论文标题
部分可观测时空混沌系统的无模型预测
Constraining Single-Field Inflation with MegaMapper
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
We forecast the constraints on single-field inflation from the bispectrum of future high-redshift surveys such as MegaMapper. Considering non-local primordial non-Gaussianity (NLPNG), we find that current methods will yield constraints of order $σ(f_{\rm NL}^{\rm eq})\approx 23$, $σ(f_{\rm NL}^{\rm orth})\approx 12$ in a joint power-spectrum and bispectrum analysis, varying both nuisance parameters and cosmology, including a conservative range of scales. Fixing cosmological parameters and quadratic bias parameter relations, the limits tighten significantly to $σ(f_{\rm NL}^{\rm eq})\approx 17$, $σ(f_{\rm NL}^{\rm orth})\approx 8$. These compare favorably with the forecasted bounds from CMB-S4: $σ(f_{\rm NL}^{\rm eq})\approx 21$, $σ(f_{\rm NL}^{\rm orth})\approx 9$, with a combined constraint of $σ(f_{\rm NL}^{\rm eq})\approx 14$, $σ(f_{\rm NL}^{\rm orth})\approx 7$; this weakens only slightly if one instead combines with data from the Simons Observatory. We additionally perform a range of Fisher analyses for the error, forecasting the dependence on nuisance parameter marginalization, scale cuts, and survey strategy. Lack of knowledge of bias and counterterm parameters is found to significantly limit the information content; this could be ameliorated by tight simulation-based priors on the nuisance parameters. The error-bars decrease significantly as the number of observed galaxies and survey depth is increased: as expected, deep dense surveys are the most constraining, though it will be difficult to reach $σ(f_{\rm NL})\approx 1$ with current methods. The NLPNG constraints will tighten further with improved theoretical models (incorporating higher-loop corrections and improved understanding of nuisance parameters), as well as the inclusion of additional higher-order statistics.