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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
