论文标题
Fisher对持续同源性原始非高斯的预测
Fisher Forecasts for Primordial non-Gaussianity from Persistent Homology
论文作者
论文摘要
我们研究了基于本地和等边类型原始非高斯性的大规模结构的多尺度拓扑构建的摘要统计数据的信息内容。我们使用从大尺度上的数值N体模拟产生的光环目录作为观察到的星系的代理。除了计算真实空间中的Halos的Fisher矩阵外，我们还检查了红移空间中更现实的场景。不需要进行遥远的观察者近似，我们将观察者放在盒子的角落。我们还添加了模仿光谱和光度样品的红移误差。我们执行多项测试来评估我们的Fisher矩阵的可靠性，包括摘要统计和收敛性的高斯性。 We find that the marginalized 1-$σ$ uncertainties in redshift space are $Δf_{\rm NL}^{\rm loc} \sim 16$ and $Δf_{\rm NL}^{\rm equi} \sim 41 $ on a survey volume of $1$ $($Gpc$/h)^3$.这些约束受到红移错误的影响。我们通过猜测如何通过利用（非）区域来对小规模不确定性做出强大的不确定性来结束。
We study the information content of summary statistics built from the multi-scale topology of large-scale structures on primordial non-Gaussianity of the local and equilateral type. We use halo catalogs generated from numerical N-body simulations of the Universe on large scales as a proxy for observed galaxies. Besides calculating the Fisher matrix for halos in real space, we also check more realistic scenarios in redshift space. Without needing to take a distant observer approximation, we place the observer on a corner of the box. We also add redshift errors mimicking spectroscopic and photometric samples. We perform several tests to assess the reliability of our Fisher matrix, including the Gaussianity of our summary statistics and convergence. We find that the marginalized 1-$σ$ uncertainties in redshift space are $Δf_{\rm NL}^{\rm loc} \sim 16$ and $Δf_{\rm NL}^{\rm equi} \sim 41 $ on a survey volume of $1$ $($Gpc$/h)^3$. These constraints are weakly affected by redshift errors. We close by speculating as to how this approach can be made robust against small-scale uncertainties by exploiting (non)locality.
