EGO · Exploiting Gravitational-Wave Observations: Modeling Coalescing Black Hole Binaries and Extreme Mass Ratio Inspirals

The existence of gravitational radiation is one of the most important predictions of Einstein's general theory of relativity. The successful observation of gravitational waves (GWs) will have a tremendous impact on physics, astrophysics, and cosmology. A worldwide effort is currently underway to achieve the first direct detection of GWs by using kilometer-scale, ground-based laser interferometers such as Advanced Virgo and GEO-HF in Europe, Advanced LIGO in the US and KAGRA in Japan, as well as future space-based antennas, such as the European eLISA/NGO mission. Among the most promising sources for the detection of this gravitational radiation are inspiralling and coalescing binary systems composed of black holes and/or neutron stars (compact binaries). The detection and analysis of these signals require very accurate theoretical predictions, for use as ""gravitational waveforms"" to be cross-correlated against the output of the detectors.The main objective of this project is to develop highly accurate and physically motivated gravitational waveforms that will model the GW emission from all compact binaries (stellar-mass compact binaries, supermassive black hole binaries, extreme mass ratio inspirals). The development of such waveforms is a prerequisite for the full scientific exploitation of current and future ground-based and space-based GW detectors. I will model the inspiral part of the orbital evolution by using black hole perturbation theory, an approximation method in general relativity. The resulting perturbative waveforms will be ""hybridized"" to a set of waveforms modeling the final plunge, merger, and final black hole ringdown, generated using numerical-relativity simulations of compact binaries. These hybrid waveforms will be used to build a bank of phenomenological waveforms covering the entire space of source parameters, for use in both ground-based and space-based observatories for detection and parameter estimation.""