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

Veitch, J., Raymond, V. ORCID:, Farr, B., Farr, W., Graff, P., Vitale, S., Aylott, B., Blackburn, K., Christensen, N., Coughlin, M., Del Pozzo, W., Feroz, F., Gair, J., Haster, C.-J., Kalogera, V., Littenberg, T., Mandel, I., O'Shaughnessy, R., Pitkin, M., Rodriguez, C., Röver, C., Sidery, T., Smith, R., Van Der Sluys, M., Vecchio, A., Vousden, W. and Wade, L. 2015. Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library. Physical Review D 91 (4) , 042003. 10.1103/PhysRevD.91.042003

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Physics and Astronomy
Subjects: Q Science > QB Astronomy
Q Science > QC Physics
Publisher: American Physical Society
ISSN: 2470-0010
Date of First Compliant Deposit: 5 February 2018
Date of Acceptance: 6 February 2015
Last Modified: 05 May 2023 18:30

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