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Automated cataloging and analysis of sky survey image databases: the SKICAT system

Fayyad, Usama M. and Weir, Nicholas and Djorgovski, S. G. (1993) Automated cataloging and analysis of sky survey image databases: the SKICAT system. In: CIKM '93 Proceedings of the second international conference on Information and knowledge management. ACM , New York, NY, pp. 527-536. ISBN 0-89791-626-3.

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We describe the application of machine learning and state-of-the-art database management technology to the development of an automated tool for the reduction and analysis of a large astronomical data set. The 3 terabytes worth of images are expected to contain on the order of 5 x 10^7 galaxies and 5 x 10^8 stars. For the primary scientific analysis of these data, it is necessary to detect, measure, and classify every sky object. The size of the complete data set precludes manual reduction, requiring an automated approach. SKICAT integrates techniques for image processing, data classification, and database management. Once sky objects are detected, a set of basic features for each object are computed. The learning algorithms are trained to classify the detected objects and can classify objects too faint for visual classification with an accuracy level of about 941Z0. This increases the number of classified objects in the final catalog three-fold relative to the best results from digitized photographic sky surveys to date. The tasks of managing and matching the resulting hundreds of plate catalogs is accomplished using custom software and the Sybase relational DBMS. A full array of scientific analysis tools are provided for filtering, manipulating, plotting, and listing the data in the sky object database. We are currently experimenting with the use of machine discovery tools, such as the AUTOCLASS unsupervised classification program, on the data. SKICAT represents a system in which machine learning played a powerful and enabling role, and solved a difficult, scientifically significant problem. The primary benefits of our overall approach are increased data reduction throughput consistency of classification; and the ability to easily access, analyze, and create new information from an otherwise unfathomable data set.

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Djorgovski, S. G.0000-0002-0603-3087
Additional Information:© 1993 ACM. First and foremost we acknowledge the other members of the JPL development team: J.C. Roden, M.P. Burl, J.L. Loch, S. Burleigh, and J.S. Yu. The SKICAT effort at JPL is funded by the NASA Office of Advanced Concepts and Technology (Code C) under the Scientific Analysis Assistant Task (SAA). We would like to thank the Sky Survey team for their expertise and effort in acquiring the plate material. The POSS-11 is funded by grants from the Eastman Kodak Company, The National Geographic Society, The Samuel Oschin Foundation, NSF Grants AST 84-08225 and AST 87-19465, and NASA Grants NGL 05002140 and NAGW 1710. We thank Joe Roden for help on evaluating the performance of the learning algorithms. This work was supported in part by a NSF graduate fellowship (N. Weir), the Caltech President’s Fund, NASA contract NAS5-3 1348 (S. Djorgovski and N. Weir), and the NSF PYI Award AST-9157412 (S. Djorgovski ). The work described in this paper was carried out in part by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
Funding AgencyGrant Number
Eastman Kodak CompanyUNSPECIFIED
National Geographic SocietyUNSPECIFIED
Samuel Oschin Foundation UNSPECIFIED
NSFAST 84-08225
NSFAST 87-19465
NSF Graduate Research FellowshipUNSPECIFIED
Caltech President’s FundUNSPECIFIED
Record Number:CaltechAUTHORS:20161013-133506418
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Official Citation:Usama M. Fayyad, Nicholas Weir, and S. Djorgovski. 1993. Automated cataloging and analysis of sky survey image databases: the SKICAT system. In Proceedings of the second international conference on Information and knowledge management (CIKM '93), Bharat Bhargava, Tim Finin, and Yelena Yesha (Eds.). ACM, New York, NY, USA, 527-536. DOI=
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:71063
Deposited By: Kristin Buxton
Deposited On:13 Oct 2016 20:53
Last Modified:03 Oct 2019 16:03

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