This work develops two new 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 is 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.