A Bayesian approach to the study of white dwarf binaries in LISA data: The application of a reversible jump Markov chain Monte Carlo method
arXiv:0907.2198 · doi:10.1103/PhysRevD.80.064032
Abstract
The Laser Interferometer Space Antenna (LISA) defines new demands on data analysis efforts in its all-sky gravitational wave survey, recording simultaneously thousands of galactic compact object binary foreground sources and tens to hundreds of background sources like binary black hole mergers and extreme mass ratio inspirals. We approach this problem with an adaptive and fully automatic Reversible Jump Markov Chain Monte Carlo sampler, able to sample from the joint posterior density function (as established by Bayes theorem) for a given mixture of signals "out of the box'', handling the total number of signals as an additional unknown parameter beside the unknown parameters of each individual source and the noise floor. We show in examples from the LISA Mock Data Challenge implementing the full response of LISA in its TDI description that this sampler is able to extract monochromatic Double White Dwarf signals out of colored instrumental noise and additional foreground and background noise successfully in a global fitting approach. We introduce 2 examples with fixed number of signals (MCMC sampling), and 1 example with unknown number of signals (RJ-MCMC), the latter further promoting the idea behind an experimental adaptation of the model indicator proposal densities in the main sampling stage. We note that the experienced runtimes and degeneracies in parameter extraction limit the shown examples to the extraction of a low but realistic number of signals.
18 pages, 9 figures, 3 tables, accepted for publication in PRD, revised version
References in corpus (7)
- Gravitational-Wave Astronomy with Inspiral Signals of Spinning Compact-Object Binaries
- An overview of the second round of the Mock LISA Data Challenges
- Report on the first round of the Mock LISA Data Challenges
- Extracting galactic binary signals from the first round of Mock LISA Data Challenges
- Improved search for galactic white dwarf binaries in Mock LISA Data Challenge 1B using an F-statistic template bank
- Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets
- A How-To for the Mock LISA Data Challenges