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Surrogate model for an aligned-spin effective one body waveform model of binary neutron star inspirals using Gaussian process regression

MPG-Autoren
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Lackey,  Benjamin
Astrophysical and Cosmological Relativity, AEI-Golm, 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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Taracchini,  Andrea
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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

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Zitation

Lackey, B., Pürrer, M., Taracchini, A., & Marsat, S. (2019). Surrogate model for an aligned-spin effective one body waveform model of binary neutron star inspirals using Gaussian process regression. Physical Review D, 100(2): 024002. doi:10.1103/PhysRevD.100.024002.


Zitierlink: https://hdl.handle.net/21.11116/0000-0002-B998-2
Zusammenfassung
Fast and accurate waveform models are necessary for measuring the properties
of inspiraling binary neutron star systems such as GW170817. We present a
frequency-domain surrogate version of the aligned-spin binary neutron star
waveform model using the effective one body formalism known as SEOBNRv4T. This
model includes the quadrupolar and octopolar adiabatic and dynamical tides. The
version presented here is improved by the inclusion of the spin-induced
quadrupole moment effect, and completed by a prescription for tapering the end
of the waveform to qualitatively reproduce numerical relativity simulations.
The resulting model has 14 intrinsic parameters. We reduce its dimensionality
by using universal relations that approximate all matter effects in terms of
the leading quadrupolar tidal parameters. The implementation of the time-domain
model can take up to an hour to evaluate using a starting frequency of 20Hz,
and this is too slow for many parameter estimation codes that require $O(10^7)$
sequential waveform evaluations. We therefore construct a fast and faithful
frequency-domain surrogate of this model using Gaussian process regression. The
resulting surrogate has a maximum mismatch of $4.5\times 10^{-4}$ for the
Advanced LIGO detector, and requires 0.13s to evaluate for a waveform with a
starting frequency of 20Hz. Finally, we perform an end-to-end test of the
surrogate with a set of parameter estimation runs, and find that the surrogate
accurately recovers the parameters of injected waveforms.