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Abstract
We present the first Open Gravitationalwave Catalog (1OGC), obtained by using the public data from Advanced LIGO’s first observing run to search for compactobject binary mergers. Our analysis is based on new methods that improve the separation between signals and noise in matchedfilter searches for gravitational waves from the merger of compact objects. The three most significant signals in our catalog correspond to the binary black hole mergers GW150914, GW151226, and LVT151012. We assume a common population of binary black holes for these three signals by defining a region of parameter space that is consistent with these events. Under this assumption, we find that LVT151012 has a 97.6% probability of being astrophysical in origin. No other significant binary black hole candidates are found, nor did we observe any significant binary neutron star or neutron star–black hole candidates. We make available our complete catalog of events, including the subthreshold population of candidates.
]1OGC: The first open gravitationalwave catalog of binary mergers from analysis of public Advanced LIGO data
0000000218504587]Alexander H. Nitz \move@AU\move@AF\@affiliationMaxPlanckInstitut für Gravitationsphysik (AlbertEinsteinInstitut), D30167 Hannover, Germany \move@AU\move@AF\@affiliationLeibniz Universität Hannover, D30167 Hannover, Germany
0000000203555998]Collin Capano \move@AU\move@AF\@affiliationMaxPlanckInstitut für Gravitationsphysik (AlbertEinsteinInstitut), D30167 Hannover, Germany \move@AU\move@AF\@affiliationLeibniz Universität Hannover, D30167 Hannover, Germany
0000000186944026]Alex B. Nielsen \move@AU\move@AF\@affiliationMaxPlanckInstitut für Gravitationsphysik (AlbertEinsteinInstitut), D30167 Hannover, Germany \move@AU\move@AF\@affiliationLeibniz Universität Hannover, D30167 Hannover, Germany
0000000245996054]Steven Reyes \move@AU\move@AF\@affiliationDepartment of Physics, Syracuse University, Syracuse NY 13244, USA
0000000251927784]Rebecca White \move@AU\move@AF\@affiliationFayettevilleManlius High School, Manlius, NY 13104, USA \move@AU\move@AF\@affiliationDepartment of Physics, Syracuse University, Syracuse NY 13244, USA
0000000291805765]Duncan A. Brown \move@AU\move@AF\@affiliationDepartment of Physics, Syracuse University, Syracuse NY 13244, USA
000000033015234X]Badri Krishnan \move@AU\move@AF\@affiliationMaxPlanckInstitut für Gravitationsphysik (AlbertEinsteinInstitut), D30167 Hannover, Germany \move@AU\move@AF\@affiliationLeibniz Universität Hannover, D30167 Hannover, Germany
1 Introduction
The Advanced LIGO gravitational wave observatories (Martynov:2016fzi) performed their first observing run (O1) from September 12, 2015 to January 19, 2016. This provided a total of 51.5 days of coincident observations from the two detectors located in Hanford, WA and Livingston, LA. The binary black hole mergers observed in this observing run have been reported by the LIGO and Virgo Collaborations (LVC) in Abbott:2016blz; Abbott:2016nmj; TheLIGOScientific:2016pea. These binary black hole detections have been independently studied by Green:2017voq; Roulet:2018jbe; Antelis:2018smo.
Since the publication of the results by TheLIGOScientific:2016pea; Abbott:2016ymx, improvements to the dataanalysis methods used (TheLIGOScientific:2016qqj) have been implemented (Nitz:2017svb; Nitz:2017lco; DalCanton:2017ala). Using these improvements, we reanalyze the O1 data and provide—for the first time—a full catalog of candidate events from a matched filter search for compact binary coalescences using the O1 data, which we call 1OGC. This catalog provides estimates of the significance of previously known events and a ranked list of subthreshold candidates. Although not significant by themselves, these subthreshold candidates can be correlated with archival data or transient events found by other astronomical observatories to provide constraints on the population of compactobject mergers (Ashton:2017ykh; Burns:2018pcl).
Our catalog is based entirely on public, open data and software. We use the LIGO data available from the Gravitational Wave Open Science Center (Vallisneri:2014vxa), and analyze the data using the open source PyCBC toolkit (Usman:2015kfa; Canton:2014ena; pycbcgithub).This toolkit was also used by one of the two analyses described in TheLIGOScientific:2016qqj. The lowest mass sources targeted in our search are neutron star binaries with total mass . The search space extends to binary black hole systems that produce gravitational waveforms longer than s from Hz. This corresponds to a total mass up to for sources with high mass ratios and spins where the component aligned with the orbital angular momentum is positive and large. For binaries with negligible spin, this corresponds to total mass . The search space also includes neutron star–black hole binaries. After applying cuts for data quality (TheLIGOScientific:2016zmo; TheLIGOScientific:2017lwt), a total of 48.1 days of coincident data are searched for signals.
The three most significant signals in the catalog correspond to GW150914 (Abbott:2016blz), LVT151012 (Abbott:2016blz; TheLIGOScientific:2016pea), and GW151226 (Abbott:2016nmj), respectively. No other astrophysically significant signals are observed. In the analysis of TheLIGOScientific:2016pea, LVT151012 was the thirdmost significant event, but it was not sufficiently significant to be labeled as an unambiguous detection. With the improved methods employed here, the false alarm rate of this candidate improves by an order of magnitude and it should be considered a true astrophysical event. The analyses of TheLIGOScientific:2016pea; Abbott:2016ymx restricted the astrophysical search space to binaries with a total mass less that . Our analysis extends this target space to higher mass signals. No additional signals are detected in this region of parameter space, consistent with the results of Abbott:2017iws.
A second observing run (O2) of the Advanced LIGO detectors took place from November 30, 2016 to August 25, 2017 (Aasi:2013wya). The Virgo gravitational wave detector also collected data for part of this period, starting from August 1, 2017. The detections reported in this second observing run thus far include three additional binary black hole coalescence events (Abbott:2017vtc; Abbott:2017gyy; Abbott:2017oio), and a binary neutron star merger (TheLIGOScientific:2017qsa). However, the full O2 data set has not yet been released. The catalog presented here is therefore restricted to the first observing run, O1.
Our paper is organized as follows: In Sec. 2 and Sec. 3, we summarize our analysis methods, including the parameter space searched, the detection statistic used for ranking candidate events, and our method for calculating the statistical significance of events. The search results are summarized in Sec. 4. Our full catalog and released data are described in Sec. 5 and are available online as supplementary materials (www.github.com/gwastro/1ogc). In this paper, we focus on the detection of compact objects. Since no new astrophysical events have been observed, we do not consider measurement of the signals’ parameters and refer to TheLIGOScientific:2016pea; Biwer:2018osg for discussion of the detected events’ sourceframe properties. Consequently, we quote binary mass parameters in the detector frame in this work.
2 Search Methodology
To search for gravitational waves from compactobject mergers, we use matched filtering (Allen:2005fk) implemented in the opensource PyCBC library (Usman:2015kfa; Canton:2014ena; pycbcgithub). Our methods improve on the analyses of TheLIGOScientific:2016pea; Abbott:2016ymx; TheLIGOScientific:2016qqj by imposing a phase, amplitude and time delay consistency on candidate signals, an improved background model, and a larger search parameter space (Nitz:2017svb; Nitz:2017lco; DalCanton:2017ala).
2.1 Target Search Space
A discrete bank of gravitationalwave template waveforms (Owen:1995tm; Owen:1998dk; Brown:2012qf) is used to target binary neutron star, neutron star–black hole, and binary black hole mergers with total mass from (DalCanton:2017ala). The templates are parameterized by their component masses and their dimensionless spins , where are the spin vectors of each compact object. For compact objects with component masses greater than , the template bank covers a wide range of spins, with , where are the components aligned with the orbital angular momentum. For compact objects with masses less than , the spin is restricted to (Brown:2012qf). Templates that correspond to sources with a signal duration less than 0.15 seconds (starting from Hz) are excluded due to the difficulty in separating candidates arising from these templates from populations of instrumental glitches (DalCanton:2017ala). Consequently, the total mass boundary of the search depends strongly on the “effective spin” (Racine:2008qv; Ajith:2009bn),
(1) 
This dependence is visible in the distribution of the approximately templates required to cover the space shown in Fig. 2. A dotted line in Fig. 2 denotes the upper boundary of the O1 analysis performed in TheLIGOScientific:2016pea. For binaries with total mass greater than , we use the spinning effectiveonebody model (SEOBNRv4) (Taracchini:2013; Bohe:2016gbl) as template gravitational waveforms. For sources with total masses less than we use TaylorF2 postNewtonian waveforms with phasing accurate to 3.5 postNewtonian order and the dominant amplitude evolution (Sathyaprakash:1991mt; Droz:1999qx; Blanchet:2002av; Faye:2012we). Our choice of template bank discretization causes less than a loss in detection rate for any source within the boundaries of the template bank. Our search assumes that the source can be adequately described by only the dominant gravitationalwave mode, two component masses, nonprecessing spins, and negligible eccentricity.
2.2 Creation and Ranking of Candidate Events
For each template and each detector, we calculate the matched filter signaltonoise ratio (SNR) as a function of time (Allen:2005fk). The template bank is divided into 15 equal sized subbanks based on the chirp mass of each template. A singledetector “trigger” is a peak in the SNR time series that is greater than 4 and larger than any other peaks within 1s. For each subbank, the loudest 100 triggers (by ) are recorded in s fixed time windows. This method has been shown to improve search sensitivity, while making the rate of singledetector triggers manageable (Nitz:2018rgo). We have found this choice of subbanks to be an effective method to ensure the analysis can concurrently record triggers from separate regions of parameter space that respond differently to instrumental noise. Other choices are possible.
We use the dataquality segments provided by the GravitationalWave Open Science Center to exclude triggers that occur in times when there are problems with the detectors’ data quality (TheLIGOScientific:2016zmo; TheLIGOScientific:2017lwt). In addition, very loud transient glitches, corresponding to deviations from Gaussian noise, are excised from the strain data according to the procedure of Usman:2015kfa before calculation of the SNR time series. However, there remain many types of transient nonGaussian noise in the LIGO data which produce triggers with large values of SNR (Nuttall:2015dqa; TheLIGOScientific:2016zmo; TheLIGOScientific:2017lwt).
For every trigger with we calculate the signal consistency test, , introduced in Allen:2004gu. The statistic divides the matched filter into frequency bands and checks that the contribution from each band is consistent with the expected signal. The statistic takes values close to unity when the data contains either Gaussian noise or the expected signal and larger values for many types of transient glitches. We impose the SNR limit as the test is generally noninformative when . The value is used to reweight the SNR as (Babak:2012zx)
(2) 
For singledetector triggers from templates with total mass greater than 40 we apply an additional test, , that determines if the detector output contains power at higher frequencies than the maximum expected frequency content of the gravitationalwave signal (Nitz:2017lco). This test is only applied for higher mass systems, since these templates are shorter in duration and more difficult to separate from instrumental noise. For other systems, we set . Using this statistic, we apply a further reweighting as
(3) 
Candidate events are generated when singledetector triggers occur in both the LIGO Hanford and Livingston data within ms (the lighttravel time between the observatories extended by ms for signal timemeasurement error) and if the triggers are recorded in the same template in each detector (Usman:2015kfa). Following the procedure of Nitz:2017svb, we model the distribution of single detector triggers from each template as an exponentially decaying function, , where allows the parameters of the exponential to vary as a function of total mass, symmetric mass ratio , and . This fitted model allows us to rescale to better equalize the rate of triggers from each template.
We improve upon the ranking of candidates in Abbott:2016ymx; TheLIGOScientific:2016pea by also taking into account , which is the expected distribution of SNR and , phase difference , and arrival time delay between the two LIGO instruments for an astrophysical population (Nitz:2017svb). No assumption is made about the distribution of intrinsic source parameters in this term. The primary benefit arises from assuming the population of sources is isotropically distributed in orientation and sky location. The final ranking statistic is then calculated as
(4) 
This expression is normalized so that approximates the standard network SNR for candidates from regions of parameter space that are not affected by elevated rates of instrumental noise. Candidates from regions affected by elevated rates of noise triggers are downweighted and assigned a smaller statistic value by this method. As multiple candidates, which arise from different template waveforms, may occur in response to the same signal, we select only the highest ranked candidate within ten seconds. A simpler version of this statistic where the singledetector exponential noise model is only a function of the template duration has also been employed in the analysis of data from LIGO’s second observing run (GW170104; GW170814; Abbott:2017gyy).
2.3 Statistical Significance
The statistical significance of candidate events is estimated by measuring empirically the rate of false alarms (FAR). To measure the noise background rate, we generate additional analyses by time shifting the data from one instrument with respect to the other by multiples of 100 ms. Since this time shift is greater than the maximum astrophysical time of flight between observatories, any candidates produced in these analyses are false alarms. This time shift is much greater than the autocorrelation length of our template waveforms of (1ms). The timeslid analyses are produced following the same procedure as the search; This is a key requirement for our analysis to produce valid statistical results (TheLIGOScientific:2016qqj). The equivalent of more than 50,000 years of observing time can be generated from 5 days of data.
To provide an unbiased measure of the rate of false alarms at least as significant as a potential candidate, the singledetector triggers that compose the candidate event should be included in the background estimation (2017PhRvD..96h2002C). However, when a real signal with a large is present in the data, the rate of false alarms for candidate events with smaller tends to be overestimated. This is due to the fact that the loud singledetector triggers from the real event in one detector form coincidences with noise fluctuations in the other detector, producing loud coincident background events. As in TheLIGOScientific:2016pea, an unbiased rate of false alarms can be achieved by a hierarchical procedure whereby a candidate with large is removed from the estimation of background for candidates with smaller ; we use this procedure here.
3 Evaluating Candidates based on the Astrophysical Population
We find two candidate events with FAR per years, corresponding to GW150914 and GW151226. Although FAR does not give the probability that an event is an astrophysical signal, we can be confident that these events were not caused by chance coincidence between the detectors. It is possible that these events were caused by a correlated source between the detectors. However, detailed followup studies of GW150914 and GW151226 found no correlated noise sources between the detectors that could be mistaken for a gravitational wave (TheLIGOScientific:2016zmo; Abbott:2016nmj).
We conclude that GW150914 and GW151226 are astrophysical in origin and use them to constrain the rate of real signals. A “true discovery rate” () can be constructed for less significant events. The is defined as:
(5) 
where is the rate that signals of astrophysical origin are observed with a ranking statistic (the “true alarm rate”) and is the false alarm rate.
The true discovery rate is the complement of the false discovery rate (Benjamini:1995ram), and can be used to estimate the fraction of real signals in a population. For example, if , it means that of events with a ranking statistic are expected to be real signals. The is also independent of the observation time.
Note that is not the probability that a particular event is a signal of astrophysical origin . For that, one needs to model the distribution of signals and noise at a given . In this work, we use a simple model of these distributions as functions of the ranking statistic . Models incorporating additional parameters are also possible, but we do not consider them here. As a function of , can be computed as
(6) 
where and are the probabilities of an event having ranking statistic given the signal and noise hypotheses respectively (2009MNRAS.396..165G; Farr:2015; Abbott:2016nhf). and are the rates of signal and noise events.
Since no binary neutron star or neutron star–black hole candidates are obtained from a search of the O1 data, here we restrict the calculation of both the and to binary black hole (BBH) observations. We include signals with total mass , mass ratio (where ), and dimensionless spins . These choices are based on a combination of what has been observed (TheLIGOScientific:2016pea; GW170104; GW170814; Abbott:2017gyy) and what is expected from models of isolated binarystar evolution (“field” binaries). The mass distribution of field binaries is dependent on a number of unknown parameters, such as the metallicity of the environment (Belczynski:2014iua). Generally, it is expected that most binaries are close to equal mass, as typically less than 1 in simulated binaries have mass ratio in models of fieldbinary evolution (Dominik:2014yma). The majority of observations of nearby Xray binaries have yielded black holes with masses greater than , which has led to speculation of a “mass gap” between 3–5 (Ozel:2010su; Farr:2010tu; Kreidberg:2012ud). The signals detected so far by LIGO and Virgo are consistent with this: the smaller component mass in the lowestmass system known to date, GW170608, has an estimated mass of (Abbott:2017gyy).
The spin distribution of black holes is not well constrained (Reynolds:2013qqa). The component spins of the most significant binary black holes detected by LIGO and Virgo are only weakly constrained (TheLIGOScientific:2016pea). The best measured quantity related to spin is . All of the BBH gravitationalwave signals detected so far have . A binary with low may still have component masses with large spin magnitudes, if the spins are antiparallel or are purely in the plane of the binary. However, it seems unlikely that this would be the case for all of the detections made so far. Hence we include signals that have component spins with . This is consistent with recent population synthesis models, which indicate that black holes must have low natal spin in order to obtain a distribution of that satisfies gravitationalwave observations (Belczynski:2017gds; Wysocki:2017isg).
To estimate the rate and distribution of false alarms that arise only from the region consistent with this selected population of binary black hole mergers, we must determine which templates are sensitive to these sources. It is necessary to analyze a simulated set of signals since the template associated with a particular event is not guaranteed to share the true source parameters. We find that the region of the template bank defined by , , and is effective at recovering this population of sources. This region is shown in Fig. 2 in red.
To estimate the true rate , we use the two significant events observed during O1, GW150914 and GW151226. We do not use any of the O2 events because the full data is not yet available for analysis, making it difficult to obtain a consistent rate estimate. The total analysis time in O1 was days, giving . Given the uncertainty in this estimate based on only two events, we take the rate of observations as a Poisson process, and choose the lower 95% bound on . This yields a . For the calculation of the we use this value for all events, independent of their ranking statistic. This means we likely underestimate the for events quieter than GW151226 and GW150914, but this is a conservative bias.
To estimate the probability that a given event is astrophysical in origin , we model the distribution of signals and noise as a function of . It is reasonable to approximate the signal probability distribution as (Schutz:2011tw; Chen:2014yla). We normalize the signal number density so that the number of signals with greater than or equal to some threshold is . We make the conservative choice to place at the value of the next largest value after GW150914 and GW151226.
To approximate the noise number density , we make a histogram of the values of false alarms arising from our selected BBH region. We use only the false alarms which are uncorrelated with possible candidate events to ensure an unbiased estimate of the mean false alarm rate (2017PhRvD..96h2002C). We fit an exponential decay to this histogram from . For much less than , is not well modeled by an exponential due to the effects of applying a threshold to singledetector triggers. We note, however, there is only a chance that an event is astrophysical at , and this chance quickly becomes negligible with decreasing . The result of this procedure is shown in Fig. 3. We caution that for candidates with will be sensitive to the form of the model chosen since it is not constrained by empirically measured false alarms.
While we do not assess the astrophysical probabilities of sources outside our selected BBH region, we are not precluding that such sources exist. Our is compatible with any model of the true BBH source distribution that allows for a signal rate to be at least as high as our estimate within the chosen region. This holds irrespective of whatever other kinds of sources may also be permitted.
table \hyper@makecurrenttable
Table 0. \Hy@raisedlink\hyper@@anchor\@currentHrefCandidate events from the full search for compact binary mergers in O1 data. Candidates are sorted by FAR evaluated for the entire bank of templates. The FAR of the top two candidates is limited only by the amount of background time estimated, and only differ due to the variation in time available in their respective analyses to create background. The parameters of the template associated with each candidate are listed. Note that these are not intended as a rigorous estimation of the source parameters. Masses are given in the detector frame.
Designation  Julian Date  

150914+09:50:45UTC  2457279.910665  66000  18.45  19.67  13.38  44.21  32.16  0.09 
151226+03:38:53UTC  2457382.652426  59000  11.62  10.73  7.43  14.83  8.50  0.24 
151012+09:54:43UTC  2457307.913420  24  9.06  6.96  6.71  30.75  12.89  0.05 
151019+00:23:16UTC  2457314.516585  0.060  8.39  6.81  5.47  14.93  1.27  0.11 
150928+10:49:00UTC  2457293.951122  0.042  8.37  6.05  6.34  2.53  1.02  0.70 
151218+18:30:58UTC  2457375.271929  0.029  8.24  7.11  5.38  31.29  2.35  0.00 
160103+05:48:36UTC  2457390.742504  0.026  8.22  6.01  6.60  9.75  7.29  0.49 
151202+01:18:13UTC  2457358.554740  0.025  8.23  6.54  5.73  40.42  1.77  0.26 
160104+03:51:51UTC  2457391.661424  0.021  8.19  5.80  6.39  6.76  1.10  0.51 
151213+00:12:20UTC  2457369.508985  0.019  8.22  5.70  7.24  11.12  3.30  0.79 
150923+07:10:59UTC  2457288.799711  0.014  8.20  6.78  5.84  2.14  1.08  0.65 
151029+13:34:39UTC  2457325.066149  0.014  8.21  6.83  5.23  2.19  1.07  0.27 
151206+14:19:29UTC  2457363.097291  0.013  8.17  5.80  6.37  100.60  1.64  0.98 
151202+15:32:09UTC  2457359.147751  0.012  8.14  5.93  6.41  6.33  1.18  0.59 
151012+06:30:45UTC  2457307.771774  0.011  8.19  6.74  5.70  3.16  1.73  0.15 
151116+22:41:48UTC  2457343.446120  0.010  8.14  5.79  6.64  2.00  1.04  0.45 
151121+03:34:09UTC  2457347.649138  0.010  8.12  6.48  5.78  7.43  1.00  0.86 
150922+05:41:08UTC  2457287.737317  0.010  8.16  6.05  6.34  2.78  1.02  0.17 
151008+14:09:17UTC  2457304.090202  0.008  8.16  5.84  6.10  46.38  1.19  0.38 
151127+02:00:30UTC  2457353.584101  0.008  8.10  6.28  5.44  39.12  2.01  0.99 

4 Results
The results presented here are generated using the data from the first observing run of Advanced LIGO which ran from September 12, 2015 to January 19, 2016. We divide the 16 kHz LIGO open data into 9 consecutive periods of time and search each time period independently so that each analysis contains roughly five days of observing time. This time interval is set by the disk and memory requirements of the search pipeline, but it is sufficient to estimate the FAR of candidate events to better than 1 in 50,000 years. It is possible to combine these time intervals during the analysis to improve this limit, but we have not done so here. Our analysis is restricted to times marked as observable by the metadata provided by the GravitationalWave Open Science Center. After accounting for times which are marked as not analyzable, there remain days of data when both the Hanford and Livingston LIGO instruments were operating.
The top candidate events by FAR from the full search are given in Table 3. There are three candidates which are statistically significant. These are the binary black hole mergers GW150914, LVT151012, and GW151226, which were previously reported in TheLIGOScientific:2016pea; Abbott:2016blz; Abbott:2016nmj. The false alarm rates for GW150914 and GW151226 of 1 per 66,000 and 1 per 59,000 years, respectively, are limits based on the amount of background time available in their respective analysis. These limits are less stringent than those reported in TheLIGOScientific:2016pea as we have created less background time. There are no other individually convincing candidates. Fig. 3 shows candidate events with . The three binary black hole mergers stand out from the other candidate events and are clustered in a portion of the parameter space that is analyzed with relatively few template waveforms.
table \hyper@makecurrenttable
Table 0. \Hy@raisedlink\hyper@@anchor\@currentHrefCandidate events consistent with the selected population of binary black holes. There are three binary black hole mergers above a threshold corresponding to a true discovery rate of . The third most significant event, LVT151012, has a 97.6% probability of being astrophysical in origin. Note that the FARs indicated do not reflect the false alarm rate for the full search, but instead for the limited region of the template bank indicated in red in Fig. 2. The FARs listed for the top two events are limited by the background time generated and so are identical to those in Table 3.
Designation  Julian Date  TDR  

150914+09:50:45UTC  2457279.910665      66000  18.45  19.67  13.38  44.21  32.16  0.09 
151226+03:38:53UTC  2457382.652426      59000  11.62  10.73  7.43  14.83  8.50  0.24 
151012+09:54:43UTC  2457307.913420  0.976  0.999  446  9.06  6.96  6.71  30.75  12.89  0.05 
160103+05:48:36UTC  2457390.742504  0.061  0.517  0.396  8.22  6.01  6.60  9.75  7.29  0.49 
151213+00:12:20UTC  2457369.508985  0.047  0.455  0.309  8.22  5.70  7.24  11.12  3.30  0.79 
151216+18:49:30UTC  2457373.284799  0.017  0.223  0.106  8.09  6.10  6.01  13.92  5.03  0.41 
151222+05:28:26UTC  2457378.728506  0.012  0.169  0.075  8.03  5.67  6.46  6.86  3.26  0.74 
151217+03:47:49UTC  2457373.658627  0.006  0.088  0.036  7.96  6.69  5.57  40.02  14.77  0.84 
151009+05:06:12UTC  2457304.713060  0.005  0.087  0.035  7.99  5.66  5.90  25.55  2.73  0.05 
151220+07:45:36UTC  2457376.823761  0.003  0.053  0.021  7.87  6.55  5.39  17.50  6.17  0.82 
151104+04:12:55UTC  2457330.676062  0.003  0.053  0.021  7.91  5.94  6.33  19.25  7.22  0.71 
151120+16:20:06UTC  2457347.181049  0.003  0.047  0.018  7.86  6.11  5.44  5.49  3.10  0.79 
151216+09:24:16UTC  2457372.892271  0.003  0.045  0.017  7.86  5.76  5.66  58.56  20.84  0.66 
151128+14:37:02UTC  2457355.109478  0.003  0.040  0.016  7.83  6.79  5.02  9.25  6.22  0.87 
160109+08:08:42UTC  2457396.839798  0.003  0.035  0.014  7.82  5.24  6.23  24.29  3.45  0.98 
160111+22:49:34UTC  2457399.451507  0.003  0.035  0.013  7.82  5.10  6.55  5.75  3.43  0.23 
151124+11:25:19UTC  2457350.976339  0.002  0.033  0.013  7.81  5.65  6.27  98.89  3.89  0.45 
150912+15:39:02UTC  2457278.152523  0.002  0.032  0.012  7.84  6.23  5.23  9.86  5.33  0.01 
151006+06:06:50UTC  2457301.755168  0.002  0.031  0.012  7.89  6.77  5.47  11.59  5.31  0.05 
151015+01:40:52UTC  2457310.570466  0.002  0.029  0.011  7.85  5.37  5.92  87.87  12.52  0.75 

4.1 Binary Black Hole Candidates
Given that there are two binary black hole mergers (GW150914 and GW151226 ) that are well established from their statistical significance, we can estimate the rate of detecting binary black hole mergers by this analysis. Candidate events that are consistent with our selected binary black hole population are listed in Table 4. We estimate the false alarm rate of events for just this region of the analysis, and using our estimate of the true rate of detections, calculate the true discovery rate as a function of ranking statistic. The at the ranking statistic of the fourth most significant candidate is 0.52. This means that only 52% of candidates with at least as large are expected to be of astrophysical origin. For each candidate we estimate its individual probability of being astrophysical in origin, . The fourth event has only a 6 chance of being astrophysical. We do not report and values for the top two events since these events are assumed to be signals in the construction of these statistics.
4.2 Revisiting LVT151012
LVT151012 was first announced in TheLIGOScientific:2016qqj, with a FAR of 1 per 2.3 years. Our improved methods yield a false alarm rate for LVT151012 of 1 per 24 years. Restricting attention to our selected BBH region, which is consistent with the other observed binary black hole mergers, gives a FAR for LVT151012 in this region alone of 1 per 446 years. We combine this FAR with our conservative estimate of the rate of detections to estimate that 99.92% of binary black hole merger candidates at least as significant as LVT151012 are astrophysical in origin. We also estimate the probability that specifically LVT151012 is astrophysical in origin to be 97.59.
These measures both depend on our selected region of binary black hole sources and our estimate of the rate of true detections, but we believe our choices for both of these to be conservative. The FAR of 1 per 446 years is not a statistical statement about the search as a whole and is used only in comparison against the rate of real signals within this same region. Selecting different boundaries for this region would yield a different FAR. However, assuming that the false alarm rate and true alarm rate are both approximately uniform in this region, then and will not change.
As data from future observing runs becomes available, it will be possible to more precisely estimate this rate in a consistent way, and improve our estimate of this event’s significance. We have modeled our signal distribution and population of false alarms as being characterized by the ranking statistic alone. An improved model could take into account the variation over the parameter space and in time. Fig. 3 shows the probability distribution of our noise and signal models for the analysis which contains LVT151012. Compared to the reported in TheLIGOScientific:2016pea of 87%, our analysis has improved the ranking of candidate events, the boundaries of our selected BBH distribution differ from what was used there, and we use a more conservative estimate of the signal rate. Given a value of 97.6 we conclude that LVT151012 is astrophysical in origin. For comparison, if we had chosen the rate of observed mergers to be , which is the linear extrapolation of two detections in 48 days, we would find that LVT151012 had a probability of astrophysical origin.
5 Data Release
The 1OGC catalog contains candidate events. Our supplemental materials online provide the complete combined set of binary neutron star, neutron star–black hole, and binary black hole candidates (1OGC). A separate listing of the candidates from our selected BBH region is also made available. Each candidate is assigned an identifying name constructed from the date and UTC time. The vast majority of these candidates are not astrophysical in origin. To help distinguish between possible sources we provide our ranking statistic along with our estimate of the false alarm rate for each candidate. We also provide information such as the SNR observed by each instrument, the time of arrival, measured phases, and the results of our set of signal consistency tests. The periods of time that were analyzed are also provided. We also provide the PyCBC pipeline configuration files that allow our analysis to be reproduced.
6 Discussion
We present a full catalog of gravitationalwave events and candidates from a PyCBCbased, templated, matchedfilter search of the LIGO O1 open data. Our analysis represents an improvement over that of TheLIGOScientific:2016pea; Abbott:2016ymx by using improved ranking of candidates by considering phase, amplitude and time delay consistency, an improved background model and a template bank targeting a wider range of sources (Nitz:2017svb; Nitz:2017lco; DalCanton:2017ala). We independently verify the discovery of GW150914 and GW151226 and report an improved significance of the candidate event LVT151012, which we claim should be viewed as a confident detection. Apart from these three signals, none of the other candidate events are individually significant in our analysis. All of these candidates are listed in our catalog available at www.github.com/gwastro/1ogc, along with tools for exploring and using it. Complete gravitationalwave event catalogs of this nature will become important tools in multimessenger astronomy.
A larger data set from the second observing run of LIGO and Virgo already exists. Individual detections have been published, and short periods of data around the detections are available publicly. However, the bulk of this data has not yet been released publicly. It will be possible to create a similar open catalog with the most uptodate analysis tools when these data are released.
We thank Thomas Dent and Sumit Kumar for useful discussions and comments. We thank Stuart Anderson, Jonah Kannah, and Alan Weinstein for help accessing data from the GravitationalWave Open Science Center. We acknowledge the Max Planck Gesellschaft for support and the Atlas cluster computing team at AEI Hannover. Computations were also supported by Syracuse University and NSF award OAC1541396. DAB acknowledges NSF awards PHY1707954, OAC1443047, and OAC1738962 for support. SR acknowledges NSF award PHY1707954 and OAC1443047 for support. RW acknowledges NSF award OAC1823378 for support. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center (https://www.gwopenscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. LIGO is funded by the U.S. National Science Foundation. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. \bibliography@latexreferences