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Published April 15, 2021 | Accepted Version + Published
Journal Article Open

Statistical and systematic uncertainties in extracting the source properties of neutron star-black hole binaries with gravitational waves

Abstract

Gravitational waves emitted by neutron star black hole mergers encode key properties of neutron stars—such as their size, maximum mass, and spins—and black holes. However, it is challenging to generate accurate waveforms from these systems with numerical relativity, and not much is known about systematic uncertainties due to waveform modeling. We simulate gravitational waves from neutron star black hole mergers by hybridizing numerical relativity waveforms produced with the SpEC code with a recent numerical relativity surrogate NRHybSur3dq8Tidal. These signals are analyzed using a range of available waveform families, and statistical and systematic errors are reported. We find that at a network signal-to-noise ratio (SNR) of 30, statistical uncertainties are usually larger than systematic offsets, while at an SNR of 70, the two become comparable. The individual black hole and neutron star masses, as well as the mass ratios, are typically measured very precisely, though not always accurately at high SNR. At an SNR of 30, the neutron star tidal deformability can only be bound from above, while for louder sources, it may be measured and constrained away from zero. All neutron stars in our simulations are nonspinning, but in no case can we constrain the neutron star spin to be smaller than ∼0.4 (90% credible interval). At lower mass ratios, waveform families whose late inspiral has been tuned specifically for neutron star black hole signals typically yield the most accurate characterization of the source parameters. Their measurements are in tension with those obtained using waveform families tuned against binary neutron stars, even for mass ratios that could be relevant for both binary neutron stars and neutron star black holes mergers. At higher mass ratios, waveforms that account for higher order modes yield the best results.

Additional Information

© 2021 American Physical Society. Received 12 August 2020; accepted 14 January 2021; published 5 April 2021. Software: We acknowledge the use of the LIGO algorithm library [87], and specifically, of the lalinference inference package [83], as released through conda [166] and [167,168]. Plots were produced with matplotlib [169], and corner [170]. We acknowledge use of ipython [171], numpy [172], and scipy [173]. The authors would like to thank T. Dent, T. Dietrich, and R. Sturani for useful discussions. Y. H., C.- J. H., S. V., and S. B. acknowledge support of the National Science Foundation, and the LIGO Laboratory. S. B. is also supported by the Paul and Daisy Soros Fellowship for New Americans and the NSF Graduate Research Fellowship under Grant No. DGE-1122374. V. V. is generously supported by the Sherman Fairchild Foundation, and NSF Grants No. PHY–170212 and No. PHY–1708213 at Caltech, and by a Klarman Fellowship at Cornell. F. Foucart gratefully acknowledges support from NASA through Grant No. 80NSSC18K0565, from the NSF through Grant No. PHY-1806278, and from the DOE through CAREER Grant No. DE-SC0020435. The authors acknowledge usage of the LIGO Data Grid clusters and the MIT Engaging cluster. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under Cooperative Agreement No. PHY-0757058. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN), and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. This is LIGO Document Number DCC-P2000176.

Attached Files

Published - PhysRevD.103.083001.pdf

Accepted Version - 2005.11850.pdf

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Additional details

Created:
August 20, 2023
Modified:
October 23, 2023