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Frequency domain reduced order model of aligned-spin effective-one-body waveforms with higher-order modes

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Cotesta,  Roberto
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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2003.12079.pdf
(Preprint), 4MB

PhysRevD.101.124040.pdf
(Publisher version), 6MB

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Citation

Cotesta, R., Marsat, S., & Pürrer, M. (2020). Frequency domain reduced order model of aligned-spin effective-one-body waveforms with higher-order modes. Physical Review D, 101(12): 124040. doi:10.1103/PhysRevD.101.124040.


Cite as: http://hdl.handle.net/21.11116/0000-0006-3AF9-1
Abstract
We present a frequency domain reduced order model (ROM) for the aligned-spin effective-one-body (EOB) model for binary black holes (BBHs) SEOBNRv4HM that includes the spherical harmonics modes $(\ell, |m|) = (2,1),(3,3),(4,4),(5,5)$ beyond the dominant $(\ell, |m|) = (2,2)$ mode. These higher modes are crucial to accurately represent the waveform emitted from asymmetric BBHs. We discuss a decomposition of the waveform, extending other methods in the literature, that allows us to accurately and efficiently capture the morphology of higher mode waveforms. We show that the ROM is very accurate with median (maximum) values of the unfaithfulness against SEOBNRv4HM lower than $0.001\% (0.03\%)$ for total masses in $[2.8,100] M_\odot$. For a total mass of $M = 300 M_\odot$ the median (maximum) value of the unfaithfulness increases up to $0.004\% (0.17\%)$. This is still at least an order of magnitude lower than the estimated accuracy of SEOBNRv4HM compared to numerical relativity simulations. The ROM is two orders of magnitude faster in generating a waveform compared to SEOBNRv4HM. Data analysis applications typically require $\mathcal{O}(10^6-10^8)$ waveform evaluations for which SEOBNRv4HM is in general too slow. The ROM is therefore crucial to allow the SEOBNRv4HM waveform to be used in searches and Bayesian parameter inference. We present a targeted parameter estimation study that shows the improvements in measuring binary parameters when using waveforms that includes higher modes and compare against three other waveform models.