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dc.contributor.authorChua, AJK
dc.contributor.authorHee, S
dc.contributor.authorHandley, WJ
dc.contributor.authorHigson, E
dc.contributor.authorMoore, CJ
dc.contributor.authorGair, JR
dc.contributor.authorHobson, MP
dc.contributor.authorLasenby, AN
dc.date.accessioned2019-01-09T00:31:38Z
dc.date.available2019-01-09T00:31:38Z
dc.date.issued2018-07-21
dc.identifier.issn1365-2966
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/287689
dc.description.abstractExtreme-mass-ratio-inspiral observations from future space-based gravitational-wave detectors such as LISA will enable strong-field tests of general relativity with unprecedented precision, but at prohibitive computational cost if existing statistical techniques are used. In one such test that is currently employed for LIGO black hole binary mergers, generic deviations from relativity are represented by N deformation parameters in a generalized waveform model; the Bayesian evidence for each of its 2N combinatorial submodels is then combined into a posterior odds ratio for modified gravity over relativity in a null-hypothesis test. We adapt and apply this test to a generalized model for extreme-mass-ratio inspirals constructed on deformed black hole spacetimes, and focus our investigation on how computational efficiency can be increased through an evidence-free method of model selection. This method is akin to the algorithm known as product-space Markov chain Monte Carlo, but uses nested sampling and improved error estimates from a rethreading technique. We perform benchmarking and robustness checks for the method, and find order-of-magnitude computational gains over regular nested sampling in the case of synthetic data generated from the null model.
dc.description.sponsorshipAJKC acknowledges support from the Jet Propulsion Laboratory (JPL) Research and Technology Development programme. SH thanks the Science and Technology Facilities Council (STFC) for financial support. CJM acknowledges financial support provided under the European Union’s H2020 ERC Consolidator Grant ‘Matter and strong-field gravity: New frontiers in Einstein’s theory’ grant agreement no. MaGRaTh646597, and networking support by the COST Action CA16104. Parts of this work were performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from STFC. Parts of this work were also undertaken on the COSMOS Shared Memory system at DAMTP, University of Cambridge operated on behalf of the STFC DiRAC HPC Facility; this equipment is funded by BIS National E-infrastructure capital grant ST/J005673/1 and STFC grants ST/H008586/1, ST/K00333X/1. Parts of this work were also carried out at JPL, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
dc.publisherOxford University Press
dc.rightsPublisher's own licence
dc.titleTowards a framework for testing general relativity with extreme-mass-ratio-inspiral observations
dc.typeArticle
prism.endingPage40
prism.issueIdentifier1
prism.publicationDate2018
prism.publicationNameMonthly Notices of the Royal Astronomical Society
prism.startingPage28
prism.volume478
dc.identifier.doi10.17863/CAM.35003
dcterms.dateAccepted2018-04-21
rioxxterms.versionofrecord10.1093/mnras/sty1079
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-07-21
dc.contributor.orcidHandley, William [0000-0002-5866-0445]
dc.contributor.orcidHigson, Edward [0000-0001-8383-4614]
dc.contributor.orcidLasenby, Anthony [0000-0002-8208-6332]
dc.identifier.eissn1365-2966
dc.publisher.urlhttp://dx.doi.org/10.1093/mnras/sty1079
rioxxterms.typeJournal Article/Review
pubs.funder-project-idSTFC (1208121)
pubs.funder-project-idEuropean Research Council (646597)
pubs.funder-project-idScience and Technology Facilities Council (ST/M001172/1)
cam.issuedOnline2018-05-30


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