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Published February 6, 2015 | Submitted + Published
Journal Article Open

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

Abstract

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.

Additional Information

© 2015 American Physical Society. Received 26 September 2014; published 6 February 2015. The authors gratefully acknowledge the support of the LIGO-Virgo Collaboration in the development of the LALInference toolkit, including internal review of the codes and results. We thank Neil Cornish and Thomas Dent for useful feedback on the manuscript. The results presented here were produced using the computing facilities of the LIGO DataGrid and XSEDE, including the following: the NEMO computing cluster at the Center for Gravitation and Cosmology at UWM under NSF Grants No. PHY-0923409 and No. PHY-0600953; the Atlas computing cluster at the Albert Einstein Institute, Hannover; the LIGO computing clusters at Caltech, Livingston and Hanford; and the ARCCA cluster at Cardiff University. Figures 7–9 were produced with the help of triangle.py [89]. J. V. was supported by the research program of the Foundation for Fundamental Research on Matter (FOM), which is partially supported by the Netherlands Organization for Scientific Research (NWO), and by the U.K. Science and Technology Facilities Council (STFC) Grant No. ST/K005014/1. V. R. was supported by a Richard Chase Tolman fellowship at the California Institute of Technology (Caltech). P. G. was supported by an appointment to the NASA Postdoctoral Program at the Goddard Space Flight Center, administered by Oak Ridge Associated Universities through a contract with NASA. M. C. was supported by the National Science Foundation (NSF) Graduate Research Fellowship Program, under NSF Grant No. DGE 1144152. J. G.'s work was supported by the Royal Society. S. V. acknowledges the support of the National Science Foundation and the LIGO Laboratory. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under Grant No. PHY-0757058. N. C.'s work was supported by NSF Grant No. PHY-1204371. F. F. is supported by a research fellowship from Leverhulme and Newton Trusts. T. L., V. K. and C. R. acknowledge the support of the NSF LIGO Grant No. PHY-1307020. R. O'S. acknowledges the support of NSF Grants No. PHY-0970074 and No. PHY-1307429, and the UWM Research Growth Initiative. M. P. is funded by the STFC under Grant No. ST/L000946/1.

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Published - PhysRevD.91.042003.pdf

Submitted - 1409.7215v2.pdf

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Created:
August 20, 2023
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