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

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arxiv 2306.03148 v2 pith:SZWQ2K6P submitted 2023-06-05 gr-qc

classification gr-qc
keywords modelwaveformseffectsmemoryodotsurrogateaccuratelycaptures
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Chase Orbits, not Time: A Scalable Paradigm for Long-Duration Eccentric Gravitational-Wave Surrogates

    gr-qc 2025-09 conditional novelty 6.0 of 10

    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.

  2. Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

    gr-qc 2025-01 conditional novelty 6.0 of 10

    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 ...

  3. Surrogate modeling of gravitational waves microlensed by spherically symmetric potentials

    gr-qc 2025-01 conditional novelty 5.0 of 10

    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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