Parametrization and Classification of 20 Billion LSST Objects: Lessons from SDSS

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The Large Synoptic Survey Telescope (LSST) will be a large, wide-field ground-based system designed to obtain, starting in 2015, multiple images of the sky that is visible from Cerro Pachon in Northern Chile. About 90% of the observing time will be devoted to a deep-wide-fast survey mode which will observe a 20,000 deg{sup 2} region about 1000 times during the anticipated 10 years of operations (distributed over six bands, ugrizy). Each 30-second long visit will deliver 5{sigma} depth for point sources of r {approx} 24.5 on average. The co-added map will be about 3 magnitudes deeper, and will include 10 ... continued below

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Ivezic, Z.; /Washington U., Seattle, Astron. Dept.; Axelrod, T.; /Large Binocular Telescope, Tucson; Becker, A.C.; /Washington U., Seattle, Astron. Dept. et al. November 10, 2011.

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The Large Synoptic Survey Telescope (LSST) will be a large, wide-field ground-based system designed to obtain, starting in 2015, multiple images of the sky that is visible from Cerro Pachon in Northern Chile. About 90% of the observing time will be devoted to a deep-wide-fast survey mode which will observe a 20,000 deg{sup 2} region about 1000 times during the anticipated 10 years of operations (distributed over six bands, ugrizy). Each 30-second long visit will deliver 5{sigma} depth for point sources of r {approx} 24.5 on average. The co-added map will be about 3 magnitudes deeper, and will include 10 billion galaxies and a similar number of stars. We discuss various measurements that will be automatically performed for these 20 billion sources, and how they can be used for classification and determination of source physical and other properties. We provide a few classification examples based on SDSS data, such as color classification of stars, color-spatial proximity search for wide-angle binary stars, orbital-color classification of asteroid families, and the recognition of main Galaxy components based on the distribution of stars in the position-metallicity-kinematics space. Guided by these examples, we anticipate that two grand classification challenges for LSST will be (1) rapid and robust classification of sources detected in difference images, and (2) simultaneous treatment of diverse astrometric and photometric time series measurements for an unprecedentedly large number of objects.

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  • Journal Name: AIP Conf.Proc.1082:359-365,2008; Journal Volume: 1082; Journal Issue: 1

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  • Report No.: SLAC-PUB-14716
  • Grant Number: AC02-76SF00515
  • Office of Scientific & Technical Information Report Number: 1029150
  • Archival Resource Key: ark:/67531/metadc834322

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  • November 10, 2011

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  • May 19, 2016, 3:16 p.m.

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  • Dec. 6, 2016, 3:37 p.m.

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Ivezic, Z.; /Washington U., Seattle, Astron. Dept.; Axelrod, T.; /Large Binocular Telescope, Tucson; Becker, A.C.; /Washington U., Seattle, Astron. Dept. et al. Parametrization and Classification of 20 Billion LSST Objects: Lessons from SDSS, article, November 10, 2011; United States. (digital.library.unt.edu/ark:/67531/metadc834322/: accessed December 10, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.