Event

The galaxy distribution across cosmic time contains a wealth of cosmological information. In the era of precision cosmology, galaxy surveys like DES,HSC,DESI,Euclid,LSST and others, would deliver very accurate measurements of cosmological observables, like the power spectrum or the correlation function. This incredible experimental effort requires the theory and modeling of the the clustering of large scale structure to reach the same level of accuracy, typically at the order of a %, over a wide range of scale. And there is no obvious path to this goal. One model which has proven to be successful in extracting cosmological information from SDDS and BOSS data is the Gaussian streaming model (GSM). In this talk I'll update the ingredients of the GSM for the redshift-space clustering of biased tracers using the techniques of Lagrangian perturbation theory, effective field theory (EFT) and a generalized Lagrangian bias expansion, presenting new results for the real-space correlation function, mean pairwise velocity and pairwise velocity dispersion. I'll then compare analytical predictions in the GSM to a suite of large N-body simulations, and show the performance of the theory on the low order multipoles of the redshift-space correlation function and power spectrum.