The Formation of Supermassive Black Holes and the Evolution of Supermassive Stars

The existence of supermassive black holes is supported by a growing body of observations. Supermassive black holes and their formation events are likely candidates for detection by proposed long-wavelength, space-based gravitational wave interferometers like LISA. However, the nature of the progenitors of supermassive black holes is rather uncertain. Supermassive black hole formation scenarios that involve either the stellar dynamical evolution of dense clusters or the hydrodynamical evolution of supermassive stars have been proposed. Each of these formation scenarios is reviewed and the evolution of supermassive stars is then examined in some detail. Supermassive stars that rotate uniformly during their secular cooling phase will spin up to the mass-shedding limit and eventually contract to the point of relativistic collapse. Supermassive stars that rotate differentially as they cool will likely encounter the dynamical bar mode instability prior to the onset of relativistic collapse. A supermassive star that undergoes this bar distortion, prior to or during collapse, may be a strong source of quasiperiodic, long-wavelength gravitational radiation.Comment: 6 pages, 1 figure; submitted to a Special Issue of Classical and Quantum Gravity, Proceedings of the Third LISA Symposiu

A Three-Stage Search for Supermassive Black Hole Binaries in LISA Data

Gravitational waves from the inspiral and coalescence of supermassive black-hole (SMBH) binaries with masses ~10^6 Msun are likely to be among the strongest sources for the Laser Interferometer Space Antenna (LISA). We describe a three-stage data-analysis pipeline designed to search for and measure the parameters of SMBH binaries in LISA data. The first stage uses a time-frequency track-search method to search for inspiral signals and provide a coarse estimate of the black-hole masses m_1, m_2 and of the coalescence time of the binary t_c. The second stage uses a sequence of matched-filter template banks, seeded by the first stage, to improve the measurement accuracy of the masses and coalescence time. Finally, a Markov Chain Monte Carlo search is used to estimate all nine physical parameters of the binary. Using results from the second stage substantially shortens the Markov Chain burn-in time and allows us to determine the number of SMBH-binary signals in the data before starting parameter estimation. We demonstrate our analysis pipeline using simulated data from the first LISA Mock Data Challenge. We discuss our plan for improving this pipeline and the challenges that will be faced in real LISA data analysis.Comment: 12 pages, 3 figures, submitted to Proceedings of GWDAW-11 (Berlin, Dec. '06