The Power of Locality: Primordial Non-Gaussianity at the Map Level

TL;DR

This paper proposes a novel map-level analysis method in real space to detect primordial non-Gaussianity, overcoming nonlinear evolution challenges and enhancing constraints beyond traditional Fourier-based approaches.

Contribution

It introduces a map-level, position space approach that leverages the locality of nonlinear evolution to better detect primordial non-Gaussianity, especially for equilateral types.

Findings

Map-level analysis can break degeneracy with nonlinearities.

Significantly improves constraints on primordial non-Gaussianity.

Potential to revolutionize the search using simulation-based inference.

Abstract

Primordial non-Gaussianity is a sensitive probe of the inflationary era, with a number of important theoretical targets living an order of magnitude beyond the reach of current CMB constraints. Maps of the large-scale structure of the universe, in principle, have the raw statistical power to reach these targets, but the complications of nonlinear evolution are thought to present serious, if not insurmountable, obstacles to reaching these goals. In this paper, we will argue that the challenge presented by nonlinear structure formation has been overstated. The information encoded in primordial non-Gaussianity resides in nonlocal correlations of the density field at three or more points separated by cosmological distances. In contrast, nonlinear evolution only alters the density field locally and cannot create or destroy these long-range correlations. This locality property of the late-time non-Gaussianity is obscured in Fourier space and in the standard bispectrum searches for primordial non-Gaussianity. We therefore propose to measure non-Gaussianity in the position space maps of the large-scale structure. As a proof of concept, we study the case of equilateral non-Gaussianity, for which the degeneracy with late-time nonlinearities is the most severe. We show that a map-level analysis is capable of breaking this degeneracy and thereby significantly improve the constraining power over previous estimates. Our findings suggest that "simulation-based inference" involving the forward modeling of large-scale structure maps has the potential to dramatically impact the search for primordial non-Gaussianity.