Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.582046
Title: Predictions in multifield inflation
Author: Frazer, Jonathan
Awarding Body: University of Sussex
Current Institution: University of Sussex
Date of Award: 2013
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Abstract:
Models of inflation with more than one active field are an important class where it is not fully understood how to compute predictions. This problem can be understood in terms of two characteristics of these models: the sensitivity to initial conditions and the superhorizon evolution of the primordial density perturbation ζ. This thesis seeks to make significant progress in understanding how to overcome these two issues. To track the superhorizon evolution of ζ in general requires numerical techniques. By extending the transport method first proposed by Mulryne, Seery and Wesley, here, a computationally efficient and highly versatile method for computing the statistics of ζ is developed. The increased efficiency and versatility allows models that were previously unaccessible to be studied. Utilising this new capability two models are explored. A new toy model of inflation in the Landscape and a 6-field D-brane model of inflation first proposed by Agarwal, Bean, McAllister, and Xu. The nature of these models allows for a statistical analysis of inflationary realisations to be performed. We conclude that the fundamental ability to constrain models of this kind is determined by the scale of features in the potential. We also show the D-brane model is under considerable pressure from current observations of the spectral index and may be ruled out by future observations. Finally, I show that there exists a class of models for which the probability distribution of observables may be computed analytically. I show the peak of the density function is largely dominated by the geometry of the potential and comparatively insensitive to the distribution of initial conditions. I argue that this characteristic should be expected in a broader range of models and for such models, it is possible to make robust predictions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.582046  DOI: Not available
Keywords: QB0980 Cosmogony. Cosmology
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