  In Astrophysics, the identification of candidate Globular Clusters through
deep, wide-field, single band HST images, is a typical data analytics problem,
where methods based on Machine Learning have revealed a high efficiency and
reliability, demonstrating the capability to improve the traditional
approaches. Here we experimented some variants of the known Neural Gas model,
exploring both supervised and unsupervised paradigms of Machine Learning, on
the classification of Globular Clusters, extracted from the NGC1399 HST data.
Main focus of this work was to use a well-tested playground to scientifically
validate such kind of models for further extended experiments in astrophysics
and using other standard Machine Learning methods (for instance Random Forest
and Multi Layer Perceptron neural network) for a comparison of performances in
terms of purity and completeness.
