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Published January 4, 2013 | Published
Journal Article Open

Suitability of hybrid gravitational waveforms for unequal-mass binaries

Abstract

This article studies sufficient accuracy criteria of hybrid post-Newtonian (PN) and numerical relativity (NR) waveforms for parameter estimation of strong binary black-hole sources in second-generation ground-based gravitational-wave detectors. We investigate equal-mass nonspinning binaries with a new 33-orbit NR waveform, as well as unequal-mass binaries with mass ratios 2, 3, 4 and 6. For equal masses, the 33-orbit NR waveform allows us to recover previous results and to extend the analysis toward matching at lower frequencies. For unequal masses, the errors between different PN approximants increase with mass ratio. Thus, at 3.5 PN, hybrids for higher-mass-ratio systems would require NR waveforms with many more gravitational-wave cycles to guarantee no adverse impact on parameter estimation. Furthermore, we investigate the potential improvement in hybrid waveforms that can be expected from fourth-order post-Newtonian waveforms and find that knowledge of this fourth post-Newtonian order would significantly improve the accuracy of hybrid waveforms.

Additional Information

© 2013 American Physical Society. Received 22 October 2012; published 4 January 2013. We would like to thank Mark Hannam, Sascha Husa, and Ulrich Sperhake for useful discussions and Riccardo Sturani, Stefano Foffa, Luc Blanchet, Chad Galley, Alessandra Buonanno, and Samaya Nissanke for insights into the difficulties in computing higher-order post- Newtonian expansions. The numerical waveforms used in this work were computed with the SpEC code [29]. We gratefully acknowledge support from the NSERC of Canada, from the Canada Research Chairs Program, the Canadian Institute for Advanced Research, and the Sherman Fairchild Foundation, as well as from NSF Grants No. PHY-0969111 and No. PHY-1005426 at Cornell and No. PHY-1068881 and No. PHY-1005655 at Caltech. Computations were performed on the Zwicky cluster at Caltech, which is supported by the Sherman Fairchild Foundation and by NSF Award No. PHY- 0960291; on the NSF XSEDE network under Grant No. TG-PHY990007N; and on the GPC supercomputer at the SciNet HPC Consortium [52]. SciNet is funded by the Canada Foundation for Innovation under the auspices of Compute Canada; the Government of Ontario; Ontario Research Fund—Research Excellence; and the University of Toronto.

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August 19, 2023
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October 23, 2023