Generic inference of inflation models by local non-Gaussianity

TL;DR

This paper introduces an analytic method using higher-order statistics and saddle-point approximation to infer inflationary parameters from observational data, potentially distinguishing between different inflation models.

Contribution

It presents a fully analytic approach to infer inflation parameters from data, avoiding computationally intensive sampling methods.

Findings

Validated saddle-point approximation with a toy example

Potential to discriminate among inflation models using real data

Provides a new analytic framework for inflation inference

Abstract

The presence of multiple fields during inflation might seed a detectable amount of non-Gaussianity in the curvature perturbations, which in turn becomes observable in present data sets like the cosmic microwave background (CMB) or the large scale structure (LSS). Within this proceeding we present a fully analytic method to infer inflationary parameters from observations by exploiting higher-order statistics of the curvature perturbations. To keep this analyticity, and thereby to dispense with numerically expensive sampling techniques, a saddle-point approximation is introduced whose precision has been validated for a numerical toy example. Applied to real data, this approach might enable to discriminate among the still viable models of inflation.