al., 2012b). GRBs are traditionally divided in two main classes, long and short bursts (Kouveliotou et al., 1993), which are thought to be associated with different progenitors (e.g., Gehrels et al., 2007; Mészáros, 2006, and references therein). Long GRBs are associated with “collapsars”, the gravitational collapse of cores of massive stars (Woosley, 1993; MacFadyen & Woosley, 1999), while short GRBs may be produced by mergers of binary systems of compact objects (neutron-star/neutron-star or black-hole/neutron-star; e.g., Eichler et al., 1989; Paczynski, 1991; Narayan et al., 1992). A compact binary merger results from gravitational radiation, producing a characteristic “inspiral” of the binary orbit and a corresponding strong GW signal (e.g., Thorne, 1987; Shibata & Taniguchi, 2011). GW emission from a collapsar depends on non-spherically-symmetric flow of material during the collapse, which may be enhanced by centrifugal effects if the progenitor is rotating rapidly (Davies et al., 2002; Fryer et al., 2002; Shibata et al., 2003; Piro & Pfahl, 2007; Corsi & Mészáros, 2009; Ott, 2009; Romero et al., 2010).

High-energy emission from GRBs is thought to escape as narrow relativistic jets (e.g., Sari et al., 1999; Harrison et al., 1999; Frail et al., 2001; Racusin et al., 2009), though at least in the case of the short GRBs, there is uncertainty regarding the angular extent of typical beams (Fong et al., 2012), as well as how the beaming angle depends on wavelength (van Eerten & MacFadyen, 2011). Afterglows of both classes of GRBs have been observed over a wide range of wavelengths (Costa et al., 1997; Frail et al., 1997; van Paradijs et al., 1997; Gehrels et al., 2005; Hjorth et al., 2005; Abdo et al., 2009), from times nearly concurrent with the prompt emission to days later (e.g. Nousek et al., 2006; Molinari et al., 2007; Racusin et al., 2011, and references therein). Generally, the observed optical afterglows fade with a temporal power-law decay, with typical indices between 1 and 1.5 (e.g., Sari et al., 1998; Nakar, 2007). A wide range of luminosities have been observed, with the afterglows of short bursts tending to be less energetic than the afterglows of long bursts (Kann et al., 2011).

The merger of two neutron stars or a neutron star with a black hole may lead to a supernova-like transient, as described by Li & Paczyński (1998). In their model, heavy radioactive elements are formed in the merger ejecta through rapid neutron capture nucleosynthesis. As the newly formed isotopes decay toward stability, they release energy and heat the ejecta. Thermal emission becomes visible after the ejecta has expanded enough to allow photons to escape. The expected transient, referred to as a kilonova throughout this paper, is roughly isotropic, and the associated light curve is expected to peak about a day after the merger time (Metzger et al., 2010; Piran et al., 2013). The model has been supported by a variety of computational work (Faber & Rasio, 2012; Roberts et al., 2011), though some details of the model are still uncertain, including the amount of mass ejected from the merger and the physics of the radiative transport. These unknowns lead to uncertainties in the peak luminosity, time-scale, and color evolution of the model. For example, Barnes & Kasen (2013) found that the ejected NS material may have a high opacity, leading to light curves that peak in infrared rather than optical wavelengths; this prediction seems consistent with one recent observation (Tanvir et al., 2013; Berger et al., 2013). For testing purposes, we adopted a simple model which was intended to mimic the main features of the light curves in Metzger et al. (2010) and Piran et al. (2013) (See Table 1).

Core-collapse supernovae are expected to emit enough GW energy to be observable with current detectors within some fraction of the Milky Way, to distances of perhaps a few kpc (Ott, 2009). A rare class of core-collapse supernovae is also known to be linked to long GRBs (Galama et al., 1998; Woosley & Bloom, 2006; Soderberg et al., 2006). Indeed, optical follow-ups of GW triggers could catch optical supernovae harboring off-axis GRBs, whose gamma-ray emission would be missed because the relativistic GRB jet is not pointed towards earth (Granot et al., 2002; Rhoads, 2003; van Eerten et al., 2010). However, unlike the models discussed above, tracking a supernova light curve requires several days or weeks of observations after the GW trigger (Doggett & Branch, 1985). Slow light curves are also expected from off-axis GRBs, whose emission is expected to peak on timescales of weeks to months (e.g.; van Eerten & MacFadyen, 2011). Taking into account that the LIGO and Virgo detectors are expected to detect more merger events than core-collapse events, the cadence of our optical follow-up observations was chosen mainly for shorter optical transients, but with some observations extending to later times to possibly catch a slower transient.

The paper is organized as follows: Sec. 2 first gives a description of the ground-based telescopes involved in the follow-up program. In Sec. 3, we present the set of GW triggers that were selected and sent as alerts to the telescopes and we describe their associated follow-up observations. Sec. 4 details the methods employed to search for optical transients in the collected series of images and Sec. 5 reports the results of the searches. Finally, estimates of the search sensitivity are presented in Sec. 6.

Source Light Curve Model Normalization Condition
Short GRB 23-31 mag at 1 day from
Long GRB 16-24 mag at 1 day from
Kilonova erg s 0.7 days
erg s 0.7 days
Table 1R-band light curve models used for simulated injections. Normalizations used for the on-axis short GRB and long GRB models correspond to the full range of observed on-axis GRB afterglows in each class in the observer frame, assuming , from Kann et al. (2010, 2011). The kilonova model is intended to mimic the light curves shown in Metzger et al. (2010) and Piran et al. (2013).

2. Telescopes involved in the follow-up program

The optical follow-up program took place during times when the LIGO and Virgo observatories were operating in coincidence during 2009 and 2010. This time was divided into two segments: the “winter” run, between December 2009 and January 2010, and the “autumn” run spanning most of September and October 2010. The program was executed as a joint study between the LIGO and Virgo collaborations, and about ten teams which operated automated and remotely controlled telescopes.

During the winter run, triggers from the LIGO/Virgo network were passed to the TAROT (Klotz et al., 2009) and QUEST (Baltay et al., 2007) telescopes. For the autumn run, the optical network was expanded to include Palomar Transient Factory (Rahmer et al., 2008; Law et al., 2009; Rau et al., 2009), Pi of the Sky (POTS) (Malek et al., 2009), ROTSE III (Akerlof et al., 2003), SkyMapper (Keller et al., 2007), the Zadko Telescope (Coward et al., 2010), and the Liverpool Telescope (Steele et al., 2004). The large number (12) of telescopes participating in the autumn run allowed for better sky coverage. The main characteristics of these observatories are listed in Table 2. With the exception of the Liverpool RATCam and Zadko, they are all equipped with wide field cameras. A wide field of view (FOV) was considered an important feature for this study, due to the imprecise source localization of the GW instruments. We expected localizations of a few tens of square degrees up to 200 square degrees, and so instruments without a wide FOV would be unable to image a significant fraction of the uncertainty region (Cavalier et al., 2004; Nissanke et al., 2011; Fairhurst, 2011; Klimenko et al., 2011). However, with the limited sensitive range to an optimally aligned source (horizon distance) of initial LIGO and Virgo, it was also possible for an instrument to observe only the most likely host galaxies for a compact object merger (Abadie et al., 2012b; Kanner et al., 2008; Nuttall & Sutton, 2010).

Separate observing plans were constructed for each observatory. Some of the instruments targeted only the single most likely field for a given GW trigger, while others observed multiple fields in an effort to cover an area comparable to the GW position uncertainty (See table 2). Planned cadences were also different for each observatory. Generally, the goal was to observe at least once as quickly as possible to image a potential rapidly fading counterpart. Where possible, attempts were made to image each field over several nights following the GW trigger, in order to trace the light curves of potential transients. The details of the observations are described in Section 5.

Name Locations FOV (square degrees) Aperture (m) Exposure Time (s) Limiting Magnitude Tiles
Palomar Transient Factory 1 7.3 1.2 60 20.5 10
Pi of the Sky 1 400 0.072 10 11.5 1
QUEST 1 9.4 1 60 20.5 3
ROTSE III 4 3.4 0.45 20 17.5 1
SkyMapper 1 5.7 1.35 110 21.5 8
TAROT 2 3.4 0.25 180 17.5 1
Zadko Telescope 1 0.15 1 120 20.5 5
Liverpool Telescope - RATCam 1 0.0058 2 300 21 1
Liverpool Telescope - SkyCamZ 1 1 0.2 10 18 1
Table 2Characteristics of instruments involved in the search. The column labeled “Tiles” indicates the maximum number of different field positions that the telescope searched in response to a trigger. The shown limiting magnitudes are estimates, under ideal observing conditions. They are listed in ’ band for RATCam, band for skymapper, and band for all other instruments.

3. Gravitational-wave triggers selected for follow-up observations

3.1. Trigger Selection

Triggers for this search were identified with a collection of low-latency pipelines designed to find transient GW events in data from the three site LIGO/Virgo network. Here, we provide a brief summary of the trigger production and selection, while a more detailed description is described in Abadie et al. (2012b) and Abadie et al. (2012a). During the winter run, two pipelines were used to identify generic short-duration transients of significant signal power, or “bursts”, and estimate their source positions: the Omega () Pipeline (Searle et al., 2008; Abadie et al., 2010a) and the coherent WaveBurst (cWB) pipeline (Klimenko et al., 2011). For the autumn run, a third trigger pipeline was added: the Multi-Band Template Analysis (MBTA) (Beauville et al., 2008; Abadie et al., 2012a), which sought inspiral waveforms from coalescing compact objects. The autumn run also added a second instance of cWB, configured to target linearly polarized GW signals, as might be expected from supernovae.

To compare triggers from different pipelines and identify the ones suitable for observation, follow-up software made event candidate selections based on the estimated false alarm rate (FAR) of each trigger. The rate of background false alarms was estimated by forming a distribution of artificial triggers from data with one or more data streams shifted by at least several seconds. Time-shifting data removes correlations of possible gravitational-wave signals between detectors, so this distribution was considered to be free from any putative signals and represented the rate of triggers not due to transient GWs (Abadie et al., 2012a, c). During the winter run, a FAR threshold of 1 trigger per day was applied to triggers, and a less significant FAR was accepted in the last week to exercise the system. For the autumn run, the FAR threshold was set to 0.25 per day. Triggers which passed the automated threshold received attention from an on-call follow-up team. The on-call team checked that the trigger occurred in high quality data in each interferometer. In addition, the criteria for manual validation in the winter run included demands that the three suggested (see below) QUEST fields covered a sky area corresponding to a greater than 50% probability of containing the GW source and that follow-up requests were sent at a rate of less than one per 24 hours.

The trigger pipelines reported the estimated position of each candidate GW event as a skymap, a list of probability densities assigned to pixels in a grid covering the sky. The grid used pixels approximately 0.4 degrees on a side, selected to be similar to the degree-scale resolving power of the GW network (For example, Fairhurst, 2011; Klimenko et al., 2011; Vitale et al., 2012; Nissanke et al., 2011). The large angular size of the skymaps required a choice of where within the uncertainty region to observe. To observe the regions most likely to contain an observable GW source, we used a catalog of galaxies within 50 Mpc and Milky Way globular clusters (GWGC, White et al., 2011), thought to be around 70% complete to 50 Mpc by -band luminosity. Each pixel in the skymap was given a weight according to the formula

(1)

where is the probability of the pixel derived from the GW data alone; is the blue light luminosity of the galaxy or galaxies contained in the pixel, which is used as a proxy for the star formation rate; and is the distance to the galaxy (Nuttall & Sutton, 2010). For MBTA triggers, a slightly modified version of this approach was applied, using the maximum distance consistent with the apparent inspiral signal (Abadie et al., 2012a). The suggested fields for each telescope were those that maximized the sum of within the respective field of view. Unless unobservable due to daylight or geometrical constraints, the suggested fields were passed to each optical telescope for every GW event candidate that passed manual validation. However, a more stringent selection was applied for PTF, and only one GW trigger was sent to PTF.

3.2. Data Set

In the winter run, the on-call team was alerted a total of nine times. Three of these triggers were vetoed by the on-call team. Six triggers were approved by the on-call team and sent to the QUEST and TAROT telescopes with roughly thirty minutes of latency. Of the six requests, four were rejected as unobservable by the scheduling software of both telescopes and two triggers were followed-up with the QUEST telescope. In addition, two triggers that did not pass the automated FAR threshold were selected by the on-call team and passed to the partner observatories in an effort to expand the winter run data set (see Table 3).

In the autumn run, only one trigger was manually rejected due to data quality concerns. Six triggers resulted in alerts to the observing partners, four of which resulted in follow-up observations992 (see Table 4). Two of the triggers are worth special note. The September 16 trigger was recognized by the on-call team as having a special significance: in addition to a small estimated FAR, spectrograms of the GW data revealed frequency evolution characteristic of the late inspiral and merger of two compact objects. This event was later revealed to be a blind hardware injection, a simulated signal secretly added to the data to test the end-to-end system. The September 26 event candidate was also discovered with a low FAR estimate. In subsequent GW data analysis, this trigger was found to be the most significant cWB trigger above 200 Hz in the time period where H1, L1, and V1 were running in coincidence in this science run, though was removed from the analysis based on data quality concerns. The FAR was measured to be 0.023 events per day, or one such trigger expected for every 44 days of network livetime. Since these detectors ran in coincidence for a total of 52.2 days throughout the Virgo science run, this trigger was consistent with expectations for detector noise.

ID Date UTC Pipeline FAR Follow-up
(day)
G3821 Dec 29, 2009 15:16:33 0.66 QUEST collected 12 images
CWB1 Jan 03, 2010 20:37:22 cWB 1.3 Alert sent Jan 7; TAROT collected 6 images
G4202 Jan 06, 2010 06:49:45 4.5 QUEST collected 9 images
CWB2 Jan 07, 2010 08:46:37 cWB 1.6 QUEST collected 12 images
Table 3Gravitational wave triggers in the winter run
ID Date UTC Pipeline FAR Follow-up
(day)
G19377 Sep 16, 2010 06:42:23 cWB (unmodeled) ROTSE collected 117 images, TAROT collected 20, Zadko 129, and SkyMapper 21. Blind injection
G20190 Sep 19, 2010 12:02:25 MBTA 0.16 ROTSE collected 257 images, QUEST 23, Zadko 159, and TAROT 3
G21852 Sep 26, 2010 20:24:32 cWB (linear) 0.02 ROTSE collected 130 images, PTF 149, CAT 3 DQ
G23004 Oct 3, 2010 16:48:23 0.21 ROTSE collected 153 images, QUEST 40, Liverpool - RATCam 22, Liverpool - SkyCamZ 121, and POTS 444
Table 4Gravitational wave triggers in the autumn run

4. Searches for optical transients

A search for optical transients essentially consists of searching for fading optical point sources in a sequence of astronomical images. A few characteristics make the search for GW counterparts unique. First, there is a significant uncertainty regarding the expected light curve from a GW source; we targeted short duration (hours to days) transients consistent with GRB afterglows and kilonovae light curves. Second, the poor localization of the GW error box required searching through a large portion of the sky. This significantly differed from the arcminute-scale error box used to find optical afterglows of GRBs discovered by Swift. Finally, we designed automated pipelines with Monte-Carlo simulations to evaluate the statistical significance of any apparent counterpart.

The telescopes involved in the program included very different instruments ranging from shallow, very wide-field cameras to meter-class telescopes (Table 2). They collected images with different cadences and follow-up strategies, leading to a heterogeneous data set. This has led us to develop a similarly heterogeneous analysis approach, with techniques tailored to match the requirements of each observational data set. Where possible, we leveraged existing software already in use by the various astronomical teams. The list of techniques which were applied in some, but not all, of the developed searches included image subtraction, identification of host galaxies, cuts on shape parameters, automated transient classifiers, volunteer work by citizen scientists, and consistency checks on light curve properties.

In future searches for optical counterparts to GW sources, a critical component will be rapidly down-selecting candidate lists to allocate follow-up resources such as large aperture photometry and spectroscopy. In this work, we attempted to unify results from disparate analyses by developing two common search statistics, which were applied in multiple analyses. The first statistic was used to quantify the ability to reject false positives, and labeled the “false-alarm probability” (FAP). The FAP was defined as the probability that a set of optical images taken with a given telescope in response to a single GW trigger, and analyzed with a given pipeline, would lead to a false positive. The FAP could encompass both false positives arising from technical noise, such as procedure artifacts, and astrophysical transients not related to the GW sources, such as M dwarf flares, Galactic variable stars, and extragalactic AGN and supernovae. For most data sets, we set a FAP target of 10%. This FAP level was chosen to reduce the number of false positives to a manageable level, so that each object passing the selection criteria could, in principle, be further studied with sensitive photometric and/or spectroscopic observations. The second statistic used to characterize an analysis was the detection efficiency, defined as the recovery rate for simulated optical transients added to representative images. We measured detection efficiencies for a few different model light curves, using data and analysis procedures from several different telescopes. The FAP measurements and the Monte Carlo simulations allowed us to find a good compromise between rejection of false positives and reduction of interesting EM candidates. For example, in a study with the QUEST and TAROT data, we found that increasing the FAP to 0.20 would produce less than a 30% improvement in the sensitive distance range of the search, and so would increase the sensitive search volume by roughly a factor of two, while also doubling the number of false positives. This section describes the different methods that were used to identify potential transients consistent with our models, and reduce false positives.

4.1. Catalog-Based Search for TAROT, Zadko and QUEST Observations

This section describes the image analysis pipeline developed specifically for the TAROT, Zadko Telescope, and QUEST observations. Unlike other approaches presented in this work, the pipeline did not use image subtraction but it extracted a source catalog from each image, and sought transients by comparing the set of catalogs to a reference. For this reason, we refer to this pipeline as the “catalog-based search.”

Analysis Pipeline

The search consisted of three main steps applied to the image set (after dark, flat and sky background level corrections): data photometric calibration, reconstruction of object light curves, and transient selection to identify possible electromagnetic counterparts.

TAROT, Zadko Telescope and QUEST observed with a clear filter. The magnitude zero-point calibration was performed using the USNO-A2.0 catalog (Monet et al., 1998) as reference and resulted in red equivalent magnitudes. For the QUEST camera, which is composed of 112 individual CCDs, calibration was performed separately on each CCD. The different response, data quality, and sensitivity of each CCD prevented managing them as a single mosaic, and the data analysis was performed CCD by CCD.

The source catalog of each image was extracted using SExtractor (Bertin & Arnouts, 1996). Each list of sources was spatially cross-correlated with the star catalog USNO-A2.0 using the tool match (Droege et al., 2006). The radius used to search for common sources was set to for TAROT, for Zadko and for QUEST. These values took into account the positional uncertainties in the images and in the USNO-A2.0 catalog. Sources found to coincide in position and luminosity with objects listed in the reference catalog were excluded from the search. The lists of remaining sources were then mutually cross-correlated in position to link sources observed at different times to common astrophysical objects. This resulted in a light curve for each identified object.

At this point, two types of analyses were conducted to select GW associated transients and reject background objects. The on-source analysis was restricted to objects lying in the image regions associated with galaxies within 50 Mpc 993 and Galactic globular clusters. For each galaxy a circular region with a radius five times the galaxy’s semi-major axis (as provided by the GWGC (White et al., 2011)) was analyzed. This region (which corresponds to an average radius of about 20 kpc) accounted for the typical projected physical offsets observed between GRB afterglows and their host galaxy centers (Berger, 2010, e.g.). The whole-field analysis covered the entire field-of-view but was limited to bright objects. For the QUEST telescope, large variations in the sensitivity and image quality between different CCDs made setting a whole-field magnitude threshold unfeasible to search the expected counterparts. For this reason, we performed only the on-source analysis on the QUEST data, which allowed us to search for faint transients while limiting the number of false positives (See Sect. 4.1.2).

For both types of analysis, rapid contaminating transients, including cosmic rays, asteroids, and CCD noise, were rejected by requiring the presence of the object in a minimum number of consecutive images. Further selection of transient objects (and hence rejection of background) was performed by applying thresholds to the initial (first observation) magnitude and light curve variability of each source. Variability was characterized by assuming power-law luminosity dimming with time, , corresponding to a linear magnitude variation . The slope index was evaluated for each object. The expected slope indices for GRB afterglows and kilonova light curves are around 2.5–4 (see Table 1). To seek these transients, we applied a cut which selected slope indices greater than 0.5. Because of the small number of repeated observations with QUEST (maximum of 8 for each galaxy), a different variability measurement was used for this instrument’s analysis. A threshold on the flux variation between the first and the following nights of observation was set by requiring a dimming larger than +0.5 mag (while we expected +1 based on the light curve models and the QUEST observational cadence).

Studies of the background events (Sect 4.1.2) and the ability to detect simulated on-axis GRBs and kilonovae (Sect. 6) were used to design selection criteria yielding a FAP of under 10% (prior probability that a background event passes all the selection criteria), while also accepting a wide range of astrophysical models. The thresholds applied to the variability measure (slope index or flux variation) were designed to detect fading transients while leaving the possibility of detecting light curves showing flaring within short time-scales (hours). However, recent re-evaluations of kilonova emission by Barnes & Kasen (2013) and others have indicated that more realistic values for the opacities of the heavy radioactive elements lead to dimmer and broader light curves. These would be difficult to detect with the depth and cadence of our data set.

Background Estimation

The background was estimated by running the analysis over a series of images obtained from random time permutations of the real observation images. The first night observations were excluded from being selected as the first image in each permuted sequence to remove any astrophysical electromagnetic counterparts from the data set. The background simulation was repeated 100 times for TAROT and Zadko Telescope and for all the permutations allowed by the observations for QUEST.

Genuine optical transients would have lost their regularly fading light curve in the scrambled image set. Random sequencing thus erased them while artifacts such as CCD noise, pixel saturation, bad pixels, errors in the de-blending and source association, etc. were just as likely to pass the pipeline’s selection cuts as with the true sequencing. This procedure allowed a measurement of the rate of false positives due to “technical” noise. However, this procedure did not permit a valuable estimate of the “astrophysical” background since the randomization reduced the number of identified astrophysical transients that actually dimmed over time. A statistically significant estimate of the astrophysical background would require the study of survey data not associated with GW triggers, which was not available at this time.

An example of the distribution of technical background events (after the removal of rapid transients) detected in the FOV of TAROT for trigger G19377 is shown in Fig. 1. The cumulative distribution of their initial magnitude is shown in the left plot, and the FAP as a function of the slope index is in the central plot. The on-source analysis showed a greatly reduced background level compared to the whole-field analysis, since only objects near a local galaxy were included. In this example, the nominal slope index threshold of 0.5 reduced the FAP to less than 1% in the on-source analysis. For the whole-field analysis, in addition to the same cut on slope index, a requirement that objects showed an initial flux brighter than magnitude 14 was needed to reduce the FAP below the 10% objective.

The “technical background” rate varied significantly between different instruments due to different fields of view, limiting magnitudes, image quality, and star crowding. For TAROT and Zadko, the number per square degree of “technical false positive” brighter than a reference magnitude of 14.5 mag for TAROT and 15.5 mag for Zadko was evaluated to be less than 1 per square degree using a slope index threshold of 0.5. For QUEST, the background study was performed CCD by CCD to account for the different density of false positives on each CCD. Compared to TAROT and Zadko, the deeper sensitivity observations of QUEST led to a higher number of false positives: an average value of 6 per square degree brighter than 18 mag and with magnitude variation larger than 0.5. Reducing the analysis to the on-source regions allowed us to lower the density of background transients to less than 1 per square degree.

Analysis Tuning

For TAROT and Zadko the two types of analysis were tuned to achieve 10% FAP using the on-source and whole-field backgrounds, respectively. The nominal slope index threshold ( 0.5) resulted in the target FAP ( 10%) for half of the on-source analyses. For the other half, a threshold on the initial magnitude (in the range 12–13 mag) was also required. For the whole-field analyses, an initial magnitude threshold of 14 mag was demanded for the TAROT follow-up of G19377 and a threshold of 10 mag for the Zadko follow-up of G19377, and the Zadko and TAROT follow-up of G20190. For these last three follow-ups the presence of observations taken months after the GW trigger allowed the additional requirement of the object’s presence in the early observations and its absence in the reference ones.

For the QUEST on-source analysis, two methods were used to estimate the false positives. First, the background was evaluated directly in each on-source area. Due to the low statistics in these areas, a second estimate was also produced by rescaling the background event counts in the entire CCD to the on-source area. The target FAP (evaluated by both methods) was achieved for the majority of galaxies by demanding a magnitude variation larger than 0.5 between the first night and follow-up night observations, and an initial magnitude brighter than 17.5 for G20190, and 18.5 for G23004. For eight galaxies associated with G23004, stronger thresholds on the initial magnitude (between 15 and 18.2) were required.

Simulations have been performed for each set of images by using the exact thresholds applied for the analysis of the data associated with the GW trigger to prove the ability to detect likely EM counterparts (GRBs and kilonovae), and to evaluate the search sensitivity for the analysis procedure described above (see Sect. 6).

     

Figure 1.— Background plots for TAROT data associated with trigger G19377 obtained by performing the on-source analysis (top plots) and whole-field analysis (bottom plots). In the left plots, N gives the cumulative number of technical background events found in a permuted set of images above the magnitude threshold shown on the X-axis, averaged over 100 permutations. The right plots show the FAP as a function of the slope index (in the case of whole-field analysis the requirement of an initial magnitude brighter than 14 was applied).

4.2. ROTSE Search

The ROTSE-III network consists of four robotic telescopes at various locations around the world. For each GW trigger in the autumn run, the telescopes repeatedly observed a single field. Each field was observed in a series of 30 exposures on the first night after the trigger time. Follow-up images were collected over the next 30 nights, with observations spaced an average of every 2 nights. Each follow-up observation included 8 exposures, each 20 or 60 seconds.

We used the existing ROTSE pipeline to analyze the images taken with the network. Based on the ISIS package994, which uses a single convolution algorithm described in Alard & Lupton (1998) and Alard (2000), the ROTSE pipeline was adapted to use cross correlation to improve image subtraction results. The details of this method can be found in Yuan & Akerlof (2008). The pipeline was implemented for our analysis to require minimal user interaction and for large scale processing which enabled characterization of the background, as described in Nuttall et al. (2012).

The pipeline began by stacking images from the same night on top of one another to form a coadded image. SExtractor was used to produce a list of objects and their coordinates for each coadded image. These images were then subtracted from the coadded reference image, and several criteria were imposed on any objects found in the subtracted image. Selection criteria included requiring a full width at half maximum (FWHM) consistent with a point source, seeking a minimum fractional flux variation between images and a signal-to-noise ratio (SNR) greater than some amount. The specific criteria depended on the location of the source in an image. For example, if a source matched a star or an unknown object a flux change of 60% was required, whereas if a source was within 20% of the semi-major axis length from the center of a galaxy, but not consistent with a core, only a 3% flux change was required. The result was several lists of candidates (one from each night), which we combined to produce a single list of unique candidates which appeared in the images, and generated light curves for all candidates.

The vast majority of these candidates were due to poor subtraction, with a fraction of real but uninteresting transients (such as variable stars or asteroids). In order to remove contaminants from the list of candidate transients, each object was subjected to a series of cuts. In order to be of interest, the transient must have appeared on more than one night, shown a sufficiently decaying light curve 48 hours after the trigger, and not have been coincident with a known variable source (from the SIMBAD catalog995) or with a minor planet (Minor Planet Checker996). These cuts proved efficient at rejecting the majority of the background. Candidates were then highlighted if they overlapped with known galaxies or if their light curves were consistent with a target theoretical light curve (Metzger et al., 2010; Kann et al., 2011, 2010). They were also assigned an ad hoc ranking statistic, , defined as:

(2)

Here is the step function, is the background-subtracted magnitude of the transient in image , and is a weight factor defined by

(3)

where is the time of the GW trigger, is the time of image . The ranking statistic was designed to prefer events which were bright within a day of the trigger time and which appear in multiple images.

The ROTSE false-alarm rate was investigated by processing sets of images for each of 100 random field locations selected from the ROTSE archive. Each set contained images of the field from a month of nominally nightly observing. The FAP for each GW candidate was estimated by counting the number of transient objects visible in archived images with a similar cadence as the images collected for that GW candidate. The ranking statistic for each such transient object was calculated using Equation (2). These studies allowed us to set thresholds on the ranking statistic to keep the target light curves, while rejecting contaminants.

4.3. Catalog-Based Search for Pi of the Sky

Pi of the Sky has an unusually wide field-of-view of degrees, with a typical limiting magnitude of 11.5 for a 10 second exposure. This allowed the telescope to image a large part of the sky in response to one LIGO/Virgo trigger, over degrees on most nights. We used the standard Pi of the Sky pipeline to analyze the images taken by the telescope. A detailed description may be found in Malek et al. (2009) and Sokolowski (2008). The full analysis was carried out in two steps. First, in each image taken by the telescope, the Guide Star Catalog (Jenkner et al., 1990) was used to identify previously unknown sources. Second, Pi of the Sky’s nova recognition algorithm was applied to the list of unknown sources. To separate optical transients from contaminating sources, the algorithm utilized several types of vetoes, including checks on background saturation, nearby bright objects, satellite databases, and the GSC catalog. Objects that passed the cuts were then visually inspected.

During the human inspection stage, every candidate that was not identified as a satellite or background fluctuation was checked against lists of known sources. First, we queried the Pi of the Sky, INTA (Spain) site for observations made in 2011. Due to the long time (one year) between the autumn science run and observations from the INTA site, any objects observed by INTA were likely unrelated to the GW trigger.997 Finally, objects were cross-correlated with the SIMBAD catalog, and sources that appeared nearer than to the position of any known star or infrared source were rejected.

4.4. SkyMapper Search

SkyMapper obtained two epochs of an eight image mosaic covering a total of square degrees in response to the September 16, 2010 trigger. An image subtraction technique was applied to identify possible transients. The SkyMapper images were reduced via the normal bias subtraction, overscan correction and flat fielding using a custom made Python-based pipeline. Thereafter, frames from the two epochs were aligned with the WCSREMAP998 routine and subtracted with HOTPANTS999 to create residuals images. SExtractor was used to identify sources with SNR greater than three. Then, a series of cuts was applied to the SExtractor output parameters to identify noise and bad subtractions. These included using the ellipticity parameter, photometry from different size apertures, and catalog matching of variable stars. In addition, a study of the point spread function (PSF) of each object was performed on the subtracted images by fitting the detection with a 2D Gaussian and comparing the fit parameters to the expected, known, PSF. The remaining objects were then examined manually to verify they correspond to an object which was visible in the first epoch and not detectable/fainter in the second. The light curves were then measured using differential photometry with nearby stars.

4.5. PTF Search

The Palomar Transient Factory (PTF) accepted the trigger of September 26, 2010. Nine PTF fields, each covering 7.26 deg, were schedule automatically for observations, and they were observed beginning hours after the trigger time (since the trigger occurred during day-time on the Pacific Coast). PTF then repeated the observations on several subsequent nights. The number of follow-up observations was mainly limited by full moon constraints.

The imaged fields were searched for candidate transients using the image subtraction pipeline hosted at LBNL (Nugent et al., 2013; Gal-Yam et al., 2011). Only three of the fields imaged by PTF had previously constructed reference images. For the rest of the fields, image subtraction was performed using a reference image constructed by co-adding several images taken during the first night of observations. Image differencing inherently produces a large number of spurious candidates, and only a small fraction (less than few percent) of these are real events. As described in Bloom et al. (2012), in a typical PTF night of order residual sources are found per 100-200 square degrees of imaging, after performing subtraction of the reference image.

To distinguish between astrophysical objects and “bogus” image subtraction residuals, we made use of a classification parameter named the “realbogus” parameter (RB; Bloom et al., 2012), which was assigned by a machine-learned (ML) classifier so as to reasonably mimic the human scanning decision of real or bogus. The RB parameter ranged from 0 (definitely bogus) to 1 (definitely real), and was constructed from 28 SExtractor output parameters, including magnitude, ellipticity of the source, and distance from the candidate to reference source.

To maximize the chances of identifying a potential optical counterpart to G21852, the images collected by PTF were analyzed using two different procedures for transient identification, both based on the RB parameter as a starting point (Nugent et al., 2013). While the first procedure (hereafter, the “automated” approach) was largely based on automated machine-learned techniques and optimized for fast transients, the second (hereafter, the “citizen-based” approach) was largely based on a citizen project (Smith et al., 2011) and optimized for supernova searches. In what follows, we describe these two approaches in more detail.

Automated Approach

We identified the most promising fast transient candidates (i.e., transients with a variability on a timescale of a week or less) obtained in an image subtraction by applying the following selection criteria:

  1. in at least one detection;

  2. matching of the candidate with at least one other detection with ;

  3. the second detection should be coincident with the candidate position within on the sky;

  4. the second detection should be at least 45 minutes (and no more than 6 days) before or after the original candidate.

Candidates satisfying the above criteria were further passed through the so-called “Oarical classification routine” which, as part of the standard PTF operations, was designed to distinguish between two main classes of events, namely “transients” and “variable stars.” The classifier used both time-domain features, such as light-curve evolution, and context features, including the location of the source relative to known stars and galaxies (see Bloom et al., 2012, for details).

Candidates with high RB and high classification confidence were saved automatically in the so-called “PTF Marshal” web archive, and thus assigned an official “PTF name” and a tentative object type. Further spectroscopic follow-up was pursued only for sources that looked particularly promising in relation with the main science objectives of the PTF survey.

The main challenge of our study was to identify, among the list of candidates retrieved using the criteria described here (and in the absence of spectral classification for most of them), the ones more likely to be of interest for LIGO and Virgo, in the sense of having properties consistent with “explosive” events such as binary mergers or stellar collapses, that our search was targeting.

Citizen-Based Approach

In addition to the list of candidates described in the previous section, we also considered candidates passing selection criteria optimized for the identification of young supernovae:

  1. candidate RB parameter value ;

  2. detected at least twice;

  3. flat or rising light curve;

  4. not seen prior to 10 days before the earliest day.

As part of normal PTF operations during 2010, candidates passing the above criteria were further examined by citizen scientists through the Galaxy Zoo Supernovae project (Smith et al., 2011). The Galaxy Zoo scanners were presented with a series of detection “triplets” for each candidate. Each triplet contained three images: the current image of the field containing the candidate; the historical or reference image of the same field; and the image of the difference between the previous two (which should contain only the candidate light). Each examiner was asked a series of questions to determine if the candidate appeared consistent with a supernova, and the answers were converted into a score. The arithmetic mean of the scores from many scanners was calculated, and candidates with strong (supernova-like) scores were counted in our final list of candidates.

Selection for LIGO/Virgo Event Candidates

All of the candidates from both the automated approach and citizen-based approach were vetted by human scanners to judge which candidates deserved to be kept for further investigation as “LIGO/Virgo interesting”. To do so, we took advantage of two new parameters recently developed by the PTF team, to improve confidence in transient identification. The first parameter is the so-called “realbogus 2” (RB2; Brink et al., 2012). The RB2 parameter is similar to the RB parameter, but it was defined by using a much larger training sample (78,000 objects). The RB2 also utilized some additional features that the original RB parameter did not use, including correlations in different PTF filters. By using a sample of spectroscopically confirmed sources discovered by PTF, it has been found that selecting candidates with yields a false positive rate of , and a missed detection rate of (Brink et al., 2012).

The second parameter is known as the Supernova Zoo predictor, a machine-learned classifier that was trained using the Supernova Zoo mark up of tens of thousands of candidate transients, so as to construct a classifier capable of efficiently discovering supernovae. The Supernova Zoo predictor assigns a score (hereafter, ) to each of the candidates, which is higher for more promising candidates (i.e. the ones that are most likely to be real supernovae). By using a sample of spectroscopically confirmed supernovae discovered by PTF, it has been found that selecting candidates with yields a false positive rate of , and a missed detection rate of .

For our final selection cuts, we applied the following criteria:

  1. Was the transient classified spectroscopically as a variable star, an AGN, or a SN of type Ia? If yes, discard.

  2. Was the candidate detected for the first time before the GW trigger time? If yes, discard.

  3. Does the transient appear to have subtracted correctly? If not, discard after double checking that this is consistent with a low value of the RB2 () and of the supernova zoo predictor parameter ().

  4. Is the candidate classified as a STAR in SDSS, and/or is it spatially coincident with a known stellar or AGN source in SIMBAD? If yes, discard.

  5. If the analyzed field is not in the SDSS footprint and nothing is found in SIMBAD (see above), can the candidate be securely associated with a point-like host in the PTF reference image (or in an image taken a year after the LIGO/Virgo trigger in case a previous reference image was not available)? If yes, is the Oarical classification (see Section 4.5) consistent with a “variable star” and/or is there enough photometry to confirm a long-term variable origin from the light curve? If yes, discard.

  6. If the analyzed field is not in the SDSS footprint, nothing is found in SIMBAD, and a point-like host cannot be identified in the reference image (see above), then: Does the candidate have both RB2 and below threshold? Or, is it classified by the Oarical classifier (Section 4.5) as variable star or AGN, and is there enough photometry to confirm a long-term variable origin from the light curve? If yes, discard.

4.6. Liverpool Telescope Search

The Liverpool Telescope observed the G23004 trigger using both the 4.6 arc-minute field of view RATCam instrument and the field of view SkyCamZ camera. This produced a total of 22 SDSS -band RATCam images and 121 “clear” filter SkyCamZ images from two nights 29 days apart. In addition, 3 RATCam and 17 SkyCamZ images were taken in early 2012 to serve as reference images for image subtraction. The analysis made use of several freely available software packages, and was split into several sections written in Python.

First, we combined the images from 2012 to create our reference images. This was done by aligning the images using the WCSRemap1000 package and combining them using the SWarp1001 package. We also combined sets of 5 SkyCamZ images on each night to improve image quality and provide a similar cadence to the RATCam images. We removed 1 RATCam image and 2 SkyCamZ images due to quality issues.

Second, as the SkyCamZ images used a non-standard filter1002, they were calibrated using the USNO-B catalog of stars to determine the zero point offset required to calculate correct magnitudes, in the same way ROTSE and TAROT images were calibrated (See section 4.1). This was done by comparing the USNO-B R-band magnitude of stars in the combined SkyCamZ fields with those same stars found using SExtractor.

The images were then aligned individually to the reference images, again using WCSRemap, and the reference image was subtracted using the HOTPANTS1003 image subtraction package. SExtractor was then used to detect potential candidates in each individual field with a minimum of 4 pixels each with a flux greater than 4-sigma above the background noise of the image. This reduced the frequency of detecting uninteresting objects, such as cosmic rays, extremely faint stars and noise from the image subtraction process while allowing us to achieve a sensitivity around 20th magnitude in the narrow-field RATCam images.

Using the output of SExtractor from each of the subtracted images, a Python script combined the objects found into a master list containing every unique candidate found in those images, along with useful parameters from SExtractor. From this data, a series of cuts were made to find candidates interesting to this analysis. First, candidates found to be near an image edge (or a bad pixel strip in the case of RATCam images) were rejected. Second, a cut was made to remove artifacts due to bad subtraction. This was achieved by examining the region in the subtracted image around the candidate and calculating the total flux more than 4 sigma below the median noise of the image. Since bad subtractions are usually caused by poor alignment or convolution, they typically produce a large amount of “negative” flux in the residual image. If the total amount of flux below this threshold was the equivalent required for detection of candidates (4 pixels above 4 sigma) then the candidate was rejected. The next cut removed candidates not seen in at least half of the images available on the first night, to ensure candidates were visible long enough to be used in our analysis. We also rejected candidates that appeared close to known variable stars and minor planets. Finally, we required that a candidate must decrease in brightness by more than 5 sigma of the median error on the magnitude measurements from SExtractor, from the first night to the second night 29 days later. Since the pipeline is designed to work with images from two telescopes for this analysis which may have different magnitude errors for the same trigger, we used a threshold based on the noise in the image rather than a fixed magnitude variation in the same way as ROTSE and TAROT.

Any objects that remained after these cuts were considered likely candidates, and looked at in more detail. This was done by plotting the light curves of each object across both nights and inspecting images of the candidates in both the original and subtracted images. This allowed us to gauge whether any transients warranted further investigation.

5. Optical Transient Search Results

In this section we present the details of the associated optical images for each GW trigger. The center location of each observed field is shown in Table 5. We also present the results of the transient analysis for each data set. Data from the two periods of our search were handled differently. The winter run triggers were not observed with sufficient cadence to reconstruct light curves, so only a limited analysis was performed on those triggers. Section 5.1 describes the results of the analysis along with figures showing the position reconstruction and image locations for each winter run GW trigger (Figures 2 - 3).

The methods described in Section 4 were applied to the data collected in response to each GW trigger in the autumn run. To display the sky coverage and depth of each response, two panels are presented for each autumn run trigger (Figures 4 - 7). The left panel shows the GW skymap (without the use of galaxy weighting) along with the positions and approximate field sizes of each observed tile. The right panel shows a timeline of the observations by each observatory. The y-axes of the timeline plots display the limiting magnitudes of the observations. In cases where multiple observations were taken on one night by one telescope, the displayed value is the median limiting magnitude of all fields for the night.

The right panel of each figure also shows several models for possible EM counterparts. The off-axis long GRB model (L-GRB; solid dark green line) is from van Eerten et al. (2010), and assumes a total energy in the jets of  erg, jet half opening angle of  rad, off-axis observer’s angle of  rad, interstellar medium number density of  cm, and distance of 30 Mpc. We note that within this model, the associated optical transient peaks at  d since trigger. The off-axis low-luminosity GRB model (LL-GRB; dash-dot-dot-dotted dark green line) is from van Eerten & MacFadyen (2011), and assumes a total energy in the jets of  erg, jet half opening angle of  rad, off-axis observer’s angle of  rad, and interstellar medium density of  cm. The off-axis short GRB model (S-GRB; dashed dark green line) also refers to a total energy in the jets of  erg (and similar jet and observer’s angles), but the interstellar medium density is set to  cm. The light green line represents the case of a faint short GRB observed on-axis (see Table 1 and Kann et al., 2010, 2011). The emission from typical short GRBs and long GRBs observed on-axis lies above this line. In particular, on-axis long GRBs at 30 Mpc would appear as very bright optical transients.

The kilonova models are courtesy of Barnes & Kasen (dashed dark blue), B. Metzger (dark blue), and E. Nakar (light blue). Specifically, the light blue line represents one of the kilonova bolometric light curves from Piran et al. (2013) (BH-NS merger with BH mass of ). This light curve assumes that all of the bolometric luminosity is emitted in the -band, and it represents an upper-limit to the true -band luminosity of the kilonova event. The solid dark blue line is one of the kilonova light curves from Metzger et al. (2010), and is calculated for an ejecta mass assuming a black-body emission. Finally, the dashed dark blue line is one of the kilonova models from Barnes & Kasen (2013), for the case of low-velocity (0.1 c) low-mass () ejecta. Since the kilonova models are subject to large uncertainties, we selected these three light curves to give an indication of the possible scatter in the model predictions.

Finally, the prototype emission from a GRB-associated SN is plotted with a red dotted line: this is a tentative extrapolation to early times of the -band light curve observed for SN 1998bw (red asterisks; Clocchiatti et al., 2011), associated with GRB 980425 (Galama et al., 1998). The light curve assumes that SN 1998bw exploded at the same time at which GRB 980425 was triggered.

     

Figure 2.— The GW skymaps for triggers G3821 (left) and CWB1 (right). The colored regions show the estimated probability per square degree that each location is the true source direction before applying the galaxy weighting. The locations of the observed fields (selected using galaxy weighting) for telescopes that observed the trigger are also marked.

     

Figure 3.— The GW skymaps for triggers G4202 (left) and CWB2 (right). See Figure 2 caption for explanation.

     

Figure 4.— On the left, the GW skymap for G19377, which was later revealed to be a blind injection. The skymap shows the probability per square degree that each location is the true source direction before applying the galaxy weighting. The locations of the observed fields (selected using galaxy weighting) for telescopes that observed the trigger are also marked. On the right, a timeline showing when each telescope observed the requested fields, with time zero corresponding to the GW trigger time. Model light curves for several sources, scaled to 30 Mpc, are shown for comparison (see Section 5 for details).

     

Figure 5.— GW skymap and observations of trigger G20190. See Figure 4 caption for explanation.

     

Figure 6.— GW skymap and observations of trigger G21852. See Figure 4 caption for explanation.

     

Figure 7.— GW skymap and observations of trigger G23004. See Figure 4 caption for explanation. The shown Pi of the Sky (POTS) fields are a subset of the ten overlapping pointing positions used to observe the GW uncertainty region.

5.1. Winter Run Triggers

For each winter run trigger, images were collected only during one night. The absence of a second night’s observations prevented the construction of variability measures and limited the analyses to only identify “unknown objects”, i.e. those not listed in the USNO catalog or with a magnitude significantly different from USNO, but visible in all the collected images. For both the TAROT and QUEST image analysis procedure, at least one observation on another night would have been required to identify a unique electromagnetic counterpart.

In the winter run, TAROT responded to one trigger, CWB1, and collected 6 images starting the single night observation at T+3d11h. The QUEST camera responded to three triggers, G3821, G4202, CWB2, starting the observations at T+9h46m, T+24m, and T+16h12m, respectively. For each trigger it collected images corresponding to three fields. Each field was observed twice within 20 minutes during the same night.

The TAROT observation associated with CWB1 reached a sensitivity of 15.8 mag. Fifteen galaxies with a distance smaller than 50 Mpc were in the FOV. The analysis found 9 unknown objects in the on-source region and 46 in the entire FOV up to the limiting magnitude. No unknown objects were found with magnitude brighter than 11.8 in the on-source region and brighter than 10.7 mag in the entire FOV.

The three QUEST fields associated with G3821 included a total of 34 galaxies with a distance smaller than 50 Mpc Only 14 of the galaxies were analyzed due to the exclusion of galaxies observed only one time or lying in CCDs that did not work or had calibration problems. The average limiting magnitude was about 18.6 mag.

For trigger G4202 the three fields included a total of 17 galaxies with a distance smaller than 50 Mpc. Ten galaxies were removed from the analysis because they were observed only one time or associated with poor image quality (impacted by bad lines and pixels or by background subtraction artifacts) or calibration problems of the CCDs (astrometric calibration or flat-field problems). An average limiting magnitude of 19.2 mag was reached during the observations.

For trigger CWB2 the three fields included a total of 12 galaxies with a distance smaller than 50 Mpc. Two of the galaxies were not analyzed due to poor image quality or CCD calibration problems. An average limiting magnitude of 19.8 mag was reached during the observations.

The QUEST analysis found 9, 1 and 1 unknown objects in the on-source region with a magnitude brighter than 14 mag for the triggers G3821, G4202 and CWB2, respectively. The number of unknown objects increased to 140, 35, 6 for magnitudes brighter than 18 mag. The number of unknown objects showed a stronger dependence on the density of image artifacts and stars in the FOV than on the on-source area. No “unknown objects” were found for magnitude brighter than 9, 12, 7 mag for G3821, G4202 and CWB2, respectively.

5.2. G19377

Event G19377 was a simulated signal added to the GW detector data in order to test our data analysis pipelines. The ROTSE-IIIc telescope responded at T+12 hours when 30, 20-second exposure images were taken within 15 minutes. On subsequent follow-up nights (6-29) both ROTSE-IIIa and c telescopes gathered 80, 20-second exposure images. The images from the two scopes varied vastly in terms of image quality, which posed difficulties for injection studies. We discarded the lower quality images from the 3c telescope, leaving just the 3a images, with an average limiting magnitude of 15.1. Two galaxies at 24 Mpc (PGC 078144 and PGC 078133) were visible within the FOV. The ROTSE image processing pipeline revealed 68 unique objects, one of which passed the candidate validation. Further tests found this candidate was consistent with background, with a false alarm probability of 7%. This left no significant candidates. At the location of this background transient there is a known star (red magnitude of 13.1 from the USNO catalog), which shows no significant magnitude variation in the TAROT images associated with the same GW trigger. This location was not covered by the Swift observations taken for G19377. We also tried analyzing images from both the 3a and 3c telescopes together, and found no additional candidates.

SkyMapper observed an 8 tile mosaic, 7 days after the initial alert. An analysis was performed, but no plausible transients were discovered.

TAROT took images starting at T+43m and repeated the observations at T+2d, T+3d and T+4d. Observations from the four nights displayed an average limiting magnitude of 15.1. The on-source analysis was performed on the two same galaxies observed by ROTSE and identified no transient counterpart. The whole-field analysis was performed with an initial magnitude threshold of 14 mag, and identified one transient candidate with a slope index of 0.6. A deeper analysis showed that this candidate resulted from an artifact of the de-blending in crowded images.

The Zadko telescope observed the regions around the five galaxies evaluated to be the most likely hosts of the G19377 trigger: NGC 2380, ESO 560-004, ESO 429-012, PGC 078133, and PGC 078144; the last two being in common with ROTSE and TAROT. The observations started at T+1d12.6h and were repeated 5 months later for reference. The average limiting magnitude for both the early and reference images was 16.5 mag. No electromagnetic counterparts were identified by either the on-source on whole-field analysis.

5.3. G20190

All four ROTSE-III telescopes responded to this GW trigger, taking images spanning T+34h38m to T+29d, centered on the region around the galaxy UGC 11944. However, all images taken with the ROTSE-IIIa, b and d telescopes were discarded because of defocussing factors in addition to weather conditions at those sites being less than optimal. This resulted in 56 images being used for the analysis, with an average limiting magnitude of 15.5. The ROTSE image subtraction pipeline found 77 potential candidates, none of which passed the candidate validation procedure.

The TAROT telescope collected three images in association with G20190. Due to the full moon only an average limiting magnitude of 14.6 mag was reached. Nine months later 18 images were taken by TAROT in the same region of the sky as reference. A mean limiting magnitude of 17 mag was reached during this second observation. No counterpart with a false alarm probability less then 10% was identified by the on-source analysis. The whole-field analysis was performed with a threshold of 10 mag on the initial magnitude and the required presence in the first three images and absence in the reference images. It resulted in four identified candidates. The candidates were seen to be image artifacts linked to the spikes of saturated stars.

The Zadko telescope was pointed toward two Galactic globular clusters: NGC 7078 and NGC 7089, and three galaxies UGC 11868, NGC 7177, and NGC 7241, evaluated to be the most likely hosts of the GW source. Observations of galaxies UGC 11868 and NGC 7241 were taken about 50 minutes after the GW trigger. All five fields were observed subsequently during at least 2 nights between T+1d and T+4d. The observations were repeated eleven months later for reference. The average limiting magnitudes were 16.4 mag and 17.3 mag for the very first and reference observations, respectively. The on-source analysis identified three transient candidates associated with NGC 7078 and 15 associated with the center of NGC 7089. The candidates were found to be due to problematic de-blending in the central region of globular clusters. No transient was identified by the on-source analysis associated with the three galaxies. The whole-field analysis required a magnitude brighter than 10 and the presence during the first nights and absence in the reference images. This resulted in no detected transient.

The QUEST observations started at T+12h3m. Each field was observed twice within 15 minutes as pairs of images dithered to fill the gaps between rows of CCDs. The entire observation sequence was repeated at T+1.5d. A total of 10 galaxies with a distance smaller than 30 Mpc were identified in the three fields. Three of the galaxies were not analyzed due to poor image quality CCDs or calibration problems. The observation was taken during a full moon night that allowed an average limiting magnitude of 17.6 mag. The on-source analysis1004 identified one possible transient with a false alarm probability less then 10% (see Sec. 6) associated with the galaxy UGC 11916. A deeper analysis of the candidate showed this to be artificial. The analysis pipeline identified the possible GW host galaxy itself as a transient due to variations in the estimate of its surface photometry over the two nights. An estimate using fixed photometry apertures indicated magnitudes in agreement within the errors with no flux decrease.

5.4. G21852

ROTSE-IIIb took images spanning T+11h53m to T+29d centered on a region containing both M31 and M110. One follow-up night had to be ignored due to defocussing issues. The average limiting magnitude of the images was 16.6, with 81% of them having an exposure times of 60s. The subtraction pipeline found 187 objects, which resulted in four candidates after candidate validation. All four candidates overlapped with one of the galaxies mentioned, however all were consistent with background. The highest ranked candidate had a false alarm probability of 9%. Consequently, we found no significant candidates. Within the  2 arsec positional accuracy of PTF, the ROTSE background events are all coincident with known stars, and according to the PTF analysis criteria applied, these sources are not considered candidates.

PTF observed 9 different fields on five nights, beginning at T+6h37m. The median limiting magnitude reached in the observed fields over the observation time (and over the eleven CCDs that make the core of the PTF imager) was in the range . The images collected by PTF were analyzed using two different procedures for transient identification, one entirely based on automated selection criteria for fast transients, and the other largely based on a citizen project targeting supernovae (see Section 4.5 for more details). These procedures for transient identification were routinely used by the PTF survey (Nugent et al., 2013). By applying the selection criteria for fast transients (automated approach; see Section 4.5.1) on the images that were taken for follow-up of trigger G21852, we obtained a list of 172 candidates, none of which passed the vetting for “LIGO/Virgo interesting” transients performed according to the criteria described in Section 4.5.3. We also applied these last criteria to the candidates obtained via the citizen-based approach (optimized for supernova searches - see Section 4.5.2). Of the 218 candidates selected according to criteria (1)-(4) in Section 4.5.2 and sent out to the citizens for scanning, 28 were saved by the citizens and assigned an official PTF name. However, none of these 28 candidates passed the additional vetting described in Section 4.5.3. We also took a closer look at 55 other candidates that were not saved by the citizens, but that had a predictor score or a RB2 (see Section 4.5.3). We vetted these candidates according to the criteria (1)-(5) in Section 4.5.3, and none of them passed our screening.

5.5. G23004

The ROTSE-IIIb, c and d telescopes responded to G23004 at T+6h25m and collected data up to T+29d. These images contained one galaxy (NGC 1518) at 11.5 Mpc within the FOV. Around of the data was of poor quality; many of the images were out of focus and cloud cover was also a factor. This resulted in the analysis of 30 images with an average limiting magnitude of 16.7. The ROTSE subtraction pipeline found 124 potential candidates of which none survived the candidate validation tests.

The Liverpool Telescope observed a single field centered on the location of the galaxy NGC 1507, with one hour of observations taken at T+9h and a further one hour at T+30d. The limiting magnitude of the RATCam images was , averaged over all images, with the calibrated limiting magnitude of the SkyCamZ images averaging . We found 406 unique objects in the RATCam images and 163 unique objects in the SkyCamZ images. After applying cuts described in Section 4 we found no candidates in either the RATCam or SkyCamZ images that met our criteria.

The Pi of the Sky telescope responded at T+6h56m after the alert. On the first night the telescope used ten different pointing locations to cover an area containing 40% of the G23004 probability map. Each location was imaged twice. The limiting magnitudes for the first night’s observations spanned mag. On the first night there were over 700 cases that were recognized by the pipeline as possible optical transients, but all of them were either already included in the database of weak stars or were noise due to ice crystals on the camera. There were no real optical transients found. The same fields were followed up on the nights of October 5, 6, 7, 11, and 30. Each follow-up night’s observed area was covered by 9 pointing locations, with each location imaged at least 3 times. Images from the first four nights were searched by the pipeline for optical transients, and 40 objects were identified as existing in images over multiple nights and have been present on all frames that were taken of that field. Each of these was manually investigated, and none were found to be linked to the GW trigger. Most of the 40 objects were traced to variable stars or were caused by ice crystals on the camera.

The QUEST follow-up for this gravitational wave trigger consisted of 3 nights of observations over three different fields. The first observation began at T+11h32m and then observations were repeated at T+2.4d and T+32.4d. Each night’s observations included two visits to each of two dithered positions for each of the three field locations. A total of 32 galaxies with a distance smaller than 50 Mpc were identified in the three fields. Due to inoperative CCDs or CCD calibration problems the regions occupied by four galaxies were not analyzed. The average limiting magnitude for the three night observations was 19.7 mag. The on-source1005 analysis identified one possible transient with an “on source” false alarm probability of less then 10% (see Sec. 6). The candidate transient overlaid the extended emission of the galaxy IC0402. A deeper analysis indicated no flux change for the object: the point source immersed in the fainter galaxy edge emission has a similar neighboring object that biased its photometry. Using a suitable fixed photometry aperture the magnitudes of the object agree within the errors in all the images. The object could be a foreground star not listed in the USNO catalog or a bright knot of one of the galaxy’s arms.

6. Efficiencies for Recovering Simulated Optical Transients

Simulated transients were added to each set of images to measure the efficiency in recovering optical counterparts located at different distances from earth. The different telescope pipelines were run over the simulated data with the same analysis tuning used in the real data. For TAROT, Zadko, QUEST, ROTSE and the Liverpool Telescope the simulated transients reproduced the observed light curves (see e.g., Figs. 5 and 4 of Kann et al., 2011, 2010) of on-axis GRB afterglows and a modelled light curve for the kilonovae (Metzger et al., 2010; Piran et al., 2013). Table 1 summarizes the features of injected models. These models were scaled on the basis of the observation time from the GW trigger and the source distance. We emphasize here that while the simulated GRB afterglows cover the range of observed luminosities, kilonovae have not been observed yet and so our efficiency results are dependent on the assumed model.

6.1. TAROT and Zadko Telescope

     

Figure 8.— Efficiency in recovering simulated optical transients in the TAROT data (left) and Zadko data (right). The figure reflects the success rate in recovering transients added to the observed fields, and does not include efficiency lost due to observing only a fraction of the possible source locations. The signals have been simulated based on the models shown in Table 1, with the power law flux of each GRB randomly scaled within the shown range of normalization conditions.

For each set of images collected by TAROT and the Zadko telescopes, 100 simulated transients were added to the data for each counterpart model and distance. To model PSF variations in the wide-field images, reference model stars were identfied in each image, and the PSF of the reference star closest to the injection position was used for each simulated object. For the GRB afterglows, we used a range of magnitudes uniformly distributed between the brightest and faintest GRBs (see normalization in Table 1). The results are presented in Figure 8. Long GRB afterglows/short GRB afterglows/kilonovae were recovered with 50 % efficiency in TAROT observations to distances of 400 Mpc/18 Mpc/6.5 Mpc respectively for trigger G19377 and 355 Mpc/16 Mpc/13 Mpc for trigger G20190. For Zadko Telescope observations, we obtained 195 Mpc/8 Mpc/4 Mpc for G19377, and 505 Mpc/ 25 Mpc 13 Mpc for G20190. As expected, the results showed some dependence on the depth of the observations, the observation time after the GW trigger, and the density of stars in the field.

6.2. Quest

     

Figure 9.— Some representative success rates in recovering simulated kilonovae lightcurves with the QUEST data for triggers G20190 (left) and G23004 (right). Each curve represents the efficiency from individual on-source galaxy regions, and so does not include efficiency lost due to observing only a fraction of the possible source locations.

     

Figure 10.— Some representative success rates in recovering simulated short GRB afterglow light curves with the QUEST data for triggers G20190 (left) and G23004 (right). Each curve represents the results from individual on-source galaxy regions, and so does not include efficiency lost due to observing only a fraction of the possible source locations. Each simulated afterglow lightcurve was randomly scaled within the range of normalization conditions showed in Table 1.

     

Figure 11.— Some representative success rates recovering simulated long GRB light curves with the QUEST data for triggers G20190 (left) and G23004 (right). Each curve shows the results from individual on-source galaxy regions, and so does not include efficiency lost due to observing only a fraction of the possible source locations. Each simulated afterglow lightcurve was randomly scaled within the range of normalization conditions showed in Table 1.

The QUEST pipeline’s recovery efficiency was evaluated separately for each on-source galaxy region. As for TAROT and Zadko, 100 simulated transients were added to the images for each model (kilonova, short and long GRBs) and distance. Randomly distributed magnitudes between the brightest and faintest GRBs (see normalization in Table 1) were used. Figures 911 show some representative examples of the achieved recovery efficiencies. The wide range in the recovery efficiencies reflects variations in CCD sensitivity and rates of contaminating artifacts. In addition, bright galaxy extended emission prevented the recovery of some injections, even at close distances. A similar efficiency loss was found when a large part of the on-source region was occupied by foreground stars or image problems like bad pixels and bad lines. The results for the QUEST observations can be characterized by the mean and the standard deviation of the distances corresponding to 50% efficiency to recover injections. For trigger G20190, we found mean distances of 33 Mpc ( 7 Mpc) for kilonovae, 30 Mpc ( 6 Mpc) for short GRBs, and 820 Mpc ( 180 Mpc) for long GRBs. For G23004, a mean distance of 64 Mpc ( 25 Mpc) for kilonovae, 63 Mpc ( 30 Mpc) for short GRBs, and 1530 Mpc ( 700 Mpc) for long GRBs were found. 1006 The larger spreads for QUEST reflect CCD-to-CCD variations. For both GW triggers, the 50% efficiency distances for long GRB afterglows were well beyond the maximum distance that the LIGO and Virgo detectors could have detected signals coming from neutron star binary coalescences, while the kilonova and short GRB distances were comparable. However, the result obtained for the kilonova transients is dependent on the adopted model and relies on the fact that the QUEST observations were made around the peak time of the light curve model used for this study.

6.3. Rotse

Figure 12.— Distribution of ROTSE background (time-shifted) triggers and recovered injections for event G19377. This plot shows the distribution ranking statistic for kilonova injections simulated from 1 Mpc, short GRBs from 7.9 Mpc, and long GRBs from 200 Mpc. The GRB models correspond to the brightest observed GRB afterglows.

Figure 13.— Efficiency of the ROTSE pipeline in recovering simulated kilonovae transients (left, solid), short GRBs (middle, dash-dot), and long GRBs (right, dashed). The figure reflects the success rate in recovering transients added to the observed fields, and does not include efficiency lost due to observing only a fraction of the possible source locations. The efficiencies shown for the GRB afterglow models are based on the brightest models shown in Table 1. At very close distances, the simulated objects became so bright that they caused saturations in the data, and were missed by the pipeline. The images associated with trigger G23004 were of poor quality, so the efficiencies with this data are not shown.

For each set of images collected by ROTSE, 140 simulated transients were added to the data for each counterpart model for 10 different distances. The PSFs for the injected transients were modeled on ’good’ objects PSFs within each image, as described in White et al. (2012). The GRB models used the brightest normalizations shown in Table 1; i.e., assuming magnitude 16 (23) at 1 day from for LGRB (SGRB) afterglows. The results are presented in Figure 13. For each GW trigger, the efficiencies for the different counterpart models are very similar as functions of the injection magnitude. The efficiencies peak at % for triggers G19377 and G20190, and at % for G21852. Trigger G23004 (not shown) contained images of very poor quality and the injection efficiency only reached a maximum of %. Long GRB afterglows / short GRB afterglows / kilonovae were recovered with 50% detection efficiency to distances of 400 Mpc / 16 Mpc / 2 Mpc for trigger G19377, 1000 Mpc / 40 Mpc / 5 Mpc for trigger G20190, and 1000 Mpc / 90 Mpc / 5 Mpc for trigger G21852. The maximum sensitive distances correspond to transient magnitudes of approximately 15 on the second night. This was typical of the average limiting magnitude of ROTSE over the FOV. Since the pipeline required transients to be seen on at least two nights, the magnitude on the second night was the primary factor determining the sensitivity to each model. Transients at much smaller distances tended to suffer from saturation and were discarded in the image subtraction. The maximum detection efficiency was less than 100% because the pipeline was not always able to produce the background-subtracted lightcurve for a transient; this depended on the position in the image and on the image quality, as sixteen reference stars were needed in the region around the transient for accurate image subtraction. Variations in efficiency between triggers were due mainly to differences in image quality and also differences in CCD performance between the different telescopes in the ROTSE network.

An example of the distribution of injections against the background can be seen in Figure 12. This figure shows that of all the injections that produced a nonzero ranking statistic with the specific distance scales shown, more than 60% of the injections were recovered with a rank comparable to the most highly ranked background event. However none of the injections were found with a ranking statistic higher than loudest background event. As the injection distances increased, the injections fell more and more within the background.

6.4. Liverpool Telescope

     

Figure 14.— Success rates recovering simulated short GRB afterglows, long GRB afterglows and kilonova light curves for the Liverpool Telescope, using the RATCam (left) and SkyCamZ (right). The figure reflects the success rate in recovering transients added to the observed fields, and does not include efficiency lost due to observing only a fraction of the possible source locations. The shown results for GRB afterglows are based on the brightest models that we considered.

The efficiency of the Liverpool Telescope pipeline was measured with the same methods used for ROTSE. A Python script was written to inject 100 transient objects per 10 Mpc bin per model, with light curves following the three models described in Table 1, assuming the brightest normalization for the GRB models. These images were then analyzed using the pipeline, and a script used to find and flag injections found in the pipeline output. Figure 14 shows that we obtained efficiencies around 90% for injections brighter than the limiting magnitude, including saturated objects normally discarded in other image subtraction methods. For RATCam, any of the tested models would have been observable out to 100 Mpc or more - well beyond the initial LIGO/Virgo horizon distance for neutron star mergers. For SkyCamZ, we found similar efficiencies, over smaller distance ranges.

6.5. Pi of the Sky

The efficiency of the Pi of the Sky transient search was investigated by adding simulated stars to existing images and reprocessing them. The objects that were injected had different magnitudes and were chosen from real observed stars during the autumn science run. Unlike the other simulations described in this paper, objects added to Pi of the Sky data did not follow model light curves, but instead measured the ability of the pipeline to recover a transient of a given magnitude using data from a single night. Stars injected in one image were also injected in subsequent images of that field taken during the same night. Only injections that were made to the inner part of the CCD chip, at least 150 pixels from CCD borders, were considered to estimate transient detection efficiency. The border part of the CCD was rejected by the off-line optical transient recognition algorithm due to the possibility of CCD anomalies that might be mistaken as short optical transients. Also, only injections starting on a good quality image were considered in efficiency estimation. This means that the effective field of view for optical transient recognition corresponds to . At each stage of the processing it was determined how many of the injected objects were detected.

Figure 15.— Success rate of the Pi of the Sky pipeline in recovering simulated transients of various magnitudes. The figure reflects the success rate in recovering transients added to the observed fields, and does not include efficiency lost due to observing only a fraction of the possible source locations.

Figure 15 shows two curves demonstrating the efficiency of the Pi of the Sky pipeline. The first one describes how many of the injected objects were detected in at least one image and the second curve shows how many of the injections were detected in five or more images. The first case corresponds to the minimal criterion that was required for the candidate to be classified as an optical transient and be inspected by a human. The second case reflects the criteria used for an optical transient to have been automatically classified as a nearly certain real event. On both curves we see that the maximal efficiency did not reach near , even for very bright sources. This can be attributed to several causes. An important loss of efficiency came from areas excluded from the search due to the presence of previously discovered stars. Objects injected within a radius of of stars listed in the Pi of the Sky star catalogue were not recognized as optical transients and discarded by the pipeline, resulting in a impact to the injection recovery rate. Additional sources were lost to structure in the CCD: of the CCD area consisted of wire guiding electric charge. A significant part of the losses also came from quality checks in the algorithm preprocessing. At this stage transients that were fainter than 11th magnitude, or observed on multiple low quality frames, were discarded. This impacted the efficiency by for bright transients, and up to for faint transients injected with brightness around magnitude 11. Other cuts in the data processing pipeline resulted in an additional loss of efficiency.

7. Discussion and Summary

This paper describes the first end-to-end searches for optical transients associated with GW candidate events. Unfortunately, no convincing transient counterpart was found. This effort included a range of different types of telescopes, as well as a range of different analysis strategies. While the variety of analysis strategies employed presents a challenge for interpretating the results, we believe that this approach is forward-looking. The LIGO and Virgo collaborations have recently made an open call for partners to search for EM counterparts to GW events discovered with the next generation of GW detectors1007. It is likely that partners will use a variety of facilities and instruments, and each apply their own data analysis techniques. Both the successes and lessons of this work should serve as useful guideposts to investigators pursuing similar searches with the up-coming “advanced” generation of gravitational wave detectors. Strategies are also being discussed in the literature (Metzger & Berger, 2012; Nissanke et al., 2013; Singer et al., 2013).

Rapidly down-selecting candidates for follow-up observations, integrating results for astrophysical interpretation, and communicating findings will require a common framework to describe transients discovered with disparate techniques. In this work, we presented two complementary statistics for characterizing the results of a transient classification pipeline, the false-alarm probability and the detection efficiency. These statistics were calculated for several different analyses, so that objects discovered in the searches could be quantitatively evaluated and compared. This paradigm, where results from transient searches with different selection criteria must be discussed in a common language, is likely to be a theme that becomes more common as survey instruments evolve.

Because GW event candidates are poorly localized, searches for counterparts need to consider the large population of optically variable sources that could produce false positive coincidences (Kulkarni & Kasliwal, 2009). Classification based on light curves, spectroscopy and other properties can help, but these strategies are complicated by the fact that the light curves associated with compact object mergers and other potential GW sources are largely uncertain. However, we were able to demonstrate several automated strategies that reduced false positives, while selecting for a wide range of models. These techniques included demands on the rate of dimming in objects, spatial coincidence with galaxies within the GW observable distance, anti-coincidence with cataloged stars and asteroids, and shapes consistent with point objects. For a variety of data sets over wide areas, we showed how these cuts could be applied to reduce the rate of false positives to less than 10%, meaning that a single telescope taking a series of images in response to a GW trigger would have less than a 10% chance of reporting a false positive. Monte Carlo simulations of model light curves were used to show that this false-positive rejection was possible while still maintaining sensitivity to models of both GRB afterglow light curves and kilonova light curves.

Follow-up observations of the type presented in this paper will probably be just the first stage in efforts to find Advanced LIGO/Virgo counterparts. While essential to identify candidate counterparts, wide field imaging is unlikely to be sufficient to make definitive associations with a GW trigger. Further observations, including sensitive photometry and spectroscopy, will be needed to confirm possible associations and characterize the source. The level of false-positive rejection achieved by software in this work, if promptly applied to collected optical image data, would reduce candidate objects associated with a LIGO/Virgo trigger to a manageable level, such that they could be pursued with further follow-up observations. The challenge presented by false positives is likely to increase with the advent of Advanced LIGO/Virgo, when a larger horizon distance will require imaging to fainter magnitudes, and so increase the number of potential contaminants.

The Monte Carlo studies we performed demonstrated that we typically recovered a range of light curve models to a depth consistent with the limiting flux of the observations, proving the validity of our selection criteria. During the observing periods, typical position averaged sensitive ranges for NS-NS mergers was 18 Mpc, or 35 Mpc for NS-BH mergers (Abadie et al., 2012c). The efficiency curves shown in Figures 9 through 15 show that the data sets with better limiting magnitudes (QUEST, Liverpool Telescope) were succesful in recovering all the considered models at these distance scales. The less sensitive data sets (ROTSE, TAROT, Zadko) would have missed a kilonova at these distances, but were potentially sensitive to GRB afterglows. Looking towards the future, the simulation results show that short exposures ( minute) with small aperture telescopes, with observations to depths of less than 18th magnitude, failed to recover short GRB or kilonova light curves at distances comparable to the expected 200 Mpc range of advanced GW detectors to NS-NS mergers (Abadie et al., 2010b; Aasi et al., 2013). This means that, while smaller telescopes may be valuable in searching for counterparts to galactic GW sources, they may require long total exposures, and/or a hierarchical observing strategy with larger telescopes, to be able to detect the expected optical signature of distant compact object mergers. Another factor that is likely to impact transient recovery in the advanced detector era is the incompleteness of available galaxy catalogs (Nissanke et al., 2013). Currently, catalogs are missing a significant fraction of the extragalactic starlight within 200 Mpc, however, planned surveys can help address this problem (Nissanke et al., 2013).

This study has been a valuable exercise that will help the preparation of the data analysis and observing strategies for the up-coming second generation GW detectors, which are anticipated to begin operating in 2015 and to improve in sensitivity over the following few years (Aasi et al., 2013). Searches for optical and other transient counterparts will become even more compelling as the range of the detectors increases. Moreover, the rapid growth of large area survey instruments, including plans for the Large Synoptic Survey Telescope (LSST) (Ivezic et al., 2011), means that the problem of choosing among rapidly fading candidates selected with different criteria is likely to become a theme that extends beyond GW related searches. The LIGO and Virgo collaborations are committed to providing prompt triggers for astronomers to follow up, with a more open model to allow broader participation (LIGO Scientific Collaboration & Virgo Collaboration, 2012). We can therefore hope that future searches will yield multi-messenger transient events that reveal the astrophysical sources and processes that produce them.

We thank J. Barnes, D. Kasen, B. Metzger, and E. Nakar for providing the kilonova model light curves that we have used in our Figures 4-7. The authors gratefully acknowledge the support of the United States National Science Foundation for the construction and operation of the LIGO Laboratory, the Science and Technology Facilities Council of the United Kingdom, the Max-Planck-Society, and the State of Niedersachsen/Germany for support of the construction and operation of the GEO600 detector, and the Italian Istituto Nazionale di Fisica Nucleare and the French Centre National de la Recherche Scientifique for the construction and operation of the Virgo detector. The authors also gratefully acknowledge the support of the research by these agencies and by the Australian Research Council, the International Science Linkages program of the Commonwealth of Australia, the Council of Scientific and Industrial Research of India, the Istituto Nazionale di Fisica Nucleare of Italy, the Spanish Ministerio de Economia y Competitividad, the Conselleria d’Economia Hisenda i Innovacio of the Govern de les Illes Balears, the Foundation for Fundamental Research on Matter supported by the Netherlands Organisation for Scientific Research, the Polish Ministry of Science and Higher Education, the FOCUS Programme of Foundation for Polish Science, the Royal Society, the Scottish Funding Council, the Scottish Universities Physics Alliance, The National Aeronautics and Space Administration, OTKA of Hungary, 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 National Science and Engineering Research Council Canada, the Carnegie Trust, the Leverhulme Trust, the David and Lucile Packard Foundation, the Research Corporation, FIRB 2012 Project RBFR12PM1F (Italian Ministry of Education, University and Research), and the Alfred P. Sloan Foundation. This work is based on results partially obtained at the ESO observatory, La Silla. The Liverpool Telescope is operated on the island of La Palma by Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias with financial support from the UK Science and Technology Facilities Council. This document has been assigned the identifier LIGO-P1200171-v19.
GW Trigger Telescope R.A. Decl. R.A. Decl. R.A. Decl.
G3821 QUEST 104.89 -27.94 133.88 -5.24 227.61 -64.26
CWB1 TAROT 207.21 -48.80
G4202 QUEST 89.34 -0.70 86.33 -9.78 89.34 -5.24
CWB2 QUEST 81.00 -32.49 75.63 -50.65 91.23 -41.57
G19377 ROTSE-c 115.56 -30.00
SkyMapper 115.43 -30.03 120.01 -29.91 110.78 -29.92
115.40 -34.00 115.39 -25.99 110.94 -25.93
110.58 -33.91 120.22 -33.90
TAROT 115.40 -30.00
Zadko 110.98 -27.53 114.75 -22.05 115.25 -32.07
115.80 -29.98 115.85 -29.22
G20190 ROTSE-abcd 333.25 18.03
TAROT 333.33 18.00
Zadko 322.49 12.17 323.37 -0.82 329.77 18.18
330.17 17.74 333.96 19.23
QUEST 336.29 8.50 334.49 10.63 331.61 17.57
G21852 ROTSE-b 11.04 41.61
PTF 11.39 41.62 55.80 -19.12 52.20 -19.12
56.93 -21.37 39.42 -7.87 52.25 -28.12
55.24 -16.87 51.15 -25.87 34.38 -32.62
G23004 ROTSE-bcd 61.97 -20.91
Liverpool 61.11 -2.20
Pi of the Sky Various
Table 5Center locations of all fields observed. All coordinates are in degrees using the J2000 equinox.

Footnotes

  1. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  2. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  3. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  4. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  5. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  6. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  7. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  8. affiliation: INFN, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  9. affiliation: Università di Salerno, Fisciano, I-84084 Salerno, Italy
  10. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  11. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  12. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  13. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  14. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  15. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  16. affiliation: Instituto Nacional de Pesquisas Espaciais, 12227-010 - São José dos Campos, SP, Brazil
  17. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  18. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  19. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  20. affiliation: Leibniz Universität Hannover, D-30167 Hannover, Germany
  21. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  22. affiliation: Università di Siena, I-53100 Siena, Italy
  23. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  24. affiliation: University of Florida, Gainesville, FL 32611, USA
  25. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  26. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  27. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  28. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  29. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  30. affiliation: The University of Mississippi, University, MS 38677, USA
  31. affiliation: California State University Fullerton, Fullerton, CA 92831, USA
  32. affiliation: Leibniz Universität Hannover, D-30167 Hannover, Germany
  33. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  34. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  35. affiliation: Leibniz Universität Hannover, D-30167 Hannover, Germany
  36. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  37. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  38. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  39. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-14476 Golm, Germany
  40. affiliation: Montana State University, Bozeman, MT 59717, USA
  41. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  42. affiliation: Syracuse University, Syracuse, NY 13244, USA
  43. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  44. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  45. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  46. affiliation: INFN, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  47. affiliation: Università di Salerno, Fisciano, I-84084 Salerno, Italy
  48. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  49. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  50. affiliation: APC, AstroParticule et Cosmologie, Université Paris Diderot, CNRS/IN2P3, CEA/Irfu, Observatoire de Paris, Sorbonne Paris Cité, 10, rue Alice Domon et Léonie Duquet, F-75205 Paris Cedex 13, France
  51. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  52. affiliation: Columbia University, New York, NY 10027, USA
  53. affiliation: Stanford University, Stanford, CA 94305, USA
  54. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  55. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  56. affiliation: Università di Pisa, I-56127 Pisa, Italy
  57. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  58. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  59. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  60. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  61. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-14476 Golm, Germany
  62. affiliation: CAMK-PAN, 00-716 Warsaw, Poland
  63. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  64. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  65. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  66. affiliation: Columbia University, New York, NY 10027, USA
  67. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  68. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  69. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  70. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  71. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  72. affiliation: San Jose State University, San Jose, CA 95192, USA
  73. affiliation: Stanford University, Stanford, CA 94305, USA
  74. affiliation: Moscow State University, Moscow, 119992, Russia
  75. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  76. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  77. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  78. affiliation: LAL, Université Paris-Sud, IN2P3/CNRS, F-91898 Orsay, France
  79. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  80. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  81. affiliation: NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA
  82. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  83. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  84. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  85. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  86. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  87. affiliationmark:
  88. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  89. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  90. affiliation: Institut de Physique de Rennes, CNRS, Université de Rennes 1, F-35042 Rennes, France
  91. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  92. affiliation: Università di Pisa, I-56127 Pisa, Italy
  93. affiliation: Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, Université de Lyon, F-69622 Villeurbanne, Lyon, France
  94. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  95. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  96. affiliation: Washington State University, Pullman, WA 99164, USA
  97. affiliation: INFN, Sezione di Perugia, I-06123 Perugia, Italy
  98. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  99. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  100. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  101. affiliation: Moscow State University, Moscow, 119992, Russia
  102. affiliation: INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy
  103. affiliation: Università degli Studi di Urbino ’Carlo Bo’, I-61029 Urbino, Italy
  104. affiliation: Washington State University, Pullman, WA 99164, USA
  105. affiliation: University of Oregon, Eugene, OR 97403, USA
  106. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  107. affiliation: Laboratoire Kastler Brossel, ENS, CNRS, UPMC, Université Pierre et Marie Curie, F-75005 Paris, France
  108. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  109. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  110. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  111. affiliation: LAL, Université Paris-Sud, IN2P3/CNRS, F-91898 Orsay, France
  112. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  113. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  114. affiliation: Syracuse University, Syracuse, NY 13244, USA
  115. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  116. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  117. affiliation: Astronomical Observatory Warsaw University, 00-478 Warsaw, Poland
  118. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  119. affiliation: VU University Amsterdam, 1081 HV Amsterdam, The Netherlands
  120. affiliation: University of Maryland, College Park, MD 20742, USA
  121. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  122. affiliation: APC, AstroParticule et Cosmologie, Université Paris Diderot, CNRS/IN2P3, CEA/Irfu, Observatoire de Paris, Sorbonne Paris Cité, 10, rue Alice Domon et Léonie Duquet, F-75205 Paris Cedex 13, France
  123. affiliation: Stanford University, Stanford, CA 94305, USA
  124. affiliation: University of Massachusetts - Amherst, Amherst, MA 01003, USA
  125. affiliation: Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, Université de Lyon, F-69622 Villeurbanne, Lyon, France
  126. affiliation: Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain
  127. affiliation: INFN, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  128. affiliation: Università di Napoli ’Federico II’, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  129. affiliation: NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA
  130. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  131. affiliation: Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, Ontario, M5S 3H8, Canada
  132. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  133. affiliation: Tsinghua University, Beijing 100084, China
  134. affiliation: University of Maryland, College Park, MD 20742, USA
  135. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  136. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  137. affiliation: University of Michigan, Ann Arbor, MI 48109, USA
  138. affiliation: Rochester Institute of Technology, Rochester, NY 14623, USA
  139. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  140. affiliation: The University of Mississippi, University, MS 38677, USA
  141. affiliation: LAL, Université Paris-Sud, IN2P3/CNRS, F-91898 Orsay, France
  142. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  143. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  144. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  145. affiliation: INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
  146. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  147. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  148. affiliation: National Tsing Hua University, Hsinchu Taiwan 300
  149. affiliation: Charles Sturt University, Wagga Wagga, NSW 2678, Australia
  150. affiliation: APC, AstroParticule et Cosmologie, Université Paris Diderot, CNRS/IN2P3, CEA/Irfu, Observatoire de Paris, Sorbonne Paris Cité, 10, rue Alice Domon et Léonie Duquet, F-75205 Paris Cedex 13, France
  151. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  152. affiliation: Caltech-CaRT, Pasadena, CA 91125, USA
  153. affiliation: INFN, Sezione di Genova, I-16146 Genova, Italy
  154. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  155. affiliation: Pusan National University, Busan 609-735, Korea
  156. affiliation: Australian National University, Canberra, ACT 0200, Australia
  157. affiliation: Carleton College, Northfield, MN 55057, USA
  158. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  159. affiliation: Australian National University, Canberra, ACT 0200, Australia
  160. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  161. affiliation: University of Florida, Gainesville, FL 32611, USA
  162. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  163. affiliation: Stanford University, Stanford, CA 94305, USA
  164. affiliation: University of Massachusetts - Amherst, Amherst, MA 01003, USA
  165. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  166. affiliation: INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
  167. affiliation: Università di Roma Tor Vergata, I-00133 Roma, Italy
  168. affiliation: Laboratoire Kastler Brossel, ENS, CNRS, UPMC, Université Pierre et Marie Curie, F-75005 Paris, France
  169. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  170. affiliation: Università di Roma ’La Sapienza’, I-00185 Roma, Italy
  171. affiliation: INFN, Sezione di Perugia, I-06123 Perugia, Italy
  172. affiliation: Instituto Nacional de Pesquisas Espaciais, 12227-010 - São José dos Campos, SP, Brazil
  173. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  174. affiliation: Università di Roma ’La Sapienza’, I-00185 Roma, Italy
  175. affiliation: University of Sannio at Benevento, I-82100 Benevento, Italy and INFN (Sezione di Napoli), Italy
  176. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  177. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  178. affiliation: San Jose State University, San Jose, CA 95192, USA
  179. affiliation: Montana State University, Bozeman, MT 59717, USA
  180. affiliation: The George Washington University, Washington, DC 20052, USA
  181. affiliation: Instituto Nacional de Pesquisas Espaciais, 12227-010 - São José dos Campos, SP, Brazil
  182. affiliation: University of Cambridge, Cambridge, CB2 1TN, United Kingdom
  183. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  184. affiliation: Columbia University, New York, NY 10027, USA
  185. affiliation: Syracuse University, Syracuse, NY 13244, USA
  186. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  187. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  188. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  189. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  190. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  191. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  192. affiliation: University of Minnesota, Minneapolis, MN 55455, USA
  193. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  194. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  195. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  196. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  197. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  198. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  199. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  200. affiliation: INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
  201. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  202. affiliation: Leibniz Universität Hannover, D-30167 Hannover, Germany
  203. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  204. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  205. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  206. affiliation: LAL, Université Paris-Sud, IN2P3/CNRS, F-91898 Orsay, France
  207. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  208. affiliation: The University of Sheffield, Sheffield S10 2TN, United Kingdom
  209. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  210. affiliation: Washington State University, Pullman, WA 99164, USA
  211. affiliation: INFN, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  212. affiliation: Università di Napoli ’Federico II’, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  213. affiliation: Wigner RCP, RMKI, H-1121 Budapest, Konkoly Thege Miklós út 29-33, Hungary
  214. affiliation: Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, Université de Lyon, F-69622 Villeurbanne, Lyon, France
  215. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  216. affiliation: University of Florida, Gainesville, FL 32611, USA
  217. affiliation: Laboratoire Kastler Brossel, ENS, CNRS, UPMC, Université Pierre et Marie Curie, F-75005 Paris, France
  218. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  219. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  220. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  221. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  222. affiliation: University of Sannio at Benevento, I-82100 Benevento, Italy and INFN (Sezione di Napoli), Italy
  223. affiliation: Inter-University Centre for Astronomy and Astrophysics, Pune - 411007, India
  224. affiliation: INFN, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  225. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  226. affiliation: Università di Pisa, I-56127 Pisa, Italy
  227. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  228. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  229. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  230. affiliation: The University of Mississippi, University, MS 38677, USA
  231. affiliation: Moscow State University, Moscow, 119992, Russia
  232. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  233. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  234. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  235. affiliation: INFN, Gruppo Collegato di Trento, I-38050 Povo, Trento, Italy
  236. affiliation: Università di Trento, I-38050 Povo, Trento, Italy
  237. affiliation: California Institute of Technology, Pasadena, CA 91125, USA
  238. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  239. affiliation: Tsinghua University, Beijing 100084, China
  240. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  241. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  242. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  243. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  244. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  245. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  246. affiliation: University of Florida, Gainesville, FL 32611, USA
  247. affiliation: University of Florida, Gainesville, FL 32611, USA
  248. affiliation: Wigner RCP, RMKI, H-1121 Budapest, Konkoly Thege Miklós út 29-33, Hungary
  249. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  250. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  251. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  252. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  253. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  254. affiliation: Columbia University, New York, NY 10027, USA
  255. affiliation: INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
  256. affiliation: Università di Roma Tor Vergata, I-00133 Roma, Italy
  257. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  258. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  259. affiliation: Northwestern University, Evanston, IL 60208, USA
  260. affiliation: Northwestern University, Evanston, IL 60208, USA
  261. affiliation: Montclair State University, Montclair, NJ 07043, USA
  262. affiliation: Northwestern University, Evanston, IL 60208, USA
  263. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  264. affiliation: University of Florida, Gainesville, FL 32611, USA
  265. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  266. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  267. affiliation: Università di Pisa, I-56127 Pisa, Italy
  268. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  269. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  270. affiliation: Università di Pisa, I-56127 Pisa, Italy
  271. affiliation: The Pennsylvania State University, University Park, PA 16802, USA
  272. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  273. affiliation: Syracuse University, Syracuse, NY 13244, USA
  274. affiliation: Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, Université de Lyon, F-69622 Villeurbanne, Lyon, France
  275. affiliation: California State University Fullerton, Fullerton, CA 92831, USA
  276. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  277. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  278. affiliation: INFN, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  279. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  280. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  281. affiliation: LAL, Université Paris-Sud, IN2P3/CNRS, F-91898 Orsay, France
  282. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  283. affiliation: Università di Roma ’La Sapienza’, I-00185 Roma, Italy
  284. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  285. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  286. affiliation: Rochester Institute of Technology, Rochester, NY 14623, USA
  287. affiliation: MTA-Eotvos University, ‘Lendulet’A. R. G., Budapest 1117, Hungary
  288. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  289. affiliation: University of Oregon, Eugene, OR 97403, USA
  290. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  291. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  292. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  293. affiliation: National Astronomical Observatory of Japan, Tokyo 181-8588, Japan
  294. affiliation: University of Florida, Gainesville, FL 32611, USA
  295. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  296. affiliation: University of Cambridge, Cambridge, CB2 1TN, United Kingdom
  297. affiliation: INFN, Sezione di Perugia, I-06123 Perugia, Italy
  298. affiliation: Università di Perugia, I-06123 Perugia, Italy
  299. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  300. affiliation: INFN, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  301. affiliation: Università di Napoli ’Federico II’, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  302. affiliation: NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA
  303. affiliation: INFN, Sezione di Genova, I-16146 Genova, Italy
  304. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  305. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  306. affiliation: MTA-Eotvos University, ‘Lendulet’A. R. G., Budapest 1117, Hungary
  307. affiliation: Washington State University, Pullman, WA 99164, USA
  308. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  309. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  310. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  311. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  312. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  313. affiliation: Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain
  314. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  315. affiliation: University of Florida, Gainesville, FL 32611, USA
  316. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  317. affiliation: University of Florida, Gainesville, FL 32611, USA
  318. affiliation: MTA-Eotvos University, ‘Lendulet’A. R. G., Budapest 1117, Hungary
  319. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  320. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  321. affiliation: Moscow State University, Moscow, 119992, Russia
  322. affiliation: Caltech-CaRT, Pasadena, CA 91125, USA
  323. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  324. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  325. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  326. affiliation: NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA
  327. affiliation: Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, Université de Lyon, F-69622 Villeurbanne, Lyon, France
  328. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  329. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  330. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  331. affiliation: Rutherford Appleton Laboratory, HSIC, Chilton, Didcot, Oxon, OX11 0QX, United Kingdom
  332. affiliation: Embry-Riddle Aeronautical University, Prescott, AZ 86301, USA
  333. affiliation: California State University Fullerton, Fullerton, CA 92831, USA
  334. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  335. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  336. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-14476 Golm, Germany
  337. affiliation: INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy
  338. affiliation: Università degli Studi di Urbino ’Carlo Bo’, I-61029 Urbino, Italy
  339. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  340. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  341. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  342. affiliation: University of Michigan, Ann Arbor, MI 48109, USA
  343. affiliation: Washington State University, Pullman, WA 99164, USA
  344. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  345. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  346. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  347. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  348. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  349. affiliation: Perimeter Institute for Theoretical Physics, Ontario, N2L 2Y5, Canada
  350. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  351. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  352. affiliation: American University, Washington, DC 20016, USA
  353. affiliation: Syracuse University, Syracuse, NY 13244, USA
  354. affiliation: University of Oregon, Eugene, OR 97403, USA
  355. affiliation: University of Florida, Gainesville, FL 32611, USA
  356. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  357. affiliation: National Astronomical Observatory of Japan, Tokyo 181-8588, Japan
  358. affiliation: Deceased, April 2012.
  359. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  360. affiliation: Laboratoire Kastler Brossel, ENS, CNRS, UPMC, Université Pierre et Marie Curie, F-75005 Paris, France
  361. affiliation: University of Florida, Gainesville, FL 32611, USA
  362. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  363. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  364. affiliation: LAL, Université Paris-Sud, IN2P3/CNRS, F-91898 Orsay, France
  365. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  366. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  367. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  368. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  369. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  370. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  371. affiliation: University of Massachusetts - Amherst, Amherst, MA 01003, USA
  372. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  373. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  374. affiliation: University of New Hampshire, Durham, NH 03824, USA
  375. affiliation: Caltech-CaRT, Pasadena, CA 91125, USA
  376. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  377. affiliation: College of William and Mary, Williamsburg, VA 23187, USA
  378. affiliation: University of Adelaide, Adelaide, SA 5005, Australia
  379. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  380. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  381. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  382. affiliation: Tsinghua University, Beijing 100084, China
  383. affiliation: National Tsing Hua University, Hsinchu Taiwan 300
  384. affiliation: Syracuse University, Syracuse, NY 13244, USA
  385. affiliation: Embry-Riddle Aeronautical University, Prescott, AZ 86301, USA
  386. affiliation: Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain
  387. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  388. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  389. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  390. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  391. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  392. affiliation: Australian National University, Canberra, ACT 0200, Australia
  393. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  394. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  395. affiliation: Raman Research Institute, Bangalore, Karnataka 560080, India
  396. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  397. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  398. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  399. affiliation: Korea Institute of Science and Technology Information, Daejeon 305-806, Korea
  400. affiliation: Northwestern University, Evanston, IL 60208, USA
  401. affiliation: Białystok University, 15-424 Białystok, Poland
  402. affiliation: Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain
  403. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  404. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  405. affiliation: University of Southampton, Southampton, SO17 1BJ, United Kingdom
  406. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  407. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  408. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  409. affiliation: IISER-TVM, CET Campus, Trivandrum Kerala 695016, India
  410. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  411. affiliation: Northwestern University, Evanston, IL 60208, USA
  412. affiliation: University of Minnesota, Minneapolis, MN 55455, USA
  413. affiliation: Korea Institute of Science and Technology Information, Daejeon 305-806, Korea
  414. affiliation: NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA
  415. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  416. affiliation: LAL, Université Paris-Sud, IN2P3/CNRS, F-91898 Orsay, France
  417. affiliation: Hobart and William Smith Colleges, Geneva, NY 14456, USA
  418. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  419. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  420. affiliation: Leibniz Universität Hannover, D-30167 Hannover, Germany
  421. affiliation: Caltech-CaRT, Pasadena, CA 91125, USA
  422. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  423. affiliation: National Astronomical Observatory of Japan, Tokyo 181-8588, Japan
  424. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  425. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  426. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  427. affiliation: Syracuse University, Syracuse, NY 13244, USA
  428. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  429. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  430. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  431. affiliation: Moscow State University, Moscow, 119992, Russia
  432. affiliation: Institute of Applied Physics, Nizhny Novgorod, 603950, Russia
  433. affiliation: Korea Institute of Science and Technology Information, Daejeon 305-806, Korea
  434. affiliation: Seoul National University, Seoul 151-742, Korea
  435. affiliation: Korea Institute of Science and Technology Information, Daejeon 305-806, Korea
  436. affiliation: Hanyang University, Seoul 133-791, Korea
  437. affiliation: Stanford University, Stanford, CA 94305, USA
  438. affiliation: University of Adelaide, Adelaide, SA 5005, Australia
  439. affiliation: Pusan National University, Busan 609-735, Korea
  440. affiliation: University of Adelaide, Adelaide, SA 5005, Australia
  441. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  442. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  443. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  444. affiliation: University of Florida, Gainesville, FL 32611, USA
  445. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  446. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  447. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  448. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  449. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  450. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  451. affiliation: Astronomical Observatory Warsaw University, 00-478 Warsaw, Poland
  452. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  453. affiliation: University of Minnesota, Minneapolis, MN 55455, USA
  454. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  455. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  456. affiliation: IM-PAN, 00-956 Warsaw, Poland
  457. affiliation: NCBJ, 05-400 Świerk-Otwock, Poland
  458. affiliation: Stanford University, Stanford, CA 94305, USA
  459. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  460. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  461. affiliation: Institute for Plasma Research, Bhat, Gandhinagar 382428, India
  462. affiliation: Syracuse University, Syracuse, NY 13244, USA
  463. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  464. affiliation: Stanford University, Stanford, CA 94305, USA
  465. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  466. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  467. affiliation: Stanford University, Stanford, CA 94305, USA
  468. affiliation: Utah State University, Logan, UT 84322, USA
  469. affiliation: The University of Melbourne, Parkville, VIC 3010, Australia
  470. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  471. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  472. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  473. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-14476 Golm, Germany
  474. affiliation: Tsinghua University, Beijing 100084, China
  475. affiliation: Pusan National University, Busan 609-735, Korea
  476. affiliation: Hanyang University, Seoul 133-791, Korea
  477. affiliation: Seoul National University, Seoul 151-742, Korea
  478. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  479. affiliation: California State University Fullerton, Fullerton, CA 92831, USA
  480. affiliation: INFN, Gruppo Collegato di Trento, I-38050 Povo, Trento, Italy
  481. affiliation: Università di Trento, I-38050 Povo, Trento, Italy
  482. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  483. affiliation: LAL, Université Paris-Sud, IN2P3/CNRS, F-91898 Orsay, France
  484. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  485. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  486. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  487. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  488. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  489. affiliation: Stanford University, Stanford, CA 94305, USA
  490. affiliation: Northwestern University, Evanston, IL 60208, USA
  491. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  492. affiliation: University of Brussels, Brussels 1050 Belgium
  493. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  494. affiliation: Tsinghua University, Beijing 100084, China
  495. affiliation: University of Florida, Gainesville, FL 32611, USA
  496. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  497. affiliation: SUPA, University of Strathclyde, Glasgow, G1 1XQ, United Kingdom
  498. affiliation: California State University Fullerton, Fullerton, CA 92831, USA
  499. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  500. affiliation: Embry-Riddle Aeronautical University, Prescott, AZ 86301, USA
  501. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  502. affiliation: University of Massachusetts - Amherst, Amherst, MA 01003, USA
  503. affiliation: INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
  504. affiliation: ESPCI, CNRS, F-75005 Paris, France
  505. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  506. affiliation: INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy
  507. affiliation: Syracuse University, Syracuse, NY 13244, USA
  508. affiliation: Caltech-CaRT, Pasadena, CA 91125, USA
  509. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  510. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  511. affiliation: Leibniz Universität Hannover, D-30167 Hannover, Germany
  512. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  513. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  514. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  515. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  516. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  517. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  518. affiliation: California State University Fullerton, Fullerton, CA 92831, USA
  519. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  520. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  521. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  522. affiliation: ESPCI, CNRS, F-75005 Paris, France
  523. affiliation: INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
  524. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  525. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  526. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  527. affiliation: University of Minnesota, Minneapolis, MN 55455, USA
  528. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  529. affiliation: Università di Roma ’La Sapienza’, I-00185 Roma, Italy
  530. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  531. affiliation: INFN, Sezione di Perugia, I-06123 Perugia, Italy
  532. affiliation: Università di Camerino, Dipartimento di Fisica, I-62032 Camerino, Italy
  533. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  534. affiliation: Columbia University, New York, NY 10027, USA
  535. affiliation: Columbia University, New York, NY 10027, USA
  536. affiliation: Stanford University, Stanford, CA 94305, USA
  537. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  538. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  539. affiliation: INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy
  540. affiliation: Università degli Studi di Urbino ’Carlo Bo’, I-61029 Urbino, Italy
  541. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  542. affiliation: University of Florida, Gainesville, FL 32611, USA
  543. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  544. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  545. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  546. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  547. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  548. affiliation: Syracuse University, Syracuse, NY 13244, USA
  549. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  550. affiliation: Columbia University, New York, NY 10027, USA
  551. affiliation: The University of Texas at Austin, Austin, TX 78712, USA
  552. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  553. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  554. affiliation: IISER-TVM, CET Campus, Trivandrum Kerala 695016, India
  555. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  556. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  557. affiliation: Australian National University, Canberra, ACT 0200, Australia
  558. affiliation: Southern University and A&M College, Baton Rouge, LA 70813, USA
  559. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  560. affiliation: University of Massachusetts - Amherst, Amherst, MA 01003, USA
  561. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  562. affiliation: University of Michigan, Ann Arbor, MI 48109, USA
  563. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  564. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  565. affiliation: Leibniz Universität Hannover, D-30167 Hannover, Germany
  566. affiliation: The University of Melbourne, Parkville, VIC 3010, Australia
  567. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  568. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  569. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  570. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  571. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  572. affiliation: Caltech-CaRT, Pasadena, CA 91125, USA
  573. affiliation: Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, Université de Lyon, F-69622 Villeurbanne, Lyon, France
  574. affiliation: College of William and Mary, Williamsburg, VA 23187, USA
  575. affiliation: INFN, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  576. affiliation: Università di Napoli ’Federico II’, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  577. affiliation: Australian National University, Canberra, ACT 0200, Australia
  578. affiliation: INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
  579. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  580. affiliation: Inter-University Centre for Astronomy and Astrophysics, Pune - 411007, India
  581. affiliation: Moscow State University, Moscow, 119992, Russia
  582. affiliation: University of Florida, Gainesville, FL 32611, USA
  583. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  584. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  585. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  586. affiliation: Syracuse University, Syracuse, NY 13244, USA
  587. affiliation: Rochester Institute of Technology, Rochester, NY 14623, USA
  588. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  589. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  590. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  591. affiliation: Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, Université de Lyon, F-69622 Villeurbanne, Lyon, France
  592. affiliation: National Astronomical Observatory of Japan, Tokyo 181-8588, Japan
  593. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  594. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  595. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  596. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  597. affiliation: University of Florida, Gainesville, FL 32611, USA
  598. affiliation: University of Florida, Gainesville, FL 32611, USA
  599. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  600. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  601. affiliation: University of Adelaide, Adelaide, SA 5005, Australia
  602. affiliation: Columbia University, New York, NY 10027, USA
  603. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  604. affiliation: University of Florida, Gainesville, FL 32611, USA
  605. affiliation: Wigner RCP, RMKI, H-1121 Budapest, Konkoly Thege Miklós út 29-33, Hungary
  606. affiliation: University of Florida, Gainesville, FL 32611, USA
  607. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  608. affiliation: Università di Roma ’La Sapienza’, I-00185 Roma, Italy
  609. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  610. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  611. affiliation: Università di Roma ’La Sapienza’, I-00185 Roma, Italy
  612. affiliation: IISER-Kolkata, Mohanpur, West Bengal 741252, India
  613. affiliation: University of Florida, Gainesville, FL 32611, USA
  614. affiliation: INFN, Sezione di Perugia, I-06123 Perugia, Italy
  615. affiliation: Università di Perugia, I-06123 Perugia, Italy
  616. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  617. affiliation: Australian National University, Canberra, ACT 0200, Australia
  618. affiliation: National Astronomical Observatory of Japan, Tokyo 181-8588, Japan
  619. affiliation: National Astronomical Observatory of Japan, Tokyo 181-8588, Japan
  620. affiliation: Syracuse University, Syracuse, NY 13244, USA
  621. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  622. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  623. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  624. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  625. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  626. affiliation: Rutherford Appleton Laboratory, HSIC, Chilton, Didcot, Oxon, OX11 0QX, United Kingdom
  627. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  628. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  629. affiliation: National Institute for Mathematical Sciences, Daejeon 305-390, Korea
  630. affiliation: National Institute for Mathematical Sciences, Daejeon 305-390, Korea
  631. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  632. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  633. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  634. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  635. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  636. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  637. affiliation: University of Adelaide, Adelaide, SA 5005, Australia
  638. affiliation: University of Florida, Gainesville, FL 32611, USA
  639. affiliation: National Tsing Hua University, Hsinchu Taiwan 300
  640. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  641. affiliation: The Pennsylvania State University, University Park, PA 16802, USA
  642. affiliation: California State University Fullerton, Fullerton, CA 92831, USA
  643. affiliation: IISER-TVM, CET Campus, Trivandrum Kerala 695016, India
  644. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  645. affiliation: University of Maryland, College Park, MD 20742, USA
  646. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  647. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  648. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  649. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  650. affiliation: Università di Siena, I-53100 Siena, Italy
  651. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-14476 Golm, Germany
  652. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  653. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  654. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  655. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  656. affiliation: Università di Pisa, I-56127 Pisa, Italy
  657. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  658. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  659. affiliation: Rochester Institute of Technology, Rochester, NY 14623, USA
  660. affiliation: Hobart and William Smith Colleges, Geneva, NY 14456, USA
  661. affiliation: Syracuse University, Syracuse, NY 13244, USA
  662. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  663. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  664. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  665. affiliation: INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy
  666. affiliation: Università degli Studi di Urbino ’Carlo Bo’, I-61029 Urbino, Italy
  667. affiliation: University of Sannio at Benevento, I-82100 Benevento, Italy and INFN (Sezione di Napoli), Italy
  668. affiliation: Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, Université de Lyon, F-69622 Villeurbanne, Lyon, France
  669. affiliation: The University of Melbourne, Parkville, VIC 3010, Australia
  670. affiliation: University of Sannio at Benevento, I-82100 Benevento, Italy and INFN (Sezione di Napoli), Italy
  671. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  672. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  673. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  674. affiliation: Università di Pisa, I-56127 Pisa, Italy
  675. affiliation: Washington State University, Pullman, WA 99164, USA
  676. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  677. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  678. affiliation: University of Minnesota, Minneapolis, MN 55455, USA
  679. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  680. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  681. affiliation: University of Sannio at Benevento, I-82100 Benevento, Italy and INFN (Sezione di Napoli), Italy
  682. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  683. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  684. affiliation: INFN, Gruppo Collegato di Trento, I-38050 Povo, Trento, Italy
  685. affiliation: Università di Trento, I-38050 Povo, Trento, Italy
  686. affiliation: Moscow State University, Moscow, 119992, Russia
  687. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  688. affiliation: INFN, Sezione di Perugia, I-06123 Perugia, Italy
  689. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  690. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  691. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  692. affiliation: University of Oregon, Eugene, OR 97403, USA
  693. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  694. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  695. affiliation: VU University Amsterdam, 1081 HV Amsterdam, The Netherlands
  696. affiliation: Wigner RCP, RMKI, H-1121 Budapest, Konkoly Thege Miklós út 29-33, Hungary
  697. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  698. affiliation: Columbia University, New York, NY 10027, USA
  699. affiliation: MTA-Eotvos University, ‘Lendulet’A. R. G., Budapest 1117, Hungary
  700. affiliation: RRCAT, Indore MP 452013, India
  701. affiliation: Tata Institute for Fundamental Research, Mumbai 400005, India
  702. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  703. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  704. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  705. affiliation: Università di Roma ’La Sapienza’, I-00185 Roma, Italy
  706. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  707. affiliation: INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
  708. affiliation: Università di Roma Tor Vergata, I-00133 Roma, Italy
  709. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  710. affiliation: Louisiana Tech University, Ruston, LA 71272, USA
  711. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  712. affiliation: SUPA, University of the West of Scotland, Paisley, PA1 2BE, United Kingdom
  713. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  714. affiliation: University of Florida, Gainesville, FL 32611, USA
  715. affiliation: INFN, Sezione di Roma, I-00185 Roma, Italy
  716. affiliation: Università di Roma ’La Sapienza’, I-00185 Roma, Italy
  717. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  718. affiliation: University of Michigan, Ann Arbor, MI 48109, USA
  719. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  720. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  721. affiliation: LAL, Université Paris-Sud, IN2P3/CNRS, F-91898 Orsay, France
  722. affiliation: INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
  723. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  724. affiliation: Northwestern University, Evanston, IL 60208, USA
  725. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  726. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  727. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  728. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  729. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  730. affiliation: INFN, Sezione di Napoli, Complesso Universitario di Monte S.Angelo, I-80126 Napoli, Italy
  731. affiliation: Università di Salerno, Fisciano, I-84084 Salerno, Italy
  732. affiliation: College of William and Mary, Williamsburg, VA 23187, USA
  733. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  734. affiliation: CAMK-PAN, 00-716 Warsaw, Poland
  735. affiliation: Institute of Astronomy, 65-265 Zielona Góra, Poland
  736. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  737. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  738. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  739. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  740. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  741. affiliation: The University of Melbourne, Parkville, VIC 3010, Australia
  742. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  743. affiliation: University of Michigan, Ann Arbor, MI 48109, USA
  744. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  745. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  746. affiliation: Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, Université de Lyon, F-69622 Villeurbanne, Lyon, France
  747. affiliation: Laboratoire des Matériaux Avancés (LMA), IN2P3/CNRS, Université de Lyon, F-69622 Villeurbanne, Lyon, France
  748. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  749. affiliation: Syracuse University, Syracuse, NY 13244, USA
  750. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  751. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  752. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  753. affiliation: Leibniz Universität Hannover, D-30167 Hannover, Germany
  754. affiliation: University of Oregon, Eugene, OR 97403, USA
  755. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  756. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  757. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  758. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-14476 Golm, Germany
  759. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  760. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  761. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  762. affiliation: Australian National University, Canberra, ACT 0200, Australia
  763. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  764. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  765. affiliation: Indian Institute of Technology, Gandhinagar Ahmedabad Gujarat 382424, India
  766. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  767. affiliation: Institute of Applied Physics, Nizhny Novgorod, 603950, Russia
  768. affiliation: Australian National University, Canberra, ACT 0200, Australia
  769. affiliation: Department of Astrophysics/IMAPP, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen, The Netherlands
  770. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  771. affiliation: Northwestern University, Evanston, IL 60208, USA
  772. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  773. affiliation: Stanford University, Stanford, CA 94305, USA
  774. affiliation: University of Maryland, College Park, MD 20742, USA
  775. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  776. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  777. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  778. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  779. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  780. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  781. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  782. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  783. affiliation: Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain
  784. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  785. affiliation: Australian National University, Canberra, ACT 0200, Australia
  786. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  787. affiliation: California State University Fullerton, Fullerton, CA 92831, USA
  788. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  789. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  790. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  791. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  792. affiliation: National Institute for Mathematical Sciences, Daejeon 305-390, Korea
  793. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  794. affiliation: Inter-University Centre for Astronomy and Astrophysics, Pune - 411007, India
  795. affiliation: INFN, Sezione di Roma Tor Vergata, I-00133 Roma, Italy
  796. affiliation: Università di Roma Tor Vergata, I-00133 Roma, Italy
  797. affiliation: Columbia University, New York, NY 10027, USA
  798. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  799. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  800. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  801. affiliation: Washington State University, Pullman, WA 99164, USA
  802. affiliation: Northwestern University, Evanston, IL 60208, USA
  803. affiliation: Australian National University, Canberra, ACT 0200, Australia
  804. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  805. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  806. affiliation: Moscow State University, Moscow, 119992, Russia
  807. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  808. affiliation: INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy
  809. affiliation: Università degli Studi di Urbino ’Carlo Bo’, I-61029 Urbino, Italy
  810. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  811. affiliation: Andrews University, Berrien Springs, MI 49104, USA
  812. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  813. affiliation: Cardiff University, Cardiff, CF24 3AA, United Kingdom
  814. affiliation: European Gravitational Observatory (EGO), I-56021 Cascina, Pisa, Italy
  815. affiliation: MTA-Eotvos University, ‘Lendulet’A. R. G., Budapest 1117, Hungary
  816. affiliation: APC, AstroParticule et Cosmologie, Université Paris Diderot, CNRS/IN2P3, CEA/Irfu, Observatoire de Paris, Sorbonne Paris Cité, 10, rue Alice Domon et Léonie Duquet, F-75205 Paris Cedex 13, France
  817. affiliation: University of Oregon, Eugene, OR 97403, USA
  818. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  819. affiliation: University of Florida, Gainesville, FL 32611, USA
  820. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  821. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  822. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  823. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  824. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  825. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  826. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  827. affiliation: Caltech-CaRT, Pasadena, CA 91125, USA
  828. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  829. affiliation: University of Florida, Gainesville, FL 32611, USA
  830. affiliation: SUPA, University of Strathclyde, Glasgow, G1 1XQ, United Kingdom
  831. affiliation: The University of Sheffield, Sheffield S10 2TN, United Kingdom
  832. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  833. affiliation: Università di Pisa, I-56127 Pisa, Italy
  834. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  835. affiliation: Università di Pisa, I-56127 Pisa, Italy
  836. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  837. affiliation: Università di Siena, I-53100 Siena, Italy
  838. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  839. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  840. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  841. affiliation: INFN, Sezione di Perugia, I-06123 Perugia, Italy
  842. affiliation: Università di Perugia, I-06123 Perugia, Italy
  843. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  844. affiliation: Columbia University, New York, NY 10027, USA
  845. affiliation: Trinity University, San Antonio, TX 78212, USA
  846. affiliation: Tata Institute for Fundamental Research, Mumbai 400005, India
  847. affiliation: Leibniz Universität Hannover, D-30167 Hannover, Germany
  848. affiliation: INFN, Sezione di Pisa, I-56127 Pisa, Italy
  849. affiliation: Università di Pisa, I-56127 Pisa, Italy
  850. affiliation: Caltech-CaRT, Pasadena, CA 91125, USA
  851. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  852. affiliation: VU University Amsterdam, 1081 HV Amsterdam, The Netherlands
  853. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  854. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  855. affiliation: Northwestern University, Evanston, IL 60208, USA
  856. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  857. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  858. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  859. affiliation: Wigner RCP, RMKI, H-1121 Budapest, Konkoly Thege Miklós út 29-33, Hungary
  860. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  861. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  862. affiliation: INFN, Sezione di Padova, I-35131 Padova, Italy
  863. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  864. affiliation: University of Adelaide, Adelaide, SA 5005, Australia
  865. affiliation: University of Washington, Seattle, WA 98195, USA
  866. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  867. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  868. affiliation: INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy
  869. affiliation: Università degli Studi di Urbino ’Carlo Bo’, I-61029 Urbino, Italy
  870. affiliation: INFN, Sezione di Firenze, I-50019 Sesto Fiorentino, Firenze, Italy
  871. affiliation: Università degli Studi di Urbino ’Carlo Bo’, I-61029 Urbino, Italy
  872. affiliation: Southern University and A&M College, Baton Rouge, LA 70813, USA
  873. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  874. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  875. affiliation: Nikhef, Science Park, 1098 XG Amsterdam, The Netherlands
  876. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  877. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  878. affiliation: INFN, Sezione di Perugia, I-06123 Perugia, Italy
  879. affiliation: Università di Perugia, I-06123 Perugia, Italy
  880. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  881. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  882. affiliation: The University of Texas at Brownsville, Brownsville, TX 78520, USA
  883. affiliation: Moscow State University, Moscow, 119992, Russia
  884. affiliation: Australian National University, Canberra, ACT 0200, Australia
  885. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  886. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  887. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  888. affiliation: Louisiana State University, Baton Rouge, LA 70803, USA
  889. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  890. affiliation: Tsinghua University, Beijing 100084, China
  891. affiliation: National Tsing Hua University, Hsinchu Taiwan 300
  892. affiliation: University of Birmingham, Birmingham, B15 2TT, United Kingdom
  893. affiliation: Tsinghua University, Beijing 100084, China
  894. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  895. affiliation: Australian National University, Canberra, ACT 0200, Australia
  896. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  897. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  898. affiliation: ARTEMIS UMR 7250, Université Nice-Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, F-06304 Nice, France
  899. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  900. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  901. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  902. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  903. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  904. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  905. affiliation: Syracuse University, Syracuse, NY 13244, USA
  906. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  907. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  908. affiliation: Rochester Institute of Technology, Rochester, NY 14623, USA
  909. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  910. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  911. affiliation: The University of Sheffield, Sheffield S10 2TN, United Kingdom
  912. affiliation: University of Florida, Gainesville, FL 32611, USA
  913. affiliation: University of Wisconsin–Milwaukee, Milwaukee, WI 53201, USA
  914. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  915. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  916. affiliation: University of Florida, Gainesville, FL 32611, USA
  917. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  918. affiliation: Southeastern Louisiana University, Hammond, LA 70402, USA
  919. affiliation: Abilene Christian University, Abilene, TX 79699, USA
  920. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  921. affiliation: Leibniz Universität Hannover, D-30167 Hannover, Germany
  922. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  923. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  924. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  925. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  926. affiliation: Albert-Einstein-Institut, Max-Planck-Institut für Gravitationsphysik, D-30167 Hannover, Germany
  927. affiliation: SUPA, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
  928. affiliation: LIGO - Hanford Observatory, Richland, WA 99352, USA
  929. affiliation: Northwestern University, Evanston, IL 60208, USA
  930. affiliation: LIGO - Livingston Observatory, Livingston, LA 70754, USA
  931. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  932. affiliation: University of Maryland, College Park, MD 20742, USA
  933. affiliation: Caltech-CaRT, Pasadena, CA 91125, USA
  934. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  935. affiliation: Southeastern Louisiana University, Hammond, LA 70402, USA
  936. affiliation: Northwestern University, Evanston, IL 60208, USA
  937. affiliation: Laboratoire d’Annecy-le-Vieux de Physique des Particules (LAPP), Université de Savoie, CNRS/IN2P3, F-74941 Annecy-le-Vieux, France
  938. affiliation: NCBJ, 05-400 Świerk-Otwock, Poland
  939. affiliation: Embry-Riddle Aeronautical University, Prescott, AZ 86301, USA
  940. affiliation: INFN, Sezione di Padova, I-35131 Padova, Italy
  941. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  942. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  943. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  944. affiliation: The Pennsylvania State University, University Park, PA 16802, USA
  945. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  946. affiliation: Deceased, May 2012.
  947. affiliation: Louisiana Tech University, Ruston, LA 71272, USA
  948. affiliation: LIGO - Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  949. affiliation: LIGO - California Institute of Technology, Pasadena, CA 91125, USA
  950. affiliation: University of Michigan, Ann Arbor, MI 48109, USA
  951. affiliation: Yale University, New Haven, CT 06520, USA
  952. affiliation: University of California Berkeley, Berkeley, CA 94720, USA
  953. affiliation: California Institute of Technology, Pasadena, CA 91125, USA
  954. affiliation: NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA
  955. affiliation: NCBJ, 05-400 Świerk-Otwock, Poland
  956. affiliation: Faculty of Physics, University of Warsaw, Hoza 69, 00-681 Warsaw, Poland
  957. affiliation: The University of Sheffield, Sheffield S10 2TN, United Kingdom
  958. affiliation: The Pennsylvania State University, University Park, PA 16802, USA
  959. affiliation: Weizmann Institute of Science, 76100 Rehovot, Israel
  960. affiliation: Hubble Fellow and Carnegie-Princeton Fellow, Observatories of the Carnegie Institution for Science, Pasadena, CA 91101, USA
  961. affiliation: Institut de Recherche en Astrophysique et Planetologie (IRAP), 31400 Toulouse, France
  962. affiliation: University of Western Australia, Crawley, WA 6009, Australia
  963. affiliationmark:
  964. affiliation: California Institute of Technology, Pasadena, CA 91125, USA
  965. affiliation: Departmentof Physics and Astronomy, University of North Carolina at Chapel Hill,Chapel Hill, NC 27599-3255
  966. affiliation: NCBJ, 05-400 Świerk-Otwock, Poland
  967. affiliation: Division of Particles and Astrophysical Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8601 Nagoya, Japan
  968. affiliation: Centre for Theoretical Physics of Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw, Poland
  969. affiliation: Centre for Theoretical Physics of Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw, Poland
  970. affiliation: NCBJ, 05-400 Świerk-Otwock, Poland
  971. affiliation: California Institute of Technology, Pasadena, CA 91125, USA
  972. affiliation: University of California Berkeley, Berkeley, CA 94720, USA
  973. affiliation: Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
  974. affiliation: Weizmann Institute of Science, 76100 Rehovot, Israel
  975. affiliation: Centre for Theoretical Physics of Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw, Poland
  976. affiliation: RIKEN, 2-1 Hirosawa, Wako, 351-0198, Saitama, Japan
  977. affiliation: Faculty of Physics, University of Warsaw, Hoza 69, 00-681 Warsaw, Poland
  978. affiliation: School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel
  979. affiliation: Yale University, New Haven, CT 06520, USA
  980. affiliation: Australian National University, Canberra, ACT 0200, Australia
  981. affiliation: University of California Berkeley, Berkeley, CA 94720, USA
  982. affiliation: The Research School of Astronomy and Astrophysics, The Australian National University, via Cotter Rd, Weston Creek, ACT 2611 Australia
  983. affiliation: Centre for Theoretical Physics of Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw, Poland
  984. affiliation: International Centre for Radio Astronomy Research - Curtin University, GPO Box U1987, Perth, WA 6845, Australia
  985. affiliation: ARC Centre of Excelence for All-sky Astrophysics (CAASTRO)
  986. affiliation: NCBJ, 05-400 Świerk-Otwock, Poland
  987. affiliation: Astrophysics Research Institute, Liverpool John Moores University, L3 5RF, United Kingdom
  988. affiliation: School of Physics and Astronomy, University of Southampton, Highfield, Southampton, SO17 1BJ, UK
  989. affiliation: Faculty of Physics, University of Warsaw, Hoza 69, 00-681 Warsaw, Poland
  990. affiliation: University of California Berkeley, Berkeley, CA 94720, USA
  991. affiliationtext: Observatoire de Haute-Provence, CNRS, 04870 Saint Michel l’Observatoire, France
  992. Of the two triggers not observed, one was the first alert generated during the autumn run and ROTSE imaged the wrong location due to a software bug, while the other was too close to the Sun to be observable by any of the telescopes.
  993. Except for trigger G20190, for which we selected galaxies within 30 Mpc in accordance with the gravitational wave horizon estimated for this event candidate.
  994. http://www2.iap.fr/users/alard/package.html
  995. http://simbad.u-strasbg.fr/simbad/
  996. http://scully.cfa.harvard.edu/cgi-bin/checkmp.cgi
  997. All Pi of the Sky telescopes have the same cameras, so data gathered is easily comparable.
  998. http://www.astro.washington.edu/users/becker/wcsremap.html
  999. http://www.astro.washington.edu/users/becker/hotpants.html
  1000. http://www. astro.washington.edu/users/becker/wcsremap.html
  1001. http://www.astromatic.net/software/swarp
  1002. http://telescope.livjm.ac.uk/Info/TelInst/Inst/SkyCam/
  1003. http://www.astro.washington.edu/users/becker/hotpants.html
  1004. The 7% of the total on-source area within the gaps between the CCDs does not have data and was not analyzed.
  1005. The 10% of the total on-source area within the gaps between the CCDs does not have data and was not analyzed.
  1006. Taking into account the galaxy regions lying in the CCD gaps, the 50% efficiency distances for G20190 (G23004) reduce to 32 (61) Mpc for kilonovae, 26 (53) Mpc for short GRBs, and 700 (1260) Mpc for long GRBs.
  1007. http://www.ligo.org/science/GWEMalerts.php

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