Improving the Accuracy, Robustness, and Computational Efficiency of the Spinning, Precessing Effective-One-Body Numerical-Relativity (SEOBNRv3/SEOBNRv4P) Codes

West Virginia University Research Corporation, Morgantown WV

Investigators

Abstract

This award supports research in relativity and relativistic astrophysics and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. In order to detect and characterize the gravitational-wave signals observed by Advanced LIGO, theoretical models that predict the detailed behavior of the signal as a function of the source parameters are required. These models must provide an accurate approximation to the exact solution of Einstein's equations, but must also be rapidly and robustly calculable, so that they can be used to predict the signal at hundreds of millions of parameter locations in order to characterize a single event. To that end, this project will create a new state-of-the-art in accurate and efficient waveform models, thus helping to ensure that future Advanced LIGO observations are not limited by the waveform models being used. In addition to the scientific benefits, this work will result in the training of a graduate student in the best practices of analytical relativity and gravitational-wave data analysis, thereby helping to train the next generation of gravitational-wave astronomers. Finally, this proposal will help facilitate impactful education and outreach throughout the state of West Virginia, by contributing to the existing Space Public Outreach Team (SPOT) program through the development of a new presentation on gravitational-wave astrophysics. The Precessing Spinning Effective-One-Body Numerical-Relativity (SEOBNRv3, and SEOBNRv4P currently under development) gravitational waveform model is one of only two models (along with IMRPhenomP) capable of facilitating complete parameter estimation (PE) of black-hole binary events. However, despite their great efficiency and reliability when compared to numerical relativity waveforms, the original SEOBNR codes were still far too slow to be directly useful for standard Markov-Chain Monte Carlo (MCMC)-based PE. To address this issue, the PI's team previously developed optimized versions of the SEOBNR approximants, which make it possible, using SEOBNRv3_opt, to perform PE on a candidate event at a much faster rate. In addition, the development of SEOBNRv3_opt uncovered occasional pathological behavior in the underlying SEOBNRv3 approximant. While only occurring in one out of every ~10,000 to 100,000 cases, this frequency nonetheless presents a major obstacle to PE, which requires the generation of 10^8 waveform realizations. Therefore, there is an urgent need to develop an approximant that is both significantly more efficient than SEOBNRv3_opt, and also substantially more robust. This project will create a new state-of-the-art approximant that is both more accurate and more robust than any currently in existence. This will require, first, the creation of a new approximant based on SEOBNRv3_opt, but replacing the ringdown attachment with the Backwards One-Body (BOB) merger-ringdown model developed by the PI, which produces merger-ringdown waveforms as accurate as NR results across the entire range of parameter space probed by NR. Because BOB can be extended to earlier times than the light ring, it can avoid the extreme sensitivity of SEOBNRv3 to the exact light ring location, thereby dramatically improving the robustness of the model. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.