Astronomy and Astrophysics – Astrophysics
Scientific paper
2007-10-24
Astronomy and Astrophysics
Astrophysics
4 pages, 1 figure, uses adassconf.sty, asp2006.sty. To appear in the proceedings of ADASS XVII, London, UK, Sep 2007
Scientific paper
We present recent results from the Laboratory for Cosmological Data Mining (http://lcdm.astro.uiuc.edu) at the National Center for Supercomputing Applications (NCSA) to provide robust classifications and photometric redshifts for objects in the terascale-class Sloan Digital Sky Survey (SDSS). Through a combination of machine learning in the form of decision trees, k-nearest neighbor, and genetic algorithms, the use of supercomputing resources at NCSA, and the cyberenvironment Data-to-Knowledge, we are able to provide improved classifications for over 100 million objects in the SDSS, improved photometric redshifts, and a full exploitation of the powerful k-nearest neighbor algorithm. This work is the first to apply the full power of these algorithms to contemporary terascale astronomical datasets, and the improvement over existing results is demonstrable. We discuss issues that we have encountered in dealing with data on the terascale, and possible solutions that can be implemented to deal with upcoming petascale datasets.
Ball Nicholas M.
Brunner Robert J.
Myers Adam D.
No associations
LandOfFree
Robust Machine Learning Applied to Terascale Astronomical Datasets does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Robust Machine Learning Applied to Terascale Astronomical Datasets, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Robust Machine Learning Applied to Terascale Astronomical Datasets will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-655333