The Data Management system for the LSST will have to perform near-real-time calibration and analysis of acquired
images, particularly for transient detection and alert generation; annual processing of the entire dataset for precision
calibration, object detection and characterization, and catalog generation; and support of user data access and analysis.
Images will be acquired at roughly a 17-second cadence, with alerts generated within one minute. The ten-year survey
will result in tens of petabytes of image and catalog data and will require ~250 teraflops of processing to reduce.
The LSST project is carrying out a series of Data Challenges (DC) to refine the design, evaluate the scientific and
computational performance of candidate algorithms, and address the challenging scaling issues that the LSST dataset
will present. This paper discusses the progress of the DCs to date and plans for future DCs.
Algorithm development must address dual requirements for the efficient use of computational resources and the accurate,
reliable processing of the deep and broad survey data. The DCs incorporate both existing astronomical images and
image data resulting from detailed photon-level simulations. The data is used to ensure that the system can scale to the
LSST field of view and 3.2 gigapixel camera scale and meet the scientific data quality requirements. Future DCs, carried
out in conjunction with the LSST Science Collaborations, are planned to deliver data products verified by computeraided
analysis and actual applications as suitable for high-quality science.