Title: "Data Management for the Dark Energy Survey"

Abstract:

The Dark Energy Survey (DES; operations 2010-2015) will address the nature of dark energy using four independent and complementary techniques. In total, the DES dataset will approach 2PB, including a 100 TB catalog database that will serve as a key science analysis tool. The data rate, volume, and duration of the survey require a new type of data management (DM) system that offers a high degree of automation and robustness, and leverages the existing high performance computing infrastructure to meet the project goals. We have tested early versions of the DES DM system using both simulated DECam data from Fermilab and the observed data from the Blanco Cosmology Survey. We also present results from the first two Data Challenges.