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Some statistical and computational challenges, and opportunities in astronomy

Babu, G. Jogesh and Djorgovski, S. George (2004) Some statistical and computational challenges, and opportunities in astronomy. Statistical Science, 19 (2). pp. 322-332. ISSN 0883-4237. http://resolver.caltech.edu/CaltechAUTHORS:BABss04

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Abstract

The data complexity and volume of astronomical findings have increased in recent decades due to major technological improvements in instrumentation and data collection methods. The contemporary astronomer is flooded with terabytes of raw data that produce enormous multidimensional catalogs of objects (stars, galaxies, quasars, etc.) numbering in the billions, with hundreds of measured numbers for each object. The astronomical community thus faces a key task: to enable efficient and objective scientific exploitation of enormous multifaceted data sets and the complex links between data and astrophysical theory. In recognition of this task, the National Virtual Observatory (NVO) initiative recently emerged to federate numerous large digital sky archives, and to develop tools to explore and understand these vast volumes of data. The effective use of such integrated massive data sets presents a variety of new challenging statistical and algorithmic problems that require methodological advances. An interdisciplinary team of statisticians, astronomers and computer scientists from The Pennsylvania State University, California Institute of Technology and Carnegie Mellon University is developing statistical methodology for the NVO. A brief glimpse into the Virtual Observatory and the work of the Penn State-led team is provided here.


Item Type:Article
Additional Information:© 2004 The Institute of Mathematical Statistics. This work was supported in part by NSF Grant DMS-01-01360. We are very thankful to Eric D. Feigelson and James P. McDermott of Penn State, Ashish Mahabal and Robert Brunner of Caltech, and Robert Nichol and Larry Wasserman of Carnegie Mellon University for providing illustrations and examples. We acknowledge useful discussions with many colleagues on these issues. S. George Djorgovski acknowledges partial support from the NASA AISRP program.
Subject Keywords:National Virtual Observatory; massive data; digital sky surveys; classification
Record Number:CaltechAUTHORS:BABss04
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:BABss04
Alternative URL:http://dx.doi.org/10.1214/088342304000000774
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:2940
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 May 2006
Last Modified:26 Dec 2012 08:51

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