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Journal Article

#### Extreme Mass-Ratio Inspirals in the Effective-One-Body Approach: Quasi-Circular, Equatorial Orbits around a Spinning Black Hole

##### Fulltext (public)

1009.6013.pdf

(Preprint), 872KB

PhysRevD.83.044044.pdf

(Any fulltext), 848KB

Erratum.pdf

(Any fulltext), 27KB

##### Supplementary Material (public)

There is no public supplementary material available

##### Citation

Yunes, N., Buonanno, A., Hughes, S. A., Pan, Y., Barausse, E., Miller, M. C., et al. (2011).
Extreme Mass-Ratio Inspirals in the Effective-One-Body Approach: Quasi-Circular, Equatorial Orbits around a Spinning Black
Hole.* Physical Review D,* *83*(4): 044044. doi:10.1103/PhysRevD.83.044044.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0023-F7F1-9

##### Abstract

We construct effective-one-body waveform models suitable for data analysis
with LISA for extreme-mass ratio inspirals in quasi-circular, equatorial orbits
about a spinning supermassive black hole. The accuracy of our model is
established through comparisons against frequency-domain, Teukolsky-based
waveforms in the radiative approximation. The calibration of eight high-order
post-Newtonian parameters in the energy flux suffices to obtain a phase and
fractional amplitude agreement of better than 1 radian and 1 % respectively
over a period between 2 and 6 months depending on the system considered. This
agreement translates into matches higher than 97 % over a period between 4 and
9 months, depending on the system. Better agreements can be obtained if a
larger number of calibration parameters are included. Higher-order mass ratio
terms in the effective-one-body Hamiltonian and radiation-reaction introduce
phase corrections of at most 30 radians in a one year evolution. These
corrections are usually one order of magnitude larger than those introduced by
the spin of the small object in a one year evolution. These results suggest
that the effective-one-body approach for extreme mass ratio inspirals is a good
compromise between accuracy and computational price for LISA data analysis
purposes.