Matching of catalogues by probabilistic pattern classification

D Rhode, Marcus Gallagher, Michael Drinkwater, Kevin Pimbblet

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15 Citations (Scopus)


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 H i Parkes All Sky Survey 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.
Original languageEnglish
Pages (from-to)2 - 14
Number of pages13
JournalMonthly Notices of the Royal Astronomical Society
Issue number1
Publication statusPublished - 2006
Externally publishedYes

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