Uploaded August 2020 | Updated September 2026, 1 hour ago
Numerical Relativity Surrogate model for eccentric binary black hole gravitational waveform
Tousif Islam, Vijay Varma, Jacqueline Lodman, Scott Field and Gaurav Khanna
Though Numerical Relativity (NR) simulations provide the most accurate gravitational waveforms for binary black holes (BBHs), they are computationally expensive, taking days to weeks to generate one single waveform. NR surrogate waveform models are computationally cheaper without sacrificing accuracy. While surrogate models for non-spinning, aligned spin and precessing BBHs are quite reliable, efforts to build an eccentric NR surrogate model are still at an early stage. We provide detailed data decomposition and parameterization strategies to build an effective NR surrogate for eccentric BBHs waveforms. Based on the proposed framework, we then present a time domain, inspiral-merger-ringdown NR surrogate model for eccentric binary black holes for mass ratio $q=1$. We call it 'NRSur2dq1Ecc'. The model includes the (l=2,m=2), (l=3,m=2) and (l=4,m=4) spin-weighted spherical harmonic modes. The odd $m$ modes are zero due to the symmetry of the system. We parameterize the effect of eccentricity via eccentric parameter $e_w$ and mean anomaly $l_w$. This model has been trained against 41 NR waveforms with mass ratio $q=1$, $e_w\lt0.175$ and $0\ltl_w\lt2\pi$ (as estimated at a reference time of $t_{ref}=-4200M$); and uses Gaussian Process Regression to build the fits across parameter space. We show that our model can accurately predict NR waveforms with mismatches $\approx 10^{-3}$ and can recover subtle effects like mode-mixing in the ringdown signal. The method proposed here would pave the way forward for constructing any future surrogate waveform model with eccentric binaries.
Numerical Relativity Surrogate model for eccentric binary black hole gravitational waveform
Tousif Islam, Vijay Varma, Jacqueline Lodman, Scott Field and Gaurav Khanna
Though Numerical Relativity (NR) simulations provide the most accurate gravitational waveforms for binary black holes (BBHs), they are computationally expensive, taking days to weeks to generate one single waveform. NR surrogate waveform models are computationally cheaper without sacrificing accuracy. While surrogate models for non-spinning, aligned spin and precessing BBHs are quite reliable, efforts to build an eccentric NR surrogate model are still at an early stage. We provide detailed data decomposition and parameterization strategies to build an effective NR surrogate for eccentric BBHs waveforms. Based on the proposed framework, we then present a time domain, inspiral-merger-ringdown NR surrogate model for eccentric binary black holes for mass ratio $q=1$. We call it 'NRSur2dq1Ecc'. The model includes the (l=2,m=2), (l=3,m=2) and (l=4,m=4) spin-weighted spherical harmonic modes. The odd $m$ modes are zero due to the symmetry of the system. We parameterize the effect of eccentricity via eccentric parameter $e_w$ and mean anomaly $l_w$. This model has been trained against 41 NR waveforms with mass ratio $q=1$, $e_w\lt0.175$ and $0\ltl_w\lt2\pi$ (as estimated at a reference time of $t_{ref}=-4200M$); and uses Gaussian Process Regression to build the fits across parameter space. We show that our model can accurately predict NR waveforms with mismatches $\approx 10^{-3}$ and can recover subtle effects like mode-mixing in the ringdown signal. The method proposed here would pave the way forward for constructing any future surrogate waveform model with eccentric binaries.
