Tuomas Multamaki

Spherically symmetric solutions of modified field equations in f(R) theories of gravity

Spherically symmetric static empty space solutions are studied in

Cosmological constraints on f(R) gravity theories within the Palatini approach

f(R)

Non-Gaussianity from preheating

theories of gravity. We reduce the set of modified Einsteins equations to a single equation and show how one can construct exact solutions in different

Static spherically symmetric perfect fluid solutions in f(R) theories of gravity

f(R)

Non-Gaussianity from instant and tachyonic preheating

models. In particular, we show that for a large class models, including e.g. the

Multiple inflation, cosmic string networks and the string landscape

f(R)=R\ensuremath{-}{\ensuremath{\mu}}^{4}/R

Energy-momentum complexes in f(R) theories of gravity

model, the Schwarzschild-de Sitter metric is an exact solution of the field equations. The significance of these solutions is discussed in light of solar system constraints on

Large scale structure and the generalised Chaplygin gas as dark energy

f(R)

On using the cosmic microwave background shift parameter in tests of models of dark energy

theories of gravity.

Current acceleration from the dilaton and stringy cold dark matter

We investigate f(R) theories of gravity within the Palatini approach and show how one can determine the expansion history, H(a), for an arbitrary choice of f(R). As an example, we consider cosmological constraints on such theories arising from the supernova type la, large-scale structure formation, and cosmic microwave background observations. We find that the best fit to the data is a non-null leading order correction to the Einstein gravity. However, the current data exhibits no significant trend toward such corrections compared to the concordance ACDM model. Our results show that the oft-considered I/R models are not compatible with the data. The results demonstrate that background expansion alone can act as a good discriminator between modified gravity models when multiple data sets are used.