Optimizing Large Scale Structure Information from Biased Tracers

Nico Hamaus
University of Zurich


Large scale structure (LSS) of the universe carries a wealth of information about the physics governing cosmological evolution. Galaxies are the observable components that allow us to investigate the nature of the universe in ever greater detail using redshift surveys. However, dark matter accounts for the dominant contribution to LSS while galaxies are only biased, stochastic tracers of this underlying density field. On large scales, this bias yields a constant enhancement in clustering amplitude and can be removed to reconstruct the dark matter power spectrum. A degree of randomness (stochasticity) in the distribution of galaxies and their dark matter halos implies a limitation on the accuracy of this reconstruction. It is thus desirable to develop estimators that are least affected by this randomness in order to provide constraints on basic cosmological parameters competitive with those from other measurements, such as the cosmic microwave background (CMB) and supernovae (SN). This, in turn, can shed more light on the cause of the universe's late time acceleration and help in the distinction between dark energy and modified gravity theories.
     I present a technique to optimize the information content encoded in the statistics of biased tracers of the dark matter density field, applied on numerical N-body simulations.  An optimal weighting scheme that minimizes the stochasticity between halos and dark matter is presented and applied to halo catalogs to show that the signal-to-noise ratio can be enhanced considerably. The issues of mass-uncertainty and redshift-space distortions are discussed.