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Paper: The Large Synoptic Survey Telescope
Volume: 351, Astronomical Data Analysis Software and Systems XV
Page: 103
Authors: Axelrod, T.S.
Abstract: The Large Synoptic Survey Telescope (LSST) is an 8.4 meter telescope with a 10 square degree field degree field and a 3 Gigapixel imager, planned to be on-sky in 2012. It is a dedicated all-sky survey instrument, with several complementary science missions. These include understanding dark energy through weak lensing and supernovae; exploring transients and variable objects; creating and maintaining a solar system map, with particular emphasis on potentially hazardous objects; and increasing the precision with which we understand the structure of the MilkyWay. The instrument operates continuously at a rapid cadence, repetitively scanning the visible sky every few nights.
The data flow rates from LSST are larger than those from current surveys by roughly a factor of 1000: A few GB/night are typical today. LSST will deliver a few TB/night. From a computing hardware perspective, this factor of 1000 can be dealt with easily in 2012. The major issues in designing the LSST data management system arise from the fact that the number of people available to critically examine the data will not grow from current levels. This has a number of implications.
For example, every large imaging survey today is resigned to the fact that their image reduction pipelines fail at some significant rate. Many of these failures are dealt with by rerunning the reduction pipeline under human supervision, with carefully \tweaked" parameters to deal with the original problem. For LSST, this will no longer be feasible. The problem is compounded by the fact that the processing must of necessity occur on clusters with large numbers of CPU's and disk drives, and with some components connected by long-haul networks. This inevitably results in a significant rate of hardware component failures, which can easily lead to further software failures. Both hardware and software failures must be seen as a routine fact of life rather than rare exceptions to normality.
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