论文标题
使用簇，星系和剪切互相关的自我校准光学星系群集选择偏差
Self-calibrating optical galaxy cluster selection bias using cluster, galaxy, and shear cross-correlations
论文作者
论文摘要
已知星系簇的聚类信号是自我校准质量观察关系的强大工具，并且与群集丰度和镜头相辅相成。在这项工作中，我们探讨了将三个相关函数结合起来的可能性 - 群集镜头，群集 - - 伴奏互相关函数和星系自动相关函数 - 以自校准光学群集选择偏差，增强的聚类和透镜信号的自我校准，在丰富的群集样品中受到预测效应。我们通过将光环占用分布（HOD）模型应用于N体模拟，并使用围绕大量光环的计数作为丰富的代理，并使用圆柱插入器作为丰富的代理，从而开发了类似红色的星系和红色磁带样星团的模拟目录。除了预测相关函数中先前已知的小规模提升外，我们发现投影效应还显着提高了3D相关函数到量表100 $ h^{ - 1} \ mathrm {mpc} $。假设调查条件类似于黑暗能源调查（DES），我们进行了一项可能性分析，并表明选择偏见可以在10％的水平上受到自持一致的约束。我们讨论将这种方法应用于真实数据的策略。我们预计将分析扩展到较小的量表并使用更深的镜头数据将进一步改善群集选择偏差的限制。
The clustering signals of galaxy clusters are known to be powerful tools for self-calibrating the mass-observable relation and are complementary to cluster abundance and lensing. In this work, we explore the possibility of combining three correlation functions -- cluster lensing, the cluster-galaxy cross-correlation function, and the galaxy auto-correlation function -- to self-calibrate optical cluster selection bias, the boosted clustering and lensing signals in a richness-selected sample mainly caused by projection effects. We develop mock catalogues of redMaGiC-like galaxies and redMaPPer-like clusters by applying Halo Occupation Distribution (HOD) models to N-body simulations and using counts-in-cylinders around massive haloes as a richness proxy. In addition to the previously known small-scale boost in projected correlation functions, we find that the projection effects also significantly boost 3D correlation functions out to scales 100 $h^{-1} \mathrm{Mpc}$. We perform a likelihood analysis assuming survey conditions similar to that of the Dark Energy Survey (DES) and show that the selection bias can be self-consistently constrained at the 10% level. We discuss strategies for applying this approach to real data. We expect that expanding the analysis to smaller scales and using deeper lensing data would further improve the constraints on cluster selection bias.
