Astroinformatics, Cloud Computing, and New Science at the Canadian Astronomy Data Centre

Details Astroinformatics, Cloud Computing, and New Science at the Canadian Astronomy Data Centre Astroinformatics, Cloud Computing, and New Science at the Canadian Astronomy Data Centre

Jan 2012

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012aas...21914511b&link_type=abstract

American Astronomical Society, AAS Meeting #219, #145.11

Astronomy and Astrophysics

Astronomy

Scientific paper

With a collection of over 0.5 petabytes of information, and serving nearly 3000 astronomers worldwide, CADC is one of the world's largest astronomy data centres. Its unique blend of astronomers and computer specialists results in a rich interaction between world experts that is ideal for the fostering of developments within astroinformatics. CADC retains science drivers as the primary motivator at each step of the process, from the receipt of raw data from telescopes to its release and use by scientists. Developments are therefore guided by maximal benefit to the astronomy community.
The Canadian Advanced Network for Astronomical Research (CANFAR) is a University of Victoria and CADC project that builds on the existing CADC infrastructure to provide storage, processing, and analysis tools needed to enable astronomers to perform data-intensive astronomy on current and next generation datasets, using their existing codes. CANFAR provides a Virtual Cluster, accessed via a Virtual Machine environment, over which the user has complete control, and access to Cloud Computing on the Compute Canada Grid. Its services are compliant with the International Virtual Observatory Alliance standards. Hence, rather than build a new infrastructure for a project such as a sky survey, an individual or collaboration may utilize CANFAR.
CANFAR's main focus is on the storage and processing of data. By analogy to the argument that CANFAR can provide the generic hardware portions of a data processing pipeline, we implement fast, scalable, data mining algorithms that simplify the generic portions of knowledge discovery in databases within current and future datasets. This is a necessary step in further enabling practical data-intensive astronomy. We show an example of the use of the SkyTree software to perform K-means clustering to determine which galaxies in the Next Generation Virgo Cluster Survey (NGVS) are cluster members. This problem is unsolved within the survey.

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