Large-scale Bias and Efficient Generation of Initial Conditions for Non-Local Primordial Non-Gaussianity
Roman Scoccimarro, Lam Hui, Marc Manera, K. C. Chan

TL;DR
This paper develops methods to generate and analyze initial conditions with non-local primordial non-Gaussianity, providing new formulas for halo bias that improve agreement with simulations and enable better constraints from galaxy surveys.
Contribution
It introduces a minimal-overhead method to generate non-local PNG initial conditions and derives a generalized bias formula accounting for non-Markovian effects and non-universality.
Findings
New bias formula matches simulations across halo masses and redshifts.
Quadratic bias results for arbitrary non-local PNG are derived.
Non-linear bias loops are small at large scales.
Abstract
We study the scale-dependence of halo bias in generic (non-local) primordial non-Gaussian (PNG) initial conditions of the type motivated by inflation, parametrized by an arbitrary quadratic kernel. We first show how to generate non-local PNG initial conditions with minimal overhead compared to local PNG models for a general class of primordial bispectra that can be written as linear combinations of separable templates. We run cosmological simulations for the local, and non-local equilateral and orthogonal models and present results on the scale-dependence of halo bias. We also derive a general formula for the Fourier-space bias using the peak-background split (PBS) in the context of the excursion set approach to halos and discuss the difference and similarities with the known corresponding result from local bias models. Our PBS bias formula generalizes previous results in the literature…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
