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Science data quality assessment for the Large Synoptic Survey Telescope

Shaw, Richard A. and Levine, Deborah and Axelrod, Timothy and Laher, Russ R. and Mannings, Vince G. (2010) Science data quality assessment for the Large Synoptic Survey Telescope. In: Software and Cyberinfrastructure for Astronomy. Proceedings of SPIE. No.7740. Society of Photo-Optical Instrumentation Engineers , Bellingham, WA, Art. No. 77400H. ISBN 978-0-81948-230-3. https://resolver.caltech.edu/CaltechAUTHORS:20161108-122306467

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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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1117/12.857293DOIArticle
http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=751708PublisherArticle
ORCID:
AuthorORCID
Laher, Russ R.0000-0003-2451-5482
Additional Information:© 2010 SPIE. The LSST development work is the result of efforts by the LSST collaboration of scientist, engineers, technicians, managers as well as the study work contracted to several outside entities. This team of dedicated and recognized experts in their field is what makes the LSST project a success. At the 2008 annual LSST all hands meeting there were 160 people that participated from the project team and Science Collaborations. LSST is a public-private partnership. Funding for design and development activity comes from the National Science Foundation, private donations, grants to universities, and in-kind support at Department of Energy laboratories and other LSSTC Institutional Members. This work is supported by in part the National Science Foundation under Scientific Program Order No. 9 (AST-0551161) and Scientific Program Order No. 1 (AST-0244680) through Cooperative Agreement AST-0132798. Portions of this work are supported by the Department of Energy under contract DE-AC02-76SF00515 with the Stanford Linear Accelerator Center, contract DE-AC02-98CH10886 with Brookhaven National Laboratory, and contract DE-AC52-07NA27344 with Lawrence Livermore National Laboratory. Additional funding comes from private donations, grants to universities, and in-kind support at Department of Energy laboratories and other LSSTC Institutional Members.
Group:Infrared Processing and Analysis Center (IPAC)
Funders:
Funding AgencyGrant Number
NSFAST-0551161
NSFAST-0244680
NSFAST-0132798
Department of Energy (DOE)DE-AC02-76SF00515
Department of Energy (DOE)DE-AC02-98CH10886
Department of Energy (DOE)DE-AC52-07NA27344
Subject Keywords:Data quality assessment, automated data analysis, quality metrics, sky surveys, software design
Series Name:Proceedings of SPIE
Issue or Number:7740
Record Number:CaltechAUTHORS:20161108-122306467
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20161108-122306467
Official Citation:Richard A. Shaw ; Deborah Levine ; Timothy Axelrod ; Russ R. Laher ; Vince G. Mannings; Science data quality assessment for the Large Synoptic Survey Telescope. Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77400H (July 19, 2010); doi:10.1117/12.857293
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:71809
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:09 Nov 2016 18:39
Last Modified:03 Mar 2020 13:01

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