Statistics – Computation
Scientific paper
Jan 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21540123d&link_type=abstract
American Astronomical Society, AAS Meeting #215, #401.23; Bulletin of the American Astronomical Society, Vol. 42, p.221
Statistics
Computation
Scientific paper
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 TFlops of processing to reduce.
The LSST project is carrying out a series of Data Challenges 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. Algorithm development must address the dual requirements for efficient use of computational resources, including emerging computing architectures, and the accurate and reliable processing of the unprecedented combination of deep and broad data resulting from the survey. This will require substantial progress beyond the state of the art from existing surveys. We anticipate the need for novel machine-learning algorithms for data quality analysis and to enable the discovery of the unexpected.
The Data Challenges incorporate both existing astronomical images and image data resulting from a detailed photon-level simulation, from sources through the atmosphere to the LSST observatory. The simulation is used to ensure that the system can scale to the LSST field of view and 3.2 gigapixel camera scale and meet the associated image and survey quality requirements. Future Data Challenges, carried out in conjunction with the LSST Science Collaborations, are planned to deliver data products suitable for high-quality science. We will report on these plans and on the progress of the Data Challenges to date.
Axelrod Tim
Becker Andrew
Becla Jacek
Burke Daniel
Connolly Amy
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