Coherent Bayesian inference on binary inspiral signals

(Christian Roever, Renate Meyer, Gianluca Guidi, 
Andrea Vicere and Nelson Christensen)


We present a Bayesian parameter estimation method for the analysis of 
a binary inspiral's `chirp' signal using data recorded simultaneously 
by a network of several interferometers at differing sites. We consider 
neutron star or black hole inspirals that are modeled to 3.5 post-Newtonian 
(PN) order in phase and to 2.5 PN order in amplitude. Inference is 
facilitated using Markov chain Monte Carlo (MCMC) methods that are adapted 
in order to efficiently explore the particular parameter space. 
Examples are shown to illustrate how and what information about the 
different parameters can be derived from given data. Nine parameters 
are estimated, including those associated with the binary system, plus 
its location on the sky.