Theoretical cosmology interim support for Liddle at Edinburgh

This grant transfers the residual FEC support for Liddle from Sussex to Edinburgh. The relevant part of the summary of the funded Sussex grant was:

Our proposal seeks funds to continue an extensive programme of research into extragalactic astrophysics and cosmology, seeking to address some of the most pressing astronomical questions of our time, such as "What are the fundamental constituents of the Universe?", "What is the nature of the dark energy believed to be driving an acceleration of the Universe?", "What processes govern the formation of the largest objects and structures in our Universe?", and "How do galaxies form and evolve?". We address these questions by a combination of theoretical work, much of it using high-performance computers, and observational surveys especially from satellite observatories. We aim to be particularly sophisticated in the statistical modelling we carry out to use the observations to learn about our models.

Theme 1: Cosmology We will advance knowledge of the fundamental constituents and processes governing our Universe. This includes the early period of cosmological inflation, believed to be responsible for the origin of structures in the Universe, explored through studies of the origin of cosmic non-gaussianity and through detailed comparisons to emerging data. We will further develop the phenomenology of models featuring cosmic defects such as cosmic strings, and explore the consequent cosmic non-gaussianity both in the microwave background and in the matter distribution. We will complete the preparation and definition stage of the Dark Energy Survey (DES) and move to exploitation of DES data to constrain dark energy phenomenology. This component of the project exploits STFC involvement in the Planck Surveyor Satellite, the Dark Energy Survey, and the COSMOS supercomputer.

The principal opportunities for impact of this work remain

a) Outreach, particularly to schoolstudents and aided by Edinburgh's extensive outreach programme.
b) Knowledge exchange concerning deployment of Bayesian methodologies such as model selection and machine learning.