On Classification of Models of Large Local-Type Non-Gaussianity

TL;DR

This paper classifies models producing large local-type non-Gaussianity using consistency relations among non-linearity parameters, aiding in distinguishing models and understanding primordial fluctuation mechanisms.

Contribution

It introduces a classification scheme for large local-type non-Gaussianity models based on consistency relations among f_{NL}^{local}, au_{NL}^{local}, and g_{NL}^{local}.

Findings

The ratio au_{NL}^{local}/(6f_{NL}^{local}/5)^2 distinguishes single-source from multi-source models.

Relations between f_{NL}^{local} and g_{NL}^{local} further classify models.

Observations of trispectrum are crucial for model discrimination.

Abstract

We classify models generating large local-type non-Gaussianity into some categories by using some "consistency relations" among the non-linearity parameters f_{NL}^{local}, \tau_{NL}^{local} and g_{NL}^{local}, which characterize the size of bispectrum for the former and trispectrum for the later two. Then we discuss how one can discriminate models of large local-type non-Gaussianity with such relations. We first classify the models by using the ratio of \tau_{NL}^{local}/(6f_{NL}^{local}/5)^2, which is unity for "single-source" models and deviates from unity for "multi-source" ones. We can make a further classification of models in each category by utilizing the relation between f_{NL}^{local} and g_{NL}^{local}. Our classification suggests that observations of trispectrum would be very helpful to distinguish models of large non-Gaussianity and may reveal the generation mechanism of primordial fluctuations.