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Automatic Bayesian inference for LISA data analysis strategies

Alexander Stroeer·Jonathan Gair·Alberto Vecchio
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Abstract

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.

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Alexander Stroeer, Jonathan Gair, Alberto Vecchio. 2006-09-04. Automatic Bayesian inference for LISA data analysis strategies. https://doi.org/10.1063/1.2405082

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