Nico Hamaus
University of Zurich
ABSTRACT
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.