In this paper we describe a Bayesian inference framework for 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 9-dimensional parameter space. Here we present intermediate results showing how, using this method, information about the 9 parameters can be extracted from the data.
@article{arxiv.0707.3969,
title = {Inference on inspiral signals using LISA MLDC data},
author = {Christian Röver and Alexander Stroeer and Ed Bloomer and Nelson Christensen and James Clark and Martin Hendry and Chris Messenger and Renate Meyer and Matt Pitkin and Jennifer Toher and Richard Umstätter and Alberto Vecchio and John Veitch and Graham Woan},
journal= {arXiv preprint arXiv:0707.3969},
year = {2008}
}
Comments
Accepted for publication in Classical and Quantum Gravity, GWDAW-11 special issue