# Non-Gaussianity from Features

## TL;DR

This paper improves the modeling of non-Gaussian signals caused by features in the inflationary power spectrum, providing more accurate formulas and analysis methods to detect these signals amidst noise.

## Contribution

It introduces refined explicit formulas for non-Gaussianity from features and compares them with analytic models and correlator-based quantifications.

## Key findings

- Enhanced formulas for non-Gaussianity improve detection accuracy.
- Comparison with analytic models validates the new approach.
- Quantitative analysis of feature signals using correlators.

## Abstract

The strongest non-Gaussianity in single-scalar potential models of inflation is associated with features in the power spectrum. We stress the importance of accurately modelling the expected signal in order for the standard estimator to minimize contamination by random noise. We present explicit formulae which improve on the approximation introduced by Adshead, Hu, Dvorkin and Peiris. We also compare with a simple, analytic model of the first feature, and quantify our results using the correlators of Hung, Fergusson and Shellard.