Extracting Cosmological Information from Large-scale Structure
The large-scale structure of the universe is a powerful probe of cosmological parameters. By connecting the observed distribution of galaxies to the initial distribution of matter, we can learn a great deal about the initial conditions and evolution of the universe. As future cosmological surveys promise to deliver vast amounts of galaxy clustering data, we must improve both our theoretical modelling and analysis techniques in order to extract all available cosmological information and improve constraints. I will discuss current work on two methods for extracting information from galaxy clustering data that go beyond current analysis techniques: higher-point statistics and Gaussianization. In the era of precision cosmology, it is crucial to develop and test methods such as these to make full use of galaxy clustering data.