Redshift distributions of galaxies in the Dark Energy Survey Science Verification shear catalogue and implications for weak lensing-猫眼课题宝

DOI：

10.1103/physrevd.94.042005

发表时间：

2015-07

期刊：

Physical Review D

影响因子：

通讯作者：

中科院分区：

物理与天体物理2区

文献类型：

作者：

C. Bonnett;M. Troxel;W. Hartley;A. Amara;B. Leistedt;M. Becker;G. Bernstein;S. Bridle;C. Bruderer;M. Busha;M. Kind;M. Childress;F. Castander;C. Chang;M. Crocce;T. Davis;T. Eifler;J. Frieman;C. Gangkofner;E. Gaztañaga;K. Glazebrook;D. Gruen;T. Kacprzak;A. King;J. Kwan;O. Lahav;G. Lewis;C. Lidman;H. Lin;N. MacCrann;R. Miquel;C. O’Neill;A. Palmese;H. Peiris;A. Réfrégier;E. Rozo;E. Rykoff;I. Sadeh;C. S'anchez;E.Sheldon;S. Uddin;R. Wechsler;J. Zuntz;T. Abbott;F. Abdalla;S. Allam;R. Armstrong;M. Banerji;A. Bauer;A.Benoit-L'evy;E. Bertin;D. Brooks;E. Buckley-Geer;D. Burke;D. Capozzi;A. Rosell;J. Carretero;C. Cunha;C. D'Andrea;L. Costa;D. Depoy;S. Desai;H. Diehl;J. Dietrich;P. Doel;A. F. Neto;E. Fernandez;B. Flaugher;P. Fosalba;D. Gerdes;R. Gruendl;K. Honscheid;B. Jain;D. James;M. Jarvis;A. Kim;K. Kuehn;N. Kuropatkin;T. Li;M. Lima;M. Maia;M. March;J. Marshall;P. Martini;Peter Melchior;C. Miller;E. Neilsen;R. Nichol;B. Nord;R. Ogando;A. Plazas;K. Reil;A. Romer;A. Roodman;M. Sako;E. Sánchez;B. Santiago;R. C. Smith;M. Soares-Santos;F. Sobreira;E. Suchyta;M. Swanson;G. Tarlé;J. Thaler;D. Thomas;V. Vikram;A. Walker

我们提出的光度红移估计用于暗能量巡天科学验证（DES SV）数据的弱透镜分析的星系。分析了四种基于模型或机器学习的光度红移方法-ANNZ 2，BPZ校准BCC-Ufig模拟，SKYNET和TPZ。为了训练，校准和测试这些方法，我们构建了一个目录的光谱证实的星系匹配DES SV数据。的方法的性能进行评估，对匹配的光谱目录，重点对弱透镜分析相关的指标，对COSMOS照片Z的额外验证。从DES SV剪切星表中的星系中，在0.3 < z < 1.3的范围内，平均红移为0.72 +/- 0.01，我们构造了三个平均z = {0.45;0.67;1.00}的层析箱。这些箱中的每一个在基准SKYNET照片-z（dz）的平均值中具有系统不确定性delta z <= 0.05。我们将红移分布中的误差传播到它们对用宇宙剪切估计的宇宙学参数的影响中，发现它们导致sigma（δ）值的约3%的偏移。对于DES SV剪切目录，该偏移在sigma（8）的一个sigma统计误差范围内。我们进一步研究了系统差异对临界表面密度Sigma（临界值）的潜在影响，发现偏倚水平安全地低于DES SV数据的统计功效。对于三个层析箱中的每个，我们推荐了宽度为0.05的n（z）平均值的光z偏差的最终高斯先验，并表明这对于相应的宇宙学分析来说是一个足够的偏差模型。

We present photometric redshift estimates for galaxies used in the weak lensing analysis of the Dark Energy Survey Science Verification (DES SV) data. Four model-or machine learning-based photometric redshift methods-ANNZ2, BPZ calibrated against BCC-Ufig simulations, SKYNET, and TPZ-are analyzed. For training, calibration, and testing of these methods, we construct a catalogue of spectroscopically confirmed galaxies matched against DES SV data. The performance of the methods is evaluated against the matched spectroscopic catalogue, focusing on metrics relevant for weak lensing analyses, with additional validation against COSMOS photo-z's. From the galaxies in the DES SV shear catalogue, which have mean redshift 0.72 +/- 0.01 over the range 0.3 < z < 1.3, we construct three tomographic bins with means of z = {0.45;0.67;1.00}. These bins each have systematic uncertainties delta z <= 0.05 in the mean of the fiducial SKYNET photo-z (dz). We propagate the errors in the redshift distributions through to their impact on cosmological parameters estimated with cosmic shear, and find that they cause shifts in the value of sigma(8) of approximately 3%. This shift is within the one sigma statistical errors on sigma(8) for the DES SV shear catalogue. We further study the potential impact of systematic differences on the critical surface density, Sigma(crit), finding levels of bias safely less than the statistical power of DES SV data. We recommend a final Gaussian prior for the photo-z bias in the mean of n(z) of width 0.05 for each of the three tomographic bins, and show that this is a sufficient bias model for the corresponding cosmology analysis.

Redshift distributions of galaxies in the Dark Energy Survey Science Verification shear catalogue and implications for weak lensing

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