MG11 
Talk detail
 

 Participant 

Institution

Session

Talk

Abstract

LISA Data Analysis - A Reversible Jump MCMC Algorithm for Characterising Galactic Binaries

Of the many data analysis problems posed by LISA, one of the most challenging is the analysis of the gravitational wave foreground produced by the tens of thousands of compact binary objects in the Milky Way. This work is a Bayesian approach to extracting both the number of these sources and their signal parameters from the data, based on a Reversible-Jump Markov Chain Monte Carlo algorithm. I will give an overview of the method we have developed, and present results from simulations using signals modulated in frequency by the motion of LISA. I will also review how a principal component analysis clarifies the role of TDI variables in LISA data analysis.