Computer Science – Learning
Scientific paper
Jan 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011aas...21734403b&link_type=abstract
American Astronomical Society, AAS Meeting #217, #344.03; Bulletin of the American Astronomical Society, Vol. 43, 2011
Computer Science
Learning
Scientific paper
The Canadian Astronomy Data Centre (CADC) is the world's largest astronomical data center, holding over 0.5 Petabytes of information, and serving nearly 3000 astronomers worldwide. Its current data collections include BLAST, CFHT, CGPS, FUSE, Gemini, HST, JCMT, MACHO, MOST, and numerous other archives and services. It provides extensive data archiving, curation, and processing expertise, via projects such as MegaPipe, and enables substantial day-to-day collaboration between resident astronomers and computer specialists. It is a stable, powerful, persistent, and properly supported environment for the storage and processing of large volumes of data, a condition that is now absolutely vital for their science potential to be exploited by the community. Through initiatives such as the Common Archive Observation Model (CAOM), the Canadian Virtual Observatory (CVO), and the Canadian Advanced Network for Astronomical Research (CANFAR), the CADC is at the global forefront of advancing astronomical research through improved data services. The CAOM aims to provide homogeneous data access, and hence viable interoperability between a potentially unlimited number of different data collections, at many wavelengths. It is active in the definition of numerous emerging standards within the International Virtual Observatory, and several datasets are already available. The CANFAR project is an initiative to make cloud computing for storage and data-intensive processing available to the community. It does this via a Virtual Machine environment that is equivalent to managing a local desktop. Several groups are already processing science data. CADC is also at the forefront of advanced astronomical data analysis, driven by the science requirements of astronomers both locally and further afield. The emergence of 'Astroinformatics' promises to provide not only utility items like object classifications, but to directly enable new science by accessing previously undiscovered or intractable information. We are currently in the early stages of implementing Astroinformatics tools, such as machine learning, on CANFAR.
Astronomy Data Centre Canadian
Ball Nicholas M.
Schade David
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