dc.contributor.author |
Ali, Asad |
en |
dc.contributor.author |
Christensen, Nelson |
en |
dc.contributor.author |
Meyer, Renate |
en |
dc.contributor.author |
Röver, Christian |
en |
dc.date.accessioned |
2015-01-07T01:52:33Z |
en |
dc.date.issued |
2012-07-21 |
en |
dc.identifier.citation |
Classical and Quantum Gravity, 2012, 29 (14), Article number 145014 |
en |
dc.identifier.issn |
0264-9381 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/23973 |
en |
dc.description.abstract |
Extreme mass ratio inspirals (EMRIs) are thought to be one of the most exciting gravitational wave sources to be detected with LISA. Due to their complicated nature and weak amplitudes the detection and parameter estimation of such sources is a challenging task. In this paper we present a statistical methodology based on Bayesian inference in which the estimation of parameters is carried out by advanced Markov chain Monte Carlo (MCMC) algorithms such as parallel tempering MCMC. We analysed high and medium mass EMRI systems that fall well inside the low frequency range of LISA. In the context of the Mock LISA Data Challenges, our investigation and results are also the first instance in which a fully Markovian algorithm is applied for EMRI searches. Results show that our algorithm worked well in recovering EMRI signals from different (simulated) LISA data sets having single and multiple EMRI sources and holds great promise for posterior computation under more realistic conditions. The search and estimation methods presented in this paper are general in their nature, and can be applied in any other scenario such as AdLIGO, AdVIRGO and Einstein Telescope with their respective response functions. |
en |
dc.relation.ispartofseries |
Classical and Quantum Gravity |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://authors.iop.org/atom/help.nsf/0/F20EC7D4A1A670AA80256F1C0053EEFF?OpenDocument http://www.sherpa.ac.uk/romeo/issn/0264-9381/ |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.title |
Bayesian inference on EMRI signals using low frequency approximations |
en |
dc.type |
Journal Article |
en |
dc.identifier.doi |
10.1088/0264-9381/29/14/145014 |
en |
pubs.issue |
14 |
en |
pubs.volume |
29 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Article |
en |
pubs.elements-id |
358552 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
Statistics |
en |
dc.identifier.eissn |
1361-6382 |
en |
pubs.number |
145014 |
en |
pubs.record-created-at-source-date |
2015-01-07 |
en |