Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published 2003 | Submitted
Book Section - Chapter Open

Challenges for Cluster Analysis in a Virtual Observatory

Abstract

There has been an unprecedented and continuing growth in the volume, quality, and complexity of astronomical data sets over the past few years, mainly through large digital sky surveys. Virtual Observatory (VO) concept represents a scientific and technological framework needed to cope with this data flood. We review some of the applied statistics and computing challenges posed by the analysis of large and complex data sets expected in the VO-based research. The challenges are driven both by the size and the complexity of the data sets (billions of data vectors in parameter spaces of tens or hundreds of dimensions), by the heterogeneity of the data and measurement errors, the selection effects and censored data, and by the intrinsic clustering properties (functional form, topology) of the data distribution in the parameter space of observed attributes. Examples of scientific questions one may wish to address include: objective determination of the numbers of object classes present in the data, and the membership probabilities for each source; searches for unusual, rare, or even new types of objects and phenomena; discovery of physically interesting multivariate correlations which may be present in some of the clusters; etc.

Additional Information

© 2003 Springer-Verlag New York, Inc. We wish to thank numerous collaborators, including R. Gal, S. Odewahn, R. de Carvalho, T. Prince, J. Jacob, D. Curkendall, and many others. This work was supported in part by the NASA grant NAG5-9482, and by private foundations. Finally, we thank the organizers for a pleasant and productive meeting.

Attached Files

Submitted - 0208246.pdf

Files

0208246.pdf
Files (168.3 kB)
Name Size Download all
md5:b9454b582a43c2a11cf99d437b0e4d3d
168.3 kB Preview Download

Additional details

Created:
August 22, 2023
Modified:
October 20, 2023