Fast and accurate prediction of numerical relativity waveforms from binary black hole coalescences using surrogate models

TL;DR

This paper introduces a surrogate model that rapidly predicts numerical relativity waveforms for binary black hole mergers with high accuracy, significantly reducing computational costs and outperforming existing models in faithfulness.

Contribution

The authors develop a new reduced order modeling approach to produce fast, accurate surrogate waveforms for non-spinning binary black hole coalescences, covering a range of mass ratios and waveform modes.

Findings

Surrogate model evaluates waveforms in milliseconds to seconds.

Model predicts unseen waveforms with errors close to numerical relativity code errors.

Surrogate outperforms Effective One Body waveforms in faithfulness for LIGO data.

Abstract

Simulating a binary black hole (BBH) coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from non-spinning BBH coalescences with mass ratios in $[1, 10]$ and durations corresponding to about $15$ orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms {\em not} used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic ${}_{-2}Y_{\ell m}$ waveform modes resolved by the NR code up to $\ell=8.$ We compare our surrogate model to Effective One Body waveforms from $50$-$300 M_\odot$ for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).