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.
@article{arxiv.1110.2144,
title = {The detection of globular clusters in galaxies as a data mining problem},
author = {M. Brescia and S. Cavuoti and M. Paolillo and G. Longo and T. Puzia},
journal= {arXiv preprint arXiv:1110.2144},
year = {2015}
}
Comments
Accepted 2011 December 12; Received 2011 November 28; in original form 2011 October 10