Data Mining Research with the LSST

Details Data Mining Research with the LSST Data Mining Research with the LSST

Dec 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007aas...21113725b&link_type=abstract

American Astronomical Society, AAS Meeting #211, #137.25; Bulletin of the American Astronomical Society, Vol. 39, p.983

Computer Science

Learning

Scientific paper

The LSST catalog database will exceed 10 petabytes, comprising several hundred attributes for 5 billion galaxies, 10 billion stars, and over 1 billion variable sources (optical variables, transients, or moving objects), extracted from over 20,000 square degrees of deep imaging in 5 passbands with thorough time domain coverage: 1000 visits over the 10-year LSST survey lifetime. The opportunities are enormous for novel scientific discoveries within this rich time-domain ultra-deep multi-band survey database. Data Mining, Machine Learning, and Knowledge Discovery research opportunities with the LSST are now under study, with a potential for new collaborations to develop to contribute to these investigations. We will describe features of the LSST science database that are amenable to scientific data mining, object classification, outlier identification, anomaly detection, image quality assurance, and survey science validation. We also give some illustrative examples of current scientific data mining research in astronomy, and point out where new research is needed. In particular, the data mining research community will need to address several issues in the coming years as we prepare for the LSST data deluge. The data mining research agenda includes: scalability (at petabytes scales) of existing machine learning and data mining algorithms; development of grid-enabled parallel data mining algorithms; designing a robust system for brokering classifications from the LSST event pipeline (which may produce 10,000 or more event alerts per night); multi-resolution methods for exploration of petascale databases; visual data mining algorithms for visual exploration of the data; indexing of multi-attribute multi-dimensional astronomical databases (beyond RA-Dec spatial indexing) for rapid querying of petabyte databases; and more. Finally, we will identify opportunities for synergistic collaboration between the data mining research group and the LSST Data Management and Science Collaboration teams.

Affiliated with

Also associated with

No associations

Rating

Data Mining Research with the LSST 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 Data Mining Research with the LSST, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Data Mining Research with the LSST will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-1482627