Classification and anomaly detection for astronomical survey data

Marc Henrion, Daniel J. Mortlock, David J. Hand, Axel Gandy

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Scopus citations


We present two statistical techniques for astronomical problems: a stargalaxy 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.

Original languageEnglish
Title of host publicationAstrostatistical Challenges for the New Astronomy
PublisherSpringer New York
Number of pages36
ISBN (Electronic)9781461435082
ISBN (Print)9781461435075
StatePublished - 1 Jan 2013
Externally publishedYes


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