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arXiv · 0707.3969

Inference on inspiral signals using LISA MLDC data

Christian Röver·Alexander Stroeer·Ed Bloomer·Nelson Christensen·James Clark·Martin Hendry·Chris Messenger·Renate Meyer·Matt Pitkin·Jennifer Toher·Richard Umstätter·Alberto Vecchio·John Veitch·Graham Woan
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Abstract

In this paper we describe a Bayesian inference framework for analysis of data obtained by LISA. We set up a model for binary inspiral signals as defined for the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte Carlo (MCMC) algorithm to facilitate exploration and integration of the posterior distribution over the 9-dimensional parameter space. Here we present intermediate results showing how, using this method, information about the 9 parameters can be extracted from the data.

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Christian Röver, Alexander Stroeer, Ed Bloomer, Nelson Christensen, James Clark, Martin Hendry, Chris Messenger, Renate Meyer, Matt Pitkin, Jennifer Toher, Richard Umstätter, Alberto Vecchio, John Veitch, Graham Woan. 2007-08-05. Inference on inspiral signals using LISA MLDC data. https://doi.org/10.1088/0264-9381%2F24%2F19%2Fs15

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