Neural Gas based classification of Globular Clusters

TL;DR

This paper explores the use of Neural Gas models, both supervised and unsupervised, for classifying Globular Clusters in astrophysics, aiming to improve computational efficiency through GPU parallelization.

Contribution

It introduces GPU-accelerated Neural Gas variants for classifying Globular Clusters, demonstrating potential for efficient, scalable data-driven astrophysical analysis.

Findings

Neural Gas models achieved high classification accuracy.

GPU implementation significantly reduced computation time.

Models validated for astrophysical data analysis.

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.