Search

The Advanced LIGO and Advanced Virgo gravitational-wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star-black hole binary and a binary black hole, where we show a cross comparison of results obtained using three independent sampling algorithms. These systems were analyzed with nonspinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analyzing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space.

The Advanced LIGO and Advanced Virgo gravitational-wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star–black hole binary and a binary black hole, where we show a cross comparison of results obtained using three independent sampling algorithms. These systems were analyzed with nonspinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analyzing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space. peerReviewed

We present here the results of a 515 days long run of the IGEC2 observatory, consisting of the four resonant mass detectors ALLEGRO, AURIGA, EXPLORER and NAUTILUS. The reported results are related to the fourfold observation time from Nov. 6 2005 until Apr. 14 2007, when Allegro ceased its operation. This period overlapped with the first long term observations performed by the LIGO interferometric detectors. The IGEC observations aim at the identification of gravitational wave candidates with high confidence, keeping the false alarm rate at the level of 1 per century, and high duty cycle, namely 57% with all four sites and 94% with at least three sites in simultaneous observation. The network data analysis is based on time coincidence searches over at least three detectors: the four 3-fold searches and the 4-fold one are combined in a logical OR. We exchanged data with the usual blind procedure, by applying a unique confidential time offset to the events in each set of data. The accidental background was investigated by performing sets of 10^8 coincidence analyses per each detector configuration on off-source data, obtained by shifting the time series of each detector. The thresholds of the five searches were tuned so as to control the overall false alarm rate to 1/century. When the confidential time shifts was disclosed, no gravitational wave candidate was found in the on-source data. As an additional output of this search, we make available to other observatories the list of triple coincidence found below search thresholds, corresponding to a false alarm rate of 1/month., Comment: 10 pages, 8 figures Accepted for publication on Phys. Rev. D