dc.contributor.author Hee, Sonke dc.date.accessioned 2018-02-20T10:11:19Z dc.date.available 2018-02-20T10:11:19Z dc.date.issued 2018-02-16 dc.date.submitted 2018-02-16 dc.identifier.uri https://www.repository.cam.ac.uk/handle/1810/273346 dc.description.abstract This 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.iso en dc.subject Bayesian inference dc.subject Cosmology dc.subject Dark Energy dc.subject LCDM dc.subject Quintessence dc.subject Statistics dc.subject Gravitational waves dc.subject Tests of GR dc.subject Computational acceleration dc.subject Nested Sampling dc.subject General Relativity dc.subject Multinest dc.subject PolyChord dc.subject Product Space MCMC dc.subject Model selection dc.subject Posterior odds dc.subject Posterior Odds Ratio dc.subject Bayes factors dc.subject Parameter estimation dc.subject Equation of state dc.subject Phantom Dark Energy dc.subject Microwave background radiation dc.subject CMB dc.subject Supernovae dc.subject Baryonic Acoustic Oscillations dc.subject Data constraints dc.subject Kullback Leibler Divergence dc.subject KL divergence dc.subject LIGO dc.subject Constraining power dc.subject Lyman alpha forest dc.subject Bayesian dc.subject Bayes theorem dc.subject Probability dc.subject Efficiency dc.subject Concordance model dc.subject Parameter reconstruction dc.subject Marginal Likelihood dc.subject Kerr waveform dc.subject Free-form reconstruction dc.subject w(z) dc.subject w=-1 dc.subject Cosmological constant dc.subject Model comparison dc.subject Evidence dc.subject CosmoMC dc.subject CAMB dc.subject Hyper-likelihood dc.subject Model averaging dc.subject Planck dc.title Computational Bayesian techniques applied to cosmology dc.type Thesis dc.type.qualificationlevel Doctoral dc.type.qualificationname Doctor of Philosophy (PhD) dc.publisher.institution University of Cambridge dc.publisher.department Department of Physics dc.date.updated 2018-02-18T22:23:17Z dc.identifier.doi 10.17863/CAM.20373 dc.publisher.college Churchill College dc.type.qualificationtitle PhD in Physics cam.supervisor Lasenby, Anthony cam.supervisor Hobson, Mike rioxxterms.freetoread.startdate 2018-02-18
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