M. Brescia, S. Cavuoti, M. Paolillo, G. Longo, T. Puzia
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.
View original:
http://arxiv.org/abs/1110.2144
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