Matching Catalogues by Probabilistic Pattern Classification

Details Matching Catalogues by Probabilistic Pattern Classification Matching Catalogues by Probabilistic Pattern Classification

2006-05-09

arxiv.org/abs/astro-ph/0605216v1

Mon.Not.Roy.Astron.Soc.369:2-14,2006

Astronomy and Astrophysics

Astrophysics

16 pages, 9 (reduced quality) figures. MNRAS (in press) 2006

Scientific paper

10.1111/j.1365-2966.2006.10304.x

We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HIPASS radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.

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