CosmoDM and its application to Pan-STARRS data

CosmoDM and its application to Pan-STARRS data
复制标题

CosmoDM 及其在 Pan-STARRS 数据中的应用

DOI:
--
复制
发表时间:
2015
期刊:
影响因子:
--
通讯作者:
M. Wetzstein
M. Wetzstein
中科院分区:
--
文献类型:
--
作者:
S. Desai;J. J. Mohr;R. Henderson;M. Kümmel;K. Paech;M. Wetzstein

文献摘要

被引文献

相似文献

宇宙学数据管理系统(CosmoDM)是一个自动化和灵活的数据管理系统,用于处理和校准光学光度测量数据。它设计为在超级计算机上运行,并最大限度地减少磁盘I/O,以便在重新处理期间能够扩展到非常高的吞吐量。它是欧几里得飞行任务所需的地面处理的一个要素的早期原型,还将用于编制eROSITA X射线全天巡天任务所需的地面数据。CosmoDM由两条主要管道组成。第一种是单历元或去趋势线,用于对原始曝光进行光度和天体测量校准。第二个是联合添加管道,它将来自个人曝光的数据合并到更深的COADD图像和科学准备目录中。CosmoDM的一个新功能是,它使用了修改后的Astroma软件堆栈,可以读写瓷砖压缩图像。自2011年以来,CosmoDM一直用于处理来自DECAM、CFHT MegaCam和PanSTARRS相机的数据。在这篇文章中,我们将描述来自CosmoDM的泛STARRS数据如何被用来光学确认和测量普朗克的Sunyaev-Zeldovich效应选定星团候选者的光度红移。
The Cosmology Data Management system (CosmoDM) is an automated and flexible data management system for the processing and calibration of data from optical photometric surveys. It is designed to run on supercomputers and to minimize disk I/O to enable scaling to very high throughput during periods of reprocessing. It serves as an early prototype for one element of the ground-based processing required by the Euclid mission and will also be employed in the preparation of ground based data needed in the eROSITA X-ray all sky survey mission. CosmoDM consists of two main pipelines. The first is the single-epoch or detrending pipeline, which is used to carry out the photometric and astrometric calibration of raw exposures. The second is the co-addition pipeline, which combines the data from individual exposures into deeper coadd images and science ready catalogs. A novel feature of CosmoDM is that it uses a modified stack of Astromatic software which can read and write tile compressed images. Since 2011, CosmoDM has been used to process data from the DECam, the CFHT MegaCam and the Pan-STARRS cameras. In this paper we shall describe how processed Pan-STARRS data from CosmoDM has been used to optically confirm and measure photometric redshifts of Planck-based Sunyaev-Zeldovich effect selected cluster candidates.