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Bayesian inference on compact binary inspiral gravitational radiation signals in interferometric data

Feb, 2006
14 pages
Published in:
  • Class.Quant.Grav. 23 (2006) 4895-4906
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Abstract: (arXiv)
Presented is a description of a Markov chain Monte Carlo (MCMC) parameter estimation routine for use with interferometric gravitational radiational data in searches for binary neutron star inspiral signals. Five parameters associated with the inspiral can be estimated, and summary statistics are produced. Advanced MCMC methods were implemented, including importance resampling and prior distributions based on detection probability, in order to increase the efficiency of the code. An example is presented from an application using realistic, albeit fictitious, data.
Note:
  • submitted to Classical and Quantum Gravity. 14 pages, 5 figures
  • 04.80.Nn
  • 02.70.Uu
  • gravitational radiation: search for
  • interferometer
  • neutron star: binary
  • statistical analysis: Bayesian
  • programming: Monte Carlo
  • numerical calculations: Monte Carlo
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