Non-Gaussianity from explicit $U(1)$-breaking interactions
Raymond T. Co, Taegyu Lee, and Sai Chaitanya Tadepalli

TL;DR
This paper explores how explicit $U(1)$ symmetry-breaking interactions during inflation can produce observable primordial non-Gaussianities in curvature and isocurvature perturbations, with potential signatures detectable by future experiments.
Contribution
It provides a detailed analysis of non-Gaussianity generation from $U(1)$-breaking interactions, including scenarios with curvaton and dark matter, and predicts observable signals in upcoming cosmological measurements.
Findings
Local NG can be suppressed to |f_NL| < 0.1 due to parameter cancellations.
Interactions can enhance isocurvature NG signals, making them potentially observable.
Oscillating correlation signals from heavy radial fields may dominate the bispectrum shape.
Abstract
We investigate primordial non-Gaussianity (NG) arising from the explicit symmetry-breaking interactions during inflation involving a nearly massless axial component of a complex scalar field . We analyze the induced NG parameter under scenarios where the axial field functions as either a curvaton or cold dark matter (CDM). In the curvaton framework, there is a conventional contribution to the local NG of . Additional positive local NG can result from either the self-interactions of axial field fluctuations, their interactions with a light radial partner, or kinetic mixing with the inflaton via symmetry-breaking terms. We identify parameter regions where the interactions lead to cancellations, suppressing the overall local NG to , while leaving the trispectrum largely unaffected. In…
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.
Taxonomy
TopicsQuantum Mechanics and Applications · Statistical Mechanics and Entropy · Complex Systems and Time Series Analysis
