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

arXiv: https://arxiv.org/abs/1502.07758 · HTML: https://mlsanity.com/paper/1502.07758/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2015.

## Built on by (most cited first, 2 of 2)

- [Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling](https://mlsanity.com/paper/2203.08434/) (2022)
- [An automated parameter domain decomposition approach for gravitational wave surrogates using hp-greedy refinement](https://mlsanity.com/paper/2212.08554/) (2022)

## Abstract (arXiv)

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 -2Yℓ m waveform modes resolved by the NR code up to ℓ=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).