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dc.contributor.authorHee, Sonke
dc.date.accessioned2018-02-20T10:11:19Z
dc.date.available2018-02-20T10:11:19Z
dc.date.issued2018-02-16
dc.date.submitted2018-02-16
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/273346
dc.description.abstractThis thesis presents work around 3 themes: dark energy, gravitational waves and Bayesian inference. Both dark energy and gravitational wave physics are not yet well constrained. They present interesting challenges for Bayesian inference, which attempts to quantify our knowledge of the universe given our astrophysical data. A dark energy equation of state reconstruction analysis finds that the data favours the vacuum dark energy equation of state $w {=} -1$ model. Deviations from vacuum dark energy are shown to favour the super-negative ‘phantom’ dark energy regime of $w {<} -1$, but at low statistical significance. The constraining power of various datasets is quantified, finding that data constraints peak around redshift $z = 0.2$ due to baryonic acoustic oscillation and supernovae data constraints, whilst cosmic microwave background radiation and Lyman-$\alpha$ forest constraints are less significant. Specific models with a conformal time symmetry in the Friedmann equation and with an additional dark energy component are tested and shown to be competitive to the vacuum dark energy model by Bayesian model selection analysis: that they are not ruled out is believed to be largely due to poor data quality for deciding between existing models. Recent detections of gravitational waves by the LIGO collaboration enable the first gravitational wave tests of general relativity. An existing test in the literature is used and sped up significantly by a novel method developed in this thesis. The test computes posterior odds ratios, and the new method is shown to compute these accurately and efficiently. Compared to computing evidences, the method presented provides an approximate 100 times reduction in the number of likelihood calculations required to compute evidences at a given accuracy. Further testing may identify a significant advance in Bayesian model selection using nested sampling, as the method is completely general and straightforward to implement. We note that efficiency gains are not guaranteed and may be problem specific: further research is needed.
dc.language.isoen
dc.subjectBayesian inference
dc.subjectCosmology
dc.subjectDark Energy
dc.subjectLCDM
dc.subjectQuintessence
dc.subjectStatistics
dc.subjectGravitational waves
dc.subjectTests of GR
dc.subjectComputational acceleration
dc.subjectNested Sampling
dc.subjectGeneral Relativity
dc.subjectMultinest
dc.subjectPolyChord
dc.subjectProduct Space MCMC
dc.subjectModel selection
dc.subjectPosterior odds
dc.subjectPosterior Odds Ratio
dc.subjectBayes factors
dc.subjectParameter estimation
dc.subjectEquation of state
dc.subjectPhantom Dark Energy
dc.subjectMicrowave background radiation
dc.subjectCMB
dc.subjectSupernovae
dc.subjectBaryonic Acoustic Oscillations
dc.subjectData constraints
dc.subjectKullback Leibler Divergence
dc.subjectKL divergence
dc.subjectLIGO
dc.subjectConstraining power
dc.subjectLyman alpha forest
dc.subjectBayesian
dc.subjectBayes theorem
dc.subjectProbability
dc.subjectEfficiency
dc.subjectConcordance model
dc.subjectParameter reconstruction
dc.subjectMarginal Likelihood
dc.subjectKerr waveform
dc.subjectFree-form reconstruction
dc.subjectw(z)
dc.subjectw=-1
dc.subjectCosmological constant
dc.subjectModel comparison
dc.subjectEvidence
dc.subjectCosmoMC
dc.subjectCAMB
dc.subjectHyper-likelihood
dc.subjectModel averaging
dc.subjectPlanck
dc.titleComputational Bayesian techniques applied to cosmology
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.publisher.departmentDepartment of Physics
dc.date.updated2018-02-18T22:23:17Z
dc.identifier.doi10.17863/CAM.20373
dc.publisher.collegeChurchill College
dc.type.qualificationtitlePhD in Physics
cam.supervisorLasenby, Anthony
cam.supervisorHobson, Mike
rioxxterms.freetoread.startdate2018-02-18


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