Inference on inspiral signals using LISA MLDC data
Christian Röver, A. Stroeer, Ed Bloomer, Nelson L Christensen, James Alexander Clark, M. A. Hendry, Christopher Messenger, Renate Meyer, M. Pitkin, Jennifer Toher, Richard Umstätter, A. Vecchio, John Veitch, G. Woan · Classical and Quantum Gravity · 2007
In this paper, we describe a Bayesian inference framework for the analysis of data obtained by LISA. We set up a model for binary inspiral signals as defined for the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte Carlo (MCMC) algorithm to facilitate exploration and integration of the posterior distribution over the nine-dimensional parameter space. Here, we present intermediate results showing how, using this method, information about the nine parameters can be extracted from the data.