## Computational Bayesian techniques applied to cosmology

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 |