arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0903.3121astro-ph.CO
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将测光红移概率密度信息转换为实空间聚类测量

Incorporating Photometric Redshift Probability Density Information into Real-Space Clustering Measurements

Adam D Myers, Martin White, Nicholas M. Ball

摘要:

光度红移在宇宙学中的应用越来越多。然而,这些photo-z通常被视为光谱观测,因为光度红移的峰值,而不是全概率密度函数(PDF),被使用。这忽略了完整PDF中固有的有用信息。我们介绍了一个新的实空间估计最常用的宇宙统计,2点关联函数,权重的PDF的个别光度对象的方式是最佳的泊松统计占主导地位时。由于我们的估计器不基于PDF峰值分箱,因此它通过有效地合并来自与感兴趣的红移分箱重叠的所有光度对象的信息来显著增强聚类信号。作为一个现实世界的应用,我们测量类星体集群在斯隆数字巡天(SDSS)。我们发现,我们最简单的分箱估计提高了聚类信号的一个因素,相当于增加调查规模的一个因素2-3。我们还介绍了一个新的实现,充分的权重对对象之间的互相关,并发现这种对加权估计提高聚类信号的方式相当于增加调查的大小由一个因素的4-5。我们的技术使用光谱数据来锚定距离尺度,并且在光谱数据(例如,来自BOSS)与更深的光度测量(例如,来自Pan-STARRS、DES或LSST)。我们还提供了简单的,信息丰富的表达式,以确定我们的估计将与光谱对象的自相关的竞争力。虽然我们使用类星体作为一个例子人口,我们的估计可以而且应该适用于任何聚类估计,使用光度对象。

英文摘要:

The use of photometric redshifts in cosmology is increasing. Often, however these photo-zs are treated like spectroscopic observations, in that the peak of the photometric redshift, rather than the full probability density function (PDF), is used. This overlooks useful information inherent in the full PDF. We introduce a new real-space estimator for one of the most used cosmological statistics, the 2-point correlation function, that weights by the PDF of individual photometric objects in a manner that is optimal when Poisson statistics dominate. As our estimator does not bin based on the PDF peak it substantially enhances the clustering signal by usefully incorporating information from all photometric objects that overlap the redshift bin of interest. As a real-world application, we measure QSO clustering in the Sloan Digital Sky Survey (SDSS). We find that our simplest binned estimator improves the clustering signal by a factor equivalent to increasing the survey size by a factor of 2-3. We also introduce a new implementation that fully weights between pairs of objects in constructing the cross-correlation and find that this pair-weighted estimator improves clustering signal in a manner equivalent to increasing the survey size by a factor of 4-5. Our technique uses spectroscopic data to anchor the distance scale and it will be particularly useful where spectroscopic data (e.g, from BOSS) overlaps deeper photometry (e.g.,from Pan-STARRS, DES or the LSST). We additionally provide simple, informative expressions to determine when our estimator will be competitive with the autocorrelation of spectroscopic objects. Although we use QSOs as an example population, our estimator can and should be applied to any clustering estimate that uses photometric objects.

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。

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