Inference on inspiral signals using LISA MLDC data
arXiv:0707.3969 · doi:10.1088/0264-9381/24/19/S15
Abstract
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.
Accepted for publication in Classical and Quantum Gravity, GWDAW-11 special issue
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