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Kirk Borne | Department of Computational and Data Sciences, GMU I will describe the new data-intensive research paradigm that astronomy and astrophysics is now entering. This is described within the context of the largest data-producing astronomy project in the coming decade – the LSST (Large Synoptic Survey Telescope). The enormous data output, database contents, knowledge discovery, and community science expected from this project will impose massive data challenges on the astronomical research community. One of these challenge areas is the rapid machine learning, data mining, and classification of all novel astronomical events from each 3-gigapixel (6-GB) image obtained every 20 seconds throughout every night for the project duration of 10 years. LSST will produce 100,000 such events each and every night. I will describe the status of the LSST project, as well as GMU's past, present, and potential future participation in the project. This interdisciplinary research program encompasses astronomical research, machine learning (data mining) research, XLDB (extremely large database) research, scientific visualization research, computational science research, science education research, and one more area of research, in addition to public outreach on a grand scale. What is that "one more area of research"? It is Astroinformatics. I will introduce the emerging research discipline of Astroinformatics and how this data-intensive scientific approach to astronomical research and education can help to address the massive petascale data challenges that are before us.
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