Christian Röver et al 2006 Class. Quantum Grav. 23 4895 doi:10.1088/0264-9381/23/15/009
Christian Röver1, Renate Meyer1 and Nelson Christensen2
Show affiliationsIn this paper we present a description of a Bayesian analysis framework for use with interferometric gravitational radiation data in search of binary neutron star inspiral signals. Five parameters are investigated, and the information extracted from the data is illustrated and quantified. The posterior integration is carried out using Markov chain Monte Carlo (MCMC) methods. Implementation details include the use of importance resampling for improved convergence and informative priors reflecting the conditions expected for realistic measurements. An example is presented from an application using realistic, albeit fictitious, data. We expect that these parameter estimation techniques will prove useful at the end of a binary inspiral detection pipeline for interferometric detectors like LIGO or Virgo.
04.80.Nn Gravitational wave detectors and experiments
60J22 Computational methods in Markov chains (See also 65C40)
Issue 15 (7 August 2006)
Received 17 February 2006, in final form 20 April 2006
Published 11 July 2006
Christian Röver et al 2006 Class. Quantum Grav. 23 4895
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