Science data quality assessment for the Large Synoptic Survey Telescope

Details Science data quality assessment for the Large Synoptic Survey Telescope Science data quality assessment for the Large Synoptic Survey Telescope

Jul 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010spie.7740e..14s&link_type=abstract

Software and Cyberinfrastructure for Astronomy. Edited by Radziwill, Nicole M.; Bridger, Alan. Proceedings of the SPIE, Volume 7

Astronomy and Astrophysics

Astronomy

Scientific paper

LSST will have a Science Data Quality Assessment (SDQA) subsystem for the assessment of the data products that will be produced during the course of a 10 yr survey. The LSST will produce unprecedented volumes of astronomical data as it surveys the accessible sky every few nights. The SDQA subsystem will enable comparisons of the science data with expectations from prior experience and models, and with established requirements for the survey. While analogous systems have been built for previous large astronomical surveys, SDQA for LSST must meet a unique combination of challenges. Chief among them will be the extraordinary data rate and volume, which restricts the bulk of the quality computations to the automated processing stages, as revisiting the pixels for a post-facto evaluation is prohibitively expensive. The identification of appropriate scientific metrics is driven by the breadth of the expected science, the scope of the time-domain survey, the need to tap the widest possible pool of scientific expertise, and the historical tendency of new quality metrics to be crafted and refined as experience grows. Prior experience suggests that contemplative, off-line quality analyses are essential to distilling new automated quality metrics, so the SDQA architecture must support integrability with a variety of custom and community-based tools, and be flexible to embrace evolving QA demands. Finally, the time-domain nature of LSST means every exposure may be useful for some scientific purpose, so the model of quality thresholds must be sufficiently rich to reflect the quality demands of diverse science aims.

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