arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2107.09460astro-ph.COhep-phhep-th
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通货膨胀的故事:慢卷和超越

Inflation Story: slow-roll and beyond

Dhiraj Kumar Hazra, Daniela Paoletti, Ivan Debono, Arman Shafieloo, George F. Smoot, Alexei A. Starobinsky

摘要:

我们使用宇宙微波背景温度、普朗克2018年数据发布的偏振数据和更新的可能性,提出了对暴胀动力学的约束和标量扰动的原始功率谱的特征。我们使用Hilltop四次势和Starobinsky $R+R^2$模型在爱因斯坦坐标系中使用普朗克2018年分类的Plik似然来约束慢滚动力学。使用山顶作为基础潜力,我们构造鞭打暴胀潜力,介绍在大角度尺度上的标量功率谱的抑制。我们注意到边际(68% C.L.)从大尺度温度角功率谱的抑制的偏好。然而,大规模的E-模式的可能性,高频仪器互谱的基础上,不支持这种抑制,并在合并的数据的偏好对抑制变得微不足道。基于Hilltop和Starobinsky模型,我们构造了Wiggly Whipped暴胀势,引入了振荡特征和抑制。我们使用最近发布的CamSpec v12.5可能性的未分组数据,更新了普朗克2018年的结果。我们比较贝叶斯证据的特征模型与他们的基线慢滚潜力。我们发现,完整的慢滚基线潜力是适度的首选对潜力产生的功能。与Planck 2015 PlikHM bin 1似然相比,我们发现由于数据分析管道中的更新,尖锐特征的重要性已经降低。我们还计算从我们的分析中获得的最佳拟合候选人的双谱。

英文摘要:

We present constraints on inflationary dynamics and features in the primordial power spectrum of scalar perturbations using the Cosmic Microwave Background temperature, polarization data from Planck 2018 data release and updated likelihoods. We constrain the slow-roll dynamics using Hilltop Quartic Potential and Starobinsky $R+R^2$ model in the Einstein frame using the Planck 2018 binned Plik likelihood. Using the Hilltop as base potential we construct Whipped Inflation potential to introduce suppression in the scalar power spectrum at large angular scales. We notice marginal (68% C.L.) preference of suppression from the large scale temperature angular power spectrum. However, large-scale E-mode likelihood, based on high frequency instrument cross spectrum, does not support this suppression and in the combined data the preference towards the suppression becomes negligible. Based on the Hilltop and Starobinsky model we construct the Wiggly Whipped Inflation potentials to introduce oscillatory features along with the suppression. We use unbinned data from the recently released CamSpec v12.5 likelihood which updates Planck 2018 results. We compare the Bayesian evidences of the feature models with their baseline slow-roll potentials. We find that the complete slow-roll baseline potential is moderately preferred against potentials which generate features. Compared to Planck 2015 PlikHM bin1 likelihood, we find that the significance of sharp features has decreased owing to the updates in the data analysis pipeline. We also compute the bispectra for the best fit candidates obtained from our analysis.

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