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University of Cambridge

1. Hee, Sonke. Computational Bayesian techniques applied to cosmology.

Degree: PhD, 2018, University of Cambridge

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-α 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.

Subjects/Keywords: Bayesian inference; Cosmology; Dark Energy; LCDM; Quintessence; Statistics; Gravitational waves; Tests of GR; Computational acceleration; Nested Sampling; General Relativity; Multinest; PolyChord; Product Space MCMC; Model selection; Posterior odds; Posterior Odds Ratio; Bayes factors; Parameter estimation; Equation of state; Phantom Dark Energy; Microwave background radiation; CMB; Supernovae; Baryonic Acoustic Oscillations; Data constraints; Kullback Leibler Divergence; KL divergence; LIGO; Constraining power; Lyman alpha forest; Bayesian; Bayes theorem; Probability; Efficiency; Concordance model; Parameter reconstruction; Marginal Likelihood; Kerr waveform; Free-form reconstruction; w(z); w=-1; Cosmological constant; Model comparison; Evidence; CosmoMC; CAMB; Hyper-likelihood; Model averaging; Planck

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hee, S. (2018). Computational Bayesian techniques applied to cosmology. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/273346 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744549

Chicago Manual of Style (16th Edition):

Hee, Sonke. “Computational Bayesian techniques applied to cosmology.” 2018. Doctoral Dissertation, University of Cambridge. Accessed October 13, 2019. https://www.repository.cam.ac.uk/handle/1810/273346 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744549.

MLA Handbook (7th Edition):

Hee, Sonke. “Computational Bayesian techniques applied to cosmology.” 2018. Web. 13 Oct 2019.

Vancouver:

Hee S. Computational Bayesian techniques applied to cosmology. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Oct 13]. Available from: https://www.repository.cam.ac.uk/handle/1810/273346 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744549.

Council of Science Editors:

Hee S. Computational Bayesian techniques applied to cosmology. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/273346 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744549


University of Cambridge

2. Hee, Sonke. Computational Bayesian techniques applied to cosmology .

Degree: 2018, University of Cambridge

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-α 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.

Subjects/Keywords: Bayesian inference; Cosmology; Dark Energy; LCDM; Quintessence; Statistics; Gravitational waves; Tests of GR; Computational acceleration; Nested Sampling; General Relativity; Multinest; PolyChord; Product Space MCMC; Model selection; Posterior odds; Posterior Odds Ratio; Bayes factors; Parameter estimation; Equation of state; Phantom Dark Energy; Microwave background radiation; CMB; Supernovae; Baryonic Acoustic Oscillations; Data constraints; Kullback Leibler Divergence; KL divergence; LIGO; Constraining power; Lyman alpha forest; Bayesian; Bayes theorem; Probability; Efficiency; Concordance model; Parameter reconstruction; Marginal Likelihood; Kerr waveform; Free-form reconstruction; w(z); w=-1; Cosmological constant; Model comparison; Evidence; CosmoMC; CAMB; Hyper-likelihood; Model averaging; Planck

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hee, S. (2018). Computational Bayesian techniques applied to cosmology . (Thesis). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/273346

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hee, Sonke. “Computational Bayesian techniques applied to cosmology .” 2018. Thesis, University of Cambridge. Accessed October 13, 2019. https://www.repository.cam.ac.uk/handle/1810/273346.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hee, Sonke. “Computational Bayesian techniques applied to cosmology .” 2018. Web. 13 Oct 2019.

Vancouver:

Hee S. Computational Bayesian techniques applied to cosmology . [Internet] [Thesis]. University of Cambridge; 2018. [cited 2019 Oct 13]. Available from: https://www.repository.cam.ac.uk/handle/1810/273346.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hee S. Computational Bayesian techniques applied to cosmology . [Thesis]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/273346

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.