Research Fellows Directory
Dr Qi Guo
University of Durham
My research interests focus on galaxy formation and cosmology.
The LCDM model has been successful in interpreting a wide variety of observations. In this standard model, around 75% of the Universe is made up by dark energy, while among 85% of the rest is made up of some, as yet unidentified, weakly interacting, non-baryonic particle (dark matter) and the baryons only occupy around 4% of the total Universe. Although baryons do not dominate, verifiable descriptions of the nature and origin of the Universe have to rely on analysis of observables. Galaxy is one of the basic populations of light emitters. They form through the condensation of gas at the centers of a hierarchically aggregating population of dark matter haloes, and are thus excellent tracers of the cosmological structures.
The large-scale structure (LSS) grows from initial perturbations in the dark matter under the effect of gravity, locally collapsing most rapidly along one direction followed by the other two. This results in the vast foam-like structure observed today, composed of voids, walls, filaments and nodes. This cosmic web contains the majority of the baryons from which galaxies form and evolve. Mass and energy are exchanged between galaxies and their environments via gas fuelling from the cosmic web into galaxies and feedback from galaxies to the cosmic web. It has become one of the most important tasks of modern astrophysics to understand the effects of the large scale distribution of matter on the physical and dynamical properties of galaxies.
In recent years, I am trying to investigate how the large-scale geometric structures shape the properties of halos and galaxies within them. We propose to apply a state-of-the-art algorithm to identify such structures both with the multi-band observational data and the cosmological simulations. Comparison of the results of the simulations with observations will help us understand the galaxy formation in the cosmological context.
Interests and expertise (Subject groups)