Prof. Arka Banerjee

The formation, evolution, and clustering of structures in the Universe, such as galaxies, is a sensitive tool to probe some of the deepest questions in physics: the properties of Dark Energy and Dark Matter, the total mass and hierarchy of the Standard Model neutrinos, among many others. On large scales, perturbation theory approaches have been successfully applied to extract almost all the information from structure formation. On smaller scales, there is a vast amount of untapped information, but perturbation theory approaches break down due to nonlinearities driven by gravitational collapse. Harnessing this small scale information will be the next big challenge in observational cosmology. I will discuss how structure formation on small scales can be accurately modeled through numerical simulations. I will briefly describe how the application of new numerical techniques are allowing us to model the nonlinear predictions of an ever-expanding set of theoretical models, such as cosmologies with massive neutrinos, Dark Matter with additional interactions, and so on. In the second part of my talk, I will focus on statistical measures to characterize the clustering of structures on small scales. The widely used 2-point correlation function fails to capture all the details of the nonlinear clustering, motivating the exploration of other summary statistics. I will discuss one such set of summary statistics - the k-nearest neighbor distributions, which are easy to compute on a given dataset while being sensitive to higher order clustering.