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Numerical relativity surrogate model with memory effects and post-Newtonian hybridization
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abstract
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 $q\leq8$ and aligned spins $\chi_{1z}, \, \chi_{2z} \in \left[-0.8, 0.8\right]$. The model spans the entire LIGO-Virgo-KAGRA (LVK) frequency band (with $f_{\text{low}}=20\text{Hz}$) for total masses $M\gtrsim2.25M_{\odot}$ and includes the $\ell\leq4$ and $(\ell,m)=(5,5)$ 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 $\lesssim2\times10^{-4}$ for total masses $2.25M_{\odot}\leq M\leq300M_{\odot}$ 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.
Forward citations
Cited by 6 Pith papers
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Merging black holes with Cauchy-characteristic matching: Computation of late-time tails
First fully nonlinear Cauchy-characteristic matching simulations of binary black hole mergers are stable and accurate, and they expose late-time tails with decay exponents near -3.5 to -3.8.
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Chase Orbits, not Time: A Scalable Paradigm for Long-Duration Eccentric Gravitational-Wave Surrogates
Eccentric inspiral waveforms are modeled against mean anomaly rather than time, yielding an order-of-magnitude compression and a 2.77e6 M surrogate that is ~20x faster to evaluate.
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Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants
A systematically optimized neural network surrogate for black-hole merger remnant properties, NRSur7dq4Remnant_NN, matches the accuracy of the GPR-based NRSur7dq4Remnant while evaluating up to 8 times faster on a CPU ...
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Gravitational memory and Ward identities in the local detector frame
Gravitational memory in TT gauge is encoded in large residual diffeomorphisms that equal BMS transformations, and their Ward identities yield soft graviton theorems and flat-space consistency relations.
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Gravitational memory and soft theorems: The local perspective
Gravitational memory is shown to be a large residual coordinate transformation in TT gauge, yielding new flat-space soft theorems for equal-time correlators that mirror inflationary consistency relations.
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Surrogate modeling of gravitational waves microlensed by spherically symmetric potentials
Surrogate models of the time-domain microlensing amplification factor for point-mass and singular isothermal sphere lenses match numerical waveforms with mismatches below about 5e-4 and evaluate in about 100 ms.
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