Searching for Gravitational Waves from the Inspiral of Precessing Binary Systems: Problems with Current Waveforms

Astronomy and Astrophysics – Astrophysics – General Relativity and Quantum Cosmology

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Replaced with a shorter version accepted for publication in Phys. Rev. D. 8 pages, 4 Figs

Scientific paper

10.1103/PhysRevD.67.042003

We consider the problem of searching for gravitational waves emitted during the inspiral phase of binary systems when the orbital plane precesses due to relativistic spin-orbit coupling. Such effect takes place when the spins of the binary members are misaligned with respect to the orbital angular momentum. As a first step we assess the importance of precession specifically for the first-generation of LIGO detectors. We investigate the extent of the signal-to-noise ratio reduction and, hence, detection rate that occurs when precession effects are not accounted for in the template waveforms. We restrict our analysis to binary systems that undergo the so-called simple precession and have a total mass close to 10 solar mass. We find that for binary systems with rather high mass ratios (e.g., a 1.4 solar mass neutron star and a 10 solar mass black hole) the detection rate can decrease by almost an order of magnitude. Current astrophysical estimates of the rate of binary inspiral events suggest that LIGO could detect at most a few events per year, and therefore the reduction of the detection rate even by a factor of a few is critical. In the second part of our analysis, we examine whether the effect of precession could be included in the templates by capturing the main features of the phase modulation through a small number of extra parameters. Specifically we examine and tested for the first time the 3-parameter family suggested by Apostolatos. We find that, even though these ``mimic'' templates improve the detection rate, they are still inadequate in recovering the signal-to-noise ratio at the desired level. We conclude that a more complex template family is needed in the near future, still maintaining the number of additional parameters as small as possible in order to reduce the computational costs.

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