Brown Dwarf Search Science Prototype: Real-Time Cross Matching of Large Catalogs
Scientific Motivation The search for brown dwarfs has been revolutionized by the latest deep sky surveys. A key attribute to discovering brown dwarfs is the federation of many surveys over different wavelengths. Such matching of catalogs is currently laborious and time consuming. This matching problem is generic to many areas of astrophysics.
Data Resources
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Sloan Digital Sky Survey (SDSS) Early Data Release (15 million objects)
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2-Micron All Sky Survey (2MASS) 2nd Incremental Point Source Catalog
(162 million objects)
What the VO Brings Today, doing the matching of these two large
datasets is user-intensive and is replicated by many different users.
Also, the correlation of these two datasets can take years of CPU time
if not done correctly. The NVO brings two key aspects to this problem.
First, it removes the need for the user to download large data to their
machine, making direct use of distributed
data. Second, the matching algorithm used here is computationally efficient
and designed to give answers in minutes rather than hours; results can
be returned to the user in real-time.
Future Prospects Catalog matching of large datasets is a generic
problem in astrophysics. Therefore, making the matching facility available
to any user for use on any dataset will greatly enhance the productivity
of scientists. Standard I/O formats allow developers to create tools to
use the matched data and easily integrate with existing visualization
and analysis tools (anomaly detector). Bringing these data together on
remote machines with enough CPU to perform analysis (GRID technology)
will allow cross-comparisons of unprecedented scale.