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Hyper-Non-Gaussianities in Inflation with Strongly Nongeodesic Motion
Jacopo Fumagalli1, Sebastian Garcia-Saenz1, Lucas Pinol1
1Institut d'Astrophysique de Paris, GReCO, UMR 7095 du CNRS et de Sorbonne Université, 98 bis boulevard Arago, Paris 75014, France.
Physical Review Letters
|December 7, 2019
Summary
Inflationary dynamics with nongeodesic motion amplify perturbations, creating "hyper-non-Gaussianities". These findings offer new constraints on inflationary models and their ultraviolet completions in high-energy physics.
Area of Science:
- Cosmology and High-Energy Physics
- Theoretical Physics
- Inflationary Cosmology
Background:
- Recent high-energy physics models propose embedding inflation using strongly nongeodesic motion in negatively curved field space.
- This dynamics can induce transient instabilities and exponential amplification of perturbations on sub-Hubble scales.
Purpose of the Study:
- To investigate the impact of nongeodesic inflationary dynamics on perturbation correlation functions.
- To explore the potential of these effects for constraining inflationary models and their ultraviolet completions.
Main Methods:
- Employed first-principles numerical computations to analyze perturbation behavior.
- Utilized the analytical insights from the effective field theory of inflation.
- Examined the bispectrum and higher-order correlation functions in flattened configurations.
Main Results:
- Demonstrated that the bispectrum is significantly enhanced in flattened configurations due to nongeodesic motion.
- Provided evidence that this enhancement extends to all higher-order correlation functions, termed 'hyper-non-Gaussianities'.
- Confirmed the transient instability and exponential amplification of sub-Hubble scale perturbations.
Conclusions:
- Hyper-non-Gaussianities serve as powerful, model-independent constraints on nonstandard inflationary attractors.
- These findings are crucial for models motivated by the search for ultraviolet completions of inflation.
- The study validates theoretical predictions with robust numerical and analytical support.
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