LISA data analysis using Markov chain Monte Carlo methods

摘要: The Laser Interferometer Space Antenna (LISA) is expected to simultaneously detect many thousands of low-frequency gravitational wave signals. This presents a data analysis challenge that very different the one encountered in ground based astronomy. LISA requires identification individual signals from stream containing an unknown number overlapping Because signal overlaps, global fit all has be performed order avoid biasing solution. However, performing such exploration enormous parameter space with dimension upwards 50 000. Markov Chain Monte Carlo (MCMC) methods offer promising solution problem. MCMC algorithms are able efficiently explore large spaces, providing estimates, error analysis, and even model selection. Here we present first application simulated demonstrate great potential approach. Our implementation uses generalized F-statistic evaluate likelihoods, annealing speed convergence chains. As final step supercool chains extract maximum likelihood estimates Bayes factorsmore » for competing models. We find approach correctly identify present, source parameters, return consistent Fisher information matrix predictions.« less