Inference on inspiral signals using LISA MLDC data

C Rover; A Stroeer; E Bloomer; N Christensen; J Clark; M Hendry; C Messenger; R Meyer; M Pitkin; J Toher; R Umstaetter; Alberto Vecchio; John Veitch; G Woan

doi:10.1088/0264-9381/24/19/S15

Inference on inspiral signals using LISA MLDC data

C Rover, A Stroeer, E Bloomer, N Christensen, J Clark, M Hendry, C Messenger, R Meyer, M Pitkin, J Toher, R Umstaetter, Alberto Vecchio, John Veitch, G Woan

Physics and Astronomy

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Abstract

In this paper, we describe a Bayesian inference framework for the 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 nine-dimensional parameter space. Here, we present intermediate results showing how, using this method, information about the nine parameters can be extracted from the data.

Original language	English
Pages (from-to)	S521-S527
Journal	Classical and Quantum Gravity
Volume	24
Issue number	19
Early online date	19 Sept 2007
DOIs	https://doi.org/10.1088/0264-9381/24/19/S15
Publication status	Published - 7 Oct 2007

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title = "Inference on inspiral signals using LISA MLDC data",

abstract = "In this paper, we describe a Bayesian inference framework for the 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 nine-dimensional parameter space. Here, we present intermediate results showing how, using this method, information about the nine parameters can be extracted from the data.",

author = "C Rover and A Stroeer and E Bloomer and N Christensen and J Clark and M Hendry and C Messenger and R Meyer and M Pitkin and J Toher and R Umstaetter and Alberto Vecchio and John Veitch and G Woan",

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T1 - Inference on inspiral signals using LISA MLDC data

AU - Rover, C

AU - Stroeer, A

AU - Bloomer, E

AU - Christensen, N

AU - Clark, J

AU - Hendry, M

AU - Messenger, C

AU - Meyer, R

AU - Pitkin, M

AU - Toher, J

AU - Umstaetter, R

AU - Vecchio, Alberto

AU - Veitch, John

AU - Woan, G

PY - 2007/10/7

Y1 - 2007/10/7

N2 - In this paper, we describe a Bayesian inference framework for the 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 nine-dimensional parameter space. Here, we present intermediate results showing how, using this method, information about the nine parameters can be extracted from the data.

AB - In this paper, we describe a Bayesian inference framework for the 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 nine-dimensional parameter space. Here, we present intermediate results showing how, using this method, information about the nine parameters can be extracted from the data.

U2 - 10.1088/0264-9381/24/19/S15

DO - 10.1088/0264-9381/24/19/S15

M3 - Article

SN - 1361-6382

VL - 24

SP - S521-S527

JO - Classical and Quantum Gravity

JF - Classical and Quantum Gravity

IS - 19

ER -

Inference on inspiral signals using LISA MLDC data

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