论文标题

随机通货膨胀中的平滑粗粒和彩色噪声动态

Smooth coarse-graining and colored noise dynamics in stochastic inflation

论文作者

Mahbub, Rafid, De, Aritra

论文摘要

我们认为使用一般的指数过滤器类别的随机通胀粗粒。这样的粗粒剂处方会产生由颜色噪声产生的通气量 - 折叠时间。首先使用一阶扰动,半分析计算的简单电位研究动力学,后来与数值模拟相比。随后的计算是使用指数相关的噪声进行的,该噪声是针对$ \ big \ big \ big \langleξ(n)ξ(n')\ big \ big \ rangle _ {(N)} \ sim \ sim \ sim \ sim \ sim \ sim \ sim \ sim \ left(n-n(n-n(n-n(n-n(n-n(N-n),)我们发现,曲率扰动的功率谱$ \ MATHCAL {p}_ζ$在早期$ e $ -folds被抑制,抑制由$ n $控制。此外,我们使用领先顺序,指数级相关的噪声并执行第一个通过时间分析来计算随机$ e $ -fold分布$ \ Mathcal {n} $的统计数据,并在平均值$ e $ e $ -folds $ \ big \ big \ big \ big \ langle \ nangle \ natcal {将分析结果与通气动力学的数值模拟进行比较,我们表明,领先的顺序噪声相关函数可以用作确切噪声的非常好的近似值,后者更难模拟。

We consider stochastic inflation coarse-grained using a general class of exponential filters. Such a coarse-graining prescription gives rise to inflaton-Langevin equations sourced by colored noise that is correlated in $e$-fold time. The dynamics are studied first in slow-roll for simple potentials using first-order perturbative, semi-analytical calculations which are later compared to numerical simulations. Subsequent calculations are performed using an exponentially correlated noise which appears as a leading order correction to the full slow-roll noise correlation functions of the type $\big\langle ξ(N)ξ(N') \big\rangle_{(n)}\sim\left( \cosh\left[ n(N-N')+1 \right] \right)^{-1}$. We find that the power spectrum of curvature perturbations $\mathcal{P}_ζ$ is suppressed at early $e$-folds, with the suppression controlled by $n$. Furthermore, we use the leading order, exponentially correlated noise and perform a first passage time analysis to compute the statistics of the stochastic $e$-fold distribution $\mathcal{N}$ and derive an approximate expression for the mean number of $e$-folds $\big\langle \mathcal{N} \big\rangle$. Comparing analytical results with numerical simulations of the inflaton dynamics, we show that the leading order noise correlation function can be used as a very good approximation of the exact noise, the latter being more difficult to simulate.

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