Local stochastic non-Gaussianity and N-body simulations

TL;DR

This paper investigates the effects of local non-Gaussianity on large-scale structure clustering, testing theoretical predictions with N-body simulations and revealing complexities in modeling stochasticity.

Contribution

It provides the first simulation-based test of stochastic halo bias predictions in local non-Gaussianity models, highlighting discrepancies and challenges in semi-analytic modeling.

Findings

Large-scale stochasticity is generated, roughly matching predictions.

The level of stochasticity is overpredicted by about 30%.

Halo bias predictions are confirmed, but stochasticity modeling remains complex.

Abstract

Large-scale clustering of highly biased tracers of large-scale structure has emerged as one of the best observational probes of primordial non-Gaussianity of the local type (i.e. f_{NL}^{local}). This type of non-Gaussianity can be generated in multifield models of inflation such as the curvaton model. Recently, Tseliakhovich, Hirata, and Slosar showed that the clustering statistics depend qualitatively on the ratio of inflaton to curvaton power \xi after reheating, a free parameter of the model. If \xi is significantly different from zero, so that the inflaton makes a non-negligible contribution to the primordial adiabatic curvature, then the peak-background split ansatz predicts that the halo bias will be stochastic on large scales. In this paper, we test this prediction in N-body simulations. We find that large-scale stochasticity is generated, in qualitative agreement with the prediction, but that the level of stochasticity is overpredicted by ~30%. Other predictions, such as \xi independence of the halo bias, are confirmed by the simulations. Surprisingly, even in the Gaussian case we do not find that halo model predictions for stochasticity agree consistently with simulations, suggesting that semi-analytic modeling of stochasticity is generally more difficult than modeling halo bias.