Speaker
Description
Statistical measurements of the clustering of galaxies is one of
the main observables from cosmological surveys, containing information both
about the initial conditions of the Universe - such as the physics of
inflation - as well as about those components that drive the expansion of
the Universe today - Dark Energy, the nature of Dark Matter, and
massive neutrinos. While most cosmology analyses in the past have focused
on two-point functions of the galaxy distribution to probe these questions,
there has been growing recognition that there is significant information in
higher order N-point functions of the highly nonlinear galaxy
distributions. In this talk, I will introduce the formalism for a new
measure of cosmological clustering - the k-Nearest Neighbor distributions.
These distributions are formally sensitive to all N-point functions, while
being computationally inexpensive to measure. I will discuss how these
statistics can also be easily extended to describing cross-correlations of
different cosmological datasets. Finally I will discuss the potential
improvements in constraints on various cosmological parameters when using
these statistics over two-point functions, as well as current efforts in
measuring these on actual data.
