Scale-dependent Bias from the Reconstruction of Non-Gaussian Distributions

Understand

Primordial non-Gaussianity introduces a scale-dependent variation in the clustering of density peaks corresponding to rare objects.

• This variation, parametrized by the bias, is investigated on scales where a linear perturbation theory is sufficiently accurate.
• The bias is obtained directly in real space by comparing the one- and two-point probability distributions of density fluctuations.
• We show that these distributions can be reconstructed using a bivariate Edgeworth series, presented here up to an arbitrarily high order.