Numerical relativity surrogate model with memory effects and post-Newtonian hybridization

arXiv (Cornell University)(2023)

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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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