Angora, G.G.AngoraBRESCIA, MassimoMassimoBRESCIACAVUOTI, STEFANOSTEFANOCAVUOTIPaolillo, MaurizioMaurizioPaolilloLongo, G.G.LongoCANTIELLO, MicheleMicheleCANTIELLOCapaccioli, M.M.CapaccioliD'Abrusco, R.R.D'AbruscoD'Ago, G.G.D'AgoHilker, M.M.HilkerIODICE, ENRICHETTAENRICHETTAIODICEMieske, S.S.MieskeNAPOLITANO, NICOLA ROSARIONICOLA ROSARIONAPOLITANOPeletier, R.R.PeletierPota, V.V.PotaPuzia, T.T.PuziaRICCIO, GIUSEPPEGIUSEPPERICCIOSPAVONE, MARILENAMARILENASPAVONE2020-12-102020-12-1020190035-8711http://hdl.handle.net/20.500.12386/28782In 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.STAMPAenArticle10.1093/mnras/stz28012-s2.0-85078499120000503993100072https://academic.oup.com/mnras/article/490/3/4080/55830722019MNRAS.490.4080AFIS/05 - ASTRONOMIA E ASTROFISICAERC sectors::Physical Sciences and Engineering::PE9 Universe sciences: astro-physics/chemistry/biology; solar systems; stellar, galactic and extragalactic astronomy, planetary systems, cosmology, space science, instrumentation