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arXiv 1111.6620astro-ph.CO
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SDSS Coadd:星系测光红移星表

The SDSS Coadd: A Galaxy Photometric Redshift Catalog

Ribamar R. R. Reis, Marcelle Soares-Santos, James Annis, Scott Dodelson, Jiangang Hao, David Johnston, Jeffrey Kubo, Huan Lin, Hee-Jong Seo, Melanie Simet

摘要:

我们提出并描述了斯隆数字巡天(SDSS)Coadd数据的星系光度红移(photo-z)的目录。我们使用人工神经网络(ANN)技术来计算photo-z的和最近邻误差(NNE)的方法来估计photo-z的$\sim$ 13万对象分类为星系的coadd与$r < 24.5$的错误。在具有SDSS测光和光谱红移的$\sim 83,000$星系样本上训练和验证了photo-z和photo-z误差估计器,所述光谱红移由SDSS数据发布7(DR 7)、加拿大观测宇宙学场星系巡天网络(hocC 2)、深河外星系演化探测数据发布3测量(DEEP 2 DR 3)、可见光成像多目标摄谱仪-甚大望远镜深度巡天(VVDS)和WiggleZ暗能量巡天。对于最好的人工神经网络方法,我们已经尝试过,我们发现,68%的星系在验证集有一个photo-z误差小于$σ_{68} =0.031$。在介绍了我们的结果和质量测试之后,我们为用户访问公共数据提供了一个简短的指南。

英文摘要:

We present and describe a catalog of galaxy photometric redshifts (photo-z's) for the Sloan Digital Sky Survey (SDSS) Coadd Data. We use the Artificial Neural Network (ANN) technique to calculate photo-z's and the Nearest Neighbor Error (NNE) method to estimate photo-z errors for $\sim$ 13 million objects classified as galaxies in the coadd with $r < 24.5$. The photo-z and photo-z error estimators are trained and validated on a sample of $\sim 83,000$ galaxies that have SDSS photometry and spectroscopic redshifts measured by the SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology Field Galaxy Survey (CNOC2), the Deep Extragalactic Evolutionary Probe Data Release 3(DEEP2 DR3), the VIsible imaging Multi-Object Spectrograph - Very Large Telescope Deep Survey (VVDS) and the WiggleZ Dark Energy Survey. For the best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than $σ_{68} =0.031$. After presenting our results and quality tests, we provide a short guide for users accessing the public data.

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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