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GRIST: Grid Data Mining for Astronomy

The Grist project is investigating and implementing ways for working astronomers, scientists, and the public to interact with the "grid" projects that are being constructed worldwide, to bring to flower the promise of easy, powerful, distributed computing. Our objectives are to u nderstand the role of service-oriented architectures in astronomical research, to bring the astronomical community to the grid -- particularly TeraGrid and to w ork with the NVO to build a library of compute-based web services.

The scientific motivation of Grist derives from creation and mining of wide-area federated images, catalogs, and spectra. An astronomical image collection will generally cover an area of sky several times -- in different wavebands, different times, etc -- and the data analysis should combine these multiple observations to a unified ("federated") understanding of the physical processes in the Universe. The familiar way to do this is to identify sources from each image, then cross-match source lists from different images. However, there is growing interest in another way to federate images: by reprojecting each image to a common set of pixel planes, then stacking images and detecting sources therein. While this has been done for years for small pointing fields, we are working on wide areas of sky in a systematic way, using data from the Palomar-Quest survey (see below). We expect to detect much fainter sources than can be detected in any individual image; to detect unusual objects such as transients; and to deeply compare (eg. principle component analysis) the big surveys such as SDSS, 2MASS, DPOSS, etc. Grist is also using PQ data to find high redshift quasars, to study peculiar variable objects, and search for transients in real-time. Grist is also using TeraGrid resources for fitting of SDSS QSO spectra to measure black hole masses. ( Read More )