LISA Data Analysis using MCMC methods

Details LISA Data Analysis using MCMC methods LISA Data Analysis using MCMC methods

2005-06-10

arxiv.org/abs/gr-qc/0506059v1

Phys.Rev. D72 (2005) 043005

Astronomy and Astrophysics

Astrophysics

General Relativity and Quantum Cosmology

14 pages, 7 figures

Scientific paper

10.1103/PhysRevD.72.043005

The Laser Interferometer Space Antenna (LISA) is expected to simultaneously detect many thousands of low frequency gravitational wave signals. This presents a data analysis challenge that is very different to the one encountered in ground based gravitational wave astronomy. LISA data analysis requires the identification of individual signals from a data stream containing an unknown number of overlapping signals. Because of the signal overlaps, a global fit to all the signals has to be performed in order to avoid biasing the solution. However, performing such a global fit requires the exploration of an enormous parameter space with a dimension upwards of 50,000. Markov Chain Monte Carlo (MCMC) methods offer a very promising solution to the LISA data analysis problem. MCMC algorithms are able to efficiently explore large parameter spaces, simultaneously providing parameter estimates, error analyses and even model selection. Here we present the first application of MCMC methods to simulated LISA data and demonstrate the great potential of the MCMC approach. Our implementation uses a generalized F-statistic to evaluate the likelihoods, and simulated annealing to speed convergence of the Markov chains. As a final step we super-cool the chains to extract maximum likelihood estimates, and estimates of the Bayes factors for competing models. We find that the MCMC approach is able to correctly identify the number of signals present, extract the source parameters, and return error estimates consistent with Fisher information matrix predictions.

Affiliated with

Also associated with

No associations

Rating

LISA Data Analysis using MCMC methods does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with LISA Data Analysis using MCMC methods, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and LISA Data Analysis using MCMC methods will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-730162