PIRSA:13010019

Parameterizing dark sector perturbations

Pearson, J. (2013). Parameterizing dark sector perturbations. Perimeter Institute. https://pirsa.org/13010019

Pearson, Jonathan. Parameterizing dark sector perturbations. Perimeter Institute, Jan. 15, 2013, https://pirsa.org/13010019 

          @misc{ pirsa_PIRSA:13010019,
            doi = {10.48660/13010019},
            url = {https://pirsa.org/13010019},
            author = {Pearson, Jonathan},
            keywords = {Cosmology},
            language = {en},
            title = {Parameterizing dark sector perturbations},
            publisher = {Perimeter Institute},
            year = {2013},
            month = {jan},
            note = {PIRSA:13010019 see, \url{https://pirsa.org}}
          }

Jonathan Pearson

Durham University

Talk number
PIRSA:13010019
Collection
Talk Type
Subject
Abstract
When recent observational evidence and the GR+FRW+CDM model are combined we obtain the result that the Universe is accelerating, where the acceleration is due to some not-yet-understood "dark sector". There has been a considerable number of theoretical models constructed in an attempt to provide an "understanding" of the dark sector: dark energy and modified gravity theories. The proliferation of modified gravity and dark energy models has brought to light the need to construct a "generic" way to parameterize the dark sector.   We will discuss our new way of approaching this problem, looking at linearised perturbations. Our approach is inspired by that taken in particle physics, where the most general modifications to the standard model are written down for a given field content that is compatible with some assumed symmetry (which we take to be isotropy of the background spatial sections). Our emphasis is on constructing a theoretically motivated toolkit which can be used to extract meaningful information about the dark sector (such as its field content). We find, for example, that the observational impact of very broad classes of theories can be encoded by a very small (less than 5) number of parameters. It is these parameters which we hope to measure with observational data.