Parametrization and classification of 20 billion LSST objects: Lessons from SDSS

Ž Ivezić, T. Axelrod, A. C. Becker, J. Becla, K. Borne, D. L. Burke, C. F. Claver, K. H. Cook, A. Connolly, D. K. Gilmore, R. L. Jones, M. Jurić, S. M. Kahn, K. T. Lim, R. H. Lupton, D. G. Monet, Philip A Pinto, B. Sesar, C. W. Stubbs, J. A. Tyson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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 2 region about 1000 times during the anticipated 10 years of operations (distributed over six bands, ugrizy). Each 30-second long visit will deliver 5σ depth for point sources of r ∼ 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.

Original languageEnglish (US)
Title of host publicationAIP Conference Proceedings
Pages359-365
Number of pages7
Volume1082
DOIs
StatePublished - 2008
EventClassification and Discovery in Large Astronomical Surveys - Ringberg Castle, Germany
Duration: Oct 14 2008Oct 17 2008

Other

OtherClassification and Discovery in Large Astronomical Surveys
CountryGermany
CityRingberg Castle
Period10/14/0810/17/08

Fingerprint

telescopes
color
stars
galaxies
Chile
binary stars
asteroids
point sources
metallicity
sky
proximity
kinematics
physical properties
orbitals

Keywords

  • Asteroids
  • Methods: data analysis
  • Stars

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Ivezić, Ž., Axelrod, T., Becker, A. C., Becla, J., Borne, K., Burke, D. L., ... Tyson, J. A. (2008). Parametrization and classification of 20 billion LSST objects: Lessons from SDSS. In AIP Conference Proceedings (Vol. 1082, pp. 359-365) https://doi.org/10.1063/1.3059076

Parametrization and classification of 20 billion LSST objects : Lessons from SDSS. / Ivezić, Ž; Axelrod, T.; Becker, A. C.; Becla, J.; Borne, K.; Burke, D. L.; Claver, C. F.; Cook, K. H.; Connolly, A.; Gilmore, D. K.; Jones, R. L.; Jurić, M.; Kahn, S. M.; Lim, K. T.; Lupton, R. H.; Monet, D. G.; Pinto, Philip A; Sesar, B.; Stubbs, C. W.; Tyson, J. A.

AIP Conference Proceedings. Vol. 1082 2008. p. 359-365.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ivezić, Ž, Axelrod, T, Becker, AC, Becla, J, Borne, K, Burke, DL, Claver, CF, Cook, KH, Connolly, A, Gilmore, DK, Jones, RL, Jurić, M, Kahn, SM, Lim, KT, Lupton, RH, Monet, DG, Pinto, PA, Sesar, B, Stubbs, CW & Tyson, JA 2008, Parametrization and classification of 20 billion LSST objects: Lessons from SDSS. in AIP Conference Proceedings. vol. 1082, pp. 359-365, Classification and Discovery in Large Astronomical Surveys, Ringberg Castle, Germany, 10/14/08. https://doi.org/10.1063/1.3059076
Ivezić Ž, Axelrod T, Becker AC, Becla J, Borne K, Burke DL et al. Parametrization and classification of 20 billion LSST objects: Lessons from SDSS. In AIP Conference Proceedings. Vol. 1082. 2008. p. 359-365 https://doi.org/10.1063/1.3059076
Ivezić, Ž ; Axelrod, T. ; Becker, A. C. ; Becla, J. ; Borne, K. ; Burke, D. L. ; Claver, C. F. ; Cook, K. H. ; Connolly, A. ; Gilmore, D. K. ; Jones, R. L. ; Jurić, M. ; Kahn, S. M. ; Lim, K. T. ; Lupton, R. H. ; Monet, D. G. ; Pinto, Philip A ; Sesar, B. ; Stubbs, C. W. ; Tyson, J. A. / Parametrization and classification of 20 billion LSST objects : Lessons from SDSS. AIP Conference Proceedings. Vol. 1082 2008. pp. 359-365
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AU - Ivezić, Ž

AU - Axelrod, T.

AU - Becker, A. C.

AU - Becla, J.

AU - Borne, K.

AU - Burke, D. L.

AU - Claver, C. F.

AU - Cook, K. H.

AU - Connolly, A.

AU - Gilmore, D. K.

AU - Jones, R. L.

AU - Jurić, M.

AU - Kahn, S. M.

AU - Lim, K. T.

AU - Lupton, R. H.

AU - Monet, D. G.

AU - Pinto, Philip A

AU - Sesar, B.

AU - Stubbs, C. W.

AU - Tyson, J. A.

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N2 - 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 2 region about 1000 times during the anticipated 10 years of operations (distributed over six bands, ugrizy). Each 30-second long visit will deliver 5σ depth for point sources of r ∼ 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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