CyberinfraV1, Main, Exploration, bibRecord, 000A68

Science Data Quality Assessment for the Large Synoptic Survey Telescope

Identifieur interne : 000A68 ( Main/Exploration ); précédent : 000A67; suivant : 000A69

Science Data Quality Assessment for the Large Synoptic Survey Telescope

Auteurs : Richard A. Shaw [États-Unis] ; Deborah Levine [États-Unis] ; Timothy Axelrod [États-Unis] ; Russ R. Laher [États-Unis] ; Vince G. Mannings [États-Unis]

Source :

Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2010.

RBID : Pascal:11-0004522

Descripteurs français

Pascal (Inist)
- Intégrité donnée, Contrôle qualité, Système gestion donnée technique SGDT, Traitement automatique, Télescope, Astrophysique, Ciel, Nuit, Métrique, Modélisation, Méthode indirecte, Hors ligne, Intégrabilité.

English descriptors

KwdEn :
- Astrophysics, Automatic processing, Data integrity, Indirect method, Integrability, Metric, Modeling, Night, Off line, Product data management PDM, Quality control, Sky, Telescope.

Abstract

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.

Affiliations:

Links toward previous steps (curation, corpus...)

to stream PascalFrancis, to step Corpus: 000132
to stream PascalFrancis, to step Curation: 000113
to stream PascalFrancis, to step Checkpoint: 000074
to stream Main, to step Merge: 000A73
to stream Main, to step Curation: 000A68

Le document en format XML

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<term>Integrability</term>
<term>Metric</term>
<term>Modeling</term>
<term>Night</term>
<term>Off line</term>
<term>Product data management PDM</term>
<term>Quality control</term>
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<front><div type="abstract" xml:lang="en">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.</div>
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Science Data Quality Assessment for the Large Synoptic Survey Telescope

Science Data Quality Assessment for the Large Synoptic Survey Telescope

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri