# A Numerical Relativity Waveform Surrogate Model for Generically Precessing Binary Black Hole Mergers

## TL;DR

This paper introduces NRSur7dq2, a fast and accurate surrogate model for gravitational waves from precessing binary black hole mergers, covering all 7 intrinsic parameters with high fidelity, enabling efficient data analysis.

## Contribution

The paper presents the first surrogate model for precessing binary black hole waveforms that includes all 7 intrinsic parameters, built from 744 numerical relativity simulations.

## Key findings

- NRSur7dq2 covers spin magnitudes up to 0.8 and mass ratios up to 2.
- The model evaluates in approximately 50 milliseconds.
- Errors are comparable to numerical relativity simulation errors.

## Abstract

A generic, non-eccentric binary black hole (BBH) system emits gravitational waves (GWs) that are completely described by 7 intrinsic parameters: the black hole spin vectors and the ratio of their masses. Simulating a BBH coalescence by solving Einstein's equations numerically is computationally expensive, requiring days to months of computing resources for a single set of parameter values. Since theoretical predictions of the GWs are often needed for many different source parameters, a fast and accurate model is essential. We present the first surrogate model for GWs from the coalescence of BBHs including all $7$ dimensions of the intrinsic non-eccentric parameter space. The surrogate model, which we call NRSur7dq2, is built from the results of $744$ numerical relativity simulations. NRSur7dq2 covers spin magnitudes up to $0.8$ and mass ratios up to $2$, includes all $\ell \leq 4$ modes, begins about $20$ orbits before merger, and can be evaluated in $\sim~50\,\mathrm{ms}$. We find the largest NRSur7dq2 errors to be comparable to the largest errors in the numerical relativity simulations, and more than an order of magnitude smaller than the errors of other waveform models. Our model, and more broadly the methods developed here, will enable studies that would otherwise require millions of numerical relativity waveforms, such as parameter inference and tests of general relativity with GW observations.