Indexed on: 11 Feb '11Published on: 11 Feb '11Published in: General Relativity and Quantum Cosmology
The Einstein Telescope (ET) is a proposed third generation ground-based interferometer, for which the target is a sensitivity that is a factor of ten better than Advanced LIGO and a frequency range that extends down to about 1Hz. ET will provide opportunities to test Einstein's theory of relativity in the strong field and will realize precision gravitational wave astronomy with a thousandfold increase in the expected number of events over the advanced ground-based detectors. A design study for ET is currently underway, so it is timely to assess the science that could be done with such an instrument. This paper is the first in a series that will carry out a detailed study of intermediate-mass-ratio inspirals (IMRIs) for ET. In the context of ET, an IMRI is the inspiral of a neutron star or stellar-mass black hole into an intermediate mass black hole (IMBH). In this paper we focus on the development of IMRI waveform models for circular and equatorial inspirals. We consider two approximations for the waveforms, which both incorporate the inspiral, merger and ringdown phases in a consistent way. One approximation, valid for IMBHs of arbitrary spin, uses the transition model of Ori and Thorne  to describe the merger, and this is then matched smoothly onto a ringdown waveform. The second approximation uses the Effective One Body (EOB) approach to model the merger phase of the waveform and is valid for non-spinning IMBHs. In this paper, we use both waveform models to compute signal-to-noise ratios (SNRs) for IMRI sources detectable by ET. At a redshift of z=1, we find typical SNRs for IMRI systems with masses 1.4+100 solar masses, 10+100 solar masses, 1.4+500 solar masses and 10+500 solar masses of about 10-25, 40-80, 3-15 and 10-60, respectively. We also find that the two models make predictions for non-spinning inspirals that are consistent to about ten percent.
Join Sparrho today to stay on top of science
Discover, organise and share research that matters to you