arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1302.2401astro-ph.CO
收藏

大尺度结构与CosmoLike的组合探测

Combining Probes of Large-Scale Structure with CosmoLike

Tim Eifler, Elisabeth Krause, Peter Schneider, Klaus Honscheid

摘要:

发展精确的分析技术来结合宇宙学的各种探测器,对于加强对宇宙学参数的约束和检查我们的宇宙模型中的不一致性至关重要。在本文中,我们开发了一个联合分析框架,从三个示踪剂的暗物质密度场,即星系的位置,剪切和放大率计算六个不同的二阶统计。我们扩展的剪切剪切统计(所谓的COSEBI)的背景下开发的数据压缩方案的其他五个二阶统计,从而显着减少联合数据向量中的数据点的数量。我们使用CosmoLike,一个新开发的软件框架联合似然分析,预测参数约束的暗能量调查(DES)。模拟MCMCs覆盖了一个五维宇宙学参数空间,将单个探测器的信息内容与几个组合探测器(CP)的数据向量进行比较。考虑到这些二阶统计量的显著相关性,我们对协方差矩阵中的所有交叉项进行建模;此外,我们超越了高斯协方差近似,并使用晕模型来包括密度场的高阶相关性。我们发现,添加放大信息(包括具有剪切和聚类的交叉探针)显著增加了信息内容,并且协方差的正确建模(即,考虑非高斯性和交叉项)对于来自CP数据向量的精确似然轮廓是必不可少的。我们还确定了几个零测试的基础上退化的放大率和剪切统计,可用于量化污染的数据集的天体物理系统学和/或校准问题。

英文摘要:

Developing accurate analysis techniques to combine various probes of cosmology is essential to tighten constraints on cosmological parameters and to check for inconsistencies in our model of the Universe. In this paper we develop a joint analysis framework for six different second-order statistics calculated from three tracers of the dark matter density field, namely galaxy position, shear, and magnification. We extend a data compression scheme developed in the context of shear-shear statistics (the so-called COSEBIs) to the other five second-order statistics, thereby significantly reducing the number of data points in the joint data vector. We use CosmoLike, a newly developed software framework for joint likelihood analyses, to forecast parameter constraints for the Dark Energy Survey (DES). The simulated MCMCs cover a five dimensional cosmological parameter space comparing the information content of the individual probes to several combined probes (CP) data vectors. Given the significant correlations of these second-order statistics we model all cross terms in the covariance matrix; furthermore we go beyond the Gaussian covariance approximation and use the halo model to include higher order correlations of the density field. We find that adding magnification information (including cross probes with shear and clustering) noticeably increases the information content and that the correct modeling of the covariance (i.e., accounting for non-Gaussianity and cross terms) is essential for accurate likelihood contours from the CP data vector. We also identify several nulltests based on the degeneracy of magnification and shear statistics which can be used to quantify the contamination of data sets by astrophysical systematics and/or calibration issues.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑