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Surrogate model of hybridized numerical relativity binary black hole waveforms

Varma, Vijay and Field, Scott E. and Scheel, Mark A. and Blackman, Jonathan and Kidder, Lawrence E. and Pfeiffer, Harald P. (2019) Surrogate model of hybridized numerical relativity binary black hole waveforms. Physical Review D, 99 (6). Art. No. 064045. ISSN 2470-0010.

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Numerical relativity (NR) simulations provide the most accurate binary black hole gravitational waveforms, but are prohibitively expensive for applications such as parameter estimation. Surrogate models of NR waveforms have been shown to be both fast and accurate. However, NR-based surrogate models are limited by the training waveforms’ length, which is typically about 20 orbits before merger. We remedy this by hybridizing the NR waveforms using both post-Newtonian and effective one-body waveforms for the early inspiral. We present NRHybSur3dq8, a surrogate model for hybridized nonprecessing numerical relativity waveforms, that is valid for the entire LIGO band (starting at 20 Hz) for stellar mass binaries with total masses as low as 2.25  M⊙. We include the ℓ ≤ 4 and (5, 5) spin-weighted spherical harmonic modes but not the (4, 1) or (4, 0) modes. This model has been trained against hybridized waveforms based on 104 NR waveforms with mass ratios q ≤ 8, and |χ_(1z)|,|χ_(2z)| ≤ 0.8, where χ_(1z) (χ_(2z)) is the spin of the heavier (lighter) black hole in the direction of orbital angular momentum. The surrogate reproduces the hybrid waveforms accurately, with mismatches ≲ 3×10^(−4) over the mass range 2.25  M⊙ ≤ M ≤ 300  M⊙. At high masses (M ≳ 40  M⊙), where the merger and ringdown are more prominent, we show roughly 2 orders of magnitude improvement over existing waveform models. We also show that the surrogate works well even when extrapolated outside its training parameter space range, including at spins as large as 0.998. Finally, we show that this model accurately reproduces the spheroidal-spherical mode mixing present in the NR ringdown signal.

Item Type:Article
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URLURL TypeDescription Paper
Varma, Vijay0000-0002-9994-1761
Blackman, Jonathan0000-0002-7113-0289
Pfeiffer, Harald P.0000-0001-9288-519X
Additional Information:© 2019 American Physical Society. Received 28 January 2019; published 27 March 2019. We thank Matt Giesler for helping carry out the new SpEC simulations used in this work. We thank Michael Boyle, Kevin Barkett, Matt Giesler, Yanbei Chen, and Saul Teukolsky for useful discussions. We thank Patricia Schmidt for careful and detailed feedback on an earlier draft of this manuscript. V. V. and M. S. are supported by the Sherman Fairchild Foundation and NSF Grants No. PHY-170212 and No. PHY-1708213 at Caltech. L. E. K. acknowledges support from the Sherman Fairchild Foundation and NSF Grant No. PHY-1606654 at Cornell. S. E. F. is partially supported by NSF Grant No. PHY-1806665. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant No. ACI-1548562. 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 No. 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. Simulations were performed on NSF/NCSA Blue Waters under allocation NSF PRAC-1713694; on the Wheeler cluster at Caltech, which is supported by the Sherman Fairchild Foundation and by Caltech; and on XSEDE resources Bridges at the Pittsburgh Supercomputing Center, Comet at the San Diego Supercomputer Center, and Stampede and Stampede2 at the Texas Advanced Computing Center, through allocation TG-PHY990007N. Computations for building the model were performed on Wheeler and Stampede2.
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Sherman Fairchild FoundationUNSPECIFIED
State of IllinoisUNSPECIFIED
Issue or Number:6
Record Number:CaltechAUTHORS:20190327-093055801
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94205
Deposited By: Tony Diaz
Deposited On:27 Mar 2019 16:47
Last Modified:03 Oct 2019 21:01

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