Data Mining by Grid Computing in the Search for Extrasolar Planets

A system is presented here to provide improved precision in ensemble differential photometry. This is achieved by using the power of grid computing to analyse astronomical catalogues. This produces new catalogues of optimised pointings for each star, which maximise the number and quality of reference stars available. Astronomical phenomena such as exoplanet transits and small-scale structure within quasars may be observed by means of millimagnitude photometric variability on the timescale of minutes to hours. Because of atmospheric distortion, ground-based observations of these phenomena require the use of differential photometry whereby the target is compared with one or more reference stars. CCD cameras enable the use of many reference stars in an ensemble. The more closely the reference stars in this ensemble resemble the target, the greater the precision of the photometry that can be achieved. The Locus Algorithm has been developed to identify the optimum pointing for a target and provide that pointing with a score relating to the degree of similarity between target and the reference stars. It does so by identifying potential points of aim for a particular telescope such that a given target and a varying set of references were included in a field of view centred on those pointings. A score is calculated for each such pointing. For each target, the pointing with the highest score is designated the optimum pointing. The application of this system to the Sloan Digital Sky Survey (SDSS) catalogue demanded the use of a High Performance Computing (HPC) solution through Grid Ireland. Pointings have thus been generated for 61,662,376 stars and 23,697 quasars