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Regression methods in waveform modeling: a comparative study

MPS-Authors
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Setyawati,  Yoshinta Eka
Binary Merger Observations and Numerical Relativity, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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Pürrer,  Michael
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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Ohme,  Frank
Binary Merger Observations and Numerical Relativity, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

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1909.10986.pdf
(Preprint), 915KB

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Citation

Setyawati, Y. E., Pürrer, M., & Ohme, F. (2020). Regression methods in waveform modeling: a comparative study. Classical and Quantum Gravity, 37(7): 075012. doi:10.1088/1361-6382/ab693b.


Cite as: http://hdl.handle.net/21.11116/0000-0004-C4B4-3
Abstract
Gravitational-wave astronomy of compact binaries relies on theoretical models of the gravitational-wave signal that is emitted as binaries coalesce. These models do not only need to be accurate, they also have to be fast to evaluate in order to be able to compare millions of signals in near real time with the data of gravitational-wave instruments. A variety of regression and interpolation techniques have been employed to build efficient waveform models, but no study has systematically compared the performance of these regression methods yet. Here we provide such a comparison of various techniques, including polynomial fits, radial basis functions, Gaussian process regression and artificial neural networks, specifically for the case of gravitational waveform modeling. We use all these techniques to regress analytical models of non-precessing and precessing binary black hole waveforms, and compare the accuracy as well as computational speed. We find that most regression methods are reasonably accurate, but efficiency considerations favour in many cases the most simple approach. We conclude that sophisticated regression methods are not necessarily needed in standard gravitational-wave modeling applications, although problems with higher complexity than what is tested here might be more suitable for machine-learning techniques and more sophisticated methods may have side benefits.