Eccentric binary black hole surrogate models for the gravitational waveform and remnant properties: comparable mass, nonspinning case

TL;DR

This paper introduces the first eccentric binary black hole surrogate waveform and remnant models trained directly on numerical relativity simulations, enabling accurate predictions for eccentric mergers relevant to gravitational-wave detection.

Contribution

The paper presents novel surrogate models for eccentric binary black hole waveforms and remnant properties, trained on numerical relativity data without assuming circularization.

Findings

Waveform model achieves mismatches around 10^{-3}

Remnant model accurately predicts final mass and spin

Model extends reasonably to mass ratios up to 3 with low mismatches

Abstract

We develop new strategies to build numerical relativity surrogate models for eccentric binary black hole systems, which are expected to play an increasingly important role in current and future gravitational-wave detectors. We introduce a new surrogate waveform model, \texttt{NRSur2dq1Ecc}, using 47 nonspinning, equal-mass waveforms with eccentricities up to $0.2$ when measured at a reference time of $5500M$ before merger. This is the first waveform model that is directly trained on eccentric numerical relativity simulations and does not require that the binary circularizes before merger. The model includes the $(2,2)$, $(3,2)$, and $(4,4)$ spin-weighted spherical harmonic modes. We also build a final black hole model, \texttt{NRSur2dq1EccRemnant}, which models the mass, and spin of the remnant black hole. We show that our waveform model can accurately predict numerical relativity waveforms with mismatches $\approx 10^{-3}$, while the remnant model can recover the final mass and dimensionless spin with absolute errors smaller than $\approx 5 \times 10^{-4}M$ and $\approx 2 \times10^{-3}$ respectively. We demonstrate that the waveform model can also recover subtle effects like mode-mixing in the ringdown signal without any special ad-hoc modeling steps. Finally, we show that despite being trained only on equal-mass binaries, \texttt{NRSur2dq1Ecc} can be reasonably extended up to mass ratio $q\approx3$ with mismatches $\simeq 10^{-2}$ for eccentricities smaller than $\sim 0.05$ as measured at a reference time of $2000M$ before merger. The methods developed here should prove useful in the building of future eccentric surrogate models over larger regions of the parameter space.