Non-Gaussian Shape Recognition

TL;DR

This paper introduces a systematic method to constrain and classify a wide range of primordial non-Gaussian shapes in cosmological data, enhancing our ability to interpret inflationary models from upcoming surveys.

Contribution

It presents a separable, divergent basis for accurately recreating various non-Gaussian shapes and grouping them into template classes with theoretical priors.

Findings

Forecasts Planck-like survey detection capabilities

Demonstrates shape discrimination improves understanding of inflation

Provides a new basis for shape reconstruction

Abstract

A detection of primordial non-Gaussianity could transform our understanding of the fundamental theory of inflation. The precision promised by upcoming CMB and large-scale structure surveys raises a natural question: if a detection given a particular template is made, what does this truly tell us about the underlying theory? In this paper we present a systematic way to constrain a wide range of non-Gaussian shapes, including general single and multi-field models and models with excited initial states. We present a separable, divergent basis able to recreate many shapes in the literature to high accuracy with between three and seven basis functions. The basis allows shapes to be grouped into broad "template classes", satisfying theoretically-relevant priors on their divergence properties in the squeezed limit. We forecast how well a Planck-like CMB survey could not only detect a general non-Gaussian signal but discern more about its shape, using existing templates and new ones we propose. This approach offers an opportunity to tie together minimal theoretical priors with observational constraints on the shape in general, and in the squeezed limit, to gain a deeper insight into what drove inflation.