Surrogate waveform model data for black hole binary systems computed in point-particle black hole perturbation theory

This repository contains all publicly available surrogate data for gravitational waveforms produced within the point-particle black hole perturbation theory framework and calibrated to numerical relativity simulations performed with the Spectral Einstein Code (SpEC). 

Several surrogate models are currently available in this catalog:

BHPTNRSur2dq1e3, for aligned spin black hole binary systems with mass-ratios varying from 3 to 1000 and spins from −0.8≤χ1≤0.8 on the larger black hole. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with calibration to numerical relativity (NR) data. The waveforms include all spin-weighted spherical harmonic modes up to ℓ=4 except the (4,1) and m=0 modes. Model details can be found in Rink et al. 2024. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the Black Hole Perturbation Toolkit (Jupyter notebook tutorial) or the GWSurrogate Python package, which can be found on PyPI or conda-forge.

BHPTNRSur1dq1e4, an updated version of the EMRISur1dq1e4 model described below. The updated version includes better calibration to NR, a smoother transition to plunge model, and more harmonic modes. Model details can be found in Islam et al. 2022. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the Black Hole Perturbation Toolkit (Jupyter notebook tutorial) or the GWSurrogate Python package, which can be found on PyPI or conda-forge.

EMRISur1dq1e4, for non-spinning black hole binary systems with mass-ratios varying from 3 to 10000. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT), with the total mass rescaling parameter tuned to NR simulations. Available modes are [(2,2), (2,1), (3,3), (3,2), (3,1), (4,4), (4,3), (4,2), (5,5), (5,4), (5,3)]. The m<0 modes are deduced from the m>0 modes. Model details can be found in Rifat et al. 2019. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the Black Hole Perturbation Toolkit (Jupyter notebook tutorial) or the GWSurrogate Python package (Jupyter notebook tutorial), which can be found on PyPI.