English

A Numerical Relativity Waveform Surrogate Model for Generically Precessing Binary Black Hole Mergers

General Relativity and Quantum Cosmology 2018-09-25 v3

Abstract

A generic, non-eccentric binary black hole (BBH) system emits gravitational waves (GWs) that are completely described by 7 intrinsic parameters: the black hole spin vectors and the ratio of their masses. Simulating a BBH coalescence by solving Einstein's equations numerically is computationally expensive, requiring days to months of computing resources for a single set of parameter values. Since theoretical predictions of the GWs are often needed for many different source parameters, a fast and accurate model is essential. We present the first surrogate model for GWs from the coalescence of BBHs including all 77 dimensions of the intrinsic non-eccentric parameter space. The surrogate model, which we call NRSur7dq2, is built from the results of 744744 numerical relativity simulations. NRSur7dq2 covers spin magnitudes up to 0.80.8 and mass ratios up to 22, includes all 4\ell \leq 4 modes, begins about 2020 orbits before merger, and can be evaluated in  50ms\sim~50\,\mathrm{ms}. We find the largest NRSur7dq2 errors to be comparable to the largest errors in the numerical relativity simulations, and more than an order of magnitude smaller than the errors of other waveform models. Our model, and more broadly the methods developed here, will enable studies that would otherwise require millions of numerical relativity waveforms, such as parameter inference and tests of general relativity with GW observations.

Keywords

Cite

@article{arxiv.1705.07089,
  title  = {A Numerical Relativity Waveform Surrogate Model for Generically Precessing Binary Black Hole Mergers},
  author = {Jonathan Blackman and Scott E. Field and Mark A. Scheel and Chad R. Galley and Christian D. Ott and Michael Boyle and Lawrence E. Kidder and Harald P. Pfeiffer and Béla Szilágyi},
  journal= {arXiv preprint arXiv:1705.07089},
  year   = {2018}
}

Comments

10 pages, 5 figures; Added report number

R2 v1 2026-06-22T19:52:50.397Z