DES科学验证数据的无光谱标样相关红移定标
Cross-Correlation Redshift Calibration Without Spectroscopic Calibration Samples in DES Science Verification Data
摘要：
具有高保真度红移样本的星系互相关具有精确校准系统光度红移不确定性的潜力，这些不确定性是由于无法获得完整和有代表性的星系训练和验证样本而产生的。然而，这种技术在暗能量调查（DES）中的应用受到相对较低的数密度，小面积，以及光度和光谱样品之间的适度红移重叠的阻碍。我们建议，而不是使用可靠的光度红移的光度表通过互相关的photo-z校准。我们验证了我们的建议的可行性，使用redMapPer集群从斯隆数字巡天（SDSS）成功地恢复SDSS光谱星系的红移分布。我们演示了如何将photo-z与互相关数据相结合来校准光度红移偏差，同时在校准或未知光度样本中对可能的聚类偏差演化进行边缘化。我们应用我们的方法DES科学验证（DES SV）的数据，以约束弱透镜研究选定的星系样本的光度红移分布，约束的平均值的层析红移分布的统计不确定性$Δz \sim \pm 0.01$。我们预测，我们的建议可以在原则上控制光度红移的不确定性在DES弱透镜实验的内在统计噪声的实验范围内redMapPer集群可用的水平附近。我们的研究结果提供了强大的动力，推出一个程序，以充分表征系统误差的偏差演变和照片的z形状在我们的校准过程。
英文摘要：
Galaxy cross-correlations with high-fidelity redshift samples hold the potential to precisely calibrate systematic photometric redshift uncertainties arising from the unavailability of complete and representative training and validation samples of galaxies. However, application of this technique in the Dark Energy Survey (DES) is hampered by the relatively low number density, small area, and modest redshift overlap between photometric and spectroscopic samples. We propose instead using photometric catalogs with reliable photometric redshifts for photo-z calibration via cross-correlations. We verify the viability of our proposal using redMaPPer clusters from the Sloan Digital Sky Survey (SDSS) to successfully recover the redshift distribution of SDSS spectroscopic galaxies. We demonstrate how to combine photo-z with cross-correlation data to calibrate photometric redshift biases while marginalizing over possible clustering bias evolution in either the calibration or unknown photometric samples. We apply our method to DES Science Verification (DES SV) data in order to constrain the photometric redshift distribution of a galaxy sample selected for weak lensing studies, constraining the mean of the tomographic redshift distributions to a statistical uncertainty of $Δz \sim \pm 0.01$. We forecast that our proposal can in principle control photometric redshift uncertainties in DES weak lensing experiments at a level near the intrinsic statistical noise of the experiment over the range of redshifts where redMaPPer clusters are available. Our results provide strong motivation to launch a program to fully characterize the systematic errors from bias evolution and photo-z shapes in our calibration procedure.
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