Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library

By J. Veitch, V. Raymond, B. Farr, W. Farr, P. Graff, S. Vitale, B. Aylott, Kent Blackburn, N. Christensen, M. Coughlin and 17 more. 2014

629 citations recorded by the source corpus.

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.

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Citations in the corpus

Multi-messenger observations of a binary neutron star merger (2017, methodology): “…2017c for full details; here, we report parameter ranges that span the 90% credible interval) were derived by employing a coherent Bayesian analysis(Veitch et al. 2015; Abbott et al. 2016b) of the three-instrument data, including marginalization over calibration uncertainties and assuming that the…”
A Future Percent-level Measurement of the Hubble Expansion at Redshift 0.8 with Advanced LIGO (2019, methodology): “After drawing a catalog of true merger parameters from the population distribution in Appendix A, we use an approximation (Fishbach et al. 2018) to the true measurement and selection process in a GW detector (Veitch et al. 2015).”
Binary Black Hole Population Properties Inferred from the First and Second Observing Runs of Advanced LIGO and Advanced Virgo (2018, methodology): “Since LALInference gives us a set of posterior samples for each event, we first divide out the priors used in the individual-event analyses before applying Equation (11) (Hogg et al. 2010; Mandel 2010; see Appendix C).”
Measuring the Binary Black Hole Mass Spectrum with an Astrophysically Motivated Parameterization (2018, methodology): ““Design” uses the output of LALInference for a three-detector advanced LIGO/Virgo network operating at design sensitivity.”
GWTC-2.1: Deep extended catalog of compact binary coalescences observed by LIGO and Virgo during the first half of the third observing run (2021, methodology): “Source parameter estimation was performed with the Bilby library [142, 166] using the Dynesty nested sampling package [300], the RIFT library [172–174] and the LALInference library [139].”
Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA (2013, methodology): “Right: Localization produced by the higher-latency (neglecting spin) LALINFERENCE (Veitch et al 2015), which also produces posterior estimates for other parameters. These algorithms are discussed in Sect. 3.2.1, and agreement between them shows that the low-latency localization is comparable to the ”
Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO and Advanced Virgo (2013, methodology): “Results are shown for the low-latency bayestar [98] and higher-latency (non-spinning) LALInference (LI) [110] codes.”
Hierarchical analysis of gravitational-wave measurements of binary black hole spin–orbit misalignments (2017, methodology): “MNRAS 000, 1–12 (2017) For any individual event α (α = 1, . . . , N) we have the posterior on Θ given by p(Θ|dα) = p(dα|Θ)p(Θ) p(dα) , (11) where p(Θ) is the prior used by LALInference, p(dα) is the evidence (which is only a normalising factor in our analysis), and we represent p(Θ|dα) by a set of d”
THE RATE OF BINARY BLACK HOLE MERGERS INFERRED FROM ADVANCED LIGO OBSERVATIONS SURROUNDING GW150914 (2016, methodology): “Under the assumption that GW150914 and LVT151012 are astrophysical, posterior distributions for system parameters can be derived (Veitch et al. 2015).”
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with Advanced LIGO Data (2017, methodology): “predicted by Deep Filtering can be used to provide instant alerts for electromagnetic follow-up campaigns and also to accelerate computationally intensive ofﬂine Bayesian parameter estimation methods [39, 40]. As deep CNNs excel at image processing, applying the same approach to analyze raw telescop”
GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs (2018, methodology): “The results were obtained using the nested sampling algorithm implemented in LALInference[225] and the precessing waveform model IMRPhenomPv2 [24, 25, 48].”