瀑布调制的α吸引子
Waterfall-modulated $α$-attractors
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中文总结 AI 辅助
本文研究瀑布调制的α吸引子，引入瀑布机制增大有效e折叠数，可沿r≅3α(1−n_s)²曲线调整预测，还探究其对精髓α吸引子n_s的影响。
中文摘要 AI 辅助
混合α吸引子模型[Kallosh:2022ggf]可在保持r≅3α(1−n_s)²（该关系对暴涨子场大值下的指数T模型和E模型有效）的同时，拥有显著更大的n_s值和更小的r值。本文研究受混合模型启发的具有特征的单场α吸引子：可提升势能，还可存在瀑布机制，该机制会在临界点φ_c附近过早终止暴涨。这使得n_s≃1−2/N_c、r≃12α/N_c²这类公式中的有效e折叠数N_c增大。通过改变瀑布的陡度和位置，可在n_s增大、r减小的r≅3α(1−n_s)²曲线上连续移动预测结果。本文还研究了瀑布插入和势能提升对描述暴涨与动力学暗能量的精髓α吸引子中n_s的影响。
英文摘要
Hybrid $α$-attractor models \cite{Kallosh:2022ggf} can have significantly greater values of $n_{s}$ and smaller $r$, while preserving the relation $r\cong 3α(1-n_s)^2$, which is valid for exponential T- and E-models at large values of the inflaton field. Here we study single-field $α$-attractors with features inspired by hybrid models: one can uplift the potential, and one can also have a waterfall regime that leads to a premature termination of inflation near the critical point $φ_c$. This allows one to increase the effective number of e-foldings $N_c$ in formulas like $n_s\simeq 1-{2\over N_c}$, $r\simeq {12 α\over N_c^2}$. By changing the waterfall's steepness and location, one can continuously move the predictions along the curves with $r\cong 3α(1-n_s)^2$ as $n_s$ increases and $r$ decreases. We also study the effect of waterfall insertions and uplift on $n_s$ in quintessential $α$-attractors that describe inflation and dynamical dark energy.
发表机构
- Leinweber Institute for Theoretical Physics at Stanford（斯坦福大学莱因韦伯理论物理研究所）
- Institute for Basic Science (IBS)（韩国基础科学研究院）
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