论文标题
原始标量功率谱中可检测的数据驱动功能
Detectable Data-driven Features in the Primordial Scalar Power Spectrum
论文作者
论文摘要
在这项工作中，我们探讨了未来大规模调查的力量，以限制与标准单场慢速通货膨胀方案的可能偏差。具体而言，我们以独立的方式参数围绕几乎规模不变的标量电源谱进行参数可能的波动。然后，我们在模拟物质分布上使用它们的烙印，如星系聚类和欧几球和平方公里阵列的弱透镜探针所观察到的，以构建最佳的可约束波动模式。为了进行比较，我们对未来派的CMB-S4样调查进行了类似的预测。发现这些模式具有相似的模式，但随着模式数量的增加，摇摆数的数量增加。对于CMB各向异性和星系聚类的预测约束最严格，具体取决于调查规范的细节。作为案例研究，我们探讨了如何通过所提出的模式重建了两种原始功率谱的物理动机模式。我们根据模式传递的信息量提出了一个优点图，以截断分析自动生成的模式层次结构。
In this work we explore the power of future large-scale surveys to constrain possible deviations from the standard single-field slow-roll inflationary scenario. Specifically, we parametrize possible fluctuations around the almost scale-invariant primordial scalar power spectrum in a model independent way. We then use their imprints on the simulated matter distribution, as observed by the galaxy clustering and weak lensing probes of Euclid and Square Kilometer Array, to construct the best constrainable patterns of fluctuations. For comparison, we make similar forecasts for a futuristic CMB-S4-like survey. The modes are found to have similar, yet shifted, patterns, with increasing number of wiggles as the mode number increases. The forecasted constraints are tightest for CMB anisotropies and galaxy clustering, depending on the details of the specifications of the survey. As case studies, we explore how two greatly different physically motivated patterns of primordial power spectrum are reconstructed by the proposed modes. We propose a figure of merit based on the amount of information delivered by the modes to truncate the mode hierarchy which is automatically generated by the analysis.
