Inference on inspiral signals using LISA MLDC data

Details Inference on inspiral signals using LISA MLDC data Inference on inspiral signals using LISA MLDC data

2007-07-26

arxiv.org/abs/0707.3969v2

Class.Quant.Grav.24:S521-S528,2007

Astronomy and Astrophysics

Astrophysics

General Relativity and Quantum Cosmology

Accepted for publication in Classical and Quantum Gravity, GWDAW-11 special issue

Scientific paper

10.1088/0264-9381/24/19/S15

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.

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