Parameter estimation for stellar-origin black hole mergers in LISA
Matthew C. Digman, Neil J. Cornish
Abstract
The population of stellar-origin black hole binaries (SOBHBs) detected by existing ground-based gravitational-wave detectors is an exciting target for the future space-based Laser Interferometer Space Antenna (LISA). LISA is sensitive to signals at significantly lower frequencies than ground-based detectors. SOBHB signals will thus be detected much earlier in their evolution, years to decades before they merge. The mergers will then occur in the frequency band covered by ground-based detectors. Observing SOBHBs years before merger can help distinguish between progenitor models for these systems. We present a new Bayesian parameter estimation algorithm for LISA observations of SOBHBs that uses a time-frequency (wavelet) -based likelihood function. Our technique accelerates the analysis by several orders of magnitude compared to the standard frequency domain approach and allows for an efficient treatment of nonstationary noise.