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Published February 15, 2019 | Published
Journal Article Open

Gravitational waveforms from spectral Einstein code simulations: Neutron star-neutron star and low-mass black hole-neutron star binaries

Abstract

Gravitational waveforms from numerical simulations are a critical tool to test and analytically calibrate the waveform models used to study the properties of merging compact objects. In this paper, we present a series of high-accuracy waveforms produced with the spectral Einstein code (SpEC) for systems involving at least one neutron star. We provide for the first time waveforms with subradian accuracy over more than twenty cycles for low-mass black hole-neutron star binaries, including binaries with nonspinning objects, and binaries with rapidly spinning neutron stars that maximize the impact on the gravitational wave signal of the near-resonant growth of the fundamental excitation mode of the neutron star (f-mode). We also provide for the first time with SpEC a high-accuracy neutron star-neutron star waveform. These waveforms are made publicly available as part of the SxS catalogue. We compare our results to analytical waveform models currently implemented in data analysis pipelines. For most simulations, the models lie outside of the predicted numerical errors in the last few orbits before merger, but do not show systematic deviations from the numerical results: comparing different models appears to provide reasonable estimates of the modeling errors. The sole exception is the equal-mass simulation using a rapidly counterrotating neutron star to maximize the impact of the excitation of the f-mode, for which all models perform poorly. This is however expected, as even the single model that takes f-mode excitation into account ignores the significant impact of the neutron star spin on the f-mode excitation frequency.

Additional Information

© 2019 American Physical Society. Received 20 December 2018; published 11 February 2019. The authors thank Maximiliano Ujevic for producing the initial data for case NSNSq1MS1b, and Jan Steinhoff and the members of the SxS collaboration for useful discussions and comments throughout this project. F. F. gratefully acknowledges support from NASA through Grant No. 80NSSC18K0565, and from the NSF through Grant No. PHY-1806278. T. H. is grateful for support from the DeltaITP. A. R. W. acknowledges support from NWO VIDI and TOP Grants of the Innovational Research Incentives Scheme (Vernieuwingsimpuls) financed by the Netherlands Organization for Scientific Research (NWO) H. P. P. gratefully acknowledges support from the NSERC Canada. M. D. D. acknowledges support through NSF Grant No. PHY-1806207. R. H. gratefully acknowledges support from NSF Grants No. ACI-1238993, No. OAC-1550514 and No. CCF-1551592. M. B. and L. E. K. acknowledge support from NSF Grant No. PHY-1606654 at Cornell, while the authors at Caltech acknowledge support from NSF Grants No. PHY-170212 and No. PHY-1708213. Authors at both Cornell and Caltech also thank the Sherman Fairchild Foundation for their support. Computations were performed on the supercomputer Briarée from the Université de Montréal, managed by Calcul Québec and Compute Canada. The operation of these supercomputers is funded by the Canada Foundation for Innovation (CFI), NanoQuébec, Réseau de Médecine Génétique Appliquée (RMGA) and the Fonds de recherche du Québec—Nature et Technologie (FRQ-NT). This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (Grants No. OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. This work is also part of the PRAC Grant No. 1713678, supported by the National Science Foundation. Simulations were also performed on the Zwicky cluster at Caltech, supported by the Sherman Fairchild Foundation and by NSF Award No. PHY-0960291.

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Published - PhysRevD.99.044008.pdf

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Additional details

Created:
August 19, 2023
Modified:
October 20, 2023