The detection of globular clusters in galaxies as a data mining problem

TL;DR

This paper applies machine learning classifiers to identify globular clusters in galaxy images, achieving high accuracy and completeness, and demonstrating the effectiveness of structural parameters in improving detection.

Contribution

The study introduces a machine learning approach for globular cluster detection that outperforms traditional methods and assesses the impact of structural parameters on classification performance.

Findings

Achieved 98.3% classification accuracy

Structural parameters improve results by only 5%

Method retrieves extreme sources missed by traditional approaches

Abstract

We present an application of self-adaptive supervised learning classifiers derived from the Machine Learning paradigm, to the identification of candidate Globular Clusters in deep, wide-field, single band HST images. Several methods provided by the DAME (Data Mining & Exploration) web application, were tested and compared on the NGC1399 HST data described in Paolillo 2011. The best results were obtained using a Multi Layer Perceptron with Quasi Newton learning rule which achieved a classification accuracy of 98.3%, with a completeness of 97.8% and 1.6% of contamination. An extensive set of experiments revealed that the use of accurate structural parameters (effective radius, central surface brightness) does improve the final result, but only by 5%. It is also shown that the method is capable to retrieve also extreme sources (for instance, very extended objects) which are missed by more traditional approaches.