The large-scale correlations of multi-cell densities and profiles,
  implications for cosmic variance estimates
  In order to quantify the error budget in the measured probability
distribution functions of cell densities, the two-point statistics of cosmic
densities in concentric spheres is investigated. Bias functions are introduced
as the ratio of their two-point correlation function to the two-point
correlation of the underlying dark matter distribution. They describe how cell
densities are spatially correlated. They are computed here via the so-called
large deviation principle in the quasi-linear regime. Their large-separation
limit is presented and successfully compared to simulations for density and
density slopes: this regime is shown to be rapidly reached allowing to get
sub-percent precision for a wide range of densities and variances. The
corresponding asymptotic limit provides an estimate of the cosmic variance of
standard concentric cell statistics applied to finite surveys. More generally,
no assumption on the separation is required for some specific moments of the
two-point statistics, for instance when predicting the generating function of
cumulants containing any powers of concentric densities in one location and one
power of density at some arbitrary distance from the rest. This exact "one
external leg" cumulant generating function is used in particular to probe the
rate of convergence of the large-separation approximation.
