Fast and faithful interpolation of numerical relativity surrogate waveforms using a meshfree approximation

Several theoretical waveform models have been developed over the years to capture the gravitational-wave emission from the dynamical evolution of compact binary systems of neutron stars and black holes. As ground-based detectors improve their sensitivity at low frequencies, the real-time computation of these waveforms can become computationally expensive, exacerbating the steep cost of rapidly reconstructing source parameters using Bayesian methods. This paper describes an efficient numerical algorithm for generating high-fidelity interpolated compact binary waveforms at an arbitrary point in the signal manifold by leveraging computational linear algebra techniques such as singular value decomposition and the meshfree approximation. The results are presented for the time-domain NRHybSur3dq8 inspiral-merger-ringdown waveform model that is fine-tuned to numerical relativity simulations and parametrized by the two component-masses and two aligned spins. For demonstration, we target a specific region of the intrinsic parameter space inspired by the previously inferred parameters of the GW200311_115853 event, a binary black hole system whose merger was recorded by the network of Advanced LIGO and Virgo detectors during the third observation run. We show that the meshfree interpolated waveforms can be evaluated in $\ensuremath{\sim}2.3\text{ }\text{ }\mathrm{ms}$, which is about 38 times faster than its brute-force (frequency-domain tapered) implementation in the pycbc software package at a median accuracy of $\ensuremath{\sim}\mathcal{O}({10}^{\ensuremath{-}5})$. The algorithm is computationally efficient and scales favorably with an increasing number of dimensions of the parameter space. This technique may find use in rapid parameter estimation and source reconstruction studies.

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Publication Details

Journal
Physical review. D/Physical review. D.
Published
2024-09-10
DOI
https://doi.org/10.1103/physrevd.110.064022
Citations
4
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
1.12

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article

Fast and faithful interpolation of numerical relativity surrogate waveforms using a meshfree approximation

A. S. Sengupta, Amit Reza, Lalit Pathak
4 citations
Physical review. D/Physical review. D.
Model Reduction and Neural Networks
1.12
article

Fast and faithful interpolation of numerical relativity surrogate waveforms using a meshfree approximation

A. S. Sengupta, Amit Reza, Lalit Pathak
article en
4 citations

Abstract

Several theoretical waveform models have been developed over the years to capture the gravitational-wave emission from the dynamical evolution of compact binary systems of neutron stars and black holes. As ground-based detectors improve their sensitivity at low frequencies, the real-time computation of these waveforms can become computationally expensive, exacerbating the steep cost of rapidly reconstructing source parameters using Bayesian methods. This paper describes an efficient numerical algorithm for generating high-fidelity interpolated compact binary waveforms at an arbitrary point in the signal manifold by leveraging computational linear algebra techniques such as singular value decomposition and the meshfree approximation. The results are presented for the time-domain NRHybSur3dq8 inspiral-merger-ringdown waveform model that is fine-tuned to numerical relativity simulations and parametrized by the two component-masses and two aligned spins. For demonstration, we target a specific region of the intrinsic parameter space inspired by the previously inferred parameters of the GW200311_115853 event, a binary black hole system whose merger was recorded by the network of Advanced LIGO and Virgo detectors during the third observation run. We show that the meshfree interpolated waveforms can be evaluated in $\ensuremath{\sim}2.3\text{ }\text{ }\mathrm{ms}$, which is about 38 times faster than its brute-force (frequency-domain tapered) implementation in the pycbc software package at a median accuracy of $\ensuremath{\sim}\mathcal{O}({10}^{\ensuremath{-}5})$. The algorithm is computationally efficient and scales favorably with an increasing number of dimensions of the parameter space. This technique may find use in rapid parameter estimation and source reconstruction studies.

Physical review. D/Physical review. D.Vol. 110(6)
Tata Institute of Fundamental Research (IN), Utrecht University (NL), Indian Institute of Technology Gandhinagar (IN), National Institute for Subatomic Physics (NL)
National Science Foundation, Tata Consultancy Services, Indian Institute of Technology Gandhinagar, Department of Science and Technology, Ministry of Science and Technology, India, Ministry of Education, Culture, Sports, Science and Technology, Academia Sinica, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, National Research Foundation of Korea, Max-Planck-Gesellschaft, Centre National de la Recherche Scientifique, Ministry of Science and ICT, South Korea, Science and Technology Facilities Council, Australian Research Council, Japan Society for the Promotion of Science, Instituto Nazionale di Fisica Nucleare
Reduced inequalities
Openalex Percentile: Top 23%
Model Reduction and Neural Networks
1.12
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