I’m interested in what the large-scale structure of the Universe can tell us about cosmology and fundamental physics. In particular, my research explores how we can go beyond traditional two-point statistics to capture the rich, non-Gaussian information contained in the cosmic web.
The non-Gaussian Universe
As cosmic structure grows under gravity, the initially near-Gaussian matter distribution develops into the complex network of clusters, filaments and voids we see today. I work on statistical descriptions of this non-Gaussian structure, particularly 1-point probability distributions, and on connecting these observables to theoretical models of gravitational evolution.
Galaxies as tracers of the cosmic web
We observe galaxies rather than the underlying dark matter distribution, so extracting cosmological information requires understanding how the two are related. My research develops models of galaxy and halo bias and stochasticity, with a particular focus on how these effects shape non-Gaussian clustering statistics on mildly non-linear scales.
Large-scale structure in redshift space
Galaxy surveys measure positions through their observed redshifts, which are affected by galaxies’ peculiar velocities. These redshift-space distortions contain valuable information about the growth of structure, but also complicate the connection between theory and observations. I study how non-Gaussian statistics are transformed in redshift space and how they can be modelled for spectroscopic galaxy surveys.
