The Laser Interferometer Space Antenna (LISA) will produce a data stream
containing a vast number of overlapping sources: from strong signals generated
by the coalescence of massive black hole binary systems to much weaker
radiation form sub-stellar mass compact binaries and extreme-mass ratio
inspirals. It has been argued that the observation of weak signals could be
hampered by the presence of loud ones and that they first need to be removed to
allow such observations. Here we consider a different approach in which sources
are studied simultaneously within the framework of Bayesian inference. We
investigate the simplified case in which the LISA data stream contains
radiation from a massive black hole binary system superimposed over a (weaker)
quasi-monochromatic waveform generated by a white dwarf binary. We derive the
posterior probability density function of the model parameters using an
automatic Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC). We show
that the information about the sources and noise are retrieved at the expected
level of accuracy without the need of removing the stronger signal. Our
analysis suggests that this approach is worth pursuing further and should be
considered for the actual analysis of the LISA data.Comment: submitted to cqg as GWDAW-10 conference proceedings, 10 pages, 4
figures, some changes to plots and numerical detail
