On the Issue of the \zeta Series Convergence and Loop Corrections in the Generation of Observable Primordial Non-Gaussianity in Slow-Roll Inflation. Part I: the Bispectrum
Heiner R. S. Cogollo (1), Yeinzon Rodriguez (1, 2), Cesar A., Valenzuela-Toledo (1) ((1) Universidad Industrial de Santander, (2), Universidad Antonio Narino)

TL;DR
This paper demonstrates that in small-field slow-roll inflation models with canonical kinetic terms, it is possible to achieve observable levels of primordial non-Gaussianity by carefully accounting for loop corrections in both the spectrum and bispectrum.
Contribution
It shows how to attain high non-Gaussianity levels in slow-roll inflation models by including loop corrections, ensuring the validity of perturbation theory and observational constraints.
Findings
High non-Gaussianity levels are achievable in small-field slow-roll models.
Loop corrections significantly impact the bispectrum and spectrum calculations.
Perturbative regime and observational constraints are compatible with large f_NL values.
Abstract
We show in this paper that it is possible to attain very high, {\it including observable}, values for the level of non-gaussianity f_{NL} associated with the bispectrum B_\zeta of the primordial curvature perturbation \zeta, in a subclass of small-field {\it slow-roll} models of inflation with canonical kinetic terms. Such a result is obtained by taking care of loop corrections both in the spectrum P_\zeta and the bispectrum B_\zeta. Sizeable values for f_{NL} arise even if \zeta is generated during inflation. Five issues are considered when constraining the available parameter space: 1. we must ensure that we are in a perturbative regime so that the \zeta series expansion, and its truncation, are valid. 2. we must apply the correct condition for the (possible) loop dominance in B_\zeta and/or P_\zeta. 3. we must satisfy the spectrum normalisation condition. 4. we must satisfy the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
