Astroinformatics-based search for globular clusters in the Fornax Deep Survey
Paolillo, M.; Peletier, R.; Hilker, M.; Brescia, M.; Cavuoti, S.; Riccio, G.; Cantiello, M.; Longo, G.; Capaccioli, M.; D'Abrusco, R.; Iodice, E.; Puzia, T.; Angora, G.; Napolitano, N.; Mieske, S.; D'Ago, G.; Pota, V.; Spavone, M.
Italy, United States, Chile, Germany, China, Netherlands
Abstract
In the last years, Astroinformatics has become a well-defined paradigm for many fields of Astronomy. In this work, we demonstrate the potential of a multidisciplinary approach to identify globular clusters (GCs) in the Fornax cluster of galaxies taking advantage of multiband photometry produced by the VLT Survey Telescope using automatic self-adaptive methodologies. The data analysed in this work consist of deep, multiband, partially overlapping images centred on the core of the Fornax cluster. In this work, we use a Neural Gas model, a pure clustering machine learning methodology, to approach the GC detection, while a novel feature selection method (ΦLAB) is exploited to perform the parameter space analysis and optimization. We demonstrate that the use of an Astroinformatics-based methodology is able to provide GC samples that are comparable, in terms of purity and completeness with those obtained using single-band HST data and two approaches based, respectively, on a morpho-photometric and a Principal Component Analysis using the same data discussed in this work.