Galaxy biasing and the reconstruction of the density field

The Large-Scale Structure (LSS) of the Universe contains a wealth of information about the origin, composition, and evolution of the Universe. In order to extract this information, non- linearities present in the late-time observables provided by LSS surveys must be understood well. In the first part of the talk, I will discuss about non-linearities due galaxy biasing and how to constrain quadratic and cubic galaxy bias parameters in dark matter halo simulations. The natural statistics to constrain quadratic and cubic bias parameters are tree-level bispectrum and trispectrum, respectively. Since these statistics are computationally quite expensive, we use efficient squared and cubic field estimators that contain integrated bispectrum and trispectrum information. We use the constraints to model the one-loop halo-matter power spectrum in the framework of the Effective Field Theory of Large-Scale Structure (EFTofLSS) and show that the results agree with simulations up to kmax = 0.1h Mpc−1 once an additional derivative bias is implemented. In the second part of the talk, I will briefly discuss the reconstruction of the linear density field based on quadratic couplings in galaxy clustering. We employ a quadratic estimator inspired by Cosmic Microwave Background (CMB) lensing reconstruction. We incorporate non-linearities due to gravity, galaxy biasing and primordial non-Gaussianity, and verify our predictions with N-body simulations. We perform a Fisher matrix analysis on how the reconstructed field in combination with the biased tracer field can improve constraints on local type primordial non-Gaussianity. The improvements come either from addition modes in the reconstruction or due to cosmic variance cancellation.