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In the last years, Astroinformatics has become a well defined paradigm formany fields of Astronomy. In this work we demonstrate the potential of amultidisciplinary approach to identify globular clusters (GCs) in the Fornaxcluster of galaxies taking advantage of multi-band photometry produced by theVLT Survey Telescope using automatic self-adaptive methodologies. The dataanalyzed in this work consist of deep, multi-band, partially overlapping imagescentered on the core of the Fornax cluster. In this work we use a Neural-Gasmodel, a pure clustering machine learning methodology, to approach the GCdetection, while a novel feature selection method ($\Phi$LAB) is exploited toperform the parameter space analysis and optimization. We demonstrate that theuse of an Astroinformatics based methodology is able to provide GC samples thatare comparable, in terms of purity and completeness with those obtained usingsingle band HST data (Brescia et al. 2012) and two approaches basedrespectively on a morpho-photometric (Cantiello et al. 2018b) and a PCAanalysis (D'Abrusco et al. 2015) using the same data discussed in this work.

Astroinformatics based search for globular clusters in the Fornax Deep Survey