Standard
Inference on inspiral signals using LISA MLDC data. / Röver, Christian; Stroeer, Alexander; Bloomer, Ed et al.
In:
Classical and Quantum Gravity, Vol. 24, No. 19, 19.09.2007, p. S521-S527.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Harvard
Röver, C, Stroeer, A, Bloomer, E, Christensen, N, Clark, J, Hendry, M, Messenger, C, Meyer, R
, Pitkin, M, Toher, J, Umstätter, R, Vecchio, A, Veitch, J & Woan, G 2007, '
Inference on inspiral signals using LISA MLDC data',
Classical and Quantum Gravity, vol. 24, no. 19, pp. S521-S527.
https://doi.org/10.1088/0264-9381/24/19/S15APA
Röver, C., Stroeer, A., Bloomer, E., Christensen, N., Clark, J., Hendry, M., Messenger, C., Meyer, R.
, Pitkin, M., Toher, J., Umstätter, R., Vecchio, A., Veitch, J., & Woan, G. (2007).
Inference on inspiral signals using LISA MLDC data.
Classical and Quantum Gravity,
24(19), S521-S527.
https://doi.org/10.1088/0264-9381/24/19/S15Vancouver
Author
Bibtex
@article{cfb32df1f0d042b0be08f356a1aab154,
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.",
keywords = "General Relativity and Quantum Cosmology",
author = "Christian R{\"o}ver and Alexander Stroeer and Ed Bloomer and Nelson Christensen and James Clark and Martin Hendry and Chris Messenger and Renate Meyer and Matthew Pitkin and Jennifer Toher and Richard Umst{\"a}tter and Alberto Vecchio and John Veitch and Graham Woan",
year = "2007",
month = sep,
day = "19",
doi = "10.1088/0264-9381/24/19/S15",
language = "English",
volume = "24",
pages = "S521--S527",
journal = "Classical and Quantum Gravity",
issn = "0264-9381",
publisher = "IOP Publishing",
number = "19",
}
RIS
TY - JOUR
T1 - Inference on inspiral signals using LISA MLDC data
AU - Röver, Christian
AU - Stroeer, Alexander
AU - Bloomer, Ed
AU - Christensen, Nelson
AU - Clark, James
AU - Hendry, Martin
AU - Messenger, Chris
AU - Meyer, Renate
AU - Pitkin, Matthew
AU - Toher, Jennifer
AU - Umstätter, Richard
AU - Vecchio, Alberto
AU - Veitch, John
AU - Woan, Graham
PY - 2007/9/19
Y1 - 2007/9/19
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.
KW - General Relativity and Quantum Cosmology
U2 - 10.1088/0264-9381/24/19/S15
DO - 10.1088/0264-9381/24/19/S15
M3 - Journal article
VL - 24
SP - S521-S527
JO - Classical and Quantum Gravity
JF - Classical and Quantum Gravity
SN - 0264-9381
IS - 19
ER -
