论文标题
删除巨人并向人群学习:一种新的SZ功率谱法和修订的康普顿$ y $ -map分析
Removing the giants and learning from the crowd: a new SZ power spectrum method and revised Compton $y$-map analysis
论文作者
论文摘要
Sunyaev-Zeldovich(SZ)效应提供了一个强大的宇宙学探针,传统上可以独立接近群集数量计数(CNC)或功率谱(PS)分析。在这里,我们通过引入调查完整性函数(通常仅在CNC分析中使用,在$ YY $ -PS建模中使用)来设计一种新方法来分析$ y $图。这提供了一种系统的方法,主要基于SZ可观察物,用于获得两个互补的$ y $图,一种是结合被检测到的/分辨率的簇,另一个仅依赖于分散/未解决的SZ贡献。我们使用在\ planck CNC分析中获得的群集的目录来定义链接这两个$ y $图的完整性函数。分裂取决于所选的信号到噪声检测阈值,我们在讨论中有所不同。我们仔细地传播了削减完整性对$ YY $ -PS分析中非高斯错误贡献的影响,从而强调了掩盖大量群集的好处。我们对未解决组件的\ planck $ yy $ -ps的分析产生了$ b = 0.15 \ pm0.04 $的质量偏见,与标准值($ b \ \ of your b = 0.4 \ b = 0.4 \ pm 0.05 $,总计$ yy $ - yy $ - $ yy $ - $ yy $ - $ yy $ - $ yy $。我们发现这种漂移的指示是由CIB-TSZ跨相关性驱动的,该相关主要起源于$ y $ -map的分辨要组件中的簇。另一个可能的解释是存在质量依赖性偏见,该偏差是从理论上动机的,可以通过我们的新方法来量化。我们此外,我们发现了第一个提示,即在$ yy $ -ps中的2次术语中存在。最后,提出的方法提供了一个新的框架,用于将CNC和PS分析的互补信息组合在即将进行的SZ调查中。
The Sunyaev-Zeldovich (SZ) effect provides a powerful cosmological probe, which traditionally is approached independently as cluster number count (CNC) or power spectrum (PS) analysis. Here, we devise a new method for analysing the $y$-map by introducing the survey completeness function, conventionally only used in the CNC analysis, in the $yy$-PS modeling. This provides a systematic method, based mainly on SZ observables, for obtaining two complementary $y$-maps, one incorporating detected/resolved clusters and the other relying only on diffuse/unresolved SZ contributions. We use the catalogue of clusters obtained in the \Planck CNC analysis to define the completeness function linking these two $y$-maps. The split depends on the chosen signal-to-noise detection threshold, which we vary in our discussion. We carefully propagate the effect of completeness cuts on the non-Gaussian error contributions in the $yy$-PS analysis, highlighting the benefits of masking massive clusters. Our analysis of the \Planck $yy$-PS for the unresolved component yields a mass bias of $b=0.15\pm0.04$, consistent with the standard value ($b\approx0.2$), in comparison to $b=0.4\pm 0.05$ for the total $yy$-PS. We find indications for this drift being driven by the CIB-tSZ cross correlation, which dominantly originates from clusters in the resolved component of the $y$-map. Another possible explanation is the presence of a mass-dependent bias, which has been theoretically motivated and can be quantified with our novel method. We furthermore find first hints for the presence of the 2-halo terms in the $yy$-PS. Finally, the proposed method provides a new framework for combining the complementary information of the CNC and PS analyses in upcoming SZ surveys.