A Three-Stage Search for Supermassive Black Hole Binaries in LISA Data
arXiv:0704.2447 · doi:10.1088/0264-9381/24/19/S22
Abstract
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.
12 pages, 3 figures, submitted to Proceedings of GWDAW-11 (Berlin, Dec. '06)
References in corpus (4)
Cited by in corpus (7)
- Report on the second Mock LISA Data Challenge
- Building a stochastic template bank for detecting massive black hole binaries
- Report on the first round of the Mock LISA Data Challenges
- Improved time-frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data
- Extracting galactic binary signals from the first round of Mock LISA Data Challenges
- A hierarchical search for gravitational waves from supermassive black hole binary mergers
- Markov chain Monte Carlo searches for Galactic binaries in Mock LISA Data Challenge 1B data sets