Abstract
We present two statistical techniques for astronomical
problems: a star-galaxy separator for the UKIRT
Infrared Deep Sky Survey (UKIDSS) and a novel anomaly
detection method for cross-matched astronomical
datasets. The star-galaxy separator is a statistical
classification method which outputs class membership
probabilities rather than class labels and allows the
use of prior knowledge about the source populations.
Deep Sloan Digital Sky Survey (SDSS) data from the
multiply imaged Stripe 82 region are used to check the
results from our classifier, which compares favourably
with the UKIDSS pipeline classification algorithm. The
anomaly detection method addresses the problem posed by
objects having different sets of recorded variables in
cross-matched datasets. This prevents the use of
methods unable to handle missing values and makes
direct comparison between objects difficult. For each
source, our method computes anomaly scores in subspaces
of the observed feature space and combines them to an
overall anomaly score. The proposed technique is very
general and can easily be used in applications other
than astronomy. The properties and performance of our
method are investigated using both real and simulated
datasets.
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