Numerical relativity surrogate model with memory effects and post-Newtonian hybridization
arXiv (Cornell University)(2023)
摘要
Numerical relativity simulations provide the most precise templates for the
gravitational waves produced by binary black hole mergers. However, many of
these simulations use an incomplete waveform extraction technique –
extrapolation – that fails to capture important physics, such as gravitational
memory effects. Cauchy-characteristic evolution (CCE), by contrast, is a much
more physically accurate extraction procedure that fully evolves Einstein's
equations to future null infinity and accurately captures the expected physics.
In this work, we present a new surrogate model, NRHybSur3dq8_CCE, built from
CCE waveforms that have been mapped to the post-Newtonian (PN) BMS frame and
then hybridized with PN and effective one-body (EOB) waveforms. This model is
trained on 102 waveforms with mass ratios q≤8 and aligned spins
χ_1z, χ_2z∈[-0.8, 0.8]. The model spans the
entire LIGO-Virgo-KAGRA (LVK) frequency band (with
f_low=20Hz) for total masses M≳2.25M_⊙ and
includes the ℓ≤4 and (ℓ,m)=(5,5) spin-weight -2 spherical
harmonic modes, but not the (3,1), (4,2) or (4,1) modes. We find that
NRHybSur3dq8_CCE can accurately reproduce the training waveforms with
mismatches ≲2×10^-4 for total masses 2.25M_⊙≤
M≤300M_⊙ and can, for a modest degree of extrapolation, capably model
outside of its training region. Most importantly, unlike previous waveform
models, the new surrogate model successfully captures memory effects.
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关键词
numerical relativity,surrogate model,hybridization,memory effects,post-newtonian
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