Inference on inspiral signals using LISA MLDC data
| Autor: | James S. Clark, Graham Woan, C. Messenger, Richard Umstätter, Alexander Stroeer, Christian Röver, Nelson Christensen, John Veitch, Renate Meyer, Matthew Pitkin, Ed Bloomer, Alberto Vecchio, Jennifer Toher, Martin Hendry |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Physics
Physics and Astronomy (miscellaneous) Posterior probability FOS: Physical sciences Inference Binary number Markov chain Monte Carlo General Relativity and Quantum Cosmology (gr-qc) Parameter space Bayesian inference General Relativity and Quantum Cosmology Set (abstract data type) symbols.namesake Data analysis symbols Algorithm |
| Zdroj: | Classical and quantum gravity |
| ISSN: | 1361-6382 0264-9381 |
| DOI: | 10.1088/0264-9381/24/19/s15 |
| Popis: | 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. Accepted for publication in Classical and Quantum Gravity, GWDAW-11 special issue |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|