Non-Gaussian Error in Galaxy Survey (Part 1)
  We propose a method to estimate non-Gaussian error bars on the matter power
spectrum from galaxy surveys in the presence of non-trivial survey selection
functions. The estimators are often obtained from formalisms like FKP and PKL,
which rely on the assumption that the underlying field is Gaussian. The Monte
Carlo method is more accurate but involves the tedious process of running and
cross-correlating a large number of N-body simulations, in which the survey
volume is embedded. From 200 N-body simulations, we extract a non-linear
covariance matrix as a function of two scales and of the angle between two
Fourier modes. All the non-Gaussian features of that matrix are then simply
parametrized in terms of a few fitting functions and Eigenvectors. We
furthermore develop a fast and accurate strategy that combines our
parameterization with a general galaxy survey selection function, and incor-
porate non-Gaussian Poisson uncertainty. We describe how to incorporate these
two distinct non-Gaussian contributions into a typical analysis pipeline, and
apply our method with the se- lection function from the 2dFGRS. We find that
the observed Fourier modes correlate at much larger scales than that predicted
by both FKP formalism or by pure N-body simulation in a "top hat" selection
function. In particular, the observed Fourier modes are already 50 per cent
correlated at k\sim0.1hMpc^(-1), and the non-Gaussian fractional variance on
the power spectrum ({\sigma} /P(k))^2 is about a factor of 3.0 larger than the
FKP prescription. At k~0.4hMpc^(-1), the deviations are an order of magnitude.
