Statistical Science

Some Statistical and Computational Challenges, and Opportunities in Astronomy

G. Jogesh Babu and S. George Djorgovski

Full-text: Open access

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.

Article information

Source
Statist. Sci., Volume 19, Number 2 (2004), 322-332.

Dates
First available in Project Euclid: 14 January 2005

Permanent link to this document
https://projecteuclid.org/euclid.ss/1105714166

Digital Object Identifier
doi:10.1214/088342304000000774

Mathematical Reviews number (MathSciNet)
MR2146945

Zentralblatt MATH identifier
1100.85500

Keywords
National Virtual Observatory massive data digital sky surveys classification

Citation

Babu, G. Jogesh; Djorgovski, S. George. Some Statistical and Computational Challenges, and Opportunities in Astronomy. Statist. Sci. 19 (2004), no. 2, 322--332. doi:10.1214/088342304000000774. https://projecteuclid.org/euclid.ss/1105714166


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