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Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

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Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets. / Stroeer, Alexander; Veitch, John; Röver, Christian et al.
2007. S541-S549.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Harvard

Stroeer, A, Veitch, J, Röver, C, Bloomer, E, Clark, J, Christensen, N, Hendry, M, Messenger, C, Meyer, R, Pitkin, M, Toher, J, Umstätter, R, Vecchio, A & Woan, G 2007, 'Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets', pp. S541-S549. https://doi.org/10.1088/0264-9381/24/19/S17

APA

Stroeer, A., Veitch, J., Röver, C., Bloomer, E., Clark, J., Christensen, N., Hendry, M., Messenger, C., Meyer, R., Pitkin, M., Toher, J., Umstätter, R., Vecchio, A., & Woan, G. (2007). Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets. S541-S549. https://doi.org/10.1088/0264-9381/24/19/S17

Vancouver

Stroeer A, Veitch J, Röver C, Bloomer E, Clark J, Christensen N et al.. Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets. 2007. doi: 10.1088/0264-9381/24/19/S17

Author

Stroeer, Alexander ; Veitch, John ; Röver, Christian et al. / Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets. 9 p.

Bibtex

@conference{aa26364c805f4f5baa654afef86a4f0b,
title = "Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets",
abstract = "We report on the analysis of selected single source data sets from the first round of the mock LISA data challenges (MLDC) for white dwarf binaries. We implemented an end-to-end pipeline consisting of a grid-based coherent pre-processing unit for signal detection and an automatic Markov Chain Monte Carlo (MCMC) post-processing unit for signal evaluation. We demonstrate that signal detection with our coherent approach is secure and accurate, and is increased in accuracy and supplemented with additional information on the signal parameters by our Markov Chain Monte Carlo approach. We also demonstrate that the Markov Chain Monte Carlo routine is additionally able to determine accurately the noise level in the frequency window of interest.",
keywords = "General Relativity and Quantum Cosmology, Astrophysics",
author = "Alexander Stroeer and John Veitch and Christian R{\"o}ver and Ed Bloomer and James Clark and Nelson Christensen and Martin Hendry and Chris Messenger and Renate Meyer and Matthew Pitkin and Jennifer Toher and Richard Umst{\"a}tter and Alberto Vecchio and Graham Woan",
year = "2007",
month = oct,
day = "1",
doi = "10.1088/0264-9381/24/19/S17",
language = "English",
pages = "S541--S549",

}

RIS

TY - CONF

T1 - Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets

AU - Stroeer, Alexander

AU - Veitch, John

AU - Röver, Christian

AU - Bloomer, Ed

AU - Clark, James

AU - Christensen, Nelson

AU - Hendry, Martin

AU - Messenger, Chris

AU - Meyer, Renate

AU - Pitkin, Matthew

AU - Toher, Jennifer

AU - Umstätter, Richard

AU - Vecchio, Alberto

AU - Woan, Graham

PY - 2007/10/1

Y1 - 2007/10/1

N2 - We report on the analysis of selected single source data sets from the first round of the mock LISA data challenges (MLDC) for white dwarf binaries. We implemented an end-to-end pipeline consisting of a grid-based coherent pre-processing unit for signal detection and an automatic Markov Chain Monte Carlo (MCMC) post-processing unit for signal evaluation. We demonstrate that signal detection with our coherent approach is secure and accurate, and is increased in accuracy and supplemented with additional information on the signal parameters by our Markov Chain Monte Carlo approach. We also demonstrate that the Markov Chain Monte Carlo routine is additionally able to determine accurately the noise level in the frequency window of interest.

AB - We report on the analysis of selected single source data sets from the first round of the mock LISA data challenges (MLDC) for white dwarf binaries. We implemented an end-to-end pipeline consisting of a grid-based coherent pre-processing unit for signal detection and an automatic Markov Chain Monte Carlo (MCMC) post-processing unit for signal evaluation. We demonstrate that signal detection with our coherent approach is secure and accurate, and is increased in accuracy and supplemented with additional information on the signal parameters by our Markov Chain Monte Carlo approach. We also demonstrate that the Markov Chain Monte Carlo routine is additionally able to determine accurately the noise level in the frequency window of interest.

KW - General Relativity and Quantum Cosmology

KW - Astrophysics

U2 - 10.1088/0264-9381/24/19/S17

DO - 10.1088/0264-9381/24/19/S17

M3 - Conference paper

SP - S541-S549

ER -