Nonlinear Galaxy Clustering for next-generation Galaxy Surveys

Abstract: Over the past decades galaxy redshift surveys have provided a wealth of information on the large-scale structure distribution of galaxies, and key information to establish our current cosmological model, LCDM. Future missions, such as EUCLID or DESI will achieve datasets orders of magnitude bigger and have the potential to revolutionize our understanding of the late time Universe. To exploit them we first need to develop robust and accurate modeling, with particular emphasis on information on nonlinear scales. In this talk I will address our current understanding of different nonlinear effects such as dark matter clustering, galaxy biasing and redshift space distortions, how we model them with perturbation theory and test those models against simulations. I will also present a machine-learning based emulator that opens up the possibility of complex analysis orders of magnitude faster.