论文标题
与$ k $ neart的邻居分布
Tracer-Field Cross-Correlations with $k$-Nearest Neighbor Distributions
论文作者
论文摘要
在天文学和宇宙学中，重大努力致力于表征和理解点之间的空间互相关 - 例如星系位置，高能中微子到达方向，X射线和AGN来源以及连续场 - 例如弱透镜和宇宙微波背景（CMB）图。最近，我们介绍了$ k $ - 最终的邻居形式主义，以更好地表征离散（点）数据集的聚类。在这里，我们将其扩展到点场互相关分析。它将点数据集的$ k $ nn测量与在许多尺度上平滑的字段的测量结果结合在一起。所得统计数据对点和场的关节聚类中的所有顺序都很敏感。我们证明，即使没有线性（高斯）相关性，即使2-PT互相关不同，这种方法也可以测量两个数据集的统计依赖性。我们进一步证明，当连续场被高水平的噪声污染时，该框架比检测互相关的两点函数要有效得多。 For a particularly high level of noise, the cross-correlations between halos and the underlying matter field in a cosmological simulation, between $10h^{-1}{\rm Mpc}$ and $30h^{-1}{\rm Mpc}$, is detected at $>5σ$ significance using the technique presented here, when the two-point cross-correlation significance is $\sim 1σ$。最后，我们表明，HALOS的$ K $ NN互相关和物质场可以通过混合有效场理论（HEFT）框架在准线性尺度上进行良好模块，并具有与两点交叉相交相同的偏差参数。使用这种方法检测互相关的统计能力的实质性改善使其成为各种宇宙学应用的有前途的工具。
In astronomy and cosmology, significant effort is devoted to characterizing and understanding spatial cross-correlations between points - e.g. galaxy positions, high energy neutrino arrival directions, X-ray and AGN sources, and continuous field - e.g. weak lensing and Cosmic Microwave Background (CMB) maps. Recently, we introduced the $k$-nearest neighbor formalism to better characterize the clustering of discrete (point) datasets. Here we extend it to the point-field cross-correlation analysis. It combines $k$NN measurements of the point data set with measurements of the field smoothed on many scales. The resulting statistics are sensitive to all orders in the joint clustering of the points and the field. We demonstrate that this approach, unlike the 2-pt cross-correlation, can measure the statistical dependence of two datasets even when there are no linear (Gaussian) correlations. We further demonstrate that this framework is far more effective than the two-point function in detecting cross-correlations when the continuous field is contaminated by high levels of noise. For a particularly high level of noise, the cross-correlations between halos and the underlying matter field in a cosmological simulation, between $10h^{-1}{\rm Mpc}$ and $30h^{-1}{\rm Mpc}$, is detected at $>5σ$ significance using the technique presented here, when the two-point cross-correlation significance is $\sim 1σ$. Finally, we show that the $k$NN cross-correlations of halos and the matter field can be well-modeled on quasilinear scales by the Hybrid Effective Field Theory (HEFT) framework, with the same set of bias parameters as are used for the two-point cross-correlations. The substantial improvement in the statistical power of detecting cross-correlations with this method makes it a promising tool for various cosmological applications.
