Curvature perturbations from stochastic particle production during inflation

被引:13
作者
Garcia, Marcos A. G. [1 ,2 ]
Amin, Mustafa A. [1 ]
Green, Daniel [3 ]
机构
[1] Rice Univ, Dept Phys & Astron, Houston, TX 77005 USA
[2] Inst Fis Teor IFT UAM CSIC, Campus Cantoblanco, Madrid 28049, Spain
[3] Univ Calif San Diego, Dept Phys, La Jolla, CA 92093 USA
来源
JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS | 2020年 / 06期
基金
美国国家科学基金会;
关键词
inflation; particle physics - cosmology connection; power spectrum; physics of the early universe; ADIABATIC REGULARIZATION; DISTRIBUTIONS;
D O I
10.1088/1475-7516/2020/06/039
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
We calculate the curvature power spectrum sourced by spectator fields that are excited repeatedly and non-adiabatically during inflation. In the absence of detailed information of the nature of spectator field interactions, we consider an ensemble of models with intervals between the repeated interactions and interaction strengths drawn from simple probabilistic distributions. We show that the curvature power spectra of each member of the ensemble shows rich structure with many features, and there is a large variability between different realizations of the same ensemble. Such features can be probed by the cosmic microwave background (CMB) and large scale structure observations. They can also have implications for primordial black hole formation and CMB spectral distortions. The geometric random walk behavior of the spectator field allows us to calculate the ensemble-averaged power spectrum of curvature perturbations semi-analytically. For sufficiently large stochastic sourcing, the ensemble-averaged power spectrum shows a scale dependence arising from the time spent by modes outside the horizon during the period of particle production, in spite of there being no preferred scale in the underlying model. We find that the magnitude of the ensemble-averaged power spectrum overestimates the typical power spectra in the ensemble because the ensemble distribution of the power spectra is highly non-Gaussian with fat tails.
引用
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页数:67
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