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Cyber-infrastructure to Support Science and Data Management for the Dark Energy Survey

C. Ngeow·J. J. Mohr·T. Alam·W. A. Barkhouse·C. Beldica·D. Cai·G. Daues·R. Plante·J. Annis·H. Lin·D. Tucker·R. C. Smith
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Abstract

The Dark Energy Survey (DES; operations 2009-2015) will address the nature of dark energy using four independent and complementary techniques: (1) a galaxy cluster survey over 4000 deg2 in collaboration with the South Pole Telescope Sunyaev-Zel'dovich effect mapping experiment, (2) a cosmic shear measurement over 5000 deg2, (3) a galaxy angular clustering measurement within redshift shells to redshift=1.35, and (4) distance measurements to 1900 supernovae Ia. The DES will produce 200 TB of raw data in four bands, These data will be processed into science ready images and catalogs and co-added into deeper, higher quality images and catalogs. In total, the DES dataset will exceed 1 PB, including a 100 TB catalog database that will serve as a key science analysis tool for the astronomy/cosmology community. The data rate, volume, and duration of the survey require a new type of data management (DM) system that (1) offers a high degree of automation and robustness and (2) leverages the existing high performance computing infrastructure to meet the project's DM targets. The DES DM system consists of (1) a grid-enabled, flexible and scalable middleware developed at NCSA for the broader scientific community, (2) astronomy modules that build upon community software, and (3) a DES archive to support automated processing and to serve DES catalogs and images to the collaboration and the public. In the recent DES Data Challenge 1 we deployed and tested the first version of the DES DM system, successfully reducing 700 GB of raw simulated images into 5 TB of reduced data products and cataloguing 50 million objects with calibrated astrometry and photometry.

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C. Ngeow, J. J. Mohr, T. Alam, W. A. Barkhouse, C. Beldica, D. Cai, G. Daues, R. Plante, J. Annis, H. Lin, D. Tucker, R. C. Smith. 2006-08-11. Cyber-infrastructure to Support Science and Data Management for the Dark Energy Survey. https://doi.org/10.1117/12.671017

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