GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run

TL;DR

GWTC-2 extends the prior gravitational-wave catalogs by reporting 39 CBC candidates from O3a, including 13 new events and 26 previously shared in real time, spanning BBH, BNS, and NSBH systems with masses up to ~150 M⊙ and redshifts up to ~0.8. The study combines three detection pipelines (cWB, GstLAL, PyCBC) and a sophisticated parameter-estimation framework across multiple waveform models, calibrations, and priors, while implementing extensive data-quality flags and glitch-subtraction to ensure robust astrophysical inferences. Key findings include the first clear appearance of highly asymmetric BBH systems, a population showing predominantly positive χ_eff and no strong negative spins, and improved localization from the triple-detector network. Public data releases accompany GWTC-2, enabling broader community analyses and population modeling in companion studies. Overall, GWTC-2 demonstrates substantial growth in CBC detections enabled by enhanced detector sensitivity and advanced analysis methods, reinforcing the astrophysical credibility of LIGO/Virgo observations and informing compact-object population studies.

Abstract

We report on gravitational wave discoveries from compact binary coalescences detected by Advanced LIGO and Advanced Virgo in the first half of the third observing run (O3a) between 1 April 2019 15:00 UTC and 1 October 2019 15:00. By imposing a false-alarm-rate threshold of two per year in each of the four search pipelines that constitute our search, we present 39 candidate gravitational wave events. At this threshold, we expect a contamination fraction of less than 10%. Of these, 26 candidate events were reported previously in near real-time through GCN Notices and Circulars; 13 are reported here for the first time. The catalog contains events whose sources are black hole binary mergers up to a redshift of ~0.8, as well as events whose components could not be unambiguously identified as black holes or neutron stars. For the latter group, we are unable to determine the nature based on estimates of the component masses and spins from gravitational wave data alone. The range of candidate events which are unambiguously identified as binary black holes (both objects $\geq 3~M_\odot$) is increased compared to GWTC-1, with total masses from $\sim 14~M_\odot$ for GW190924_021846 to $\sim 150~M_\odot$ for GW190521. For the first time, this catalog includes binary systems with significantly asymmetric mass ratios, which had not been observed in data taken before April 2019. We also find that 11 of the 39 events detected since April 2019 have positive effective inspiral spins under our default prior (at 90% credibility), while none exhibit negative effective inspiral spin. Given the increased sensitivity of Advanced LIGO and Advanced Virgo, the detection of 39 candidate events in ~26 weeks of data (~1.5 per week) is consistent with GWTC-1.

Figures (17)

• Figure 1: The number of compact binary coalescence detections versus the effective volume--time (VT) to which the gravitational wave network is sensitive to BNS coalescences. The effective VT is defined as the Euclidean sensitive volume Chen:2017wpg of the second-most sensitive detector in the network at a given time, multiplied by the live time of that network configuration. The Euclidean sensitive volume of the network is the volume of a sphere with a radius given by the BNS inspiral range Allen:2005fkChen:2017wpg (shown in Fig. \ref{['fig:range']}) of the second most sensitive detector in the network. To account for the addition of single detector candidates in O3a, a single-detector Euclidean sensitive volume was also included, defined using the inspiral range of the most sensitive detector divided by 1.5. The effective BNS VT does not account for differences in sensitivity across the entire population of signals detected, necessary cosmological corrections, or changes to analysis pipeline efficiency between observing runs, but, as shown in this figure, is consistent with the currently observed rate of detections. The colored bands indicate the three runs, O1, O2 and O3a. The black line is the cumulative number of confident detections of all compact binary coalescences (including black holes and neutron stars) for GWTC-1 LIGOScientific:2018mvr and this catalog. The blue line, dark blue band, and light blue band are the median, 50% confidence interval, and 90% confidence interval of draws from a Poisson fit to the number of detections at the end of O3a. The increase in detection rate is dominated by improvements to the sensitivity of the LIGO and Virgo detectors with changes in analysis methods between observing runs being sub-dominant.
• Figure 2: Representative amplitude spectral density of the three detectors' strain sensitivity (LIGO Livingston 5 September 2019 20:53 UTC, LIGO Hanford 29 April 2019 11:47 UTC, Virgo 10 April 2019 00:34 UTC). From these spectra we compute BNS inspiral ranges of 109 Mpc, 136 Mpc, and 50 Mpc for LIGO Hanford, LIGO Livingston, and Virgo, respectively.
• Figure 3: The BNS range of the LIGO and Virgo detectors. (Left) The evolution in time of the range over the entire duration of O3a. Each data point corresponds to the median value of the range over one-hour-long time segments. (Right) Distribution of the range and the median values for the entire duration of O3a.
• Figure 4: The rate of single interferometer glitches with SNR $>$ 6.5 and frequency between 10 Hz and 2048 Hz identified by Omicron omicronOmicron:2019 in each detector during O3a. Each point represents the average rate over a 2048 s interval. Dotted black lines show the median glitch rate for each detector in O2 and O3a.
• Figure 5: Top: time--frequency representation of the data surrounding event GW190701AGW190413AGW190413_052954GW190719AGW190719_215514GW190620AGW190620_030421GW190514AGW190514_065416GW190731AGW190731_140936GW190503AGW190503_185404GW190602AGW190602_175927GW190929AGW190929_012149GW190517AGW190517_055101GW190915AGW190915_235702GW190425AGW190425GW190512AGW190512_180714GW190630AGW190630_185205GW190521AGW190521GW190413BGW190413_134308GW190924AGW190924_021846GW190930AGW190930_133541GW190706AGW190706_222641GW190408AGW190408_181802GW190909AGW190909_114149GW190728AGW190728_064510GW190426AGW190426_152155GW190412AGW190412GW190720AGW190720_000836GW190521BGW190521_074359GW190910AGW190910_112807GW190803AGW190803_022701GW190519AGW190519_153544GW190708AGW190708_232457GW190527AGW190527_092055GW190513AGW190513_205428GW190424AGW190424_180648GW190727AGW190727_060333GW190814AGW190814GW190707AGW190707_093326GW190828AGW190828_063405GW190828BGW190828_065509GW190701AGW190701_203306GW190421AGW190421_213856 at LIGO Livingston, showing the presence of scattered light glitches (modulated arches). Bottom: The same data after glitch identification and subtraction as described in Sec. \ref{['ss:denoise']}. In both plots, the time--frequency track of the matched filter template used to identify GW190701AGW190413AGW190413_052954GW190719AGW190719_215514GW190620AGW190620_030421GW190514AGW190514_065416GW190731AGW190731_140936GW190503AGW190503_185404GW190602AGW190602_175927GW190929AGW190929_012149GW190517AGW190517_055101GW190915AGW190915_235702GW190425AGW190425GW190512AGW190512_180714GW190630AGW190630_185205GW190521AGW190521GW190413BGW190413_134308GW190924AGW190924_021846GW190930AGW190930_133541GW190706AGW190706_222641GW190408AGW190408_181802GW190909AGW190909_114149GW190728AGW190728_064510GW190426AGW190426_152155GW190412AGW190412GW190720AGW190720_000836GW190521BGW190521_074359GW190910AGW190910_112807GW190803AGW190803_022701GW190519AGW190519_153544GW190708AGW190708_232457GW190527AGW190527_092055GW190513AGW190513_205428GW190424AGW190424_180648GW190727AGW190727_060333GW190814AGW190814GW190707AGW190707_093326GW190828AGW190828_063405GW190828BGW190828_065509GW190701AGW190701_203306GW190421AGW190421_213856 is overlaid in orange. Investigations identified 7 candidate events in coincidence with similar scattering glitches, and required mitigation before further analysis. Despite the clear overlap of the signal with the glitch, the excess power from the glitch is successfully modeled and subtracted.