Capabilities of the NASA/IPAC Extragalactic Database (NED) in the Era of a Global Virtual Observatory

Details Capabilities of the NASA/IPAC Extragalactic Database (NED) in the Era of a Global Virtual Observatory Capabilities of the NASA/IPAC Extragalactic Database (NED) in the Era of a Global Virtual Observatory

2001-11-09

arxiv.org/abs/astro-ph/0111200v1

in "Astronomical Data Analysis," Jean-Luc Starck, Fionn D. Murtagh, Editors, Proceedings of SPIE Vol. 4477, p. 20 (2001)

Astronomy and Astrophysics

Astrophysics

15 pages, 6 figures; astro-ph file size limits required extensive degradation of the quality of the figures. A version with th

Scientific paper

10.1117/12.447177

We review the capabilities of the NASA/IPAC Extragalactic Database (NED, http://ned.ipac.caltech.edu) for information retrieval and knowledge discovery in the context of a globally distributed virtual observatory. Since its inception in 1990, NED has provided astronomers world-wide with the results of a systematic cross-correlation of catalogs covering all wavelengths, along with thousands of extragalactic observations culled from published journal articles. NED is continuously being expanded and revised to include new catalogs and published observations, each undergoing a process of cross-identification to capture the current state of knowledge about extragalactic sources in a panchromatic fashion. In addition to assimilating data from the literature, the team is incrementally folding in millions of observations from new large-scale sky surveys such as 2MASS, NVSS, APM, and SDSS. At the time of writing the system contains over 3.3 million unique objects with 4.2 million cross-identifications. We summarize the recent evolution of NED from its initial emphasis on object name-, position-, and literature-based queries into a research environment that also assists statistical data exploration and discovery using large samples of objects. Newer capabilities enable "Web mining" of entries in geographically distributed astronomical archives that are indexed by object names and positions in NED, sample building using constraints on redshifts and object types, and an image archive. A pilot study demonstrates how NED is being used in conjunction with linked survey archives to characterize the properties of galaxy classes to form a training set for machine learning algorithms. Opportunities for tighter integration of NED capabilities into data mining tools for astronomy archives are also discussed.

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