Paper Info
Title
Neural Gas Based Classification of Globular Clusters.
Abstract
Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multi-dimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts.
Year
DOI
Venue
2017
10.1007/978-3-319-96553-6_7
DAMDID/RCDL (Selected Papers)
Field
DocType
ISSN
Astronomy,Globular cluster,Data analysis,Parameter space,Artificial intelligence,Astroinformatics,Neural gas,Machine learning,Physics
Conference
2018 Communications in Computer and Information Science, vol 822. Springer, Cham
Citations 
PageRank 
References 
0
0.34
7
Authors
6
Name
Order
Citations
PageRank
Giuseppe Angora100.68
Massimo Brescia2148.41
Stefano Cavuoti3104.79
giuseppe riccio443.21
Maurizio Paolillo521.65
Thomas H. Puzia600.68