Astronomy and Astrophysics – Astrophysics – Instrumentation and Methods for Astrophysics
Scientific paper
2011-10-10
MNRAS Main Journal, ref. MN-11-2315-MJ.R1, 2011
Astronomy and Astrophysics
Astrophysics
Instrumentation and Methods for Astrophysics
Accepted 2011 December 12; Received 2011 November 28; in original form 2011 October 10
Scientific paper
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.
Brescia Massimo
Cavuoti Stefano
Longo Giuseppe
Paolillo Maruizio
Puzia Thomas
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