Automatic Bayesian inference for LISA data analysis strategies
摘要
We demonstrate the use of automatic Bayesian inference for the analysis of LISA data sets. In particular we describe a new automatic Reversible Jump Markov Chain Monte Carlo method to evaluate the posterior probability density functions of the a priori unknown number of parameters that describe the gravitational wave signals present in the data. We apply the algorithm to a simulated LISA data set containing overlapping signals from white dwarf binary systems (DWD) and to a separate data set containing a signal from an extreme mass ratio inspiral (EMRI). We demonstrate that the approach works well in both cases and can be regarded as a viable approach to tackle LISA data analysis challenges.
引用
@article{arxiv.gr-qc/0609010,
title = {Automatic Bayesian inference for LISA data analysis strategies},
author = {Alexander Stroeer and Jonathan Gair and Alberto Vecchio},
journal= {arXiv preprint arXiv:gr-qc/0609010},
year = {2009}
}
备注
8 pages, 2 sets of figures, submitted to the proceedings of the 6th LISA symposium