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M. J. Graham, S. G. Djorgovski, A. Mahabal, R. Williams (Caltech), E. D. Feigelson, G. J. Babu (Penn State), R. Nichol, D. Vanden Berk, L. Wasserman (CMU)
The nature of astronomical data is changing: data volumes are following Moore's law with a doubling every 18 months and data sets consisting of a billion data vectors in a 100-dimensional parameter space are becoming commonplace. Sophisticated statistical techniques are crucial to fully and efficiently exploit these and maximize the scientific return.
VOStat is the result of a cross-disciplinary collaboration between astronomers and statisticians to meet these challenges; it is a prototype knowledge-based statistical toolkit implemented within the Virtual Observatory (VO) paradigm for the entire astronomical community. VOStat consists of an easily extensible distributed web services-based framework transparently accessed via an open-source client GUI (available from our website: http://www.astro.caltech.edu/~mjg/vostat). Functionality currently offered includes multi-resolutional k-dimensional trees for clustering and outlier detection and more traditional techniques such as principal component and survival analyses.
If you would like more information about this abstract, please follow the link to http://www.astro.caltech.edu/~mjg/vostat. This link was provided by the author. When you follow it, you will leave the Web site for this meeting; to return, you should use the Back comand on your browser.
The author(s) of this abstract have provided an email address for comments about the abstract: mjg@astro.caltech.edu
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Bulletin of the American Astronomical Society, 36 #2
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