Scale-dependent bias from an inflationary bispectrum: the effect of a stochastic moving barrier

TL;DR

This paper investigates how a stochastic moving barrier influences the scale-dependent bias from primordial non-Gaussianity in large scale structure, revealing significant deviations from traditional models and providing testable predictions.

Contribution

It introduces a detailed analysis of the impact of a stochastic moving barrier on non-Gaussian bias, highlighting deviations from standard predictions and offering new insights for future surveys.

Findings

Non-Gaussian bias amplitude is ~40% larger for certain halo masses.

The effect of PNG on halo bispectrum differs from standard bias models.

Predictions are testable with upcoming N-body simulations.

Abstract

With the advent of large scale galaxy surveys, constraints on primordial non-Gaussianity (PNG) are expected to reach ${\cal O}(f_\text{NL}) \sim 1$. In order to fully exploit the potential of these future surveys, a deep theoretical understanding of the signatures imprinted by PNG on the large scale structure of the Universe is necessary. In this paper, we explore the effect of a stochastic moving barrier on the amplitude of the non-Gaussian bias induced by local quadratic PNG. We show that, in the peak approach to halo clustering, the amplitude of the non-Gaussian bias will generally differ from the peak-background split prediction unless the barrier is flat and deterministic. For excursion set peaks with a square-root barrier, which reproduce reasonably well the linear bias $b_1$ and mass function $\bar{n}_\text{h}$ of SO haloes, the non-Gaussian bias amplitude is $\sim 40$% larger than the peak-background split expectation $d\ln\bar{n}_\text{h}/d\ln\sigma_8$ for haloes of mass $\sim 10^{13} {\it h}^{-1}M_\odot$ at $z=0$. Furthermore, we argue that the effect of PNG on squeezed configurations of the halo bispectrum differs significantly from that predicted by standard local bias approaches. Our predictions can be easily confirmed, or invalidated, with N-body simulations.