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Activity Number:
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367
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #310188 |
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Title:
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Coherent Bayesian Inference on Compact Binary Inspirals Using a Network of Interferometric Gravitational Wave Detectors
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Author(s):
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Christian Röver*+ and Renate Meyer and Nelson Christensen
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Companies:
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The University of Auckland and The University of Auckland and Carleton College
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Address:
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Department of Statistics, Auckland, , New Zealand
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Keywords:
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gravitational waves ; coherent parameter estimation
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Abstract:
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Presented in this paper is the description of a Markov chain Monte Carlo (MCMC) routine for conducting coherent parameter estimation for interferometric gravitational wave observations of an inspiral of binary compact objects using multiple detectors. Data from several interferometers are processed, and all nine parameters (ignoring spin) associated with the binary system are inferred, including the distance to the source, the masses, and the location on the sky. The data is matched with time-domain inspiral templates that are 2.5 post-Newtonian (PN) in phase and 2.0 PN in amplitude. We designed and tuned an MCMC sampler so that it is able to efficiently find the posterior mode(s) in the parameter space and perform the stochastic integration necessary for inference within a Bayesian framework. Examples are given for simulated signals and data as seen by the LIGO and Virgo detectors.
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