AbstractIn ation is a period of accelerated expansion in the very early Universe that is typically invoked as a solution to the problem of originating the observed cosmic microwave background anisotropies, as well as those of the original hot big bang model. The particle content of the in ationary era is as-of-yet unknown, hence it is imperative that the best models of in ation are studied carefully for their potentially unique observational characteristics and then compared to current ob- servations in a statistically rigorous way.
In this thesis we will primarily demonstrate how additional scalar degrees of freedom | which are motivated from many high-energy embeddings | open up new observational windows onto the physics of in ation. We construct a Bayesian framework to statistically compare models with additional elds given the current astronomical data. Putting in ation to the test, we perform our analysis on the quadratic curvaton accompanying a range of in ationary potentials, where we nd that only one potential remains as a viable candidate. Furthermore, if the cur- vaton mechanism were to be con rmed by future non-Gaussianity measurements (from large scale structure surveys), the model could prove to be tremendously informative of the early in ationary history.
The initial conditions given to these scalar elds become apparent when con- sidering their fundamentally quantum behaviour. Taking this physics into ac- count leads us to develop detailed models for post-in ationary phenomenology (namely, the curvaton and freeze-in dark matter models) and to discover powerful new probes of in ation itself. We further demonstrate how this theoretical study complements our statistical approach by motivating the prior information in our i Bayesian analyses.
The thesis nishes with a discussion of the future prospects for in ationary model selection. By hypothesising different toy survey con gurations, we forecast different outcomes using information theory and our newly developed Bayesian experimental design formalism. In particular, we nd that the most likely observ- able to optimise model selection between single- eld in ationary models, through an order of magnitude precision improvement in the future, is the scalar spectral index. We conclude with a summary of the results obtained throughout.
|Date of Award||May 2019|
|Supervisor||David Wands (Supervisor), Vincent Vennin (Supervisor) & Hooshyar Assadullahi (Supervisor)|