Deriving an Accurate Formula of Scale-dependent Bias with Primordial Non-Gaussianity: An Application of the Integrated Perturbation Theory
Takahiko Matsubara

TL;DR
This paper develops a comprehensive framework using integrated perturbation theory to accurately derive the scale-dependent bias caused by primordial non-Gaussianity, including effects of redshift-space distortions, and clarifies the dependence on primordial bispectrum and bias models.
Contribution
It introduces a general formula for scale-dependent bias incorporating various bias models and redshift-space distortions, extending previous formulas derived under specific approximations.
Findings
The slope of large-scale scale-dependent bias depends only on the primordial bispectrum in the squeezed limit.
The amplitude of the bias is sensitive to the bias model used.
Redshift-space distortions have minimal impact on the monopole component but affect the quadrupole component.
Abstract
We apply the integrated perturbation theory (Matsubara 2011, PRD 83, 083518) to evaluate the scale-dependent bias in the presence of primordial non-Gaussianity. The integrated perturbation theory is a general framework of nonlinear perturbation theory, in which a broad class of bias models can be incorporated into perturbative evaluations of biased power spectrum and higher-order polyspectra. Approximations such as the high-peak limit or the peak-background split are not necessary to derive the scale-dependent bias in this framework. Applying the halo approach, previously known formulas are re-derived as limiting cases of a general formula in this work, and it is implied that modifications should be made in general situations. Effects of redshift-space distortions are straightforwardly incorporated. It is found that the slope of the scale-dependent bias on large scales is determined…
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.
