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arXiv · 1110.2144

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

M. Brescia·S. Cavuoti·M. Paolillo·G. Longo·T. Puzia
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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 HST images. Several methods provided by the DAME (Data Mining & Exploration) web application, were tested and compared on the NGC1399 HST data described in Paolillo 2011. The best results were obtained using a Multi Layer Perceptron with Quasi Newton learning rule which achieved a classification accuracy of 98.3%, with a completeness of 97.8% and 1.6% of contamination. 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%. 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.

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M. Brescia, S. Cavuoti, M. Paolillo, G. Longo, T. Puzia. 2011-12-16. The detection of globular clusters in galaxies as a data mining problem. https://doi.org/10.1111/j.1365-2966.2011.20375.x

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