Bayesian modeling of source confusion in LISA data

Umstatter, R., Christensen, N., Hendry, M., Meyer, R., Simha, V., Veitch, J. , Vigeland, S. and Woan, G. (2005) Bayesian modeling of source confusion in LISA data. Physical Review D, 72, 022001. (doi: 10.1103/PhysRevD.72.022001)

Full text not currently available from Enlighten.

Abstract

One of the greatest data analysis challenges for the Laser Interferometer Space Antenna (LISA) is the need to account for a large number of gravitational wave signals from compact binary systems expected to be present in the data. We introduce the basis of a Bayesian method that we believe can address this challenge and demonstrate its effectiveness on a simplified problem involving 100 synthetic sinusoidal signals in noise. We use a reversible jump Markov chain Monte Carlo technique to infer simultaneously the number of signals present, the parameters of each identified signal, and the noise level. Our approach therefore tackles the detection and parameter estimation problems simultaneously, without the need to evaluate formal model selection criteria, such as the Akaike Information Criterion or explicit Bayes factors. The method does not require a stopping criterion to determine the number of signals and produces results which compare very favorably with classical spectral techniques.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Veitch, Dr John and Woan, Professor Graham and Hendry, Professor Martin
Authors: Umstatter, R., Christensen, N., Hendry, M., Meyer, R., Simha, V., Veitch, J., Vigeland, S., and Woan, G.
College/School:College of Science and Engineering > School of Physics and Astronomy
Journal Name:Physical Review D
Publisher:American Physical Society
ISSN:1550-7998
ISSN (Online):1089-4918

University Staff: Request a correction | Enlighten Editors: Update this record