Inference on inspiral signals using LISA MLDC data

Röver, Christian and Stroeer, Alexander and Bloomer, Ed and Christensen, Nelson and Clark, James and Hendry, Martin and Messenger, Chris and Meyer, Renate and Pitkin, Matthew and Toher, Jennifer and Umstätter, Richard and Vecchio, Alberto and Veitch, John and Woan, Graham (2007) Inference on inspiral signals using LISA MLDC data. Classical and Quantum Gravity, 24 (19). S521-S527. ISSN 0264-9381

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Abstract

In this paper, we describe a Bayesian inference framework for the 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 nine-dimensional parameter space. Here, we present intermediate results showing how, using this method, information about the nine parameters can be extracted from the data.

Item Type:
Journal Article
Journal or Publication Title:
Classical and Quantum Gravity
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3100/3101
Subjects:
ID Code:
135802
Deposited By:
Deposited On:
30 Jul 2019 13:45
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2020 09:33