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

TL;DR

This paper introduces a new gravitational waveform surrogate model built from Cauchy-characteristic evolution waveforms, accurately capturing memory effects and hybridized with post-Newtonian and effective one-body waveforms for binary black hole mergers.

Contribution

The paper presents NRHybSur3dq8_CCE, a novel surrogate model that incorporates memory effects by using CCE waveforms and hybridization, improving physical accuracy over previous models.

Findings

Achieves mismatches less than 2e-4 for a wide mass range

Successfully captures gravitational memory effects

Models waveforms for mass ratios up to 8 and spins within [-0.8, 0.8]

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.