We describe the application of machine learning and
state-of-the-art database management technology to
the development of an automated tool for the
reduction and analysis of a large astronomical data set.
The 3 terabytes worth of images are expected to
contain on the order of 5 x 10^7 galaxies and 5 x 10^8
stars. For the primary scientific analysis of these
data, it is necessary to detect, measure, and classify
every sky object. The size of the complete data set
precludes manual reduction, requiring an automated
approach. SKICAT integrates techniques for image
processing, data classification, and database
management. Once sky objects are detected, a set of
basic features for each object are computed. The
learning algorithms are trained to classify the detected
objects and can classify objects too faint for visual
classification with an accuracy level of about 941Z0.
This increases the number of classified objects in the
final catalog three-fold relative to the best results from
digitized photographic sky surveys to date. The tasks
of managing and matching the resulting hundreds of
plate catalogs is accomplished using custom software
and the Sybase relational DBMS. A full array of
scientific analysis tools are provided for filtering,
manipulating, plotting, and listing the data in the sky
object database. We are currently experimenting with
the use of machine discovery tools, such as the
AUTOCLASS unsupervised classification program,
on the data. SKICAT represents a system in which
machine learning played a powerful and enabling role,
and solved a difficult, scientifically significant
problem. The primary benefits of our overall approach
are increased data reduction throughput consistency of
classification; and the ability to easily access, analyze,
and create new information from an otherwise
unfathomable data set.
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