Computer Science – Learning
Scientific paper
May 1994
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1994aas...184.3503d&link_type=abstract
American Astronomical Society, 184th AAS Meeting, #35.03; Bulletin of the American Astronomical Society, Vol. 26, p.915
Computer Science
Learning
Scientific paper
Processing of the Palomar-STScI Digital Sky Survey, based on the PDS scans of the Second Palomar Observatory Sky Survey, is now under way at Caltech. The result of this effort will be a gigasource database, the Palomar Northern Sky Catalog (PNSC), covering the entire northern sky in 3 colors, down to the equivalent limiting magnitude of B =~ 22(m) . We have made an extensive use of machine learning, expert systems, machine-assisted discovery, and other AI software technologies in this effort; for example, there is a major AI component in object classification applications. We are now starting to test and implement clustering analysis, unsupervised classification, and other techniques for exploration of large data spaces. A major effort has also gone into the CCD calibrations of the survey. The initial scientific tests and applications include galaxy counts and correlation function studies, a search for high-redshift quasars using discrepant colors, etc. Many other projects will be possible with this data base, including the studies of large-scale structure, Galactic structure, automatic identifications of sources from other wavelengths (radio, IRAS, or x-ray), generation of objective catalogs of clusters and groups of galaxies, searches for variable or extreme-color objects, etc. We intend to make the catalogs and the data exploration tools available to the general community, probably via Internet, with the initial releases within a year or two. This work is supported in part by Palomar Observatory, the NASA contract NAS5-32337, the NSF PYI award AST-9157412, and the funds from NASA through JPL and IPAC.
Barton Elizabeth
de Carvalho Reinaldo R.
Djorgovski Stanislav
Fayyad U.
Roden Jan
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