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Proceedings Paper

Spatial clustering of galaxies in large datasets
Author(s): Alexander Szalay; Tamas Budavari; Andrew Connolly; Jim Gray; Takahiko Matsubara; Adrian Pope; Istvan Szapudi
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Paper Abstract

Datasets with tens of millions of galaxies present new challenges for the analysis of spatial clustering. We have built a framework, that integrates a database of object catalogs, tools for creating masks of bad regions, and a fast (NlogN) correlation code. This system has enabled unprecedented efficiency in carrying out the analysis of galaxy clustering in the SDSS catalog. A similar approach is used to compute the three-dimensional spatial clustering of galaxies on very large scales. We describe our strategy to estimate the effect of photometric errors using a database. We discuss our efforts as an early example of data-intensive science. While it would have been possible to get these results without the framework we describe, it will be infeasible to perform these computations on the future huge datasets without using this framework.

Paper Details

Date Published: 19 December 2002
PDF: 12 pages
Proc. SPIE 4847, Astronomical Data Analysis II, (19 December 2002); doi: 10.1117/12.476761
Show Author Affiliations
Alexander Szalay, Johns Hopkins Univ. (United States)
Tamas Budavari, Johns Hopkins Univ. (United States)
Andrew Connolly, Univ. of Pittsburgh (United States)
Jim Gray, Microsoft Research (United States)
Takahiko Matsubara, Nagoya Univ. (Japan)
Adrian Pope, Johns Hopkins Univ. (United States)
Istvan Szapudi, Institute for Astronomy/Univ. of Hawaii (United States)


Published in SPIE Proceedings Vol. 4847:
Astronomical Data Analysis II
Jean-Luc Starck; Fionn D. Murtagh, Editor(s)

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