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Published April 2012 | Published + Accepted Version
Journal Article Open

The detection of globular clusters in galaxies as a data mining problem

Abstract

We present an application of self-adaptive supervised learning classifiers derived from the machine learning paradigm to the identification of candidate globular clusters in deep, wide-field, single-band Hubble Space Telescope (HST) images. Several methods provided by the DAta Mining and Exploration (DAME) web application were tested and compared on the NGC 1399 HST data described by Paolillo and collaborators in a companion paper. The best results were obtained using a multilayer perceptron with quasi-Newton learning rule which achieved a classification accuracy of 98.3 per cent, with a completeness of 97.8 per cent and contamination of 1.6 per cent. An extensive set of experiments revealed that the use of accurate structural parameters (effective radius, central surface brightness) does improve the final result, but only by ∼5 per cent. It is also shown that the method is capable to retrieve also extreme sources (for instance, very extended objects) which are missed by more traditional approaches.

Additional Information

© 2012 The Authors. Monthly Notices of the Royal Astronomical Society © 2012 RAS. Accepted 2011 December 12. Received 2011 November 28; in original form 2011 October 10. Published 16 March 2012. The authors wish to thank the whole DAMEWARE working group, whose huge efforts made the DM facility available to the scientific community. MP acknowledges support from PRIN-INAF 2009, and thanks the ASI Science Data Center (ASDC) for support and hospitality. GL wishes to thank Professor G. S. Djorgovski and the whole Department of Astronomy at the California Institute of Technology in Pasadena, for hospitality. We also thank the anonymous referee for useful suggestions and comments.

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Published - mnras0421-1155.pdf

Accepted Version - 1110.2144.pdf

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