Coherent Bayesian analysis of inspiral signals

Understand

We present in this paper a Bayesian parameter estimation method for the analysis of interferometric gravitational wave observations of an inspiral of binary compact objects using data recorded simultaneously by a network of several interferometers at different sites.

• We consider neutron star or black hole inspirals that are modeled to 3.5 post-Newtonian (PN) order in phase and 2.5 PN in amplitude.
• Inference is facilitated using Markov chain Monte Carlo 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 the data.